The Current Status and Control of Horseweed (Conyza Canadensis) in Ohio Soybean (Glycine Max) Production

The Current Status and Control of Horseweed (Conyza canadensis) in Ohio Soybean (Glycine max) Production

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in

the Graduate School of The Ohio State University

Alyssa Irene Lamb

Graduate Program in Horticulture and Crop Science

The Ohio State University

2018

Thesis Committee

Dr. Mark Loux, Advisor

Dr. Alex Lindsey

Dr. Emilie Regnier

Copyrighted by

Alyssa Irene Lamb

2018

Abstract

Studies were conducted from 2013 through 2017 with the objectives of: 1) optimizing a cereal rye cover crop program for the control of glyphosate-resistant (GR) horseweed in soybean production; and 2) determining the frequency, infestation level, and distribution of some of the most common and troublesome weeds in Ohio soybean fields as well as the spatial and temporal distribution of horseweed populations.

Two studies were conducted simultaneously from fall of 2016 to fall of 2017 to evaluate how the planting date and seeding rate of a cereal rye cover crop affect horseweed population density, and the utility of cereal rye to aid or replace control from spring preplant residual (Study I) or fall herbicides (Study II). There was no difference in horseweed population density as a result of rye planting date in either study. The rye seeding rate affected horseweed density throughout the season in both studies.

Horseweed density was greater in the absence of rye compared to either seeding rate. In

Study I, the flumioxazin + metribuzin spring preplant residual reduced horseweed density in June compared with no or low level residual. In July, the flumioxazin + metribuzin treatments had a reduced horseweed density compared to the nontreated, but the flumioxazin alone was not different than the mixture or nontreated. In Study II, the fall herbicide treatment reduced horseweed density until July compared with the absence of a fall treatment. These results suggest that cereal rye used as a cover crop before no-till

iii soybeans can reduce GR horseweed plant density, but that fall herbicide treatments and comprehensive spring residual programs are still important to ensure effective GR horseweed control into the growing season.

A survey was conducted annually from 2013 through 2017 in 49 to 52 counties in

Ohio soybean fields to assess the frequency, infestation level, and distribution of horseweed, giant ragweed (Ambrosia trifida), common ragweed (Ambrosia artemisiifolia), and three Amaranthus or ‘pigweed’ species. Horseweed was the most frequently encountered species in all years, followed by giant ragweed, pigweeds, and common ragweed, respectively. Horseweed also had the greatest number of infestations

(highest density) each year, followed by giant ragweed, common ragweed, and pigweed species, respectively. Spatial cores of interest, or counties identified as having significant levels of horseweed infestations or lack thereof, relative to surrounding counties, were identified in 2013, 2014, 2015 and 2016, but not 2017. However, the lowest total frequency of horseweed occurred in 2017, which coincided with second highest frequency of infestations among years. There was no distinct distribution or pattern of horseweed movement within the state from year to year, but there did seem to be an increase in counties with one to three infested fields over time compared to the early years of the survey where many counties had one or no infested fields. These results suggest that horseweed persists as a common and troublesome threat to Ohio soybean producers, and that growers should still consider making horseweed management a

priority when developing weed control programs.

Vita

May 2011 ...... Westfall High School

December 2015 ...... B.S. Agricultural Business and Applied

Economics, The Ohio State University

January 2016 ...... Graduate Research Associate, The Ohio

State University

August 2016 ...... Graduate Research Fellow, The Ohio

State University

August 2017 ...... Graduate Research Associate, The Ohio

State University

January 2018 to present ...... Graduate Teaching Associate, The Ohio

State University

Field of Study

Major Field: Horticulture and Crop Science

Table of Contents

Abstract ...... iii Vita ...... v List of Tables ...... vii List of Figures ...... ix Chapter 1. Literature Review ...... 1 Bibliography ...... 19 Chapter 2. Optimizing a Cereal Rye Cover Crop Program for the Control of Glyphosate- Resistant Horseweed in No-Till Soybeans ...... 26 Introduction ...... 26 Objectives...... 28 Materials & Methods ...... 28 Results & Discussion ...... 33 Bibliography ...... 41 Chapter 3. The Spatial and Temporal Distribution of Horseweed in Ohio Soybean Production Fields from 2013 to 2017 ...... 57 Introduction ...... 57 Objectives...... 59 Materials & Methods ...... 59 Results & Discussion ...... 62 Bibliography ...... 107 Thesis Bibliography...... 109

List of Tables

Table 2.1 ANOVA results of horseweed density measurements from Study I examining planting date and seeding rate of a rye cover crop and spring residual treatments...... 44

Table 2.2 ANOVA results of horseweed density measurements from Study II examining rye planting date and seeding rate of a rye cover crop and fall herbicide treatments...... 45

Table 2.3 Effect of rye seeding rate on horseweed density in Study I, averaged over rye planting date and spring residual herbicide level...... 46

Table 2.4 Effect of rye seeding rate on horseweed density in Study II, averaged over planting date and fall herbicide treatments...... 47

Table 2.5 Effect of spring preplant residual herbicide on horseweed density in Study I, averaged over rye planting date and seeding rate...... 48

Table 2.6 Effect of fall herbicide treatment on horseweed density in Study II, averaged over rye planting date and seeding rate...... 49

Table 2.7 Effect of interaction between rye planting date and fall herbicide treatment on horseweed density in Study II, averaged over rye seeding rate...... 50

Table 2.8 ANOVA results of rye planting date, seeding rate, and date by rate interaction on rye biomass in both studies...... 51

Table 2.9 Effect of rye planting date on fall and spring rye biomass averaged over seeding rate and herbicide treatment in both studies...... 52

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Table 2.10 Effect of rye seeding rate on fall and spring rye biomass averaged over planting date and herbicide treatment in both studies...... 53

Table 2.11 Effect of the planting date by seeding rate interaction on fall and spring rye biomass averaged over herbicide treatment in both studies...... 54

Table 3.1 Weed species with reported cases of resistance to herbicides and multiple herbicide sites of action in Ohio...... 68

Table 3.2 Overall frequency of weeds (all ratings) in soybean fields just prior to harvest –

2013 to 2017...... 69

Table 3.3 Frequency of single, isolated weeds (level 1 rating) in soybean fields just prior to harvest – 2013 to 2017...... 71

Table 3.4 Frequency of clustered groups of weeds (level 2 rating) in soybean fields just prior to harvest – 2013 to 2017...... 72

Table 3.5 Frequency of infestations (level 3 rating) in soybean fields just prior to harvest

– 2013 to 2017...... 73

Table 3.6 Number of fields with horseweed infestations (level 3 rating) by county and year...... 74

Table 3.7 Significance of fields with single, isolated horseweed plants (level 1 rating) by year from univariate local Moran’s I test...... 78

Table 3.8 Significance of fields with clustered groups of horseweed (level 2 rating) by year from univariate local Moran’s I test...... 81

Table 3.9 Significance of horseweed infestations (level 3 rating) by year from univariate local Moran’s I test...... 83

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List of Figures

Figure 2.1 Regression results of spring rye biomass and horseweed density at the time of soybean planting in May in Study II at both rye planting dates in the treatments without a fall 2,4-D application (p = 0.004)...... Error! Bookmark not defined.

Figure 2.2 Regression results of spring rye biomass and horseweed density at the time of soybean planting in May in Study II at the early planting dates in the treatments without a fall 2,4-D application (p = 0.004)...... 56

Figure 3.1 Ohio counties included in all survey years – 2013 to 2017...... 70

Figure 3.2 Gradient of the distribution of fields with single, isolated horseweed plants

(level 1 rating) in Ohio soybean fields based on the number of infestations per county –

2013...... 76

Figure 3.3 The cores and neighbors of significant clusters of fields with single, isolated horseweed plants (level 1 rating) in Ohio soybean fields – 2013...... 77

Figure 3.4 Gradient of the distribution of fields with clustered groups of horseweed (level

2 rating) in Ohio soybean fields based on the number of infestations per county – 2013. 79

Figure 3.5 The cores and neighbors of significant clusters of fields with clustered groups of horseweed (level 2 rating) in Ohio soybean fields – 2013...... 80

Figure 3.6 Gradient of the distribution of fields with horseweed infestations (level 3 rating) in Ohio soybean fields based on the number of infestations per county – 2013. .. 82

Figure 3.7 The cores and neighbors of significant clusters of horseweed infestations

(level 3 rating) in Ohio soybean fields – 2013...... 84

Figure 3.8 Gradient of the distribution of fields with single, isolated horseweed plants

(level 1 rating) in Ohio soybean fields based on the number of infestations per county –

2014...... 85

Figure 3.9 The cores and neighbors of significant clusters of fields with single, isolated horseweed plants (level 1 rating) in Ohio soybean fields – 2014...... 86

Figure 3.10 Gradient of the distribution of fields with clustered groups of horseweed

(level 2 rating) in Ohio soybean fields based on the number of infestations per county –

2014...... 87

Figure 3.11 The cores and neighbors of significant clusters of fields with clustered groups of horseweed (level 2 rating) in Ohio soybean fields – 2014...... 88

Figure 3.12 Gradient of the distribution of horseweed infestations (level 3 rating) in Ohio soybean fields based on the number of infestations per county – 2014...... 89

Figure 3.13 The cores and neighbors of significant clusters of horseweed infestations

(level 3 rating) in Ohio soybean fields – 2014...... 90

Figure 3.14 Gradient of the distribution of fields with single, isolated horseweed plants

(level 1 rating) in Ohio soybean fields based on the number of infestations per county –

2015...... 91

Figure 3.15 Gradient of the distribution of fields with clustered groups of horseweed

(level 2 rating) in Ohio soybean fields based on the number of infestations per county –

2015...... 92

Figure 3.16 Gradient of the distribution of horseweed infestations (level 3 rating) in Ohio soybean fields based on the number of infestations per county – 2015...... 93

Figure 3.17 The cores and neighbors of significant clusters of horseweed infestations

(level 3 rating) in Ohio soybean fields – 2015...... 94

Figure 3.18 Gradient of the distribution of fields with single, isolated horseweed plants

(level 1 rating) in Ohio soybean fields based on the number of infestations per county –

2016...... 95

Figure 3.19 The cores and neighbors of significant clusters of fields with single, isolated horseweed plants (level 1 rating) in Ohio soybean fields – 2016...... 96

Figure 3.20 Gradient of the distribution of fields with clustered groups of horseweed

(level 2 rating) in Ohio soybean fields based on the number of infestations per county –

2016...... 97

Figure 3.21 The cores and neighbors of significant clusters of fields with clustered groups of horseweed (level 2 rating) in Ohio soybean fields – 2016...... 98

Figure 3.22 Gradient of the distribution of horseweed infestations (level 3 rating) in Ohio soybean fields based on the number of infestations per county – 2016...... 99

Figure 3.23 The cores and neighbors of significant clusters of horseweed infestations

(level 3 rating) in Ohio soybean fields – 2016...... 100

Figure 3.24 Gradient of the distribution of fields with single, isolated horseweed plants

(level 1 rating) in Ohio soybean fields based on the number of infestations per county –

2017...... 101

Figure 3.25 Gradient of the distribution of fields with clustered groups of horseweed

(level 2 rating) in Ohio soybean fields based on the number of infestations per county –

2017...... 102

Figure 3.26 Gradient of the distribution of horseweed infestations in Ohio soybean fields based on the number of infestations (level 3 rating) per county – 2017...... 103

Figure 3.27 Regression results of fields with single, isolated horseweed plants (level 1 rating) by year (p = 0.27)...... 104

Figure 3.28 Regression results of fields with clustered groups of horseweed (level 2 rating) by year (p = 0.89)...... 105

Figure 3.29 Regression results of fields with horseweed infestations (level 3 rating) by year (p = 0.26)...... 106

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Chapter 1. Literature Review

Soybeans (Glycine max) belong to the Fabaceae family, subclass Rosidae, order

Fabales (USDA 2017). They are leguminous annual dicots grown mainly for seed production. Soybeans were domesticated by farmers in the northeastern or central regions of China between six and nine thousand years ago (Kim et al. 2012; Liu 1997). They were brought to North America in the late 1700s, but wide scale production didn’t take place until the early 1900s (Liu 1997). Today, soybeans are one of the most economically and culturally important crops, both domestically and globally.

Approximately 34 million hectares in the Unites States were dedicated to soybean production in 2016 with a value of $41 billion, while worldwide production was approximately 341 million metric tons (Soy Stats 2017). The Unites States led the world in soybean production in 2016 at 117 million metric tons, followed by Brazil and

Argentina with 108 and 56 million metric tons, respectively (Soy Stats 2017). Soybeans are roughly 350 g kg-1 protein and 170 g kg-1 oil (at 130 g kg-1 moisture) and are grown and processed primarily for soybean oil and soybean meal (Liu 1997). The byproducts of soybean processing are used in the production of many goods, most commonly in livestock feed, but also for human use in many food and industrial products (Soy Stats

2017).

Control of weeds is one of the most important factors in the production of a high yielding soybean crop. The most prevalent pests in soybeans are weeds, competing with the crop for light, water, and nutrients. Weeds can cause over 80% yield loss when left uncontrolled for the entire season (Heap 2014). Weeds alone account for 37% of annual soybean yield loss, while pathogens, viruses, and animal pests cause approximately 11, 1, and 11% yield loss, respectively (Oerke and Dehne 2004). In 2012, weeds represented the largest source of total soybean yield loss in the US at 39% and were the single largest contributor to soybean yield loss in the corn belt region (USDA-NASS 2014). In agronomic production systems such as soybean, the critical time for weed control is in the first four to six weeks after crop planting (Loux et al. 2016). There is typically an interaction between cultural field management and tillage practices, location, environment, and herbicide management in weed population dynamics. In no-tillage crop production systems, weeds generally emerge later and at a higher density as compared to conventional tillage crop production systems (Halford et al. 2001). In general, conservation and no-till systems experience an increase in winter annual, biennial, and perennial weed species as compared to conventional tillage (Buhler 1995).

Prior to the “chemical era in agriculture” with the introduction of 2,4-

Dichlorophenoxyacetic acid (2,4-D) in the 1940’s, weed control in crop production consisted of hand-weeding, hoeing, and tillage and was time consuming and laborious

(Timmons 2005). The initial herbicides and those created thereafter provided farmers a fast, efficient means of weed control. In the 1960s and 1970s, many new herbicides were created, and more than half of the currently used herbicide modes of action were

2 developed. Today, there are a number of name brand and generic herbicides marketed for commercial use. However, many of the commercial herbicides on the market rely on the same 19 sites of action that were identified decades ago (Duke 2011; Holm and Johnson

2009). The continual use of these same sites of action created ideal conditions for the formation and establishment of herbicide-resistant weeds. The first incidences of herbicide-resistant weeds were reported in 1957 when populations of wild carrot (Daucus carota) in Ontario and spreading dayflower (Commelina diffusa) in Hawaii were found to be insensitive to 2,4-D (Heap 2000; Shaner 2014; Whitehead and Switzer 1963).

Populations of field bindweed (Convolvulus arvensis) were reported to be resistant to 2,4-

D in 1964 (Whitworth 1964). In 1970, common groundsel (Senecio vulgaris) populations in Washington were reported to be insensitive to 2-chloro-4,6-bis(ethylamino)-s-triazine

(simazine) and 2-chloro-4-(ethylamino)-6-(isopropylamino)-s-triazine (atrazine) (Ryan

1970).

Following these initial reports, the number of reported resistant weeds dramatically increased, especially from the 1980s to present day. Atrazine and simazine in corn (Zea mays) created the first problematic herbicide-resistant weeds in the United

States in the 1970s. In the 1980s and 90s, acetolactate synthase (ALS) and acetyl CoA carboxylase (ACCase) inhibitors were used as a means of controlling triazine-resistant weeds. After the release of glyphosate-resistant crops, glyphosate-resistant crop seed and glyphosate, N-(Phosphonomethyl)glycine, were used on a large scale to control the triazine, ALS, and ACCase inhibitor-resistant weeds that had started to become uncontrollable (Heap 2014). Now, producers are turning to older practices like tillage and

3 implementing biotech alternatives (2,4-D- and dicamba-resistant crops) in an attempt to control the large number of glyphosate-resistant weed populations that now exist. Beyond glyphosate resistance, repeated use of the same modes of action have also led to the development of weeds that express resistance to multiple sites of action (Heap 2014).

There have been 19 reports of herbicide resistance in Ohio, with three species having resistance to multiple herbicide sites of action (Heap 2017). As of December 2017, there were 253 resistant weed species reported worldwide, with resistance to 23 out of the 26 sites of action in 70 countries, with some weeds showing insensitivity to upwards of five sites of action (Heap 2017).

Glyphosate was first created by the Swiss chemist Dr. Henri Martin in 1950 while working for Cilag (Dill et al. 2010). Glyphosate was a derivative of the amino acid glycine. Cilag was bought by Johnson and Johnson, from whom Aldrich Chemical bought research samples that included glyphosate. Aldrich Chemical sold several glyphosate samples to different companies, but no biological significance was reported

(Dill et al. 2010). Not long after, Monsanto employee Dr. John Franz began working on metabolites and identified the biological activity of glyphosate. Monsanto synthesized glyphosate in 1970 and began greenhouse trials of the product that July, and quickly passed greenhouse and field trial approvals shortly before being introduced as the

Roundup herbicide (Dill et al. 2010). Glyphosate acid and salt compounds are now commercially available in over 750 products sold in the United States (Henderson et al.

2010). Glyphosate kills plants by inhibiting the 5-enylpyruvyl shikimate-3-phosphate synthase (EPSPS) enzyme, which is critical in the synthesis of aromatic amino acids,

4 hormones, and a host of plant metabolites such as flavonoids and lignins (Dill 2005).

Glyphosate toxicity is lower than some of its herbicide predecessors. It presents low toxicity to humans, and was found to have no toxicological concerns following the examination of dietary toxicity and general exposure. It is slightly toxic to birds, but is essentially non-toxic to fish, honeybees, or aquatic invertebrates. Environmental fate is also not of great concern, as glyphosate has strong adsorption to soil and the ability to be readily degraded (EPA 1993).

The first glyphosate-resistant soybean, the Roundup Ready Soybean, was introduced to growers in 1996 by Monsanto to be used with their glyphosate postemergence product, Roundup (Carpenter and Gianessi 1999). Roundup Ready

Soybeans bypass the effects of glyphosate as a result of the introduction of the CP4

EPSPS gene originating from an Agrobacterium sp., which allows for the production of amino acids in the presence of glyphosate (Re and Padgette 1993). This product revolutionized weed control by giving producers a wide-spectrum product capable of being used in-season as needed that was also relatively safe and overall decreased the amount and number of herbicides needed in production (Green 2009). The Roundup

Ready system was quickly adapted by producers for its effective weed control and the level of management ease it allowed (USDA-NASS 2014). In 2006 the second glyphosate-tolerant soybean, the Roundup RReady2Yield Soybean, was deregulated.

This product worked similarly to the Roundup Ready Soybean and provided the same level of weed control and management ease, but was said to be four to seven percent higher yielding as compared to the original line (Meyer et al. 2006). The creation of these

5 glyphosate-tolerant soybean products changed the face of American agriculture by providing an unparalleled level of simplicity and flexibility in weed control management.

One of the most common soybean weeds, horseweed (Conyza canadensis) is a member of the Asteraceae family, subclass Asteridae, order Asterales (USDA 2017).

Native to North America, horseweed can be found in 50 states as well as all over the world in over 40 different crops (Fine et al. 2016). Horseweed, also known as marestail or Canadian fleabane, infests a number of different cultivated row crops including corn and soybeans, as well as perennial crops, orchards, fallow fields, and fencerows (Uva et al. 1997). It also inhabits non-agricultural land sites such as roadsides, waste sites, floodplains, railroads, and urban areas (Bryson and DeFelice 2010; Fine et al. 2016).

Horseweed begins its life cycle as a basal rosette, followed by a period of stem elongation and flowering, and has a short fibrous taproot (Weaver 2001). Horseweed reproduces only by seed and can demonstrate both winter and summer annual characteristics, with winter survival correlating to rosette size which itself is a function of root length (Davis and Johnson 2008; Weaver 2001). Horseweed populations are considered to be self- thinning, but seed production per unit area has been found to be relatively consistent among various densities (Weaver 2001). Horseweed is self-compatible and mainly reproduces by self-pollinating, but up to 15% outcrossing has been observed (Loux et al.

2004). Seeds mature about three weeks after fertilization and exhibit a lack of dormancy.

The majority of horseweed seeds do not remain viable longer than two to three years, but have been shown to remain viable in seed banks despite the absence of vegetation for ten to twenty years (Fine et al. 2016; Weaver 2001).

Herbicide resistance in horseweed was first discovered in 1980 when populations resistant to paraquat were reported in Japanese orchards and riverbanks (Heap 2017).

Several instances of herbicide resistance in horseweed around the world followed, but the first in the United States occurred in 1994, when horseweed in Mississippi soybean fields was found to have 100-fold resistance to paraquat (Heap 2017). Glyphosate resistance in horseweed was first documented in Delaware in 2000, and Ohio was the eighth state to report resistance, in 2002 (Heap 2017; VanGessel 2001). Since these early reports, glyphosate-resistant horseweed quickly became one of the most common and problematic weeds in Midwestern crop production fields, causing substantial yield loss at rates up to

90% at high densities if not controlled (Huang et al. 2015; Van Wychen 2016).

Glyphosate-resistant horseweed is currently found on four continents, in 13 countries, and in 25 states. Five states have confirmed cases of resistance to multiple herbicide sites of action, one of which is Ohio. In Ohio, horseweed exhibiting resistance to both EPSPS inhibitors (glyphosate) and ALS inhibitors (chlorimuron-ethyl and cloransulam-methyl) have been documented (Heap 2017). The level of glyphosate resistance exhibited by horseweed is dependent on growth stage. Glyphosate-resistant horseweed seedlings are more sensitive to glyphosate, and sensitivity decreases with increasing plant size.

Horseweed control by glyphosate increases with repeated applications. However, herbicides with different modes of action should always be implemented as missed targets can spread glyphosate resistance (VanGessel 2009).

The ability of horseweed to spread and thrive is largely due to: i) biological qualities that allow it to survive in a variety of environmental conditions; and ii)

7 management practices that have led to favorable conditions for growth and development of glyphosate-resistant horseweed. One of the biological qualities that makes horseweed so tenacious is that it can act as either a winter or summer annual, with five to 32% of total germination occurring in the spring (Buhler and Owen 1997; Loux et al. 2004). The variable emergence can make horseweed control troublesome as different seasons require different management strategies. Additionally, horseweed can produce roughly 200,000 seeds per plant (Bhowmik and Bekech 1993), 80% of which can germinate immediately

(Loux et al. 2004). Horseweed’s readily germinating seeds can spread to concentrations of 12,500 seeds m-2 six meters from the seed head, and 126 seeds m-2 122 meters from the plant (Regehr and Bazzaz 1979). Horseweed seed has been shown to spread as far as

500 meters from the seed source, and is thought to be capable of further dispersal distances (Dauer et al. 2007). These factors allow horseweed to spread over long distances and grow in nearly any season.

Horseweed’s ability to grow in almost any condition has been exacerbated by selection pressure applied through current weed control and agronomic practices. Limited use of alternative control options for late-season control of horseweed led to the extreme overuse of glyphosate, especially following the introduction of glyphosate-tolerant crops

(Loux et al. 2004). Glyphosate-tolerant crops in combination with a high number of glyphosate-only herbicide systems provided an effective means of weed control with reduced tillage (Owen 2008). Horseweed is a surface germinator and does not germinate well when buried below 0.5 cm, making tillage an effective control option (Nandula et al.

2006). However, in an effort to preserve soil, there has been an increase in the number of

8 hectares farmers are managing with no-till production (Vilsack and Clark 2014; Wade et al. 2015). Horseweed then becomes a greater problem in no-till production systems where seeds are left on the soil surface undisturbed (Brown and Whitwell 1988). The reduced- till systems used in conjunction with glyphosate-tolerant crops and herbicide programs utilizing only glyphosate created perfect conditions for the selection of glyphosate- resistant horseweed biotypes, which contributed to the resistance issues experienced today (Boerboom and Owen 2006).

An increasing number of farmers have started to utilize cover crops for their wide range of benefits, which include soil conservation, water quality preservation, reduced nutrient leaching, and weed suppression. In 2012, approximately 4.2 million hectares were planted with cover crops in the Unites States, excluding ground in conservation reserve programs (Vilsack and Clark 2014). Due to an increased interest in conservation, the 2012 USDA census included a section that contained data about tile drainage, rotational grazing, tillage practices, and cover crops. The number of cover crop acres planted have been increasing yearly as farmers discover their benefits and implications for the long-term sustainability of soil and cropland (Watts et al. 2014). Cover crops establish in soil during times it would otherwise lay bare and vulnerable to erosion via wind and rain (Dabney 1998; Dabney et al. 2001). They also can improve water quality and aid in nutrient management by reducing the off-target movement of applied nutrients into nearby waterways, keeping them in the cropping system (Reicosky and Forcella

1998). With repeated use, cover crops are capable of improving soil structure and increasing organic matter (Dabney 1998). Cover crops can also be of use in weed

9 management by shading out weeds and competing for resources, and some cover crops even possess means of chemical suppression (Teasdale 1996).

One of the most widely used cover crops, cereal rye (Secale cereale) is a member of the Poaceae family, subclass Commelinidae, order Cyperales (USDA 2017).

Speculated to have originated somewhere in Eurasia, it was discovered and documented as a weedy species in south Asian wheat, where it had coevolved with wheat and barley for 2000 years. It was then brought to the United States by English and Dutch settlers.

Currently less than half the rye grown in the United States is used for grain. Its main economic uses include the production of livestock feed, alcohol, food, and seed products.

(Casey 2012; Oelke et al. 1990). The two main states currently involved in cereal rye production are Georgia and Oklahoma. In 2016, approximately 770 thousand hectares of cereal rye was planted, roughly 160,000 hectares were harvested, and production totaled almost 343 million kilograms (USDA-NASS 2017). The discrepancy between hectares planted and harvested is likely due to the planting of cereal rye for use in grazing or as a cover crop, where it is not harvested for grain. Cereal rye has been used widely as a cover crop for its agronomic traits and environmental benefits, namely winter hardiness, biomass production, soil conservation, nitrate leaching management, and weed suppression.

The main goal of using a cover crop to improve soil conservation is to provide a living vegetative crop with roots that can hold onto the soil. The most commonly used groups of cover crops are legumes and grasses, each of which typically serve two different purposes. Fabaceae, or legume, species such as hairy vetch (Vicia villosa) and

10 crimson clover (Trifolium incarnatum), are selected mainly for their nitrogen fixation which can add up to 112 kilograms per hectare to the soil (Magdoff and Van Es 2009).

Poaceae, or grass, species are chosen more for their nutrient scavenging and extensive root systems, which can help slow soil erosion. Some of the most commonly used grass cover crop species include cereal rye, oats (Avena sativa), and annual ryegrass (Lolium multiflorum). Among these species, cereal rye has continued to be one of the most popular in use for multiple reasons. It has the ability to germinate at 1°C and can have vegetative growth at temperatures as low as 3°C, indicating that it is not easily winter- killed (Clark 2012). This temperature range may allow for a more flexible planting window for farmers that intend to plant cereal rye as a cover crop in the fall. In a comparison of the hardiest cultivars of various grass species, it was found that cereal rye was the most cold-hardy, surviving temperatures of -30°C whereas wheat (Triticum aestivum), barley (Hordeum vulgare), and oats could only withstand -21, -15, and -13°C, respectively (Fowler and Carles 1979).

Cereal rye is likely recognized most for its relatively high production of biomass, which is a function of its winter hardiness. Used as a cover crop, cereal rye on average produces 3300 to 4500 kg of biomass per hectare in a season in the Northeastern US, and can produce up to 11200 kg dry matter per hectare in some instances (Clark 2012). The greatest biomass production often occurs when cereal rye is planted early in the fall and terminated late in the spring; there can be an approximately 65% difference in biomass between early and late planted rye (Mirsky et al. 2011). In using a cover crop system for soybeans that includes cereal rye, the concern lies in maintaining soybean seed yield in

11 the face of large amounts of living biomass, or killed residue. In some climatic regions and environmental locations, the use of a cereal rye cover crop can potentially reduce soybean yields (Dabney et al. 2001; Williams et al. 2000). In other cases its inclusion can maintain yield, but reduce profitability (De Bruin et al. 2005; Reddy 2003). One study showed that late termination of rye provided sufficient weed control but reduced soybean yield, while early termination provided similar weed control as the late termination and allowed for comparable or better yields than soybeans grown in corn residue (Liebl et al.

1992).

However, studies exist that illustrate the ability to use a cereal rye cover crop and attain high levels of biomass to achieve sufficient weed control without a significant impact on soybean grain yield (Moore et al. 1994; Reddy 2003; Ruffo et al. 2004). In fact, some studies have shown that use of a cereal rye cover crop can increase soybean yields (ILF and PFI 2017; Williams et al. 2000). A cereal rye cover crop is sometimes terminated with a roller/crimper, which can provide high levels of residue on the soil surface, allowing for adequate weed control and still maintaining soybean yield (Mischler et al. 2010). Other factors can also play a role in determining soybean yield following a cereal rye cover crop. When cereal rye biomass is high, a narrow soybean row spacing of

19 or 38 cm can result in a yield advantage over a wider spacing of 76 cm and produce yields comparable to weed-free controls, averaged over herbicide use and soybean planting date (Wells et al. 2014). Overall, the variability in soybean grain yield following a cereal rye cover crop is caused by a host of environmental conditions and management factors.

When used in a no-till corn-soybean rotation, repeated use of a cereal rye cover crop can improve soil aggregate stability by 55 and 29% in the top 0-10 and 10-20 cm of soil, respectively, in just four years (Rodrick and Kladivko 2017). Soil aggregate stability, or the ability of the soil to resist disruption from outside forces, can improve water filtration and protects against erosion and crusting (NSSC 1996). Cereal rye produces a fibrous root system, which allows it to serve as a scavenger crop by capturing anywhere from 60 to 100% of the nitrogen (N) that otherwise would have been leached following a corn crop (Clark 2012). Like biomass production, the earlier the cereal rye is seeded the more successful it is at scavenging N due to an extended fall establishment window.

Incorporating a cereal rye cover crop in a corn-soybean rotation after corn can reduce soil N by 42% (Dozier et al. 2017). This reduction is first evident in the soil area between the root zone and water table, where cereal rye retains the N that would have otherwise been lost to leaching, and can be near 80% (Staver and Brinsfield 1998). It has even been shown to maintain N losses to less than 5.6 kg ha-1 when overseeded into soybeans and allowed to grow from September to May, which can be a difference of 207 kg ha-1 compared to a non-covered control (Clark 2012; Kaspar et al. 2007). The use of a cereal rye cover crop terminated with a roller-crimper can suppress weeds while leaving crop yield relatively unaffected by the N scavenging of the cover crop as a result of N fixation by the soybeans (Wells et al. 2014). Utilizing a cover crop in this way ensures that more of the nutrients the grower applies remain in the field benefitting the crop.

Weed suppression by a cover crop is often most effective when the cover crop produces dense stands and is allowed long windows of growth (Dabney et al. 2001).

Cereal rye is one of the most impressive cover crop species in terms of its ability to provide weed control, largely due to the aforementioned high levels of biomass production. In one study comparing ten different cover crop species and species mixes, cereal rye provided the most winter annual weed suppression at 72% control, but did not attain as much control as a fall herbicide program in the absence of rye, at 99% control

(Cornelius and Bradley 2017). When used in combination with preemergence herbicides, a cereal rye cover crop can provide weed control that is comparable to an intensive herbicide only program that utilizes both preemergence and postemergence herbicides

(Price et al. 2006). Used as a living mulch, where the cover crop is kept alive to grow alongside the cash crop, cereal rye can provide adequate weed control, but often at the cost of soybean vigor (Ateh and Doll 1996). In general, it has been found that weed biomass decreases as the biomass of a cereal rye cover crop increases, but that as weed pressure increases supplemental control methods such as herbicide applications are still necessary to achieve acceptable weed control (Nord et al. 2011).

Cereal rye also possesses some allelopathic properties, allowing it to chemically suppress nearby growing vegetation. It is capable of producing a number of chemical compounds, including: b-phenyllactic acid, b-hydroxybutyric acid, and a number of benzoxazolinone compounds (Liebman and Davis 2000). The effects of these compounds on crops can be reduced while still reaping weed control benefits by allowing a couple of weeks to pass between rye termination and crop planting (Liebman and Davis

2000), but it is also important to be cognizant of the seed characteristics of the crop that follows. In general, allelopathic compounds have the greatest effect on germination and growth of smaller-seeded weeds and crops than larger seeded weeds and crops, and legumes as a whole are rather insensitive or even stimulated by the compounds from rye residue (Liebman and Davis 2000; Putnam and DeFrank 1983). Additionally, monocots have been shown to be less sensitive to the allelopathic chemicals of cereal rye residue than dicots. Horseweed was found to have markedly reduced germination when grown in soil with cereal rye root residue, and experienced similar rates of germination reduction despite seeding rate, suggesting a threshold effect (Przepiorkowski and Gorski 1994).

Current recommendations for the control of glyphosate-resistant horseweed in

Ohio soybean production can be found in “The Weed Control Guide for Ohio, Indiana, and Illinois”. (Loux et al. 2016). This guide serves as one of the main resources used by crop producers in the Midwest for herbicide and general management decisions. In this guide, an entire section has been dedicated to the control of glyphosate-resistant horseweed in no-till soybean production. Studies conducted by Ohio State in 2010 show that when managed according to the guide, growers could gain nearly 940 kg ha-1 in soybean yield versus situations where horseweed is not adequately controlled (Loux et al.

2016). It is also mentioned that the majority of Ohio horseweed is the glyphosate- resistant biotype, and a growing number also exhibits resistance to ALS herbicides. The guide also suggests glufosinate-resistant LibertyLink soybeans as the most effective tool to control glyphosate-resistant horseweed, in conjunction with burndown and residual herbicides (Loux et al. 2016). However, using these tolerant crop systems as well as a

15 number of difference herbicides and application timings can increase the cost of production over less intensive methods. The potential for cover crops to be another tool that may replace even a single herbicide application would be beneficial to producers.

“The Weed Control Guide for Ohio, Indiana, and Illinois” details a five-step approach for management and control of glyphosate-resistant horseweed in no-till soybeans. Overall, the message is that the more important factors in a herbicide program include the use of a fall herbicide treatment to control fall-emerging horseweed and the application of a residual herbicide in the spring to control horseweed into the growing season (Loux et al. 2016). The first step in the program is a fall treatment that is primarily

2,4-D or a mix including 2,4-D with the intent to control winter annual broadleaf weeds such as horseweed. The second step is the application of a spring burndown in order to ensure a clean field at the time of cash crop planting. The third recommendation is to include a non-ALS herbicide in the spring residual application for most effective control, as ALS resistance is also an issue in horseweed, for control in the weeks that follow planting. The fourth step is to split spring treatments into two applications, in early spring and before planting, if a fall treatment was not applied. The fifth and final recommendation for the control of glyphosate-resistant horseweed in soybeans is not an instruction so much as a sentiment. It is made clear that unless horseweed control has been exceptional in the past and populations are well-known, one spring pre-plant treatment is often not effective when it comes to controlling the year-long germinating horseweed in no-till situations (Loux et al. 2016).

There has yet to be a study published that quantifies the control of glyphosate- resistant horseweed achieved by a terminated cereal rye cover crop at different planting dates and seeding rates with various spring and fall herbicide treatments in no-till soybean production systems. Unpublished preliminary results that come from a study conducted at Pennsylvania State University suggest the density and size of horseweed plants in spring prior to planting can be reduced up to 75% using a cover crop (Wallace et al. 2016). They are investigating the role of cover crops for control of winter annual weeds in no-till soybeans. The study included several cover crop species and mixes of species, and found that even in the mixes cereal rye provided the most consistent weed suppression. It was also observed that the extent of fall cover crop growth was most important in determining control levels (Wallace et al. 2016). The Ohio State University conducted a study from 2012-2013 investigating the effect of three factors on the control of horseweed. These factors were a fall 2,4-D treatment, cover crop termination date, and spring residual application on horseweed control at three sites. Results showed that termination date was the most consistently significant factor across all sites, with control decreasing the earlier the cover was terminated (Loux 2013).

Several gaps in the literature exist as to the specifics of a cereal rye cover crop program that can provide consistent control of glyphosate-resistant horseweed. It is clear that earlier planting dates of cereal rye can produce higher levels of biomass. It is unclear however whether the difference in weed suppression makes earlier planting a necessity, or whether Ohio soybean producers have a longer planting window that can achieve similar control, making cover crops potentially more accessible to a higher number of

17 growers. Several recommendations for the seeding rate of cereal rye as a grain crop, cover crop, and forage exist. However, it is important to relate the suppression of horseweed to seeding rate before making recommendations. To date, herbicides have been the most important form of horseweed control. It is vital to better understand the levels of control from the different recommended applications in this system to determine if cover crop supplement practices can aid or replace herbicide applications in no-till soybeans. This research aims to provide a more detailed approach for the use of a cereal rye cover crop program to control glyphosate-resistant horseweed in order to provide effective recommendations to Ohio soybean producers.

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Chapter 2. Optimizing a Cereal Rye Cover Crop Program for the Control of Glyphosate- Resistant Horseweed in No-Till Soybeans

Introduction

Horseweed (Conyza canadensis) has long been a problematic weed in Ohio soybean (Glycine max) production fields, reducing yields greatly if not adequately controlled (Bruce and Kells 1990). A survey performed by the Weed Science Society of

America in 2016 found horseweed to be both the number one most common and troublesome weed in Ohio soybean production (Van Wychen 2016). It possesses a number of characteristics that give it the ability to spread over long distances and grow in practically any season. Horseweed can exhibit as both a winter or summer annual, with five to 32% of total germination occurring in the spring (Buhler and Owen 1997; Loux et al. 2004). It has become even more problematic since the evolution of the herbicide- resistant biotypes that now infest fields. Meanwhile, increased interest in conservation management has led to an increase in the number of acres that farmers are managing with no-tillage and cover crops (Vilsack and Clark 2014; Watts et al. 2014). This has exacerbated control issues as horseweed is a surface germinator and thrives in no-till conditions, exhibiting poor germination if buried below 0.5 cm (Nandula et al. 2006).

Current recommendations for the control of horseweed consist of comprehensive herbicide programs. These programs can be difficult for growers to implement and manage, as they most often require both fall and spring herbicide applications to achieve 26 acceptable horseweed control. The applications outside of the growing season are necessary because there are few postemergence herbicide options for the late-season control of horseweed, especially if the population exhibits resistance to glyphosate (Loux et al. 2016). Horseweed must be controlled as both a summer and winter annual, an additional rationale for the requirement of both fall and spring applications. Cover crops may be able to provide some weed suppression and could potentially reduce the need for chemical applications (Teasdale 1996). Cereal rye (Secale cereale), henceforth referred to as rye, is one of the most widely used cover crops. It is capable of producing high levels of biomass, and can outcompete winter and summer annual weeds (Cornelius and

Bradley 2017). There has yet to be a comprehensive rye cover crop management program developed that integrates the use of fall or spring applied herbicides and the specifics of rye management to control glyphosate-resistant horseweed.

The goal of this study was to develop a more complete management program that addressed the use of rye as an alternative or supplemental form of glyphosate-resistant horseweed control. The planting date and seeding rate of a rye cover crop were two factors that could potentially impact horseweed population density and control. The hypothesis was that earlier planting dates and higher seeding rates, which may allow for greatest rye biomass production, are most effective in controlling horseweed. Horseweed population density and control could also be effected by the presence and absence of spring residual herbicides, but the relationship in the presence of rye was unclear. The hypothesis was that the addition of a rye cover crop will supplement the spring residual herbicides in terms of reducing horseweed density. It was also uncertain whether

27 optimizing the fall rye growth could consistently replace the fall herbicide treatment. The hypothesis was that the fall rye cover crop growth, aided by earlier planting dates and higher seeding rates, would be sufficient to achieve acceptable control and replace the fall applied herbicide treatment. The overall expected outcome was that adding a rye cover crop to a no-till soybean production system would aid in controlling glyphosate-resistant horseweed, and possibly even replace some of the conventionally used herbicide inputs, such as fall 2,4-D and spring preplant residuals.

Objectives

The objectives of this research were to: (1) determine the effect of planting date and seeding rate of a rye cover crop on horseweed population density and control in the subsequent soybean crop; (2) determine the effects of different levels of spring residual herbicides on horseweed population density and control (referred to as Study I); and (3) determine whether optimizing the fall rye growth can consistently replace the fall herbicide treatment that has been an important component of herbicide programs for horseweed control (described as Study II).

Materials & Methods

Two field studies were conducted simultaneously in the growing season from fall

2016 to fall 2017 at the OARDC Western Agricultural Research Station in South

Charleston, Ohio (39°51'43.58"N, 83°40'2.37"W). At both sites, there was a natural infestation of glyphosate-resistant horseweed, henceforth referred to as horseweed. The fields used for each study had previously been in wheat production and were fallow at the start of the project. The soil type in Study I was a Kokomo silty clay loam with a soil

28 organic matter content of 2.8% and a pH of 6.4. In Study II, the soil was a Crosby silty clay with an organic matter content of 1.6% and a pH of 6. In order to incorporate all the variables of the three objectives, a randomized complete block design in a split-split plot randomization restriction with four replications was utilized. Individual plots were three meters wide by nine meters long. Study I consisted of 18 treatments with the following factors: two rye planting dates, three rye seeding rates, and three levels of a spring residual herbicide. Study II consisted of 12 treatments with the following factors: two rye planting dates, three rye seeding rates, and two levels of a fall herbicide, all to be described in depth in the following section. For each study, rye planting date was the main plot factor, rye seeding rate was the subplot factor, and the herbicide treatment was the sub-subplot factor.

Rye, variety not stated (Cisco Company; Indianapolis, IN), was disc drilled at a depth of 1.3 cm in 19 cm rows in both studies. For both studies, the first rye planting date was September 27 and the second planting date was October 26. These planting dates were representative of both the beginning and most active times of grain corn (Zea mays) harvest, directly after which cover crops can be planted with a subsequent planting of soybeans in a conventional crop rotation (USDA-NASS 2010). These establishment timings are also suitable for fields that were previously in wheat (Triticum aestivum) production and harvested mid-summer. Rye seeding rates were 0, 50, and 100 kg of seed ha-1. The 50 and 100 kg ha-1 rates represented the low and mid to high ends of the recommended seeding rate range for rye used as a cover crop (Hayden et al. 2014;

Winger et al. 2010). The 100-kg ha-1 seeding rate also represented the recommended

29 seeding rate for rye planted as a grain or forage (Bruening 2015), and the 0 kg ha-1 rate served as the control. Study I received a broadcast application to all plots of 2,4-D at 0.49 kg ae ha-1 on November 28 so that management of the trial was consistent with current recommendations, and the residual herbicide factor could be isolated for evaluation. In

Study I, the three levels of spring preplant residual herbicides were non-treated, flumioxazin at 0.09 kg ai ha-1, and flumioxazin plus metribuzin at 0.09 and 0.42 kg ai ha-

1, respectively. These herbicides were applied on April 25, 22 days prior to soybean planting. Spring preplant residual levels were representative of common agronomic practices in Ohio, with many producers starting with a flumioxazin or sulfentrazone product, and then adding metribuzin for greater control if needed. These treatments were utilized because many of the horseweed populations across Ohio exhibit ALS resistance

(Trainer et al. 2005). The two levels of fall herbicide in Study II were non-treated and

2,4-D at 0.49 kg ae ha-1. A fall application of 2,4-D is the first step in recommendations for Ohio producers attempting to control horseweed (Loux et al. 2016). Study II also received a broadcast application to all plots of flumioxazin at 0.09 kg ai ha-1 on April 25 to enable isolation of the fall herbicide treatment as a factor. Both studies received a spring broadcast preplant treatment of glyphosate at 0.89 kg ae ha-1 on April 25 to terminate the rye.

Soybeans resistant to glyphosate and dicamba, Asgrow AG36X6, were planted on

May 17 at a rate of 432000 seeds ha-1 in rows spaced 38 cm apart. Soybeans emerged on

May 29. Both studies received a postemergence application of glyphosate at 0.89 kg ae ha-1 on June 16 to remain consistent with current recommendations and common

30 practices. In Study II, 1.11 kg ai ha-1 of acetochlor was included with the glyphosate for residual control of pigweed and grass species based on the history of the experimental

-1 field. Herbicide treatments were applied at a volume of 140 L ha with a CO2 pressurized backpack sprayer equipped with a three meter boom using Air Injection Extended Range

(AIXR) TeeJet® tips. The other broadcast treatments were applied in 140 L ha-1 using a tractor-mounted sprayer.

Measurements in both studies included horseweed population density and visual evaluation of control, aboveground rye biomass, and soybean population density and seed yield. Horseweed density and control were measured on the day prior to the spring preplant herbicide application (April 24), at soybean planting on May 17, four days after the summer postemergence application on June 20, late-season just prior to canopy closure on July 7, and just prior to soybean harvest on October 17. To measure horseweed density, two 0.25 m2 quadrats were established in the front and back half of each plot, and maintained throughout the duration of the study. This ensured that the population density was always measured within the same area. Horseweed control was evaluated using a scale of 0 to 100, where 0 was no control, similar to the non-treated without rye, and 100 represented complete control, or an absence of horseweed. Rye biomass was measured on November 28 of the fall planting by harvesting the aboveground rye growth from a 0.25 m2 quadrat placed at random in the middle of the plot, taking care to include the same number of drilled rows each time. The rye samples were dried at 55° C for three days and weighed immediately to assess biomass based on dry weight. A rising plate meter (Jenquip; Feilding, NZ) was used to measure spring rye

31 biomass on April 24, the day prior to rye termination (Michell et al. 1983). Ten samples outside the treated plot area were measured using the plate meter and then cut, dried, and weighed in order to obtain the calibration equation used for subsequent non-destructive measurements within plots. The mean of five measurements from each plot were used to derive kg ha-1 of dry matter using the established calibration equation, and these were then averaged per plot for analysis.

Soybean density was measured on July 18 utilizing a method from the University of Kentucky. This required counting the number of soybeans in 1.5 m of the front and back of each center plot row, adding the values and entering the sum in a formula based on the number of soybeans per foot of row and row width, to give an estimate of plants per acre (Lee and Herbek 2005). Soybeans were harvested mechanically on October 20 and seed yield measured and adjusted to a moisture of 130 g kg-1. Results were similar for both horseweed density and visual estimation measurements. As such, the horseweed density results will primarily be presented. Data were analyzed as a factorial in a randomized complete block using the GLIMMIX procedure in SAS 9.4. Fixed factors were the rye planting date, rye seeding rate, and herbicide level. Replication was the random factor, with replication, date by replication, and rate by date and replication.

Treatment means were compared using Fisher’s protected LSD (α = 0.05). Linear regression analysis was conducted to determine the relationship between rye biomass and horseweed density, using data collected in May. Data were transformed using square root and log functions in an attempt to improve normality. Neither transformation had an

32 effect on normality or outcomes, therefore non-transformed data were analyzed and presented here.

Results & Discussion

The main effects of rye seeding rate and herbicide level had the most consistent impact on horseweed population density in both studies. There was no difference in horseweed population density as a result of rye planting date in Study I or Study II with the exception of a date by rate interaction in Study II (Table 2.1 and 2.2). This corresponds with results of a study that assessed rye planted after both wheat in August and after corn in September and October that found both had produced relatively similar levels of biomass by spring, which could lead to similar mid and late-season control of horseweed (Murrell et al. 2017). It has also been shown that differences in planting dates can cause variability among roller-crimped spring biomass levels, especially between mid to late fall planting dates, and that earlier planting dates can reduce total weed densities

(Mirsky et al. 2011). However, the same study found that the date of rye planting from

August through October did not have an effect on the density of broadleaf weeds.

Compared to treatments with rye, horseweed population density was higher in the absence of rye (averaged over other factors) at the time of the spring preplant herbicide application in late April, as well as in May at soybean planting, but there was no difference between rye seeding rates (Tables 2.3 and 2.4). Spring horseweed population densities remained relatively constant in the rye treatments from early spring through the time of soybean planting in May.

Horseweed population density in both studies remained affected by seeding rate at the time of the postemergence herbicide application in mid-June and just before soybean canopy closure in early July. In Study I, horseweed density in mid-June was higher in the absence of rye compared to the highest cereal rye seeding rate (Table 2.3). In Study II, horseweed density was higher in the absence of rye compared to both the high and low seeding rates, consistent with the results found in April and May (Table 2.4). It is important to note that Study II had a higher natural infestation of horseweed than Study I.

In July, horseweed density in Study I was lower in the presence of rye versus the absence, with an average of 7.7, 3.2, and 1.8 plants m-2 for the 0, 50, and 100 kg ha-1 seeding rates, respectively (Table 2.3). At this time in Study II, horseweed density was lower at the high seeding rate versus the absence of rye, with an average of 22.5, 18.5, and 12.5 plants m-2 for the 0, 50, and 100 kg ha-1 rates, respectively (Table 2.4). These results suggest that, averaged over other factors, inclusion of a rye cover crop in no-till soybeans can decrease horseweed population density, and that using higher rye seeding rates may help to achieve more effective season-long control. This has been observed in other studies that found the inclusion of a rye cover crop can reduce weed density (Mischler et al.

2010) as well as a positive relationship between rye seeding rates and weed suppression

(Boyd et al. 2009; Ryan et al. 2011).

Beyond planting date and seeding rate, the purpose of Study I was to determine whether or not a rye cover crop could aid or replace the horseweed control provided by spring-applied residual herbicides. In May, roughly a month after application, spring residual herbicide level did not have an effect on horseweed density (Table 2.1).

However, horseweed density in mid-June was lower with the flumioxazin and metribuzin residual application, averaged over other factors, compared with flumioxazin alone or an absence of herbicides. At this time, there was an average of roughly 6.8, 5.5, and 1.3 horseweed plants m-2 in the non-treated, flumioxazin, and flumioxazin and metribuzin treatments, respectively (Table 2.5). In early July, the flumioxazin and metribuzin treatments had a reduced horseweed density compared to the non-treated across rye treatments. However, the flumioxazin treatments were not different than the mixture or non-treated. In July there were an average of 6.3, 4.8, and 1.7 horseweed plants m-2 in the non-treated, flumioxazin, and flumioxazin and metribuzin treatments, respectively (Table

2.5). These results suggest that a spring residual is necessary for acceptable horseweed control, comparable to a comprehensive herbicide-only program, but the use of a rye cover crop can reduce horseweed density and may allow for a less intense preplant residual herbicide application, in terms of the number of active ingredients necessary, to achieve relatively similar late-season control. This is consistent with the findings of another study that illustrated the ability of rye to suppress summer annual weeds compared non-treated controls, other cover crop species, and even other herbicide programs early season, but not to the same degree as spring preplant residual herbicides late season (Cornelius and Bradley 2017).

Study II was designed in part to determine whether rye growth was capable of replacing the fall herbicide treatment that has been an important part of horseweed control. Horseweed population density was impacted by the fall herbicide treatment throughout the season (Table 2.2). Treatments that included a fall herbicide had reduced

35 horseweed density compared to those that did not from April through June, averaged over other factors (Table 2.6). In late April, horseweed density was greatly reduced in the treatments that included a fall herbicide compared to those that did not. Treatments without the fall herbicide had an average of 52.2 horseweed plants m-2, while treatments that included a fall herbicide had an average of 6.5 plants m-2 (Table 2.6). These population differences remained constant through the time of soybean planting in May, where plots that received the fall herbicide had an average of 5.3 horseweed plants m-2, while those that did not had an average of 23.7 plants m-2 (Table 2.6). These findings again remained constant through mid-June, with horseweed density being reduced when treated with a fall herbicide. Herbicide level did not have an effect on horseweed density in July, with fall treated plots having an average of 20.9 plants m-2 and the plots without the fall treatment an average of 14.8 plants m-2 (Table 2.6). In July there was a rye planting date by fall herbicide interaction in terms of horseweed plant density. The early plant date and no herbicide combination had a greater horseweed density than the early plant date and fall herbicide treatment (Table 2.7). This indicates that late season, early planting of rye alone is not enough to replace the control from a fall herbicide treatment.

These results suggest that a fall herbicide treatment is still important for the control of horseweed, even in the presence of a rye cover crop at any plant date, especially for early season control. These results are in agreeance with another study that showed rye generally does not control weeds, especially winter annuals, to the same extent as fall applied herbicides (Cornelius and Bradley 2017). The understanding of this effect and

36 that of the spring preplant residual is important as horseweed can act as both a winter or summer annual, and thus must be controlled as both.

Rye biomass in the fall was influenced by plant date, seeding rate, and interactions between the two (Table 2.8). At the time of biomass collection in late-

November, earlier planted rye produced greater amounts of biomass in both studies. In the first study, the early planted rye produced an average of 401 more kg ha-1 than the later planted. In the second study, the early planted rye generated 195 more kg-1 of biomass than the late planted, averaged over other factors (Table 2.9). In the fall, the high seeding rate out produced the low seeding rate by an average of 207 and 131 kg ha-1 in

Study I and II, respectively (Table 2.10). In terms of the date by rate interaction, the early planted high seeding rate combination produced the most fall biomass, followed by the early low, then two late planting dates, which were not different from one another in either study (Table 2.11). These results suggest that an early rye planting date combined with a high seeding rate can achieve the highest level of fall biomass. However, regressions relating fall rye biomass levels to horseweed density in November showed no relationship between the two in the treatments in Study II that received no fall 2,4-D application at both planting dates or the early only planting date (data not shown).

In the spring, early planted rye in Study I produced more biomass than late planted, averaged over other factors. Rye biomass in the spring showed no difference based on planting date in Study II (Table 2.9). At this time, the early planting date in

Study I produced 717 more kg ha-1 than the late planting date (Table 2.9), and the high seeding rate produced 660 more kg ha-1 than the low, averaged over other factors (Table

2.10). The early plant date and high seeding rate produced more biomass than the late plant date at either seeding rate in Study I (Table 2.11). In Study II, the late and early planted rye did not yield different amounts of biomass come spring (Table 2.9), but the high seeding rate produced 809 more kg ha-1 of biomass than the low seeding rate (Table

2.10). In Study II, the early and late planted 100 kg ha-1 seeding rate treatments produced more biomass in the spring than rye seeded at 50 kg ha-1, regardless of planting date

(Table 2.11). Taking into account these results and the horseweed population density measurements, rye seeding rate may have more of an effect on the amount of rye biomass than planting date by spring termination. This could however be a result of the relatively mild temperatures during the 2016-2017 winter that allowed the later planted rye to catch up in growth to the early planted, therefore providing similar levels of weed control, averaged over the other factors. Regressions performed on Study II treatments without the fall 2,4-D application showed an inverse relationship between spring rye biomass and horseweed density at the time of soybean planting in May at both planting dates, but the fit was better when only the early planting dates were included (Figures 2.1 and 2.2).

These results suggest that in the absence of a fall 2,4-D application, a rye cover crop can provide a reduction in horseweed density and that earlier planting dates may allow for greater biomass production, which may allow for greater control than later planted rye.

There was no treatment effect on soybean plant density or seed yield in either study. There were an average of 271997 soybean plants ha-1 in Study I and 211421 soybean plants ha-1 in Study II. Treatments averaged 4620 to 5246 kg ha-1 soybean seed yield in Study I and 4230 to 4909 kg ha-1 in Study II. Overall, whether the rye was

38 planted in late September or late October was not critical in its effect on horseweed density in either study, averaged across other factors. Just prior to cover crop termination in April and at the time of soybean planting in May, horseweed density in both studies was reduced by the presence of rye regardless of seeding rate. In June and July, horseweed density was reduced by the presence of rye, and Study I had better control at the higher seeding rate. Horseweed density in June was reduced with the inclusion of a spring residual at the high level. However, in July, the rye cover crop provided similar horseweed density management as the medium residual level, averaged over other factors. Treatments that included a fall herbicide had consistently lower levels of horseweed plant density throughout the season than those that did not until July.

These results suggest that inclusion of a rye cover crop can reduce horseweed density overall. Further, earlier rye planting dates can result in higher cover crop biomass in late fall, but higher seeding rates may increase control more consistently throughout the season. At the time of the postemergence herbicide application late-June, the high seeding rate provided the highest level of horseweed control of any main effect at 91% and 84% in studies one and two, respectively, averaged across other factors (data not shown). Rye used as a cover crop before no-till soybeans can add value to a weed management program in terms of reducing the number of horseweed plants, and potentially allowing for a less intense preplant residual application. This research confirms the results of other studies that illustrate the ability of rye to reduce weed pressure, but reinforces that herbicides are an essential component of weed management

(Cornelius and Bradley 2017; Reddy 2001; Ateh and Doll 1996). Fall herbicide

39 treatments and comprehensive spring residual programs are still important to ensure effective horseweed control into the growing season, especially where there are no postemergence herbicide options, as in the case of glyphosate-resistant horseweed in no- till soybean production systems. Incorporating a rye cover crop with a comprehensive herbicide program can be part of the integrated weed management strategy necessary to control glyphosate-resistant horseweed in no-till soybean production systems.

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Murrell EG, Schipanski ME, Finney DM, Hunter MC, Burgess M, LaChance JC, Baraibar B, White CM, Mortensen DA, Kaye JP. Achieving Diverse Cover Crop Mixtures: Effects of Planting Date and Seeding Rate. Agron J. 109(1):259-271.

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Table 2.1 ANOVA results for the effect of rye planting date and seeding rate, and spring residual herbicides on horseweed population density in Study I.

April May June July Planting Date NS NS NS NS Seeding Rate ** * * * Spring Residual NA NS * * a For each main effect, * and ** indicate significance with p ≤ 0.05 and p ≤ 0.01, respectively, and NS indicates nonsignificant with p > 0.05.

Table 2.2 ANOVA results for the effect of rye planting data and seeding rate, and fall herbicide treatment on horseweed population density measurements in Study II.

April May June July Planting Date NS NS NS NS Seeding Rate * ** ** * Fall Herbicide * ** ** * Date*Fall Herb NS NS NS * a For each main effect, * and ** indicate significance with p ≤ 0.05 and p ≤ 0.01, respectively, and NS indicates nonsignificant with p > 0.05.

Table 2.3 Effect of rye seeding rate on horseweed density in Study I, averaged over rye planting date and spring residual herbicide level.

Rye Seeding Rate April May June July kg ha-1 ——————————– plants m-2 ——————————– 100 0.2 b 0.3 b 2.1 b 1.8 b 50 0 b 1 b 3.3 ab 3.2 b 0 6.6 a 8.2 a 8.2 a 7.7 a LSD 4.1 6.5 5.1 4.3 a Means within a column followed by the same letter are not significantly different based on LSD at α = 0.05

Table 2.4 Effect of rye seeding rate on horseweed density in Study II, averaged over planting date and fall herbicide treatments.

Rye Seeding Rate April May June July kg ha-1 ——————————– plants m-2 ——————————– 100 2.9 b 0.9 b 9.9 b 12.5 b 50 12.3 b 11.1 b 17.3 b 18.5 ab 0 72.9 a 31.4 a 29.6 a 22.5 a LSD 56.4 14.1 11.6 8.1 a Means within a column followed by the same letter are not significantly different based on LSD at α = 0.05

Table 2.5 Effect of spring preplant residual herbicide on horseweed density in Study I, averaged over rye planting date and seeding rate.

Spring Preplant Residual June July

––––––– plants m-2 –––––––

Flumioxazin + metribuzin 1.3 b 1.7 b

Flumioxazin 5.5 a 4.8 ab

Non-treated 6.8 a 6.3 a

LSD 3.5 3.4 a Means within a column followed by the same letter are not significantly different based on LSD at α = 0.05

Table 2.6 Effect of fall herbicide treatment on horseweed density in Study II, averaged over rye planting date and seeding rate.

Herbicide April May June July ——————————– plants m-2 ——————————– 2,4-D 6.5 b 5.3 b 11.8 b 14.8 a Non-treated 52.2 a 23.7 a 26.0 a 20.9 a LSD 37.9 11.1 9.1 6.4 a Means within a column followed by the same letter are not significantly different based on LSD at α = 0.05

Table 2.7 Effect of interaction between rye planting date and fall herbicide treatment on horseweed density in Study II, averaged over rye seeding rate.

Rye Planting Date Fall Treatment July

Early Non-treated 23.5 a Early 2,4-D 9.9 b Late Non-treated 18.3 ab Late 2,4-D 19.7 ab LSD 10.1 a Means within a column followed by the same letter are not significantly different based on LSD at α = 0.05

Table 2.8 ANOVA results for the effect of rye planting date, seeding rate, and date by rate interaction on rye biomass.

Study I Study II Fall Biomass Spring Biomass Fall Biomass Spring Biomass Date ** * ** NS Rate ** ** ** ** Date*Rate ** ** ** NS a For each main effect and interaction, * and ** indicate significance with p ≤ 0.05 and p ≤ 0.01, respectively, and NS indicates nonsignificant with p > 0.05

Table 2.9 Effect of rye planting date on fall and spring rye biomass averaged over seeding rate and herbicide treatment.

Date Study I Study II Fall Biomass Spring Biomass Fall Biomass Spring Biomass –———————————— kg ha-1 —————————— Early 439.1 a 3153.7 a 219.6 a 2718.3 a Late 37.5 b 2436.3 b 24.9 b 2774.3 a a Means within a column followed by the same letter are not significantly different based on LSD at α = 0.05

Table 2.10 Effect of rye seeding rate on fall and spring rye biomass averaged over planting date and herbicide treatment.

Rye Seeding Rate Study I Study II Fall Biomass Spring Biomass Fall Biomass Spring Biomass kg ha-1 –———————————— kg ha-1 ——————————– 100 465.4 a 4522.4 a 249.2 a 4524.0 a 50 249.5 b 3862.6 b 117.7 b 3715.6 b 0 0 c 0 c 0 c 0 c a Means within a column followed by the same letter are not significantly different based on LSD at α = 0.05

Table 2.11 Effect of the planting date by seeding rate interaction on fall and spring rye biomass averaged over herbicide treatment.

Date*Rate Study I Study II Fall Biomass Spring Biomass Fall Biomass Spring Biomass kg ha-1 –———————————— kg ha-1 ——————————–——– Early 100 854.8 a 4874.0 a 441.3 a 4592.9 a Early 50 462.5 b 4587.0 ab 218.0 b 3563.4 b Early 0 0 c 0 d 0 c 0 c Late 100 76.1 c 4170.9 b 57.1 c 4455.2 a Late 50 36.5 c 3138.1 b 17.5 c 3867.8 b Late 0 0 c 0 d 0 c 0 c a Means within a column followed by the same letter are not significantly different based on LSD at α = 0.05

Figure 2.1 Regression for the effect of spring rye biomass on horseweed population density at the time of soybean planting in May in Study II. Regression analysis included data from both rye planting dates in the absence of a fall 2,4-D application (p = 0.004).

Figure 2.2 Regression for the effect of spring rye biomass on horseweed population density at the time of soybean planting in May in Study II. Regression analysis included data from the early rye planting date in the absence of a fall 2,4-D application (p = 0.004).

Chapter 3. The Spatial and Temporal Distribution of Horseweed in Ohio Soybean Production Fields from 2013 to 2017

Introduction

Soybean (Glycine max) has been one of the most economically important crops in

Ohio. In 2016, 1.9 million hectares of soybeans were harvested with a value of over 2.5 billion dollars (USDA-NASS 2018a). From the middle of 2012 to the middle of 2014, soybean prices growers received ranged from $0.45 to $0.59 kg-1. Mid 2014 through the early months of 2018, soybean prices hovered in the $0.31 to $0.37 kg-1, dropping to the lowest level since 2009 in February of 2016 at $0.31 kg-1 (USDA-NASS 2018b). This was an industrywide trend as farmers paid on average anywhere from 5 to 15% more on input costs than they received from 2014 to 2017 (USDA-NASS 2018c). As soybean prices remained low, it became even more important to maximize yield in order to offset rising input costs. The presence of weeds continued to be one of the most yield-limiting factors, and caused up to 39% of soybean yield loss in the Midwestern corn belt region

(USDA-NASS 2014). According to a survey performed by the Weed Science Society of

America, the five most common soybean weeds in Ohio in 2016 were horseweed

(Conyza canadensis), giant ragweed (Ambrosia trifida), common ragweed (Ambrosia artemisiifolia), common lambsquarters (Chenopodium album), and volunteer corn (Zea mays), respectively (Van Wychen 2016). The survey also identified the first three of these weeds as the three most troublesome weeds in Ohio soybean production, with 57 horseweed at number one in both categories. An increase in the number of herbicide- resistant weeds and their spread in Ohio have made weed control efforts more complex and expensive. As of 2017, there have been 11 weed species with reported resistance in

Ohio to five herbicide sites of action, and three species have resistance to multiples sites of action (Table 3.1; Heap 2018). Several of these species with herbicide-resistant biotypes are also the most common and troublesome weeds in Ohio.

A single horseweed plant can produce up to 200,000 seeds, capable of spreading as far as 500 meters from the seed source (Bhowmik and Bekech 1993; Dauer et al.

2007). Additionally, horseweed can exhibit summer annual, winter annual, or biennial life cycles (Buhler and Owen 1997). This complicates the control of horseweed as contamination can come from a number of sources (temporally and spatially) and germination is unpredictable. Beyond biological advantages, herbicide resistance has made horseweed increasingly difficult to control. In Ohio, there have been documented horseweed populations with resistance to acetolactate synthase (ALS) inhibitors (site 2), or 5-enolpyruvylshikimate-3-phosphat (EPSP) synthase inhibitors (site 9), and multiple resistance to both sites of action (Heap 2018). Failure to control horseweed can cause soybean yield loss of up to 940 kg ha-1 in Ohio soybean production, a value of roughly

$320 ha-1 (Loux et al. 2016).

On-site surveys of weed populations in fields provide information on the relative occurrence and density of weeds that can be useful to growers in that region (Loux and

Berry 1991). While national surveys can be useful, it is important to also conduct higher resolution surveys as weeds that are common and troublesome in larger regions of the

58 country are not always the most problematic in a certain state (Rankins et al. 2005).

These surveys can also be used to monitor temporal shifts in species within selected geographical areas (Rankins et al. 2005). Land owners, producers, extension agents, and weed scientists all have something to gain from the data generated by weed surveys, as it can aid in the management of weed issues by monitoring the movement of problem weeds and forecasting areas susceptible to infestations (Korres et al 2015). Maps are helpful tools in examining the geographical distribution and spread of weed species, which can be supplemented by information on species abundance and infestations, to illustrate regional differences and predict species occurrences (Hanzlik and Gerowitt

2016). Results reported here from the weed surveys conducted in Ohio from 2013 to

2017 provided OSU weed scientists, county educators, and growers with these types of information, which aided in planning educational efforts and weed management programs.

Objectives

The objectives of this research were to: (1) determine the frequency, infestation level, and distribution of horseweed, giant ragweed, common ragweed, redroot pigweed, waterhemp, and Palmer amaranth in soybean fields in 49 Ohio counties; (2) determine the spatial and temporal distribution of horseweed at each rating level in soybean fields and identify any significant spatial clusters or movement trends.

Materials & Methods

A survey of soybean fields in 49 to 52 Ohio counties was conducted annually each fall from 2013 to 2017, just prior to soybean harvest. Visual evaluations of

59 frequency and population density were measured for six weed species: horseweed, giant ragweed, common ragweed, redroot pigweed, waterhemp, and Palmer amaranth.

Amaranthus species (redroot pigweed, waterhemp, and Palmer amaranth) were grouped and rated together as ‘pigweeds’. Of counties surveyed, only the 49 that were surveyed in all years were included here. A total of approximately 3400 to 4900 total fields were evaluated each year (Table 3.2). The state was divided into six regions that were evaluated sequentially from most to least dry based on the United States Drought Monitor for Ohio in order to capture as many unharvested fields as possible (Bathke 2017).

Counties with at least 4046 hectares of soybean production were surveyed, mostly located in the central and western regions of the state (Figure 3.1).

Routes were creating using Google Earth with the intention to drive diagonal transects across each county, during which large cities were avoided. These routes were loaded onto a Garmin GPS system for navigation. In order to account for predominant corn-soybean crop rotations annually, the same routes were driven for two years and then adjusted. Soybean fields passed on these routes were assessed for weed infestations, based on visibility of weeds above the soybean canopy. A rating scale of zero to three was used to assess the level of each weed species, as follows: zero –not present; one - single, isolated weeds scattered in the field; two - clustered groups dispersed throughout the field; and three - dense, widespread clusters, indicative of an infestation. For the purpose of this survey, the presence and infestations of all weed species were discussed relative to one another, but only the horseweed data was broken down by rating and further analyzed.

QGIS software was used to create gradient maps of Ohio demonstrating the movement of horseweed by rating level from year to year at the county scale and also to illustrate the results from the analytical software, GeoDA. GeoDa spatial data analysis software was used to identify notable amounts of horseweed populations by rating, or lack thereof, by county in relation to surrounding counties. In order to determine whether the changes in horseweed populations over time were spatially clustered, the univariate local Moran’s I test was performed to identify spatial hotspots of interest, a means of autocorrelation (Anselin 1995). This tested the number of fields at a certain rating in a county compared to neighboring counties using the queen’s case contiguity (or Moore neighborhood) by running a number of conditional permutations, a method of numerically testing for significance that yields a pseudo significance value (Anselin

1995). The parameters for this evaluation were set to 99999 permutations at a significance level of 0.01 in order to avoid false findings as a result of the pseudo p- values and multiple comparisons (Anselin 2017). Significant observations in the classical sense were referred to as points of interest, which may be more appropriate for this type of analysis (Anselin 2017; Efron and Hasti 2016). The output was a number of local indicators of spatial association (LISA) maps revealing the core clusters of interest or outliers of horseweed infestations in the state, as well as a Moran’s I value for the model.

The Moran’s I value can be used to detect spatial relationships; the cutoff is typically values greater than 2k/n (k = number of explanatory variables; n = sample size), which in this case was 0.082. Positive I values indicated spatial clusters with similar high or low

61 values, whereas negative I values indicated cores with neighbors having contradictory high or low values (Anselin 1995).

Results & Discussion

Horseweed was encountered in the most fields relative to the other weed species surveyed, and was present in all of the 49 counties surveyed across the state from 2013 to

2017. It was observed in 24 to 39% of fields, whereas giant ragweed was in 15 to 24%, pigweeds were in 1 to 4%, and common ragweed was in 1 to 3% (Table 3.2). Horseweed was also found in the greatest number of fields compared to the other weed species among each rating level, relative to total fields (Tables 3.3, 3.4 and 3.5). Horseweed given a rating of one was highest in 2015 at 27.2%, and decreased overall in the later years of the survey (Table 3.5) The highest amount of level two rated horseweed populations occurred in 2015 at 7.5%, but remained relatively stagnant throughout the years. Infestations, fields having been given a rating of three, were also highest in 2015 at

2.9%, which coincided with the highest overall frequency of horseweed, in 39% of fields.

The lowest percentage of total fields with horseweed present was in 2017 at 24%, but this was paired with the second highest percentage of infestations, 2.3%. The number of horseweed populations at each rating level per county was variable from year to year, with some counties having at least two infestations per year and some counties having no more than one infestation per year (Table 3.6).

In 2013, horseweed was found in 35% of the surveyed soybean fields, with an infestation rate of 1.9%. The number and distribution of horseweed populations were variable across the state. Most counties seemed to have somewhere between 10 to 40

62 fields designated a rating of one, with higher frequency counties located mainly in the central part of the state (Figure 3.2). According to the LISA map and the Moran’s I value generated by GeoDa, four counties were identified as points of interest in 2013 at the one rating. Madison and Union were high-high counties, or counties with a high number fields rated one for horseweed, surrounded by counties that also had a high number of fields rated one for horseweed. Butler and Preble were low-low counties, or counties with low levels of horseweed rated a one surrounded by counties also low in fields with horseweed rated a one (Figure 3.3; Table 3.7). Similarly, most of the counties with a greater number of fields rated a two seemed to be in the west central part of the state

(Figure 3.4). Two counties in 2013 were identified as points of interest at the level two rating; Logan and Champaign counties were high-high counties (Figure 3.5; Table 3.8).

Fields given a rating of three had fairly even numbers across the state, with Fulton county having a much greater number of fields at this rating than any other county (Figure 3.6).

Auglaize county was identified as a low-low county, meaning it had a relatively low level of horseweed infestations and was surrounded by counties that also had low numbers of horseweed infestations. At 10 fields, Fulton county had the highest number of infestations in 2013, or 12% of the fields surveyed in the county (Table 3.6). Fulton county was identified a point of interest in 2013 as a high-low county, meaning it had a high number of infestations and neighboring counties had relatively low levels of infestations (Figure

3.7; Table 3.9).

In 2014, horseweed infestations occurred in 1.9% of total fields, and was present in 32% of fields. Fields at the level one rating seemed to be mostly in the 10-30 fields per

63 county range, with most of the higher counties centrally located (Figure 3.8). One county of interest was identified at the one rating in 2014. Fayette was a low-high county, or a county with a low level of one rated horseweed fields surrounded by counties with higher levels of fields rated one for horseweed (Figure 3.9; Table 3.7). Counties in the south and west central regions of the state had more counties with a higher number of fields given a rating of two (Figure 3.10), and five counties were identified as points of interest. Union,

Champaign, and Miami were high-high counties, and Van Wert and Allen were low-low counties (Figure 3.11; Table 3.8). Infestations also seemed to be more frequent in the central and lower regions of the state in 2014 (Figure 3.12). Three spatial clusters of interest were illustrated by the LISA map, the cores of which were Clermont,

Montgomery, and Logan counties (Figure 3.13; Table 3.9). Both Clermont and

Montgomery were identified as low-high cores, with their infestation levels being relatively low compared to surrounding counties, a number of which they shared. This was logical as the southern region of the state appeared to have a higher number of infestations than the more northern, leaving these two counties that had a lower number of infestations as outliers. Logan county was defined as a high-low county, or having a high number fields with infestations relative to surrounding counties which were in the more west central part of the state.

The 2015 survey resulted in the highest overall frequency and infestations of horseweed at 38 and 2.9%, respectively. The number of fields given a rating of one in

2015 seemed to be fairly even throughout the state, which appeared darker overall than in previous years, indicating more fields given a rating of one. Paulding however had a

64 much higher number of fields at the one rating than the other counties (Figure 3.14). No counties were designated as points of interest at the one rating in 2015 (Table 3.7).

Similarly, there seemed to be more fields given a rating of two in 2015 than in previous years, but no counties were points of interest according to the LISA maps or Moran’s I value (Figure 3.15; Table3.8) It seemed that in 2015, the northwest region of Ohio experienced an exceptionally high frequency of horseweed infestations in comparison to the rest of the state (Figure 3.16). Williams, Henry, and Wood county were counties of interest in that year. Each were identified as high-high counties, meaning they had high levels of horseweed infestations, as did their neighboring counties (Figure 3.17; Table

3.9).

In 2016, counties with a greater number of fields given a rating of one seemed to be in the central and more northern regions of the state (Figure 3.18). Three counties of interest were identified at the one rating level. Union and Logan counties were high-high counties, and Wyandot was a low-high county (Figure 3.19; Table 3.7) Similarly, the counties with the most fields at the two rating level were located in the central and northern region of the state (Figure 3.20). One county of interest was identified at the two rating level in 2016, where Champaign was found to be a high-high county (Figure 3.21;

Table 3.8). There seemed to be many counties with a higher frequency of infestations, or the three rating, that were dispersed fairly evenly across the state (Figure 3.22). Only one county of interest, Ross, was identified in 2016 (Figure 3.23; Table 3.9). This indicated a high-low relationship to the surrounding counties that were surveyed, meaning Ross

65 county had a higher number of horseweed infestations relative to neighboring counties in the surrounding region.

In 2017, fields given a rating of one were fairly evenly dispersed throughout the state, with most counties appearing to be somewhere in the 10-20 fields range (Figure

3.24). Fields given a rating of two seemed to be more frequent in the more central and northern regions of the state (Figure 3.25). In 2017 there was a lower number of counties with a very high frequency of infestations, but there seemed to have been more counties with a mid-range frequency of infestations compared to previous years that had a number of low-range counties (Figure 3.26). No counties were identified as points of interest at any horseweed rating level in 2017 (Table 3.7; Table 3.8; Table 3.9).

The results of the univariate local Moran’s I displayed by the LISA maps are supported by the gradient maps generated by QGIS, in that the interesting counties often have a much higher or lower number of infestations in comparison to neighboring counties. Lighter indicates a lesser frequency of infestations, and increasingly darker indicates increasingly frequent infestations. Looking at the overall frequency of horseweed at each rating level, there does not appear to be any real trend in the increase or decrease of horseweed populations as a whole from 2013 to 2017. However, there was an overall, gradual darkening from year to year on the gradient maps of Ohio in terms of the lower to mid-levels of infestations. Regression analyses were used in an attempt to understand the change in horseweed populations at the various ratings over the years.

While the regressions were not significant, they confirmed that the overall survey trend in terms of the years presented was a decrease in horseweed present in fields as isolated

66 plants, a static amount of fields with horseweed in clusters, and an increase in the amount of fields with infestations (Figure 3.27, 3.28 and 3.29).

The lowest overall total frequency of horseweed occurred in 2017, but this year also had the second highest frequency of infestations among years. While there may have been a lower overall frequency of horseweed throughout the state (Table 3.2), the areas where it was present it was also more likely to be at the level three rating, or an infestation (Table 3.5). These results suggest that overall during these five years, growers may have implemented effective programs for the management and control of horseweed, reducing its frequency and presence in Ohio soybean production fields. Where horseweed was present, there may have been a lack of effective management practices, and infestations may have been more likely to occur making the crop more prone to yield loss. Overall, horseweed persists as a common and troublesome threat to Ohio soybean producers. Over the five years of this survey, horseweed occurred in soybean fields more frequently and at higher densities within fields than other species. However, there did not seem to be a distinct distribution or pattern of movement of horseweed populations at any rating level over these five years. While the overall frequency of horseweed at the one and two ratings decreased with time, the frequency of infestations, or the level three rating, remained relatively consistent with the overall trend suggesting an increase. These results suggest that Ohio soybean producers should still consider making horseweed management a priority when developing weed control programs. One goal would certainly be to avoid the potentially increasing frequency of infestations that could be detrimental to crop yield if not adequately controlled.

Table 3.1 Weed species with reported cases of resistance to herbicides and multiple herbicide sites of action in Ohio.

Species Common Name Site of Action Chenopodium album Common lambsquarters 2, 5 Daucus carota Wild carrot 4 Amaranthus tuberculatus/rudis Tall waterhemp 2, 9 Amaranthus powellii Powell amaranth 2 Ambrosia artemisiifolia Common ragweed 2, 2 & 9, 2 & 14 Ambrosia trifida Giant ragweed 2, 9, 2 & 9 Xanthium strumarium Common cocklebur 2 Sorghum bicolor Shattercane 2 Conyza canadensis Horseweed 2, 9, 2 & 9 Amaranthus hybridus/quitensis Smooth Pigweed 2 Amaranthus palmeri Palmer amaranth 9 a Table adapted from Heap 2018. Sites of action: 2 – ALS inhibitors; 4 – Synthetic auxins; 5 – Photosystem II inhibitors; 9 – EPSP synthase inhibitors; 14 – PPO inhibitors.

Table 3.2 Overall frequency of weeds (all ratings) in soybean fields just prior to harvest – 2013 to 2017.

Year Total Fields Horseweed Pigweeds G. Ragweed C. Ragweed 2013 3610 35% 3% 24 % 3% 2014 3410 32% 1% 16% 1% 2015 3536 38% 1% 19% 2% 2016 4938 26% 2% 15% 1% 2017 3795 24% 4% 20% 2%

Figure 3.1 Ohio counties included in all survey years – 2013 to 2017.

Table 3.3 Frequency of single, isolated weeds (level 1 rating) in soybean fields just prior to harvest – 2013 to 2017.

Year Total Fields Horseweed Pigweed G. Ragweed C. Ragweed

2013 3610 26.1% 2.8% 18.5% 2.3% 2014 3410 24.8% 0.3% 12.5% 0.9% 2015 3536 27.2% 0.5% 13.7% 1.0% 2016 4938 18.8% 1.1% 11.3% 0.7% 2017 3795 17.0% 2.5% 14.8% 1.3%

Table 3.4 Frequency of clustered groups of weeds (level 2 rating) in soybean fields just prior to harvest – 2013 to 2017.

Year Total Fields Horseweed Pigweed G. Ragweed C. Ragweed 2013 3610 7.1% 0.4% 4.1% 0.6% 2014 3410 5.4% 0.2% 2.4% 0.2% 2015 3536 7.5% 0.1% 3.9% 0.7% 2016 4938 5.7% 0.4% 3.3% 0.2% 2017 3795 5.1% 1.1% 4.0% 0.4%

Table 3.5 Frequency of infestations (level 3 rating) in soybean fields just prior to harvest – 2013 to 2017.

Year Total Fields Horseweed Pigweeds G. Ragweed C. Ragweed 2013 3610 1.9% 0.1% 1.4% 0.4% 2014 3410 1.9% 0.1% 1.0% 0.1% 2015 3536 2.9% 0.0% 1.4% 0.3% 2016 4938 1.8% 0.1% 0.9% 0.1% 2017 3795 2.3% 0.6% 1.0% 0.2%

Table 3.6 Number of fields with horseweed infestations (level 3 rating) by county and year. Continued County 2013 2014 2015 2016 2017 Allen 0 1 5 5 0 Ashland 1 0 0 3 5 Auglaize 1 0 0 1 2 Brown 2 6 3 0 3 Butler 3 2 2 2 2 Champaign 3 0 2 2 0 Clark 0 3 0 8 6 Clermont 0 1 0 1 0 Clinton 4 3 1 4 4 Crawford 2 0 0 1 1 Darke 0 2 1 0 1 Defiance 1 1 2 0 1 Delaware 5 4 2 0 1 Erie 1 0 4 2 1 Fairfield 1 0 1 2 0 Fayette 0 1 3 0 0 Fulton 10 1 11 5 1 Greene 4 4 2 3 3 Hancock 2 1 2 7 1 Hardin 0 0 1 5 8 Henry 0 1 6 1 1 Huron 0 1 1 4 3 Knox 2 1 2 2 2 Licking 1 1 1 1 3 Logan 0 2 1 1 2 Lorain 1 0 0 4 1 Madison 1 3 2 5 2 Marion 1 2 2 0 3 Mercer 0 0 0 1 0 Miami 1 6 3 2 5 Montgomery 3 1 1 0 0 Morrow 2 0 2 0 2 Ottowa 0 1 10 0 0 Paulding 2 0 0 0 1 Pickaway 1 1 1 0 2 Preble 0 1 2 0 2 Putnam 2 1 4 0 1 Richland 0 2 2 1 3 Ross 3 0 1 2 2 74

Table 3.6 Continued

Sandusky 1 3 2 1 3 Seneca 0 0 4 1 4 Shelby 0 1 1 0 0 Union 0 0 0 2 2 Van Wert 1 0 0 0 0 Warren 2 3 2 1 0 Wayne 1 1 2 6 0 Williams 0 0 5 1 1 Wood 1 1 4 2 1 Wyandot 2 1 0 1 1

Figure 3.2 Gradient of the distribution of fields with single, isolated horseweed plants (level 1 rating) in Ohio soybean fields based on the number of infestations per county – 2013.

Figure 3.3 The cores and neighbors of significant clusters of fields with single, isolated horseweed plants (level 1 rating) in Ohio soybean fields – 2013.

Table 3.7 Significance of fields with single, isolated horseweed plants (level 1 rating) by year from univariate local Moran’s I test.

Year Moran’s I Value Pseudo P-Value 2013 0.30 0.00 2014 0.15 0.04 2015 -0.04 0.44 2016 0.19 0.02 2017 -0.06 0.34

Figure 3.4 Gradient of the distribution of fields with clustered groups of horseweed (level 2 rating) in Ohio soybean fields based on the number of infestations per county – 2013.

Figure 3.5 The cores and neighbors of significant clusters of fields with clustered groups of horseweed (level 2 rating) in Ohio soybean fields – 2013.

Table 3.8 Significance of fields with clustered groups of horseweed (level 2 rating) by year from univariate local Moran’s I test.

Year Moran’s I Value Pseudo P-Value 2013 0.19 0.02 2014 0.21 0.01 2015 0.03 0.29 2016 0.20 0.02 2017 -0.06 0.34

Figure 3.6 Gradient of the distribution of fields with horseweed infestations (level 3 rating) in Ohio soybean fields based on the number of infestations per county – 2013.

Table 3.9 Significance of horseweed infestations (level 3 rating) by year from univariate local Moran’s I test.

Year Moran’s I Value Pseudo P-Value 2013 -0.11 0.13 2014 0.14 0.05 2015 0.30 0.00 2016 0.14 0.05 2017 -0.08 0.28

Figure 3.7 The cores and neighbors of significant clusters of horseweed infestations (level 3 rating) in Ohio soybean fields – 2013. 84

Figure 3.8 Gradient of the distribution of fields with single, isolated horseweed plants (level 1 rating) in Ohio soybean fields based on the number of infestations per county – 2014.

Figure 3.9 The cores and neighbors of significant clusters of fields with single, isolated horseweed plants (level 1 rating) in Ohio soybean fields – 2014.

Figure 3.10 Gradient of the distribution of fields with clustered groups of horseweed (level 2 rating) in Ohio soybean fields based on the number of infestations per county – 2014.

Figure 3.11 The cores and neighbors of significant clusters of fields with clustered groups of horseweed (level 2 rating) in Ohio soybean fields – 2014.

Figure 3.12 Gradient of the distribution of horseweed infestations (level 3 rating) in Ohio soybean fields based on the number of infestations per county – 2014. 89

Figure 3.13 The cores and neighbors of significant clusters of horseweed infestations (level 3 rating) in Ohio soybean fields – 2014.

Figure 3.14 Gradient of the distribution of fields with single, isolated horseweed plants (level 1 rating) in Ohio soybean fields based on the number of infestations per county – 2015. 91

Figure 3.15 Gradient of the distribution of fields with clustered groups of horseweed (level 2 rating) in Ohio soybean fields based on the number of infestations per county – 2015. 92

Figure 3.16 Gradient of the distribution of horseweed infestations (level 3 rating) in Ohio soybean fields based on the number of infestations per county – 2015.

Figure 3.17 The cores and neighbors of significant clusters of horseweed infestations (level 3 rating) in Ohio soybean fields – 2015.

Figure 3.18 Gradient of the distribution of fields with single, isolated horseweed plants (level 1 rating) in Ohio soybean fields based on the number of infestations per county – 2016.

Figure 3.19 The cores and neighbors of significant clusters of fields with single, isolated horseweed plants (level 1 rating) in Ohio soybean fields – 2016.

Figure 3.20 Gradient of the distribution of fields with clustered groups of horseweed (level 2 rating) in Ohio soybean fields based on the number of infestations per county – 2016. 97

Figure 3.21 The cores and neighbors of significant clusters of fields with clustered groups of horseweed (level 2 rating) in Ohio soybean fields – 2016.

Figure 3.22 Gradient of the distribution of horseweed infestations (level 3 rating) in Ohio soybean fields based on the number of infestations per county – 2016. 99

Figure 3.23 The cores and neighbors of significant clusters of horseweed infestations (level 3 rating) in Ohio soybean fields – 2016. 100

Figure 3.24 Gradient of the distribution of fields with single, isolated horseweed plants (level 1 rating) in Ohio soybean fields based on the number of infestations per county – 2017.

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Figure 3.25 Gradient of the distribution of fields with clustered groups of horseweed (level 2 rating) in Ohio soybean fields based on the number of infestations per county – 2017.

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Figure 3.26 Gradient of the distribution of horseweed infestations in Ohio soybean fields based on the number of infestations (level 3 rating) per county – 2017.

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Figure 3.27 Regression results of fields with single, isolated horseweed plants (level 1 rating) by year (p = 0.27).

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Figure 3.28 Regression results of fields with clustered groups of horseweed (level 2 rating) by year (p = 0.89).

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Figure 3.29 Regression results of fields with horseweed infestations (level 3 rating) by year (p = 0.26).

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