enhancing efficacy of herbicides to control …

ENHANCING EFFICACY OF HERBICIDES TO CONTROL CHEATGRASS ON

MONTANA RANGE, PASTURE, AND CONSERVATION

RESERVE PROGRAM (CRP)

by

Krista Ann Ehlert

A thesis submitted in partial fulfillment of the requirements for the degree

of

Master of Science

in

Land Resources and Environmental Sciences

MONTANA STATE UNIVERSITY Bozeman, Montana

April 2013

©COPYRIGHT

by

Krista Ann Ehlert

2013

All Rights Reserved

ii

APPROVAL

of a thesis submitted by

Krista Ann Ehlert

This thesis has been read by each member of the thesis committee and has been found to be satisfactory regarding content, English usage, format, citation, bibliographic style, and consistency and is ready for submission to The Graduate School.

Dr. Jane Mangold (Co-Chair)

Dr. Richard Engel (Co-Chair)

Approved for the Department of Land Resources and Environmental Sciences

Dr. Tracy Sterling

Approved for The Graduate School

Dr. Ronald W. Larsen

iii

STATEMENT OF PERMISSION TO USE

In presenting this thesis in partial fulfillment of the requirements for a master’s

degree at Montana State University, I agree that the Library shall make it available to

borrowers under rules of the Library.

If I have indicated my intention to copyright this thesis by including a copyright

notice page, copying is allowable only for scholarly purposes, consistent with “fair use”

as prescribed in the U.S. Copyright Law. Requests for permission for extended quotation

from or reproduction of this thesis in whole or in parts may be granted only by the

copyright holder.

Krista Ann Ehlert April 2013

iv

ACKNOWLEDGEMENTS I would like to thank my advisors, Drs. Jane Mangold and Rick Engel, for

providing their thoughtful feedback and encouragement throughout my project, as well as

my committee member, Dr. Cathy Zabinski. Help in guiding me through graduate school

and field work was provided whole-heartedly and selflessly by Hilary Parkinson, Noelle

Orloff, and Rosie Wallander. A big thank you goes to all the people who helped with

long and hot field work days, followed by hours of weighing and sorting samples: Sam

Carlson, Hazal Ural, Laura Bosacker, Torrin Daniels, and Daniel France. Parts of my

project would not have been possible if not for the following people providing access to

equipment and support: Dr. Barbara Keith, Dr. Zach Miller, Dr. Fabian Menalled, and Dr.

Linnea Skoglund. I would not have been able to get through this project if not for help

from the MSU PGC staff, especially Dave Baumbauer. Cooperation for this project was

greatly appreciated from Jeff Hockett in Havre, MT, and Roger Hammersmark in Big

Timber, MT, whom allowed access to their land for my field studies. Thanks goes to the

NRCS CIG program, as well as the Montana Noxious Weed Trust Fund, for providing

funding for my project, in addition to scholarships I received from the Montana Weed

Control Association. I would further like to thank my friends for all of their

encouragement, those that are near and far. And I would lastly like to say thank you a

million times to my family, especially my mom and dad, who always seem to have the

right thing to say at the right time. This truly has been a memorable learning experience,

and an enjoyable one at that.

v

TABLE OF CONTENTS

1. PROJECT BACKGROUND AND OBJECTIVES ........................................................ 1 Introduction ..................................................................................................................... 1 Literature Review ............................................................................................................ 2

Cheatgrass Origin and Distribution ........................................................................ 2 Biology .................................................................................................................... 5 Impacts .................................................................................................................... 6 Integrated Management of Cheatgrass .................................................................... 8 Chemical Control .................................................................................................. 10 Biological Control ................................................................................................. 14

Project Justification and Objectives ................................................................................ 19

2. EFFECTS OF IMAZAPIC RATE, APPLICATION TIMING, AND PLANT LITTER ON CHEATGRASS-INFESTED RANGE AND CRP LANDS ........................................ 20

Introduction ................................................................................................................... 20

Materials and Methods ................................................................................................. 22 Site Description ..................................................................................................... 22 Weather Data ........................................................................................................ 23 Experimental Design ............................................................................................. 23 Herbicide Applications ......................................................................................... 24 Vegetation Sampling ............................................................................................. 26 Statistical Analysis ................................................................................................ 26

Results .......................................................................................................................... 27 Climate (Average Precipitation) .......................................................................... 27 Experiment I. – Rate x Litter ................................................................................ 30

Cheatgrass (BRTE) ................................................................................... 30 Perennial Grasses (PG) ............................................................................. 35 Exotic Perennial Forbs (EPF) ................................................................... 35 Native Perennial Forbs (NPF) .................................................................. 35

Experiment II. – Rate x Timing ............................................................................ 35 Cheatgrass (BRTE) .................................................................................. 35 Perennial Grasses (PG) ............................................................................ 38 Exotic Perennial Forbs (EPF) .................................................................. 38 Native Perennial Forbs (NPF) .................................................................. 38

Discussion .................................................................................................................... 39 Implications ........................................................................................................... 42

vi

TABLE OF CONTENTS – CONTINUED

3. IMAZAPIC PERSISTENCE IN A SEMIARID CLIMATE AT CHEATGRASS-INFESTED RANGELAND AND CRP SITES ........................... 44

Introduction ................................................................................................................... 44

Materials and Methods ................................................................................................. 46 Site and Field Experimental Description .............................................................. 46 Soil Sampling ........................................................................................................ 48 Bioassay ................................................................................................................ 49 Statistical Analysis ................................................................................................ 51

Results .......................................................................................................................... 51 Big Timber ............................................................................................................ 51

Cucumber ................................................................................................. 51 Cheatgrass ................................................................................................ 55

Havre ..................................................................................................................... 57 Cucumber ................................................................................................. 57 Cheatgrass ................................................................................................ 61


4. INTEGRATING THE HERBICIDE IMAZAPIC AND THE FUNGAL PATHOGEN PYRENOPHORA SEMENIPERDA TO CONTROL CHEATGRASS ................................................................................... 67

Introduction ................................................................................................................... 67

Materials and Methods ................................................................................................. 71 Experimental Design ............................................................................................. 71 Pyrenophora semeniperda Inoculum Preparation ................................................ 71 Pyrenophora semeniperda Inoculum Application ................................................ 72 Greenhouse Conditions and Seed Planting ........................................................... 73 Data Collection ..................................................................................................... 74 Statistical Analysis ................................................................................................ 74

Results .......................................................................................................................... 74 Cheatgrass Emergence .......................................................................................... 74 Cheatgrass Density ................................................................................................ 76 Cheatgrass Biomass .............................................................................................. 77


vii

TABLE OF CONTENTS – CONTINUED

5. SUMMARY OF FINDINGS AND DIRECTIONS FOR FUTURE RESEARCH ................................................................................................. 85

LITERATURE CITED ..................................................................................................... 88 APPENDICES ................................................................................................................ 100 APPENDIX A: Chapter Two Statistical Model .................................................. 101 APPENDIX B: Chapter Three Statistical Model ................................................ 103 APPENDIX C: Chapter Four Statistical Model and Supplemental Information .................................................................................. 112

viii

LIST OF TABLES

Table Page

1.1. Common rangeland herbicides for cheatgrass management ............................................................................................. 11 2.1. Application date, cheatgrass growth stage, and weather conditions for Experiment I (Rate x Litter) and Experiment II (Rate x Timing) at Big Timber and Havre for 2011 and 2012 ...................................................................................................... 25 2.2. Vegetation sampling date, application timing from the previous year, and weeks post-application for Experiment I (Rate x Litter) and Experiment II (Rate x Timing) at Big Timber and Havre for 2011 and 2012 ............................................................. 27 2.3. Species found at each site by plant functional group ................................................. 28 2.4. Experiment I – Rate x Litter. P-values from ANOVA on functional group cover and biomass at Big Timber and Havre ........................................................................................... 31 2.5. Experiment II – Rate x Timing. P-values from ANOVA on functional group cover and biomass at Big Timber and Havre ........................................................................................... 36 3.1. Application date and weather conditions for 2011 and 2012 at Big Timber and Havre .................................................................................. 48 3.2. Soil sampling date and corresponding days post-application (DPA) at Big Timber and Havre for 2011 and 2012 .............. 49 3.3. P-values from ANOVA on cucumber and cheatgrass biomass at each sampling date (days post-application, DPA) for Big Timber ........................................................................................................... 52 3.4. Mean absolute biomass (mg plant-1) for cucumber and cheatgrass at each sampling date (days post-application, DPA) for the 2011 and 2012 bioassays for Big Timber .................................................... 53

ix

LIST OF TABLES - CONTINUED

Table Page 3.5. P-values from ANOVA on cucumber and cheatgrass biomass at each sampling date (days post-application, DPA) for Havre .................................................................... 58 3.6. Absolute mean biomass (mg plant-1) for cucumber and cheatgrass at each sampling date (days post-application, DPA) for the 2011 and 2012 bioassays for Havre ............................................................... 59 4.1. P-values from ANOVA on cheatgrass emergence ..................................................... 75 4.2. P-values from ANOVA on cheatgrass density and biomass ..................................... 76

x

LIST OF FIGURES

Figure Page

1.1. Cheatgrass .................................................................................................................... 3 1.2. Cheatgrass infested rangeland ...................................................................................... 3

1.3. Cheatgrass seeds bearing Pyrenophora semeniperda stromata ................................................................................................ 17 2.1. Precipitation for the long-term average, September 2010-August 2011, and September 2011-August 2011 for a) Big Timber and b) Havre ................................................................................. 29 2.2. Cheatgrass cover as affected by year, imazapic rate, and litter at Big Timber for Experiment I (Rate x Litter) .................................. 32 2.3. Cheatgrass biomass as affected by year, imazapic rate, and litter at Big Timber for Experiment I (Rate x Litter) .................................. 33 2.4. Cheatgrass biomass as affected by year, imazapic rate, and litter at Havre for Experiment I (Rate x Litter) ........................................... 34 2.5. Cheatgrass cover as affected by year, imazapic rate, and application timing at Havre for Experiment I (Rate x Timing) .................. 37 3.1. Cucumber biomass as percent of the control for the a) 2011 and b) 2012 bioassays for Big Timber .................................................. 54 3.2. Cheatgrass biomass as percent of the control for the a) 2011 and b) 2012 bioassays for Big Timber ................................................... 56 3.3. Cucumber biomass as percent of the control for the a) 2011 and b) 2012 bioassays for Havre ............................................................ 60 3.4. Cheatgrass biomass as percent of the control for the a) 2011 and b) 2012 bioassays for Havre ............................................................ 62 4.1. Cheatgrass emergence as affected by Pyrenophora semeniperda and seeding depth ........................................................... 75

xi

LIST OF FIGURES - CONTINUED

Figure Page 4.2. Cheatgrass density as affected by Pyrenophora semeniperda, imazapic, and seeding depth ..................................................................................... 77 4.3. Cheatgrass biomass as affected by Pyrenophora semeniperda and imazapic treatments ............................................................................................ 78 4.4. Cheatgrass biomass as affected by Pyrenophora semeniperda

treatment and seeding depth ..................................................................................... 79

xii

ABSTRACT

Chemical control of cheatgrass has recently focused on imazapic; factors such as application rate and timing and the presence of plant litter can influence imazapic’s efficacy. Herbicides minimally impact the seedbank so integrating a seed-killing pathogen like Pyrenophora semeniperda may result in more effective and sustainable control. My research objectives were to 1) test the effect of imazapic application rate and timing and plant litter on cheatgrass and desired plant species in range and Conservation Reserve Program (CRP) lands, 2) conduct a soil bioassay to determine imazapic persistence as affected by imazapic rate, presence of plant litter, and time after herbicide application, and 3) determine whether the fungal pathogen P. semeniperda combined with a single imazapic application would provide greater control of cheatgrass than either strategy used alone.

Objective 1 was carried out in range and CRP lands over two years with a factorial combination of four imazapic rates, two litter manipulation treatments and/or two application timings. In general, all three imazapic rates were equally effective in controlling cheatgrass compared to the non-sprayed control. Litter manipulation treatments had little effect on imazapic efficacy, but early application of imazapic resulted in more consistent cheatgrass control.

Objective 2 was conducted in the greenhouse using soil samples collected over a six month period from the field study for Objective 1. Cucumber and cheatgrass were used as indicator species. All three herbicide rates reduced both species’ biomass below that of the control. Again, litter manipulation had a minimal effect, and imazapic was found to persist through the following spring after spraying.

Objective 3 was explored in a greenhouse experiment using a factorial combination of two imazapic treatments, two P. semeniperda treatments, and three seeding depths. Pyrenophora semeniperda reduced cheatgrass emergence, while cheatgrass biomass and density were affected by imazapic and the integration of imazapic and P. semeniperda. Imazapic and P. semeniperda did not favorably interact to reduce biomass and density; however, integrating these two tools holds promise as P. semeniperda can reduce the seedbank, and imazapic can control seedlings that escape pathogen-caused mortality.

1

CHAPTER ONE

PROJECT BACKGROUND AND OBJECTIVES

Introduction

Cheatgrass (Bromus tectorum) is an annual grass native to Eurasia that currently

infests millions of hectares of rangeland in western North America (Mack 1981, Rice

2005). Control of cheatgrass has often centered on the application of synthetic herbicides

(Davison and Smith 2007, Elseroad and Rudd 2011, Hirsch et al. 2012, Pellant et al.

1999, Peterson et al. 2010). However, a single control method is often not effective in

achieving sustainable control of an invasive weed (DiTomaso 2000, Sheley and Krueger-

Mangold 2003). Integrated pest management (IPM) allows land managers more effective

and long-term means to manage the invasion of cheatgrass. Integrated pest management

is an ecologically sound mix of available and effective control tactics that include

physical, biological, and chemical control methodologies (Norris 2011).

The overall goal of this project was to refine cheatgrass control practices for

Montana’s semiarid environment, which include the use of synthetic herbicides, and new

practices that combine chemical and biological control to provide more effective and

long-term control. Specifically, this project focused on the use of the herbicide imazapic

(Plateau®) and the fungal pathogen Pyrenophora semeniperda. First, I explored the

efficacy of imazapic in the field as it relates to application rate and timing and plant litter

presence. Second, I used a greenhouse bioassay to investigate how imazapic application

rate and plant litter influence imazapic persistence in the soil. Lastly, I investigated the

2 integration of imazapic with P. semeniperda and assessed cheatgrass control as it relates

to location of cheatgrass seeds within the soil profile. My intent was to determine

whether a favorable response exists by integrating chemical and biological tactics, thus

enhancing cheatgrass control.

In this chapter, I review the literature relevant to this project, starting with

cheatgrass origin and distribution. I then discuss cheatgrass biology, impacts, and

strategies for its management. Lastly, I provide an in-depth description of chemical

control and biological control, focusing on imazapic and P. semeniperda.

Literature Review

Cheatgrass Origin and Distribution

The invasion of cheatgrass (Figures 1.1 and 1.2) is considered one of the most

significant plant invasions in North America (Mack 2011). It was introduced to North

America through contaminated crop seed and ship ballasts that originated in eastern

Europe and western Asia (Mack 1981). Cheatgrass was first reported in British Columbia

in 1890 (Mack 1981) and now infests 22.3 million hectares in 17 western states (Rice

2005). The spread of cheatgrass is believed to be due to overgrazing in the late 1800s,

abandoned homesteads during the Great Depression, and heavy plowing practices that

produced disturbed sites suitable for invasion (Pickford 1932, Rickard and Cline 1980).

3

Figure 1.1. Cheatgrass. (USDA NRCS PLANTS Database).

Figure 1.2. Cheatgrass infested rangeland. (redorbit.com)

Cheatgrass has been especially problematic in the Great Basin, an area consisting

of portions of Nevada, Utah, Idaho, Oregon, and California (National Park Service 2012).

Plant communities within the Great Basin have experienced a decrease in species

richness, leading to fewer native bunchgrasses and annual forbs (Knapp 1996). While

cheatgrass invasion in the Great Basin was partly due to overgrazing in the late 1800s

that led to substantial range deterioration into the early 1900s (Pickford 1932, Vale

1974), cheatgrass also seized an unoccupied niche that was open because of the lack of

4 native annual grasses (Knapp 1996). Moreover, cheatgrass has a high degree of

phenotypic plasticity because of genetic variation among individual plants within a

population and the ability of these plants to cross-pollinate (Ashley and Longland 2007).

This results in hybrid vigor and provides an advantage over native grasses (Ashley and

Longland 2007, Mack and Pyke 1983).

Cheatgrass was first reported in Montana in 1898, and now exists in every county

(Rice 2003). Like the Great Basin, Montana’s semiarid climate is favorable for cheatgrass

given the seasonal timing of precipitation that leads to water availability during the fall,

winter, and spring months. This provides a broad window of time for cheatgrass

germination (Bradford and Lauenroth 2006) that is followed by drought-like conditions

in the summer. Cheatgrass can germinate even during the winter, as long as the soil is not

frozen (Mack 2011). The seasonal precipitation patterns in Montana and the Great Basin

are similar to those found in cheatgrass’ native range (Knapp 1996).

Global climate change is predicted to enhance cheatgrass expansion in Montana

and the western U.S. (Bradley 2009). Bioclimatic envelope models using summer

precipitation variables as the best predictors of cheatgrass presence indicate that

decreased summer precipitation (June – September) will result in large portions of

Montana, Wyoming, Utah, and Colorado becoming climatically suitable for cheatgrass

invasion (Bradley 2009). Thus, because native perennial grasses grow later in the spring

relative to cheatgrass, and because their growth continues through the summer, they are

at a competitive disadvantage for water resources under decreased summer precipitation

(Bradley 2009, Stewart and Hull 1949). With the spread of cheatgrass into previously

5 unoccupied areas, the need to develop new management strategies becomes even more

imperative.

Biology

Cheatgrass’ competitive advantages result from prolific seed production, a

continuous (fall through spring) germination strategy, and the ability to utilize soil water

and nitrogen (N) efficiently. Cheatgrass is capable of producing up to 6,000 seeds plant-1

(Young and Evans 1978), creating densities of 4,800 – 19,000 seeds m-2 (Hempy-Mayer

and Pyke 2008, Humphrey and Schupp 2001). Although cheatgrass is considered a winter

annual that germinates in the fall after primary dormancy ends, it can also behave as a

summer annual that germinates in the spring when moisture is present. This continuous

germination characteristic is due to secondary dormancy (Harmon et al. 2012). Seeds

entering secondary dormancy during the winter add to the carryover spring seed bank

(Young and Evans 1975, Young et al. 1969). In semiarid climates, seed production and

seed bank persistence vary with site attributes and precipitation patterns. Above-normal

precipitation leads to greater seed production and higher seed densities (13,942 seeds m-2)

relative to drought conditions (3,567 seeds m-2) (Smith et al. 2008). If precipitation

patterns following cheatgrass seed production favor fall germination, then the fraction of

seeds carried over to the spring is reduced. In general, cheatgrass seeds do not persist

beyond the second carryover year (Smith et al. 2008).

Nitrogen and water are co-limiting resources in semiarid rangelands, hence

effective use of soil water and N are critical for species survival (Archer and Bowman

2002). Cheatgrass produces more biomass, tillers, and root length per unti leaf N relative

6 to native perennials under a wide range of available N environments (Vasquez et al.

2008). James (2008b) attributed this response to higher leaf N productivity, defined as the

rate of dry matter gain per leaf N per unit time (Garnier et al. 1995). Cheatgrass also

efficiently utilizes water relative to native plants (Pellant 1996). Cheatgrass had greater

root length relative to perennial grass species like bluebunch wheatgrass

(Pseudoroegneria spicata), bottlebrush squirreltail (Elymus elymoides), and crested

wheatgrass (Agropyrum desertorum) under varying soil N levels (James 2008a). This

allows cheatgrass to effectively use soil moisture and nutrients, making them less

available to surrounding native plants (Chambers et al. 2007, Harris 1967, Sperry et al.

2006, Stewart and Hull 1949). Thus, cheatgrass has competitive traits that allow it to

outperform natives, in addition to its life history characteristics of prolific seed

production and carryover seed bank.

Impacts

The impacts of cheatgrass on rangelands are negative and include a reduction in

the diversity of native plants, altered fire regimes, and economic losses. Cheatgrass

decreases native plant diversity such that revegetation is sometimes necessary to restore

productive, properly functioning ecosystems (Epanchin-Niell et al. 2009). Cheatgrass-

invaded sites had approximately 10% perennial grass cover compared to uninvaded sites

with > 50% perennial grass cover (Leger 2008). Additionally, cheatgrass dries out early

in the summer and acts as a continuous carpet of highly flammable fine fuel. Cheatgrass-

infested rangeland is 10-500 times more likely to burn relative to rangeland dominated by

native bunchgrasses, and the fire season can be extended up to three months (Hull 1965).

7 Large and frequent fires weaken native vegetation but have little impact on cheatgrass

populations. This positive feedback loop between cheatgrass and the fire cycle

contributes to the success of cheatgrass within the Great Basin (Peters and Bunting 1994,

Whisenant 1990).

Economic impacts from cheatgrass invasions are associated with control costs and

decreased rangeland forage quality. The estimated costs of revegetation, fire suppression,

and chemical control, are $185, $175, and $173 per hectare, respectively (BLM 1999).

Cheatgrass monocultures result in an unpredictable forage base due to the plant’s

dependency on precipitation for biomass production. Cheatgrass also has a narrow

window of palatability because of awned seeds and its tendency to dry out by late spring

or early summer (Klemmedson and Smith 1964, Murray et al. 1978, Rice 2005). While

cheatgrass crude protein (CP) is relatively high (27%) early in the season relative to other

grasses (16% for Idaho fescue (Festuca idahoensis)), it has the fewest days of adequate

CP (7.5% CP) across the entire growing season compared to other common rangeland

grasses (Ganskopp and Bohnert 2001). Furthermore, the CP concentration of common

rangeland forage species, e.g.western wheatgrass (Pascopyrum smithii), increases when

cheatgrass is suppressed (Haferkamp et al. 2001a). Cheatgrass suppression reduces soil

water use, allowing perennial forage species to continue growth later into the growing

season. Prescribing management strategies that improve cheatgrass control in rangelands

can reduce the economic losses associated with control costs and decreased forage quality

that occur because of cheatgrass infestations.

8 Integrated Management of Cheatgrass

Improving degraded communities and decreasing noxious weed invasion and

spread is a key goal for rangeland management, which can successfully be met by using

economical and sustainable integrated strategies (DiTomaso 2000). Integrated pest

management (IPM) incorporates an ecological sound mix of available and effective

control tactics including physical, biological, and chemical control methodologies (Norris

2011).

Physical or mechanical methods attempt to remove the targeted plant or damage it

to the point where it is unable to survive (DiTomaso et al. 2010). Tillage is a common

mechanical control method used for cheatgrass in cropping systems, but is not practicable

for many rangeland sites where soils are thin or rocky. In addition, the disturbance from

tillage can create disturbed sites that cheatgrass or other invasive plants are quick to

occupy (DiTomaso et al. 2010). Prescribed grazing strategies have sometimes been used

to control invasive plants, such as cheatgrass, in rangelands. Prescribed grazing requires

that the timing, intensity, and frequency of grazing events be manipulated to have the

greatest effect on the targeted weed (DiTomaso et al. 2010). Grazing to control

cheatgrass is most applicable during the spring when biomass, nutritional value, and

palatability are high (Klemmedson and Smith 1964, Murray et al. 1978, Rice 2005);

however, recent work suggested fall grazing of cheatgrass can reduce fuel loads on

Nevada rangeland and potentially decrease the risk of fire in subsequent years (Schmelzer

et al. 2008). Grazing reduces cheatgrass seed production and seed bank density which can

9 lead to improved rangeland health (Call et al. 2008), provided grazing is managed to

minimize the impact on desirable species (DiTomaso et al. 2010).

Prescribed fire can be integrated with other tools to improve management of

cheatgrass and other annual invasive grasses (DiTomaso et al. 2010). An imazapic

treatment following prescribed fire reduced cheatgrass cover from 82% to 9% over a two-

year period in Colorado (Calo et al. 2012). Prescribed fire can also stimulate re-sprouting

perennial species, which may improve their ability to compete against annual grasses

(DiTomaso et al. 2010). For example, imazapic application after a prescribed burn

resulted in ten times more perennial bunchgrass cover relative to a control treatment in a

study investigating revegetation of medusahead (Taeniatherum caput-medusae)-infested

rangeland in Oregon (Davies 2010). The synergistic effect of prescribed fire with

herbicide applications likely reflects better contact between the herbicide and the targeted

plant because of a reduction in plant litter at the soil surface (Fowers 2011, Sheley et al.

2007).

Cheatgrass control methods are often integrated with revegetation when rangeland

is so severely deteriorated that few desirable species are present (DiTomaso et al. 2010).

Research has focused on using competitive native and introduced perennial species in

cheatgrass-infested lands (Cox and Anderson 2004, Davies et al. 2010, Hull and Stewart

1948). Selecting desirable species adapted to the site’s soil conditions, elevation, climate,

precipitation, and fire regimes improves the probability of success (Allen 1995,

DiTomaso 2000, Epanchin-Niell et al. 2009, Jacobs et al. 1999). Revegetation may be the

best option for long-term, sustainable management of cheatgrass invasions (DiTomaso

10 2000). For example, one model parameterized for the Wyoming big sagebrush

community, predicted that if revegetation was not implemented, 72% of the landscape

would be covered by a cheatgrass monoculture after 50 years (Epanchin-Niell et al.

2009).

Chemical Control

Synthetic herbicides are widely used to control cheatgrass in cropping systems.

However, application to rangelands may be limited to severely affected sites where

herbicide use will lead to the improved productivity of desired plant species and grazing

opportunities. Herbicides reduce labor costs associated with hand and mechanical

weeding and are easy to apply while being highly effective (Ross and Lembi 1999,

Radosevich et al. 2007). Rangeland herbicides used to control cheatgrass include

sulfometuron methyl, rimsulfuron, glyphosate, and imazapic (Bussan and Dyer 1999)

(Table 1.1).

Sulfometuron methyl and rimsulfuron are acetolacetate synthase (ALS) inhibitors

that hinder the synthesis of branched chain amino acids needed for cell growth (Peterson

et al. 2010). Sulfometuron methyl has been used on Great Basin rangelands to control

cheatgrass and reduce its competition with seeded species (Pellant et al. 1999). Remnant

perennial grasses in treated plots showed improved plant vigor relative to a non-sprayed

control, potentially improving rangeland rehabilitation results (Pellant et al. 1999).

11

Table 1.1. Common rangeland herbicides for cheatgrass management.

Name Trade Name Family Chemical Structure

Avg. Soil

Half-Life

(days)

Sulfometuron methyl +

chlorosulfuron Landmark® Sulfonylurea

30

Rimsulfuron Matrix® Sulfonylurea

18-21

Glyphosate RoundUp® Amino acid derivative

47

Imazapic Plateau® Imidazolinone

120

Herbicide effectiveness can be influenced by soil properties such as pH. Hirsch et

al. (2012) investigated rimsulfuron effects on cheatgrass and two revegetation species,

crested wheatgrass (Agropyrum desertorum) and bottlebrush squirreltail (Elymus

elymoides), in salt desert shrub and sagebrush sites. The pH of the soil was 7.9 at the

sagebrush site and was 9.5 at the salt desert site. Herbicide activity as measured by

seedling emergence, biomass production, and seedling mortality was greater in the

sagebrush soil. The authors attributed this effect to greater herbicide adsorption and

12 residual bioavailability at the lower pH, resulting in prolonged exposure of the seedlings

to rimsulfuron.

Glyphosate is an aromatic amino acid synthesis inhibitor that selectively controls

cheatgrass if application occurs in the spring while cheatgrass is actively growing but

before desirable species break dormancy (Bussan and Dyer 1999). Glyphosate was

applied as part of a management strategy for cheatgrass control and perennial grass

establishment in Wyoming rangelands (Whitson and Koch 1998). Cheatgrass control was

greater than 92%, on average, after three sequential years of glyphosate application; a

single year of glyphosate application limited control to less than 60%. However,

glyphosate reduced the perennial grasses available to compete with cheatgrass.

Cheatgrass control is possible with repeated glyphosate applications combined with

replacing annual grasses with competitive cool-season perennials to improve competition

(Whitson and Koch 1998).

Recent cheatgrass management on western rangelands has focused on imazapic, a

member of the imidazolinone herbicide family (Davison and Smith 2007, Elseroad and

Rudd 2011, Morris et al. 2009). Imazapic is an ALS inhibitor that is absorbed through

leaves, stems, and roots (BASF Corporation 2008, Peterson et al. 2010). Imazapic

efficacy has been evaluated across the western U.S. with variable results. In Oregon,

imazapic reduced cheatgrass frequency to zero without affecting native species

abundance in semiarid grassland and shrub-steppe (Elseroad and Rudd 2011). In Nevada,

cheatgrass biomass decreased by more than 50% with imazapic application relative to a

non-sprayed control after two growing seasons (Davison and Smith 2007). In Utah,

13 variation in cheatgrass control and seeded species establishment was observed in salt

desert shrub and Wyoming big sagebrush communities that were seeded with perennial

plants following imazapic application (Morris et al. 2009). The survival of seeded

perennial plants, such as crested wheatgrass, can be inhibited if cheatgrass densities are

above a critical threshold of 43 seedlings m-2 (Evans 1961). This suggests sites can return

to pretreatment levels within two years if cheatgrass densities are not below this critical

threshold, despite seeding after imazapic application (Morris et al. 2009).

In Montana imazapic application for cheatgrass control has produced inconsistent

results. A meta-analysis of 25 trials from across Montana found cheatgrass control

ranging from 20% to 95% (Mangold et al., in review). The meta-analysis revealed post-

emergent (foliar) application to be more effective than pre-emergent (soil) application,

although both application methods are recommended on the label (BASF Corporation

2008). Disconnection between empirical data and label recommendations suggest the

need for an investigation into the factors influencing imazapic persistence.

Imazapic persistence is related to how quickly it degrades, and thus, its

corresponding availability for plant uptake. Imazapic is primarily degraded through soil

microbial activity. The average soil half-life of imazapic is 120 days (Tu et al. 2001), but

the half-life can range from 31 to 233 days (American Cyanamid 2000). Soil properties

can influence imazapic persistence. For example, the persistence of imidazolines

increases as soil pH decreases (Loux and Reese 1992). High clay and soil organic matter

also increase imazapic persistence due to greater surface area and charge density

(Colquhoun 2006, Ulbrich et al. 2005). Increased adsorption in such soils corresponds

14 with decreased availability for microbial degradation (Loux and Reese 1992). Microbial

degradation is favored in warm, moist soils (Prostko et al. 2005, Ulbrich et al. 2005).

Additionally, imidazolines can be adsorbed by plant litter at the soil surface,

which can further reduce imazapic efficacy. This is demonstrated in studies investigating

the invasive annual grass medusahead. Kyser et al. (2007) observed limited uptake of

imazapic by medusahead because of the presence of plant litter. Monaco et al. (2005)

found that medusahead control using imazapic was greater after implementing a complete

burn to eliminate litter. Decreasing or removing litter may reduce the concentration of

imazapic needed to reduce medusahead cover because of improved herbicide contact

with the target plant (DiTomaso et al. 2006, Sheley et al. 2007).

Biological Control

Biological control may offer alternatives for improving cheatgrass control, with

the deliberate use of natural enemies to achieve the goal of not eradicating the weed, but

rather reduce its presence to a tolerable level (Watson 1989, Wilson and McCaffrey

1999). Biological control can potentially address the need for targeted and effective

environmentally benign methods that can be used in conjunction with restoration seeding

(Meyer et al. 2008a). Fungal pathogens have been the focus of most research

investigating biological control of cheatgrass (Dooley and Beckstead 2010, Meyer et al.

2008a, Stewart 2009).

Three pathogens that target different stages of cheatgrass’s life history are the

focus of current research (Meyer et al. 2008a). Ustilago bullata, a head smut pathogen

with high host-specificity is endemic in nearly every cheatgrass population and is easily

15 grown in culture (Meyer et al. 2008a). Ustilago bullata infects cheatgrass seedlings that

emerge during the fall. The pathogen overwinters in vegetative tissues and grows upward

during bolting in the spring, eventually infecting floral meristems, and preventing seed

set (Meyer et al. 2008a). However, major differences exist in U. bullata susceptibility

among cheatgrass genotypes because of resistance polymorphism among and within

cheatgrass populations (Meyer et al. 2001).

Tilletia fusca is a host specific chestnut bunt that infects seedlings and can persist

long term because of a soil spore bank (Meyer et al. 2008a). Tilletia fusca infects

cheatgrass seedlings and grows systemically, preventing seed production (Meyer et al.

2008a). Infection is enhanced at colder temperatures and under persistent snow cover

(Meyer et al. 2008a). Tilletia fusca is most likely effective in mesic habitats where

autumn precipitation and persistent snow cover is likely to occur (Meyer et al. 2008a).

Pseudomonas fluorescens D7, a deleterious rhizobacterium, is an alternative

biological control to fungal pathogens proposed by Meyer et al. (2008a). Pseudomonas

fluorescens D7 is host specific to cheatgrass and widespread effects on non-target species

do not exist (Kennedy et al. 2001). A phytotoxin of P. fluorescens D7 reduces cheatgrass

biomass by inhibiting root elongation and reducing seedling vigor (Kennedy et al. 1991,

Tranel et al. 1993). However, cheatgrass seeds inoculated with P. fluorescens D7 did not

experience mortality under laboratory or field conditions (Kennedy et al. 1991).

Pyrenophora semeniperda is a generalist grass fungal pathogen that causes minor

leaf spot, seed infection, and death of at least 36 genera of annual and perennial grasses

(Medd et al. 2003, Meyer et al. 2008a). The name “black fingers of death” (BFOD) is

16 ascribed to this pathogen because of black finger-like stromata that emerge from infected

seeds (Figure 1.3) (Meyer et al. 2008a). Pyrenophora semeniperda can be observed as its

anamorph, or asexual state, Drechslera campanulata (Medd et al. 2003). Pyrenophora

semeniperda over-summers as mycelium in seed and plant debris, with stromata

developing from the mycelium to produce conidiophores and conidia (Medd et al. 2003).

Conidia that are carried to the inflorescence directly infect the developing ovary of

cheatgrass seeds during anthesis, resulting in seed death (Medd et al. 2003).

Pyrenophora semeniperda is most effective in arid environments where drought

conditions occur during seed ripening, leading to greater disease incidence on dormant

seeds in the seed bank relative to mesic environments (Meyer et al. 2008b). Cheatgrass

populations in arid habitats (199 mm mean annual precipitation (MAP)) experienced 50

times more seed mortality than in mesic habitats (486 mm MAP) as a result of P.

semeniperda infection (Beckstead et al. 2007, Meyer et al. 2007). In mesic habitats, non-

dormant cheatgrass seeds germinate more quickly and escape P. semeniperda-caused

mortality (Beckstead et al. 2007). In essence, germinating seeds compete with P.

semeniperda for seed resources, so a “race for survival” occurs between the fungal

pathogen and the seed (Beckstead et al. 2007). In contrast, cheatgrass seeds that fail to

germinate in the fall, remain dormant over the winter, and germinate in the spring are

more susceptible to P. semeniperda infection and mortality.

17

Figure 1.3. Cheatgrass seeds bearing Pyrenophora semeniperda stromata.

An advantage of using P. semeniperda as a biological control for annual grasses is

its ability to infect the carryover seed bank and inhibit spring cheatgrass germination,

especially under dry fall conditions (Medd and Campbell 2005). However, if conditions

are favorable for fall cheatgrass germination, it may still be beneficial to use P.

semeniperda. Fall herbicide application can decrease growth or kill cheatgrass seedlings

while P. semeniperda can infect and kill non-germinating seeds over the winter.

Disadvantages of using P. semeniperda include logistics of mass production,

environmental constraints, and spillover effects. Mass-producing inoculum is expensive

and complex depending on what type of inoculum is used; conidial suspensions are more

costly relative to mycelium fragments (Medd and Campbell 2005). Sodium alginate can

be used to prepare pellets containing mycelium fragments as a more cost-effective

inoculum type; however, such pellets have one-third the infection level of conidial

suspensions.

Spillover effects of P. semeniperda onto sensitive crops and native grasses are a

major concern associated with its use as a biological control (Medd and Campbell 2005).

Pathogen spillover occurs when one host species supports high pathogen loads, causing

18 indirect disease-mediated consequences for co-occurring host species (Beckstead et al.

2010). Cheatgrass serves as a reservoir for P. semeniperda, so the potential exists for it to

negatively affect native grass seeds before germination or emergence occurs (Beckstead

et al. 2010). Beckstead et al. (2010) reported spillover effects on five native grasses that

co-occur with cheatgrass: Indian ricegrass (Achnatherum hymenoides), squirreltail

(Elymus elymoides), needle and thread (Hesperostipa comate), Sandberg bluegrass (Poa

secunda), and bluebunch wheatgrass. Of these grasses, bluebunch wheatgrass, Sandberg

bluegrass, and Indian ricegrass experienced 35-80% P. semeniperda-caused seed

mortality (Beckstead et al. 2010).

To mitigate spillover effects on native grasses, P. semeniperda could be applied

where pure cheatgrass monocultures exist, especially in situations where revegetation is

necessary. Grass species with low susceptibility to P. semeniperda could be used for

revegetation, or seeds could be treated with a fungicide prior to seeding (Miller et al., in

preparation, Meyer et al. 2008a). The risks associated with spillover effects must be

evaluated against the benefits of using P. semeniperda as a biological control. Cheatgrass

seed mortality can reach > 90% due to P. semeniperda infection, which may provide

seeded desirable species the chance to establish in cheatgrass-infested rangeland

(Beckstead et al. 2010).

Pyrenophora semeniperda has potential as a biological control agent, but further

research is necessary to overcome the limitations of mass production, environmental

constraints, and spillover effects. This pathogen integrated with other management tools

like herbicides and revegetation will potentially improve control of one of the most

19 problematic annual grasses on western rangeland. Combining herbicides and P.

semeniperda may be beneficial because neither tool used alone has proven one hundred

percent effective. To my knowledge, no studies exist that have investigated integrating

chemical and biological control for cheatgrass.

Project Justification and Objectives

Improving invasive plant management with integrated management techniques,

such as herbicides and biological controls, has been recognized as a sustainable and

economic approach to aggressive invaders like cheatgrass (DiTomaso 2000, DiTomaso et

al. 2010, Krueger-Mangold et al. 2006, Masters and Sheley 2001). This project focused

on the use of imazapic and the fungal pathogen Pyrenophora semeniperda for control of

cheatgrass in Montana. The first study investigated imazapic efficacy as affected by

application rate and timing and plant litter. The second study determined imazapic

persistence in the soil as affected by application rate and plant litter. The third study

integrated imazapic application with P. semeniperda inoculation to determine whether

the two methods produced a response that would lead to greater cheatgrass control than

either method alone.

20

CHAPTER TWO

EFFECTS OF IMAZAPIC RATE, APPLICATION TIMING, AND PLANT LITTER

ON CHEATGRASS-INFESTED RANGE AND CRP LANDS

Introduction

The invasion of cheatgrass (Bromus tectorum) is one of the most significant plant

invasions in North America, with 22.3 million infested hectares in 17 western states (Rice

2005). Cheatgrass has been problematic in the Great Basin and more recently in Montana

where its expansion may be enhanced even further in the future because of global climate

change (Bradley 2009). The impacts of cheatgrass on rangelands include a reduction in

plant community diversity, altered fire regimes, economic losses, and an unpredictable

forage base for livestock and wildlife (BLM 1999, Epanchin-Niell et al. 2009, Hull 1965,

Klemmedson and Smith 1964, Leger 2008, Murray et al. 1978, Peters and Bunting 1994,

Rice 2005, Whisenant 1990). Improved cheatgrass management can reduce the ecological

and economic impacts associated with large-scale infestations.

Cheatgrass can outperform many native plants due to its prolific seed production

(Humphrey and Schupp 2001), carryover seed bank (Harmon et al. 2012), and effective

use of soil moisture (Pellant 1996) and nutrients (Chambers et al. 2007, Harris 1967,

James 2008b, Sperry et al. 2006, Stewart and Hull 1949, Vasquez et al. 2008). As a

winter annual, cheatgrass usually germinates in the fall after primary seed dormancy

ends. However, in Montana’s semiarid climate where moisture is sometimes scarce in the

fall, a significant fraction of the cheatgrass seed bank may not germinate until the spring

21 when rainfall becomes more plentiful. The ability to continually germinate from the fall

through the spring makes control of this weed particularly difficult.

Chemical control of cheatgrass on western rangelands has recently focused on

imazapic (Plateau®, BASF Corporation 2008), a member of the imidazolinone herbicide

family (Davison and Smith 2007, Elseroad and Rudd 2011, Morris et al. 2009). Imazapic

is an acetolacetate synthase (ALS) inhibitor that is absorbed through leaves, stems, and

roots (BASF Corporation 2008, Peterson et al. 2010). It is currently labeled for both

foliar (post-emergent) and soil applications (pre-emergent), although results from

preliminary trials have revealed improved efficacy with post-emergent application

relative to pre-emergent application (Mangold et al., in review). Reduced efficacy of pre-

emergent application may be due to surface plant litter that often accumulates at

cheatgrass-infested sites. Surface litter may serve as sorption sites for herbicide resulting

in less contact with cheatgrass seedlings. Actions to reduce plant litter, including

prescribed fire, increase the efficacy of imazapic (Calo et al. 2012, DiTomaso et al. 2006,

Sheley et al. 2007). However, direct contact between imazapic and foliage is more likely

to occur once seedlings have emerged, compared to pre-emergent applications, regardless

of the presence of surface plant litter.

Optimal imazapic application rates for cheatgrass control have not been reviewed

in detail in the literature. Recommended ates of 40 to 240 g active ingredient (a.i.) ha-1 (2

to 12 oz. product ha-1) are on the Plateau® label (BASF Corporation 2008). Morris et al.

(2009) reported that as imazapic rate increased, cheatgrass cover decreased; this

response, though, was dependent on year and site characteristics including precipitation,

22 soil organic matter, and disturbance history. Kyser et al. (2007) found that control of

medusahead (Taeniatherum caput-medusae) and other annual grasses, including

cheatgrass, increased as imazapic rate increased. Imazapic efficacy was improved by

reducing the litter layer with tillage, mowing and raking, or burning, suggesting that

imazapic was adsorbing to plant litter (Kyser et al. 2007). Lack of consensus on an

optimal imazapic application rate, especially as it relates to site characteristics, likely

contributes to the variable cheatgrass control that often occurs with imazapic use.

The objective of this study was to test the effect of imazapic application rate and

timing, and plant litter on cheatgrass control and desired plant species in range and

Conservation Reserve Program (CRP) lands. It was predicted that greater cheatgrass

control and an increase in desired perennial grasses and forbs would occur with

increasing imazapic application rate. Further, greater cheatgrass control would occur

when plant litter was reduced. Lastly, it was hypothesized that early post-emergent

application of imazapic would result in greater cheatgrass control and an increase in

desired perennial grasses and forbs relative to late post-emergent application.

Materials and Methods

Site Description

Field studies were conducted over two years at a rangeland site 35 km south of

Big Timber, Montana (45° 35’ 45.01”, -110° 10’ 28.23”), and a CRP site 23 km south of

Havre, Montana (48° 27’ 9.59”, -109° 52’ 33.42”). Soil at Big Timber is a Winspect

cobbly loam (Typic Calciustoll) and has a pH of 6.6. Mean annual precipitation and air

temperature are 387 mm and 7.2°C, respectively. Soil at Havre is an Evanston loam

23 (Aridic Agriustoll) and has a pH of 7.1. Mean annual precipitation is 295 mm, and air

temperature is 5°C.

Weather Data

Monthly precipitation data from 1894-2012 (Big Timber) and 1961-2012 (Havre)

were compiled from the Western Regional Climate Center (NCDC 2002). Observations

were taken from stations located at Big Timber, Montana (Big Timber) and Havre

Weather Service Office (WSO), Havre, Montana (Havre).

Experimental Design

Two experiments were established at each field site. Experiment I (Rate x Litter)

consisted of a factorial combination of four imazapic application rates (0, 80, 160, and

240 g a.i. ha-1; hereafter referred to as control, low, medium, and high) and two litter

treatments (reduced, ambient). The experiment was arranged in a randomized split-block

design with imazapic rate as the whole plot (18.3 m x 3.0 m), and litter treatment as the

subplot (9.1 m x 3.0 m), with four replications. The reduced litter treatment was achieved

by hand raking with a lawn rake immediately prior to imazapic application. The ambient

litter treatment was undisturbed.

Experiment II (Rate x Timing) consisted of a factorial combination of four

imazapic application rates (0, 80, 160, and 240 g a.i. ha-1; hereafter referred to as control,

low, medium, and high) and two application timings (early, late). Early application

occurred when cheatgrass seedlings had one to two leaves, on average; late application

occurred when cheatgrass seedlings had three to four leaves, on average. The experiment

was arranged in a randomized split-block design with imazapic rate as the whole plot

24 (18.3 m x 3.0 m), and application timing as the subplot (9.1 m x 3.0 m), with four

replications. Experiments I and II were run over two years (2011, 2012) at different

locations within the same fields.

Herbicide Applications

Imazapic was applied as Plateau® (BASF Corporation 2008). The herbicide was

mixed with water plus the non-ionic surfactant (0.10% volume/volume) Penetrator®

(Helena Chemical Company) and applied using a CO2 backpack sprayer delivering 157 L

ha-1 water at 3 kg cm-2 pressure across a boom width of 3 m. Date of application,

cheatgrass growth stage, and weather conditions at the time of imazapic application for

Experiment I and Experiment II are summarized in Table 2.1. Experiment I herbicide

applications corresponded with the early application for Experiment II.

25

Table 2.1. Application date, cheatgrass growth stage, and weather conditions for Experiment I (Rate x Litter) and Experiment II (Rate x Timing) at Big Timber and Havre

for 2011 and 2012. Experiment I herbicide applications corresponded with the early application for Experiment II.

Timing Date Growth Stage Weather ConditionsEarly 9/29/2010 1-2 leaf 1.0 km hr-1 wind

10˚C63% rel. humidity

Late 10/18/2010 1-2 leaf 5.0 km/hr wind13˚C

29% rel. humidity



Late 10/12/2010 3-4 leaf 0 km hr-1 wind14˚C

33% rel. humidity

Timing Date Growth Stage Weather ConditionsEarly 9/20/2011 Pre-emergent 8.0 km hr-1 wind


Late 10/18/2011 1-2 leaf 7 km hr-1 wind-1˚C

54% rel. humidity



Late 10/13/2011 3-4 leaf 2.0 km hr-1 wind4˚C

66% rel. humidity

Havre

Big Timber2011

Big Timber

Havre

2012

26 Vegetation Sampling

Vegetation sampling occurred 36-42 weeks post-herbicide application for both

years of Experiment I and Experiment II (Table 2.2). Sampling dates corresponded to

proximate peak standing crop, except at Big Timber in 2012 where cheatgrass was

starting to drop seed due to droughty conditions. Foliar cover was recorded by species in

three 20 x 50 cm frames (Daubenmire 1959) randomly placed within each sub-plot, and

then species’ data were placed into functional groups for analysis. Biomass was clipped

by functional group from the same Daubenmire frames used to estimate percent cover.

Biomass was dried at 65°C for a minimum of 72 hours at the Plant Growth Center,

Montana State University, Bozeman, MT, USA Functional groups included cheatgrass

(BRTE), perennial grasses (PG), exotic perennial forbs (EPF), and native perennial forbs

(NPF). Species found at each site are listed by functional group in Table 2.3.

Statistical Analysis

Analysis of variance (ANOVA) of cover and biomass were performed using Proc

Mixed in SAS 9.3 (SAS Institute Inc. 2012, Appendix A). Sites were analyzed separately

for Experiment I and II. For Experiment I (Rate x Litter), year (2011, 2012) and imazapic

application rate (control, low, medium, and high) were treated as fixed effects. For

Experiment II (Rate x Timing), year, imazapic application rate and application timing

(early, late) were treated as fixed effects. Block was a random effect for both

experiments. Means separations tests were performed using the PDIFF option in the

LSMEANS statement when the main effects or interactions were significant at ɑ < 0.05.

27

Table 2.2. Vegetation sampling date, application timing from the previous year, and weeks post-herbicide application for Experiment I (Rate x Litter) and Experiment II (Rate

x Timing) at Big Timber and Havre for 2011 and 2012. Experiment I weeks post-application corresponded with the early application timing for Experiment II.

Sampling Date Timing Weeks Post-ApplicationEarly 40Late 37




6/25 - 6/27/2012

7/2 - 7/3/2012

7/11 - 7/13/2011

Big Timber

Havre

7/5 - 7/8/2011

2011

2012

Havre

Big Timber

Results

Climate (Average Precipitation)

At Big Timber, precipitation for September 2010 – August 2011 fluctuated

throughout the year and was consistently higher than the long-term average from

February – May 2011 (Figure 2.1a). Average monthly precipitation for September 2011 –

May 2012 aligned closely with the long-term average, but was lower than the long-term

average for June – August 2012. Additionally, precipitation for September 2011 – May

2012 was lower than that for September 2010 – August 2011.

28

Table 2.3. Species found at each site by plant functional group. An “X” in the column under a site indicates that the species was found at that site.

Scientific Name Big Timber Havre

Agropyron cristatum - XBromus inermis - XKoeleria macrantha X XPascopyrum smithii (Agropyron smithii) X XPoa compressa X XPoa secunda X XPseudoroegneria spicata (Agropyron spicata) X XStipa comata - -Stipa viridula X -Leymus cinereus - X

Medicago sativa X XCirsium arvense X -Cynoglossum officinale X -Taraxacum officinale X XTragopogon dubius X X

Arabis drummondii X -Artesima fridgida X XArtemisia ludoviciana X -Astragalus adsurgens (Astragalus laxmannii) X -Comandra umbellata X -Liatris spp. X -Liatris punctata X -Phlox spp. X -Potentilla pensylvanica X -Psoralea tenuiflora (Psoralidium tenuiflorum) X -Sphaeralcea grossulariifolia X XVicia americana X -Lygodesmia spp. X -

Perennial Grasses (PG)

Native Perennial Forbs (NPF)

Exotic Perennial Forbs (EPF)

29

Month

Sept. Oct.Nov.

Dec.Jan.

Feb.March

April MayJune July Aug.

Prec

ipita

tion

(mm

)

0

50

100

150

200

250

Long-term Average Sept. 2010 - Aug. 2011 Sept. 2011 - Aug. 2012

a) Big Timber.

Month

Sept. Oct.Nov.

Dec.Jan.

Feb.March

April MayJune July Aug.

Prec

ipita

tion

(mm

)

0

20

40

60

80

100Long-term Average Sept. 2010 - Aug. 2011 Sept. 2011 - Aug. 2012

b) Havre. Figure 2.1. Precipitation for the long-term average, September 2010-August 2011, and September 2011-August 2011 for Big Timber and Havre.

30 At Havre, monthly average precipitation varied for September 2010 – August

2011, and was generally higher than the long-term average (Figure 2.1b). Monthly

average precipitation for September 2011 – February 2012 was lower than the long-term

average, and increased during the spring from March – May 2012. During the summer

from June – August 2012, the monthly average precipitation was lower than the long-

term average.

Experiment I. – Rate x Litter

Cheatgrass (BRTE) At Big Timber, the interaction of imazapic rate, litter, and

year influenced BRTE cover (P = 0.0064) and biomass (0.0426) (Table 2.4). Cheatgrass

cover was higher in 2011 than 2012. In 2011, reduced litter resulted in similar BRTE

cover in the control (43%) and low (34%) imazapic rate treatments; cover was lower and

similar in the medium (19%) and high (16%) imazapic rate treatments (Figure 2.2). This

differed from ambient litter, which resulted in similar BRTE cover in the control, low and

high imazapic rate treatments (19, 24, and 15%, respectively); cheatgrass cover was

highest at 33% in the medium imazapic rate treatment. In 2012, reduced litter had similar

BRTE cover across all imazapic rates. Cheatgrass cover in ambient litter was highest in

the control at 23%; cheatgrass cover was similarly decreased by the low, medium, and

high imazapic rate treatments.

31

Table 2.4. Experiment I – Rate x Litter. P-values from ANOVA on functional group cover and biomass at Big Timber and Havre.

Fixed effects df BRTE PG EPF NPFYear 1 < 0.0001 0.6561 0.6077 0.0356Litter 1 0.8915 0.3579 0.0805 0.3772Rate 3 < 0.0001 0.0827 0.1462 0.1298Litter x Year 1 0.1181 0.6777 0.4319 0.9426Rate x Year 3 0.1232 0.7924 0.5632 0.0557Rate x Litter 3 0.1060 0.2610 0.8305 0.0798Rate x Litter x Year 3 0.0064 0.0691 0.9404 0.9834

Fixed effects df BRTE PG EPF NPFYear 1 < 0.0001 0.4934 0.8587 0.0640Litter 1 0.2025 0.9626 0.0617 0.8923Rate 3 0.0006 0.2060 0.4098 0.4811Litter x Year 1 0.1712 0.3906 0.9447 0.6987Rate x Year 3 0.0588 0.7786 0.9730 0.1324Rate x Litter 3 0.6706 0.4902 0.4016 0.1042Rate x Litter x Year 3 0.0426 0.2997 0.9731 0.4286

Fixed effects df BRTE PG EPF NPFYear 1 0.2453 0.0219 0.1463 N/SLitter 1 0.1086 0.6663 0.7286 N/SRate 3 < 0.0001 0.3691 0.4832 N/SLitter x Year 1 0.5983 0.0803 0.7286 N/SRate x Year 3 0.1422 0.0625 0.4173 N/SRate x Litter 3 0.0523 0.1243 0.6455 N/SRate x Litter x Year 3 0.5130 0.0638 0.8468 N/S

Fixed effects df BRTE PG EPF NPFYear 1 0.1982 0.0008 0.2763 N/SLitter 1 0.1003 0.5174 0.0537 N/SRate 3 0.0006 0.8388 0.7392 N/SLitter x Year 1 0.0924 0.4363 0.0817 N/SRate x Year 3 0.0766 0.8369 0.1157 N/SRate x Litter 3 0.1594 0.4533 0.2492 N/SRate x Litter x Year 3 0.0167 0.3485 0.2515 N/S

HavreCover

Biomass

Big TimberCover

Biomass

Functional groups indicated as follows: cheatgrass (BRTE), perennial grasses (PG), exotic perennial forbs (EPF), and native perennial forbs (NPF). N/S signifies a non-significant model because of the infrequent

occurrence of observations.

32

Che

atgr

ass

Cov

er (%

)

0

10

20

30

40

50Control Low Medium High

Litter Treatment

2011 2012

Reduced Ambient Reduced Ambient

Figure 2.2. Cheatgrass cover as affected by year, imazapic rate, and litter at Big Timber for Experiment I (Rate x Litter). Error bars indicate 1 SE of the mean. Lower case letters indicate means that are different within a litter treatment for each year. An “*” indicates means that are different between litter treatments across an imazapic rate for each year.

Similar to cover, cheatgrass biomass at Big Timber was higher in 2011 than 2012.

In the reduced litter treatment, BRTE biomass decreased as imazapic rate increased.

Biomass was reduced by about 30, 60, and 80%, in the low, medium and high imazapic

rate treatments, respectively, compared to the control (Figure 2.3). Similar results

occurred in ambient litter in 2011 where BRTE biomass decreased with increasing

imazapic rate, although the relationship was not as strong. In 2012, reduced litter had

* b

b

* a

* a a

* b

ab

a

a

a

a

a a a a

b

33 similar BRTE biomass across all imazapic rates. In ambient litter, BRTE biomass was

reduced compared to the control by the low and medium imazapic rates but not the high

rate. C

heat

gras

s bi

omas

s (g

m-2

)

0

100

200

300

400

500

600 Control Low Medium High

Litter Treatment

2011 2012


Figure 2.3. Cheatgrass biomass as affected by year, imazapic rate, and litter at Big Timber for Experiment I (Rate x Litter). Error bars indicate 1 SE of the mean. Lower case letters indicate means that are different within a litter treatment for each year. An “*” indicates means that are different between litter treatments across an imazapic rate for each year.

At Havre, imazapic rate affected BRTE cover (P < 0.0001) and the interaction of

imazapic rate, litter, and year affected BRTE biomass (P = 0.0167) (Table 2.4). Cover in

the control treatment was highest at 20 ± 4% and decreased to similar levels across the

b b

* c

* b

ab

a

ab * a

a a a

* b

ab

a

aa

34 low, medium, and high imazapic rate treatments (3 ± 1, 1 ± 0, and 0 ± 0%, respectively).

Reduced and ambient litter treatments resulted in similar trends for BRTE biomass in

2011; biomass exhibited a trend of decreasing biomass with increasing imazapic rate,

however statistically the means for the low, medium, and high imazapic rates were not

different from each other (Figure 2.4). In 2012, the reduced and ambient litter treatments

resulted in similar BRTE biomass across all imazapic rates.

Che

atgr

ass

Biom

ass

(g m

-2)

0

200

400

600

800


Litter Treatment

2011 2012


Figure 2.4. Cheatgrass biomass as affected by year, imazapic rate, and litter at Havre for Experiment I (Rate x Litter). Error bars indicate 1 SE of the mean. Lower case letters indicate means that are different within a litter treatment for each year. An “*” indicates means that are different between litter treatments across an imazapic rate, for each year.

* b

a

a a

* b

a a a

a

a a a

a

a

a a

35 Perennial Grasses (PG) Perennial grass cover and biomass were not affected by

treatments at Big Timber (Table 2.4). At Havre, year influenced PG cover (P = 0.0219)

and biomass (P = 0.0008) (Table 2.4). Perennial grass cover in 2011 was 12 ± 2%, which

was three times higher than it was in 2012 (4 ± 1%). The same trend occurred for PG

biomass, which was nearly 20 times higher in 2011 (829.2 ± 170.3 g m-2) compared to

2012 (42.3 ± 15.4 g m-2).

Exotic Perennial Forbs (EPF) Exotic perennial forb cover and biomass were not

affected by treatments at either site (Table 2.4).

Native Perennial Forbs (NPF) At Big Timber, NPF cover was affected by year (P

= 0.0356, Table 2.4). Native perennial forb cover in 2011 was 9 ± 1%, and decreased to 5

± 1% in 2012. Infrequent occurrence of NPF at Havre resulted in non-significant models

for cover and biomass.

Experiment II. – Rate x Timing

Cheatgrass (BRTE) Cheatgrass cover was affected by year and imazapic rate (P =

0256 and 0.0029, respectively) and biomass (P = 0.0002 and 0.0005, respectively) at Big

Timber (Table 2.5). Cheatgrass cover was lower in 2012 than in 2011 (8 ± 3% versus 25

± 3%). Cheatgrass cover decreased from 28 ± 4% in the control treatment to similar

levels in the low (13 ± 5%), medium (14 ± 4%), and high (10 ± 3%) imazapic rate

treatments, across years. Similar trends occurred for cheatgrass biomass. Cheatgrass

biomass decreased from 268.5 ± 29.9 g m-2 in 2011 to 37.2 ± 9.6 g m-2 in 2012. Biomass

decreased compared to the control (249.95 ± 39.28 g m-2) as imazapic rate increased,

36 with the low, medium, and high imazapic rates resulting in similar BRTE biomass, across

years (144.1 ± 47.2, 133.3 ± 46.6, and 83.9 ± 27.0 g m-2, respectively).

Table 2.5. Experiment II – Rate x Timing. P-values from ANOVA on functional group cover and biomass at Big Timber and Havre.

Fixed effects df BRTE PG EPF NPFYear 1 0.0256 0.0065 0.7682 0.0216Timing 1 0.4431 0.1411 0.8287 0.7598Timing x Year 1 0.2176 0.7618 0.1132 0.7414Rate 3 0.0029 0.0283 0.2478 0.9680Rate x Year 3 0.1125 0.2263 0.8915 0.0863Rate x Timing 3 0.4758 0.7489 0.8379 0.6278Rate x Timing x Year 3 0.4780 0.7356 0.1317 0.2920

Fixed effects df BRTE PG EPF NPFYear 1 0.0002 0.0014 0.0794 0.3319Timing 1 0.2672 0.0534 0.8325 0.4750Timing x Year 1 0.1599 0.6727 0.2117 0.3939Rate 3 0.0005 0.0338 0.3577 0.7837Rate x Year 3 0.4211 0.1265 0.7245 0.2309Rate x Timing 3 0.0875 0.5505 0.6519 0.4593Rate x Timing x Year 3 0.1043 0.5484 0.7651 0.9686

Fixed effects df BRTE PG EPF NPFYear 1 < 0.0001 0.2475 0.0215 N/STiming 1 0.2012 0.3862 0.1891 N/STiming x Year 1 0.2156 0.0532 0.1080 N/SRate 3 < 0.0001 0.7221 0.7000 N/SRate x Year 3 < 0.0001 0.5341 0.7631 N/SRate x Timing 3 0.0124 0.1907 0.6794 N/SRate x Timing x Year 3 0.0105 0.1761 0.5948 N/S

Fixed effects df BRTE PG EPF NPFYear 1 0.0013 0.1541 0.0819 N/STiming 1 0.7943 0.0208 0.2479 N/STiming x Year 1 0.9915 0.1125 0.2011 N/SRate 3 0.2816 0.6888 0.3435 N/SRate x Year 3 0.4672 0.4428 0.2533 N/SRate x Timing 3 0.0682 0.1140 0.5529 N/SRate x Timing x Year 3 0.1484 0.6467 0.5861 N/S

HavreCover

Biomass

Big TimberCover

Biomass

Functional groups indicated as follows: cheatgrass (BRTE), perennial grasses (PG), exotic perennial forbs (EPF), and native perennial forbs (NPF). N/S signifies a non-significant model because of the infrequent

occurrence of observations.

37 At Havre, year, imazapic rate, and timing interacted to influence cheatgrass cover

(P = 0.0105) (Table 2.5). In 2011, the early application was more effective in reducing

BRTE cover than the late application (Figure 2.5). In the early application treatment, all

imazapic rates reduced BRTE cover below that of the control. With the late application,

only the high imazapic rate reduced BRTE cover below that of the control. In 2012,

BRTE cover was zero regardless of imazapic rate or timing.

Che

atgr

ass

Cov

er (%

)

0

20

40

60

80


Application Timing

2011 2012

Early Late Early Late

Figure 2.5. Cheatgrass cover as affected by year, imazapic rate, and application timing at Havre for Experiment II (Rate x Timing). Error bars indicate 1 SE of the mean. Lower case letters indicate means that are different within a litter treatment for each year. An “*” indicates means that are different between litter treatments across an imazapic rate, for each year.

a a a a

a a a a

* b * *

ab b

a

* b

* * a a a

38 Perennial Grasses (PG) At Big Timber, the main effects of year and imazapic rate

influenced PG cover (P = 0.0065 and 0.0283, respectively) and biomass (P = 0.0014 and

0.0338) (Table 2.5). Perennial grass cover was approximately two times higher in 2011

(14 ± 1%) than it was in 2012 (6 ± 1%). Perennial grass cover was 6 ± 1% in the control

treatment. Applying imazapic at any rate doubled PG cover to 12 ± 2% in the low

imazapic rate treatment and 11 ± 2% in the medium and high imazapic rate treatments.

Similar trends occurred for PG biomass. In 2011 PG biomass was approximately two

times higher than it was in 2012 (181.3 ± 14.1 g m-2 versus 78.0 ± 12.4 g m-2,

respectively). Biomass was similar in the low, medium, and high imazapic rate treatments

(141.2 ± 29.7, 143.0 ± 18.1, 148.6 ± 22.4 g m-2, respectively), which were all higher than

the control (85.7 ± 16.9 g m-2).

Perennial grass cover at Havre was not affected by imazapic rate or timing

treatments, however, PG biomass was influenced by timing (P = 0.0208, Table 2.5). The

late application (534.0 ± 115.2 g m-2) doubled PG biomass relative to the early

application (261.4 ± 34.7 g m-2).

Exotic Perennial Forbs (EPF) Treatments did not effect EPF cover and biomass at

Big Timber; however, EPF cover at Havre was affected by year (P = 0.0215, Table 2.5).

There was no EPF cover in 2011; in 2012, EPF cover increased to 5 ± 1%.

Native Perennial Forbs (NPF) Native perennial forb cover was influenced by year

(P = 0.0216, Table 2.5) at Big Timber. In 2011, NPF cover was 13 ± 1%, which

decreased to 7 ± 1% in 2012. At Havre, infrequent occurrence of NPF resulted in non-

significant models for cover and biomass.

39

Discussion

Successful chemical control of cheatgrass in rangeland and CRP is dependent on

understanding herbicide efficacy as it relates to application rate and timing and plant

litter. Imazapic rate influenced cheatgrass control at both Big Timber and Havre for

Experiments I (Rate x Litter) and II (Rate x Timing). For Experiment I, cheatgrass cover

at Big Timber was highly variable; however, cheatgrass biomass decreased as imazapic

rate increased. At Havre, the low, medium, and high imazapic rates were equally

effective in reducing cheatgrass biomass. Few differences were seen among imazapic

rates at either site in 2012, which may be attributed to minimal cheatgrass presence even

in the control treatment. These results demonstrate the year-to-year variation in

cheatgrass populations that can be associated with site characteristics such as

precipitation, temperature regimes, disturbance, and competitive abilities of co-occurring

species (Chambers et al. 2007).

For Experiment II, the low, medium, and high imazapic rates were equally

effective in reducing cheatgrass cover and biomass at Big Timber. This was also the case

at Havre for the early application in 2011, however, only the high imazapic rate reduced

cheatgrass cover below that of the control for the late application. In general, cheatgrass

control was achieved even with a low imazapic rate, which reduces the potential of non-

target injury. If application is delayed (i.e. late post-emergent versus early post-emergent)

then higher imazapic rates may be warranted. Further, similar to Experiment I, there was

minimal cheatgrass presence at Big Timber and Havre in 2012, again demonstrating the

year-to-year variation in cheatgrass populations.

40 Annual variability in precipitation timing and amount greatly influences

cheatgrass establishment and growth (Mack and Pyke 1983, Miller et al. 2006,

Schwinning and Ehleringer 2001). For example, precipitation patterns from the previous

year regulated the size and persistence of cheatgrass carryover seed banks in semiarid

sites in western Utah (Smith et al. 2008). Smith et al. (2008) state that above-average

precipitation resulted in higher seed production, which in turn would likely lead to greater

cheatgrass populations, which is what occurred in my experiment at both sites in 2011.

Cheatgrass emergence in the fall and growth the following spring depend on

precipitation. While variable at both sites, monthly average precipitation in fall 2010 and

March – May 2011 was near or higher than the long-term average, accounting for higher

cheatgrass presence in 2011. During fall 2011, average monthly precipitation was lower

than the long-term average, which may have resulted in much lower cheatgrass in

summer 2012.

Precipitation patterns may also explain the response of perennial grasses at Havre

in 2011. Perennial grasses typically rely on precipitation for the current spring and

summer in which they are actively growing, instead of precipitation from the previous

fall (Cable 1975). Consequently, lower average monthly precipitation during the summer

months explains the decrease in perennial grasses, even though cheatgrass was generally

controlled with imazapic. Furthermore, the increase in perennial grasses at Havre in 2011

may be associated with higher than average precipitation that occurred in June at this site.

However, precipitation patterns are not solely responsible for increases in perennial

grasses, as perennial grasses increased at Big Timber as a result of imazapic application.

41

Litter treatment had little effect on cheatgrass control, suggesting that decreasing

plant litter will not necessarily increase the efficacy of imazapic. This conflicts with

studies investigating medusahead and other annual grass control, which have

demonstrated increased control with imazapic when it is applied after a prescribed burn

(Davies 2010, Monaco et al. 2005, Sheley et al. 2007). In Experiment I, litter was

reduced by manual raking, which may not be as effective as prescribed burning in terms

of increasing physical contact between the herbicide and targeted plant (Calo et al. 2012,

Sheley et al. 2007, DiTomaso et al. 2006). Moreover, the effect of litter was only visible

in the non-sprayed control plots, suggesting that litter manipulation by manual raking

may influence microsite conditions to be more favorable to cheatgrass growth.

My results suggest that imazapic efficacy can be improved by properly

implementing the correct application timing. There was no influence of application

timing on cheatgrass control at Big Timber; however, the targeted application timings

were not as well-achieved at Big Timber as they were at Havre (Table 2.1). Application

timing was more significant at Havre where both the early and late application occurred

at the targeted cheatgrass growth stage. In 2011, the early application resulted in better

cheatgrass control than the late application, and control was more consistent across rates.

This coincides with results from other trials in Montana, which suggested improved

efficacy with early post-emergent (1-2 leaves) application relative to late post-emergent

(3-4 leaves) application (Mangold et al., in review). In a study investigating the use of

glufosinate for annual weed control, growth stage influenced herbicide efficacy; water

soluble herbicides can penetrate the cuticle of younger plants more effectively than older

42 plants (Steckel et al. 1997). Early post-emergent application offers the added benefit of

treating cheatgrass once it is visible and land managers can be more confident that

cheatgrass will be present the following growing season. This is in contrast to pre-

emergent application where land managers risk applying herbicide even though

cheatgrass may not emerge because of unfavorably dry fall conditions.

Implications

Overall, the findings of these experiments provide evidence that successful

control of cheatgrass in Montana can occur with imazapic application; however, results

are highly dependent on the annual variability of cheatgrass populations, which is linked

to seasonal precipitation. Findings from this study suggest that all imazapic rates perform

similarly, providing land managers with an economically viable option of applying a low

imazapic rate (~80 g a.i. ha-1). In addition, early application of imazapic is recommended,

as it provides more consistent cheatgrass control. Finally, surface litter removal is likely

to have little or no impact on imazapic efficacy and cheatgrass control.

Although imazapic provided cheatgrass control, the annual variability in

cheatgrass populations raises concerns about being able to predict the appropriate

imazapic rate to apply or whether herbicide application is necessary at all. More long-

term (> 2 years) research is needed to address the annual variability in cheatgrass

populations. Consequently, future research should incorporate climate data, specifically

precipitation, into models that are capable of forecasting cheatgrass populations. Doing

so would help land managers prioritize areas for management (Maxwell et al. 2009, Rew

et al. 2005) and reduce costs associated with cheatgrass control. Furthermore, future

43 research should also focus on other tools that may either overcome herbicide limitations

(i.e. when/if to spray, rates to spray), or augment herbicide efficacy in the field. Other

tools that could be incorporated into cheatgrass management include biological controls,

as well as their integration with herbicides.

44

CHAPTER THREE

IMAZAPIC PERSISTENCE IN A SEMIARID CLIMATE AT CHEATGRASS-

AFFECTED RANGELAND AND CRP SITES

Introduction

The invasion of cheatgrass (Bromus tectorum) is considered one of the most

significant plant invasions in North America with 22.3 million infested hectares in 17

western states (Rice 2005). Cheatgrass has been problematic in the Great Basin, and more

recently in Montana, where its expansion may be enhanced in the future by global

climate change (Bradley 2009). As a winter annual, cheatgrass germinates in the fall after

primary seed dormancy ends, overwinters as a seedling and resumes growth in the early

spring (Mack and Pyke 1983, Pellant 1996). The impacts of cheatgrass on rangelands

include a reduction in plant community diversity, altered fire regimes, economic losses,

and an unpredictable forage base for livestock and wildlife (BLM 1999, Epanchin-Niell

et al. 2009, Hull 1965, Klemmedson and Smith 1964, Leger 2008, Murray et al. 1978,

Peters and Bunting 1994, Rice 2005, Whisenant 1990).

Chemical control of cheatgrass on rangelands has recently focused on imazapic

(Plateau®, BASF Corporation 2008), a member of the imidazolinone herbicide family

(Davison and Smith 2007, Elseroad and Rudd 2011, Morris et al. 2009). Imazapic is an

acetolacetate synthase (ALS) inhibitor that is absorbed through leaves, stems, and roots

(BASF Corporation 2008, Peterson et al. 2010). It is currently labeled for both foliar

(post-emergent) and soil (pre-emergent) applications. Field trials in Montana have

45 revealed post-emergent applications are more effective than pre-emergent applications

(Mangold et al., in review). Reduced efficacy of pre-emergent applications may be a

result of herbicide sorption to surface plant litter that often accumulates at cheatgrass

affected sites. Disagreement between field results and label recommendations for

imazapic suggest the need for an investigation into the factors influencing efficacy,

including persistence in soils of Montana’s semiarid climate.

Imazapic persistence has been studied under a range of conditions and is often

linked to soil properties. Imidazolinone persistence increases as soil pH decreases (pH 4.5

to 6.5) (Loux and Reese 1992). In addition, imazapic persistence increases in soils with

high clay and soil organic matter content (Colquhoun 2006, Ulbrich et al. 2005), because

of increased adsorption which decreases availability for microbial degradation (Loux and

Reese 1992). Imazapic persistence has been studied extensively in cropping systems for

the purpose of determining rotation restrictions. In Brazil, imazapic persistence reduced

yield of a non-tolerant genotype of rice at 371 days post-application and plant injury was

evident up to 705 days post-application (Marchesan et al. 2010). Marchesan et al. (2010)

explained that the pronounced persistence of imazapic may be attributed to the saturated

nature and low pH (4.5) of the soils in their study system. In Georgia, only four months

was required for imazapic to degrade sufficiently such that oat production was not

affected; this likely occurred because the study was conducted under irrigated conditions,

which when combined with rainfall, exceeded the long-term rainfall averages (~540 mm)

(Prostko et al. 2005). In the cooler and more arid climate of Alberta, Canada,

46 imidazolinone herbicides reduced yields of rotational crops that were seeded one year

post-application (Moyer and Esau 1996).

Soil bioassays are a common method used to assess herbicide persistence

(Cobucci et al. 1998, Eberle and Gerber 1976, Streibig 1988). A soil bioassay is defined

as a measure of a plant’s response to soil herbicide residue (Ranft et al. 2010). Because

soil bioassays are simple and easily repeatable, they are conducted to determine the

influence of soil organic matter, soil pH, and microbial degradation on herbicide

persistence (Cutulle et al. 2009, Ranft et al. 2010). Species like cucumber (Cucumis

sativus) are often used for bioassays because they are highly sensitive to herbicide

residues (Ulbrich et al. 2005).

The objective of this study was to conduct a soil bioassay using cucumber and

cheatgrass to determine imazapic persistence as it relates to imazapic rate, presence of

plant litter, and time after herbicide application. It was predicted that cucumber and

cheatgrass biomass would decrease with increasing imazapic rate. Further, higher

cucumber and cheatgrass biomass would occur with the presence of plant litter because

of imazapic’s adsorption to the litter layer, reducing its susceptibly to degradation. Lastly,

it was hypothesized that the effect of imazapic on cucumber and cheatgrass biomass

reduction would diminish with time as a result of herbicide degradation.


Site and Field Experiment Description

Soil for the bioassay was collected from field trials conducted at two locations

during 2010-2011 and 2011-2012, hereafter referred to as 2011 and 2012, respectively.

47 The field sites included a rangeland site 35 km south of Big Timber, Montana (45° 35’

45.01”, -110° 10’ 28.23”), and a Conservation Reserve Program (CRP) site 23 km south

of Havre, Montana (48° 27’ 9.59”, -109° 52’ 33.42”). Soil at Big Timber is a Winspect

cobbly loam (Typic Calciustoll) with a pH of 6.6 (0-10 cm depth). Mean annual

precipitation and air temperature are 387 mm and 7.2°C, respectively. The plant

community at Big Timber consists of perennial grasses (e.g. Pascopyrum smithii and

Psedoroegneria spicata), exotic perennial forbs (e.g. Taraxacum officinale and

Tragopogon dubius), and native perennial forbs (e.g. Artemesia fridgida) (Table 2.3,

Chapter 2). The soil at Havre is an Evanston loam (Aridic Agriustoll) with a pH of 7.1 (0-

10 cm depth). Mean annual precipitation and air temperature are 295 mm and 5 °C,

respectively. The plant community at Havre is dominated by seeded native and perennial

grasses and to a lesser extent exotic perennial forbs, most of which are also found at Big

Timber (Table 2.3, Chapter 2). Both sites are infested with cheatgrass.

The field experiment consisted of a factorial combination of four imazapic

application rates (0, 80, 160, and 240 g a.i. ha-1; hereafter referred to as control, low,

medium, and high) and two litter treatments (reduced, ambient). The experiment was

arranged in a randomized split-block design at each site with imazapic rate as the whole

plot (18.3 m x 3.0 m) and litter treatment as the subplot (9.1 m x 3.0 m), with four

replications. The reduced litter treatment was achieved by hand raking with a lawn rake

immediately prior to imazapic application. The ambient litter treatment was undisturbed.

Imazapic was applied as Plateau® (BASF Corporation 2008). The herbicide was mixed

with water plus the non-ionic surfactant (0.10% volume/volume) Penetrator® (Helena

48 Chemical Company) and applied using a CO2 backpack sprayer delivering 157 L ha-1

water at 3 kg cm-2 pressure across a boom width of 3 m. Date of application and weather

conditions at the time of imazapic application are summarized in Table 3.1.

Table 3.1. Application date and weather conditions for 2011 and 2012 at Big Timber and Havre.

Date Weather Conditions Date Weather Conditions9/29/2010 1.0 km hr-1 wind 9/20/2011 8.0 km hr-1 wind

10˚C 8˚C63% rel. humidity 62% rel. humidity

Date Weather Conditions Date Weather Conditions9/23/2010 1.0 km hr-1 wind 9/28/2011 6.0 km hr-1 wind

17˚C 18˚C51% rel. humidity 23% rel. humidity

Havre

Big Timber2011

Big Timber

Havre

2012

Soil Sampling

At Havre, five soil sampling events occurred in 2011 and six sampling events

occurred in 2012 (Table 3.2). At Big Timber, four sampling events occurred in 2011 and

six sampling events occurred in 2012. In 2011, no sampling event was conducted ~56

days post-application (DPA) at Big Timber because of snow cover and frozen soil. In

2012, both field sites were sampled during the summer because 2011 results suggested

imazapic persisted in the soil beyond ~190-200 DPA. At each sampling event, three soil

cores were dug at three random locations within each plot (9 cores per plot) using a 7 cm

dia. tulip bulb planter. Soil core depth was 10 cm at Havre but only 8 cm at Big Timber

due to rock fragments. The nine soil cores collected from each plot were composited and

placed in 2 L Ziploc© freezer bags and frozen (0°C) within four hours of sampling. Soil

49 samples remained frozen until all sampling events were completed. Soils were then

prepared for the bioassay beginning with drying in an oven at 50°C for 72 hours followed

by sieving to remove coarse rock fragments (> 2 mm). All coarse litter material was

retained.

Table 3.2. Soil sampling date and corresponding days post-application (DPA) at Big Timber and Havre for 2011 and 2012.

Date DPA Date DPA9/29/2010 0 9/20/2011 010/13/2010 14 10/5/2011 1510/29/2010 30 10/18/2011 28N/A - 11/17/2011 58

4/10/2011 193 3/23/2012 185N/A - 7/3/2012 287

Date DPA Date DPA9/24/2010 0 9/29/2011 010/8/2010 14 10/12/2011 1310/22/2010 28 10/25/2011 2611/19/2010 56 11/28/2011 604/14/2011 202 3/29/2012 182N/A - 6/27/2012 272

Big Timber Big Timber2011

HavreHavre

2012

N/A indicates that no soil sampling occurred.

Bioassay

Soil bioassays were conducted in 1.3 L pots filled with approximately 500 g soil.

For each sampling event, 32 samples were collected (four imazapic rates, two litter

treatments, four blocks). Processed soil samples from each sampling event were

separated into two different pots, one for cheatgrass and the other for cucumber. In 2011

50 there were a total of 256 pots for Big Timber and 320 pots for Havre, and 384 pots each

for Big Timber and Havre in 2012.

Cheatgrass seeds were collected prior to the study and stored in cold-wet storage

until use. Cucumber seeds (cv. Straight Eight, American Meadows, Williston, VT) were

kept at room temperature (20°C) until use. Seeds were pre-germinated and five seeds of

cucumber or cheatgrass were planted in each pot. Pots were placed in a greenhouse at the

Montana State University Plant Growth Center (PGC), Bozeman, MT USA. Greenhouse

light and temperature conditions were maintained at 21.1˚C day/12.8˚C night.

Supplemental light was applied as needed to achieve 12-hour days. Pots were moved by

block every five to seven days to minimize the effect of location in the greenhouse. Pots

were watered daily throughout both trials to avoid drought stress (i.e. visual evidence of

wilting). Two soil bioassays were conducted corresponding with the 2011 and 2012 field

trials.

In 2012, cheatgrass seedlings that emerged from the seedbank were removed

from both cheatgrass and cucumber pots approximately 14 days after planting. Pots were

then thinned every 14 days until harvest. At 28 days, total biomass (roots and shoots) of

each species was harvested and weighed. Root were separated from the soil, and gently

triple-washed with water. After harvest, biomass was dried in a drying oven at the PGC at

65°C for a minimum of 48 hours and summarized as mg plant-1for analysis, based on the

count of plants present in each pot.

51 Statistical Analysis

Analysis of variance (ANOVA) of cucumber and cheatgrass biomass was

performed using Proc Mixed in SAS 9.3 (SAS Institute Inc. 2012, Appendix B). The

result of Levene’s test for homogeneity of variances indicated that the variances between

imazapic rates were not equal. Thus, a model with unequal variances was specified in

Proc Mixed by using the REPEATED statement with the GROUP = option; GROUP =

rate was used to specify a different residual variance for each imazapic rate. Imazapic

application rate (control, low, medium, high), plant litter (reduced, ambient), block, and

the interaction of rate and litter were included as independent effects. The DIFF option in

the LSMEANS statement was used to separate means when independent effects were

found to be significant (α = 0.05). The analysis indicated that in general, the low,

medium, and high imazapic rates similarly reduced cucumber and cheatgrass biomass

within each sampling date relative to the control. Consequently, biomass means within

each sampling date were averaged across each respective rate and normalized as percent

of the control. Tables of mean absolute biomass and graphs of relative biomass are

presented for each site.

Results

Big Timber

Cucumber Imazapic significantly reduced cucumber biomass at each sampling

event in 2011 (Table 3.3). However, there was no difference in response among the three

application rates (low, medium, and high) relative to the control (Table 3.4). In 2011,

52 biomass was 39-55%, 37-43%, and 34-43% of the control across all sampling dates for

the low, medium, and high imazapic rates, respectively (Figure 3.1a). In 2012, imazapic

affected cucumber biomass only at 58 DPA, when it was equally reduced by the low

(32% of control), medium (31% of control), and high (25% of control) imazapic rates

(Figure 3.1b).

Litter influenced cucumber biomass only at 287 DPA (P = 0.0351, Table 3.3).

Between litter treatments, ambient litter resulted in higher cucumber biomass relative to

reduced litter, but only for the low imazapic rate (143 ± 25 versus 82 ± 17 mg plant-1).

Table 3.3. P-values from ANOVA on cucumber and cheatgrass biomass at each sampling date (days post-application, DPA) for Big Timber.

Effect df 0 14 30 N/A 193 N/ARate 3 0.0004 0.0046 0.0103 - 0.0063 -Litter 1 0.8199 0.5928 0.6625 - 0.4561 -Litter x Rate 3 0.9191 0.7973 0.7040 - 0.4117 -

Effect df 0 15 28 58 185 287Rate 3 0.0584 0.2637 0.0560 0.0121 0.1027 0.3051Litter 1 0.9510 0.2721 0.9514 0.4921 0.7879 0.0351Litter x Rate 3 0.6131 0.3186 0.6932 0.8153 0.7873 0.0672

Effect df 0 14 30 N/A 193 N/ARate 3 < 0.0001 0.0001 < 0.0001 - 0.0015 -Litter 1 0.5837 0.3447 0.4950 - 0.4359 -Litter x Rate 3 0.8901 0.4252 0.8082 - 0.1953 -

Effect df 0 15 28 58 185 287Rate 3 0.0319 0.0052 0.0005 0.0004 < 0.0001 < 0.0001Litter 1 0.2194 0.8143 0.4052 0.6767 0.3222 0.3842Litter x Rate 3 0.6311 0.8156 0.4908 0.1519 0.4036 0.4459

Big TimberCucumber

2011

2012

2012

Cheatgrass2011


53

Table 3.4. Mean absolute biomass (mg plant-1) for cucumber and cheatgrass at each sampling date (days post-application, DPA) for the 2011 and 2012 bioassays for Big

Timber.

Rate 0 14 30 N/A 193 N/AControl 121 b 116 b 108 b - 201 b -Low 48 a 49 a 60 ab - 106 a -Medium 45 a 48 a 46 a - 85 a -High 41 a 43 a 43 a - 87 a -

Rate 0 15 28 58 185 287Control 163 b 117 a 302 b 355 b 277 a 114 aLow 87 a 87 a 98 a 113 a 162 a 113 aMedium 81 a 86 a 76 a 108 a 120 a 100 aHigh 92 ab 80 a 99 a 88 a 114 a 90 a

Rate 0 14 30 N/A 193 -Control 30 c 33 b 43 c - 92 c -Low 6 b 8 a 9 b - 25 b -Medium 5 ab 7 a 5 a - 24 b -High 4 a 4 a 4 a - 11 a -

Rate 0 15 28 58 185 287Control 35 b 21 b 135 c 155 c 188 b 33 cLow 3 a 4 a 8 b 11 b 46 a 28 bMedium 3 a 2 a 6 ab 5 a 16 a 27 abHigh 3 a 3 a 3 a 4 a 11 a 25 a

Days Post-Application (DPA)2012

Days Post-Application (DPA)



2012

Cheatgrass2011

Big TimberCucumber

2011

N/A indicates that no soil sampling occurred. Within a column, means followed by the

same letter are not significantly different at the P < 0.05 level for each species.

54


0 50 100 150 200 250 300

Biom

ass a

s per

cent

of c

ontro

l

0.0

0.2

0.4

0.6

0.8

1.0Low Medium High

a) 2011.


0 50 100 150 200 250 300

Biom

ass a

s per

cent

of c

ontro

l

0.0

0.2

0.4

0.6

0.8

1.0

Low Medium High

b) 2012. Figure 3.1. Cucumber biomass as percent of the control for the 2011 and 2012 bioassays for Big Timber.

55

Cheatgrass Cheatgrass biomass was affected by imazapic rate at each sampling

date in 2011 and 2012 (Table 3.3). Within each sampling date in 2011, cheatgrass

biomass was reduced below that of the control by the low, medium, and high imazapic

rates (Table 3.4). Specifically, across sampling dates biomass was 20-27% of the control

with the low imazapic rate, 12-27% with the medium rate, and 10-13% with the high

imazapic rate (Figure 3.2a). Significant differences among rates occurred at 0, 30, and

193 DPA (Table 3.4).

In 2012, all imazapic rates reduced cheatgrass biomass below that of the control

(Table 3.4). The low imazapic rate resulted in cheatgrass biomass that was 6-84% of the

control across sampling dates, which was a similar range for the low (3-80%) and high

(2-74%) imazapic rates (Figure 3.2b). For all three imazapic rates, biomass became

nearer that of the control toward the end of the sampling period (287 DPA).

Differences in rates were observed at 28, 58, and 287 DPA (Table 3.4). At 28

DPA, the high imazapic rate resulted in the lowest biomass (3 ± 1 mg plant-1) relative to

the control (135 ± 18 mg plant-1), with intermediate biomass occurring with the medium

imazapic rate (6 ± 2 mg plant-1), followed by the low imazapic rate (8 ± 1 mg plant-1);

this same trend occurred at 287 DPA. At 58 DPA, the medium and high imazapic rates

resulted in similarly low biomass (5 ± 1 and 4 ± 0 mg plant-1, respectively), relative to the

low imazapic rate (11 ± 2 mg plant-1) and the control (155 ± 22 mg plant-1).

56


0 50 100 150 200 250 300

Bio

mas

s as p

erce

nt o

f con

trol

0.0

0.2

0.4

0.6

0.8

1.0 Low Medium High

a) 2011.


0 50 100 150 200 250 300

Biom

ass a

s per

cent

of c

ontro

l

0.0

0.2

0.4

0.6

0.8

1.0Low Medium High

b) 2012. Figure 3.2. Cheatgrass biomass as percent of the control for the 2011 and 2012 bioassays for Big Timber.

57 Havre

Cucumber In 2011, imazapic rate had no effect on cucumber biomass except at

202 DPA (Table 3.5). At 202 DPA, the low (179 ± 18 mg plant-1), medium (100 ± 13 g

plant-1), and high (116 ± 13 mg plant-1) imazapic rates resulted in similar biomass, but

only the medium and high rates were different than the control (289 ± 42 mg plant-1)

(Table 3.6). In general, biomass was 40-85% of the control across sampling dates and

imazapic rates (Figure 3.3a).

The interaction of imazapic rate and litter influenced biomass at 14 DPA (P =

0.0345, Table 3.5) in 2011. Reduced litter resulted in higher biomass (143 ± 12 mg plant-

1) than ambient litter (57 ± 21mg plant-1), but only for the control. Overall, the effect of

litter was minimal, and no general trend occurred.

In 2012, imazapic rate influenced cucumber biomass at each sampling date (Table

3.5). In general, cucumber biomass was similar across the low, medium, and high

imazapic rates (Table 3.6). However, at 182 DPA biomass with the medium (79 ± 8 mg

plant-1) and high (83 ± 10 mg plant-1) rates was significantly lower than biomass at the

low rate (145 ± 14 mg plant-1) (Table 3.6). Additionally, at 272 DPA only the high

imazapic rate (68 ± 7 mg plant-1) resulted in significantly lower biomass than the control

(153 ± 22 mg plant-1). Across sampling dates, the low imazapic rate resulted in biomass

that was 13-64% of the control; biomass was generally lower with the medium (11-49%

of the control) and high (8-45%) imazapic rates (Figure 3.3b).

58 Table 3.5. P-values from ANOVA on cucumber and cheatgrass biomass at each sampling

date (days post-application, DPA) for Havre.

Effect df 0 14 28 56 202 N/ARate 3 0.1717 0.0896 0.1291 0.0758 0.0112 -Litter 1 0.9069 0.2534 0.8212 0.8442 0.1881 -Litter x Rate 3 0.8251 0.0345 0.6762 0.6580 0.6968 -

Effect df 0 13 26 60 182 272Rate 3 0.0032 0.0057 < 0.0001 0.0199 0.0031 0.0204Litter 1 0.1578 0.6818 0.0956 0.3544 0.0562 0.4623Litter x Rate 3 0.2057 0.6889 0.4830 0.7681 0.1636 0.7140

Effect df 0 14 28 56 202 N/ARate 3 0.0017 0.0036 0.1519 0.0018 < 0.0001 -Litter 1 0.6215 0.3233 0.3521 0.1009 0.8801 -Litter x Rate 3 0.7328 0.2440 0.2501 0.4196 0.8900 -

Effect df 0 13 26 60 182 272Rate 3 0.0001 0.0029 0.0021 0.0012 0.0019 0.0020Litter 1 0.3982 0.7606 0.0446 0.6058 0.9510 0.7542Litter x Rate 3 0.2521 0.9402 0.1826 0.8778 0.8920 0.0695

HavreCucumber

2011

2012

2012

Cheatgrass2011


59

Table 3.6. Absolute mean biomass (mg plant-1) for cucumber and cheatgrass at each sampling date (days post-application, DPA) for the 2011 and 2012 bioassays for Havre.

Rate 0 14 28 56 202 N/AControl 111 a 100 a 108 a 120 a 289 b -Low 64 a 70 a 76 a 78 a 179 ab -Medium 75 a 84 a 64 a 60 a 100 a -High 56 a 59 a 58 a 65 a 116 a -

Rate 0 13 26 60 182 272Control 359 b 438 b 392 b 273 b 557 c 153 bLow 45 a 89 a 75 a 79 ab 145 b 98 abMedium 40 a 57 a 50 a 44 a 79 a 75 abHigh 49 a 34 a 53 a 49 a 83 a 68 a

Rate 0 14 28 56 202 N/AControl 22 c 23 b 13 a 17 b 89 b -Low 9 abc 9 b 9 a 10 b 59 b -Medium 5 ab 7 ab 9 a 6 ab 24 ab -High 3 a 5 a 6 a 5 a 13 a -

Rate 0 13 26 60 182 272Control 39 b 94 c 55 b 71 c 123 b 42 bLow 12 a 19 b 18 a 12 b 34 a 33 bMedium 6 a 6 a 5 a 6 ab 8 a 17 aHigh 4 a 6 a 4 a 4 a 8 a 17 a

HavreCucumber

2011Days Post-Application (DPA)


2012

Cheatgrass2011



2012

N/A indicates that no soil sampling occurred. Within a column, means followed by the

same letter are not significantly different at the P < 0.05 level for each species.

60


0 50 100 150 200 250 300

Biom

ass a

s per

cent

of c

ontro

l

0.0

0.2

0.4

0.6

0.8

1.0Low Medium High

a) 2011.


0 50 100 150 200 250 300

Biom

ass a

s per

cent

of c

ontro

l

0.0

0.2

0.4

0.6

0.8

1.0Low Medium High

b) 2012. Figure 3.3. Cucumber biomass as percent of the control for the 2011 and 2012 bioassays for Havre.

61

Cheatgrass In 2011, imazapic rate influenced cheatgrass biomass at each sampling

date except at 28 DPA (Table 3.5). The low, medium, and high imazapic rates reduced

cheatgrass biomass below that of the control at 0 and 14 DPA (Table 3.6). At 56 DPA,

the medium and high imazapic rates reduced biomass the most (6 ± 0 and 5 ± 0 mg plant -

1, respectively). The low imazapic rate resulted in 10 ± 2 mg plant-1, which was similar to

the control (17 ± 2 mg plant-1). This same trend occurred at 202 DPA, when biomass

began approaching that of the control; the medium and high imazapic rates resulted in 24

± 4 mg plant-1 and 13 ± 3 mg plant-1, respectively. The low imazapic rate (59 ± 11 mg

plant-1) and the control (89 ± 11 mg plant-1) were similar. Across all sampling dates, the

low imazapic rate resulted in biomass that was 37-73% of the control, and the medium

imazapic rate was 23-66% of the control (Figure 3.4a). Biomass was 14-45% of the

control with the high imazapic rate.

In 2012, imazapic rate influenced cheatgrass biomass at each sampling date and

litter affected biomass at 26 DPA (Table 3.5). Generally, the low, medium, and high

imazapic rates equally reduced cheatgrass biomass below that of the control (Table 3.6).

At the last sampling date (272 DPA), the control (42 ± 6 mg plant-1) and low (33 ± 4 mg

plant-1) imazapic rate were similar, while the medium and high imazapic rates equally

reduced cheatgrass biomass (17 ± 3 and 17 ± 4 mg plant-1, respectively) (Table 3.6).

Across all sampling dates, the low imazapic rate resulted in biomass that was 16-78% of

the control, while the medium and high imazapic rates resulted in biomass that ranged

from 6-40% of the control (Figure 3.4b).

62


0 50 100 150 200 250 300

Biom

ass a

s per

cent

of c

ontro

l

0.0

0.2

0.4

0.6

0.8

1.0Low Medium High

a) 2011.


0 50 100 150 200 250 300

Biom

ass a

s per

cent

of c

ontro

l

0.0

0.2

0.4

0.6

0.8

1.0Low Medium High

b) 2012. Figure 3.4. Cheatgrass biomass as percent of the control for the 2011 and 2012 bioassays for Havre.

63

Discussion

Imazapic degradation in Montana’s semiarid climate appears to occur slowly, as

imazapic persisted in the soil through the following spring. Consequently, imazapic is

able to inhibit cheatgrass growth both in the fall and the following spring. In general,

across both sites and both trials, all three imazapic rates reduced cucumber and cheatgrass

biomass below that of the control, supporting the idea of slow herbicide degradation and

increased imazapic persistence. These findings led me to reject the hypothesis that a

higher reduction in biomass would occur with high imazapic rates relative to low rates. In

addition, imazapic degradation did not appear to occur at either site in 2011; however, it

did appear that degradation occurred in 2012 somewhere between the fifth (~180 DPA)

and sixth sampling dates (~280 DPA), which was accompanied by an increase in

cucumber and cheatgrass biomass. These findings demonstrate the importance of the

additional sampling event that occurred in 2012. Thus, I can partially accept my

hypothesis that as time after imazapic application increased, imazapic would become less

persistent in the soil.

Similar annual grass control for one or more years across different imazapic rates

has been reported elsewhere (Sheley et al. 2007, Elseroad and Rudd 2011, Davison and

Smith 2007). In a study investigating medusahead control in southeast Oregon, unburned

fields that were sprayed with 175 to 210 g a.i. ha-1of imazapic resulted in similarly low

annual grass cover (Sheley et al. 2007). The imazapic rates used by Sheley et al. (2007)

coincide with the medium (160 g a.i. ha-1) and high (240 g a.i. ha-1) imazapic rates used

in my study. Even an imazapic rate of 70 g a.i. ha-1, slightly lower than my low rate of 80

64 g a.i. ha-1, has been reported to reduce cheatgrass compared to unsprayed plots (Elseroad

and Rudd 2011). In another study, plots sprayed with 105 g a.i. ha-1 resulted in reduced

cheatgrass productivity in a Nevada fuelbreak (Davison and Smith 2007).

While most studies do not directly investigate imazapic persistence, they do

suggest that imazapic is able to persist in the soil and provide cheatgrass control. Davison

and Smith (2007) and others (Owen et al. 2011, Sheley et al. 2007) found cheatgrass

control one year post-treatment at a range of application rates. Further, Elseroad and

Rudd (2011) suggest that cheatgrass control can last three to four years after imazapic

application. In contrast, my study investigated imazapic persistence only ~9 months post-

application. However, my findings suggest that imazapic persists through the following

spring and likely undergoes degradation sometime between the spring and summer (~180

to 270 DPA). Thus, fall spraying provides control of cheatgrass by acting on seedlings

that emerge in both the fall and spring.

Imazapic appears to degrade slowly over time in Montana’s semiarid climate.

Microbial degradation is the primary route of imazapic degradation in the soil and is

favored in warm, moist soils (Prostko et al. 2005, Ulbrich et al. 2005). This is evident in a

study investigating rotation restrictions for oat in Georgia; four months post-application,

imazapic had degraded to such a degree that it no longer affected oat yields (Prostko et al.

2005). However, in Alberta, Canada, which has a climate more similar to Montana,

imidazolinone herbicides reduced yields of rotational crops that were seeded one year

post-application (Moyer and Esau 1996), which indicates that microbial degradation

occurs more slowly in dry, cool climates.

65

The presence of plant litter had a minimal effect on either cucumber or cheatgrass

biomass and therefore did not appear to affect imazapic persistence. Thus, I rejected my

hypothesis that the presence of plant litter would result in higher biomass because of

imazapic’s adsorption and decreased degradation. This is in contrast to studies

investigating control of medusahead and other annual grasses, which have shown

increased annual grass control with imazapic after implementing a prescribed burn to

eliminate litter (Davies 2010, Monaco et al. 2005, Sheley et al. 2007). In my study, litter

was reduced by manual raking, which may not have been as effective as prescribed

burning in terms of increasing physical contact between the herbicide and targeted plant

(Calo et al. 2012, DiTomaso et al. 2006, Sheley et al. 2007).

Soil bioassays provide insight into herbicide persistence and degradation that

perhaps is not immediately visible in a field setting. Although soil bioassays have been

conducted with cucumber (Ulbrich et al. 2005), my findings suggest that cucumber is

perhaps not sensitive enough to imazapic residues. This is supported by the higher

biomass that occurred for cucumber plants (< 180 DPA) relative to cheatgrass.

Consequently, cucumber may be as good of an indicator species as the targeted species

when imazapic persistence and degradation over time are investigated in a semiarid

region.

Implications

Overall, the findings of this study provide evidence that imazapic degradation

occurs slowly in Montana’s semiarid climate, suggesting that fall applications of

imazapic will continue to provide cheatgrass control through the following spring.

66 Regardless of imazapic rate applied, cheatgrass control is not dependent on the presence

or absence of plant litter. Controlling cheatgrass seedlings in both fall and spring

increases the likelihood of successful management, and control is possible even when

applying low imazapic rates (80 g a.i. ha-1).

Because of imazapic’s persistence into the spring, questions arise as to whether or

not its soil residual activity negatively affects desired plant communities. However,

results from my field experiment investigating imazapic application rate and timing and

plant litter (Chapter 2) and other studies (Davison and Smith 2007) indicate that desired

plant communities are not negatively affected by imazapic’s soil residual activity. This is

encouraging in that carefully timed imazapic applications at low rates can control

cheatgrass without negatively impacting the resident plant community.

67

CHAPTER FOUR

INTEGRATING THE HERBICIDE IMAZAPIC AND THE FUNGAL PATHOGEN

PYRENOPHORA SEMENIPERDA TO CONTROL CHEATGRASS

Introduction

Cheatgrass (Bromus tectorum) is an exotic winter annual grass that infests

millions of hectares of rangeland in western states, outcompeting native species and

negatively affecting ecosystems (BLM 1999, Epanchin-Niell et al. 2009, Rice 2005,

Sperry et al. 2006, Whisenant 1990, Young et al. 1969). As a prolific seed producer, a

single cheatgrass plant is capable of producing up to 6,000 seeds (Young and Evans

1978), which can result in densities of 4,800-19,000 seeds m-2 (Hempy-Mayer and Pyke

2008, Humphrey and Schupp 2001). In addition, cheatgrass is able to germinate from fall

through early spring, allowing a portion of the seed bank to carryover and escape control

from a single herbicide application. Cheatgrass infested sites often have a dense layer of

plant litter that can serve as a sorption site for herbicides, consequently limiting plant

herbicide-uptake. Thus, controlling cheatgrass with traditional management methods such

as synthetic herbicides becomes more difficult when compounded by its carryover seed

bank and accompanying dense plant litter layer.

Cheatgrass control on western rangelands has recently focused on imazapic

(Plateau®, BASF Corporation 2008), a member of the imidazolinone herbicide family

(Davison and Smith 2007, Elseroad and Rudd 2011, Morris et al. 2009). Imazapic is an

acetolacetate synthase (ALS) inhibitor that is absorbed through leaves, stems, and roots

68 (BASF Corporation 2008, Peterson et al. 2010). In Montana, imazapic efficacy has been

inconsistent (Mangold et al., in review).

Imazapic efficacy can be influenced by several factors. First, application timing

can impact herbicide efficacy (DiTomaso 2000). Imazapic is labeled for application to

the soil prior to cheatgrass emergence (pre-emergent) or to cheatgrass foliage after

emergence (post-emergent) (BASF Corporation 2008). Second, plant litter may reduce

efficacy, especially with pre-emergent application. Surface litter may serve as a sorption

site for herbicides resulting in less contact with cheatgrass seedlings. Third, efficacy can

be influenced by application rate. Rates of 2 to 12 oz. product ha-1 (corresponding to 40

and 240 g a.i. ha-1) are suggested on the Plateau® label (BASF Corporation 2008).

Varying rates have been tested for annual grass control, including cheatgrass and

medusahead (Taeniatherum caput-medusae); however, responses have been highly

variable and often depend on site characteristics (i.e. precipitation, soil organic matter,

disturbance history) (Kyser et al. 2007, Morris et al. 2009). Integrating other control tools

with herbicides is necessary because herbicides alone are not consistently effective

(DiTomaso et al. 2010, Masters and Sheley 2001). Furthermore, incorporating other tools

may result in more economical and sustainable control of cheatgrass.

The integration of the fungal pathogen Pyrenophora semeniperda with imazapic

may improve cheatgrass management (Dooley and Beckstead 2010, Meyer et al. 2008a,

Stewart 2009). Pyrenophora semeniperda is a generalist grass fungal pathogen that

causes minor leaf spot, seed infection, and death of at least 36 genera of annual and

perennial grasses (Medd et al. 2003, Meyer et al. 2008a). The name “black fingers of

69 death” is ascribed to this pathogen because of black, finger-like stromata that emerge

from infected seeds (Meyer et al. 2008a). Conidia carried to the inflorescence infect the

developing ovary of cheatgrass seeds during anthesis (Medd et al. 2003). These infected

mature seeds disperse to the ground, where the fungus over summers as mycelium;

stromata develop from the mycelium, producing more conidia to further the infection

cycle, resulting in seed death, with seed mortality reaching 90-100% (Beckstead et al.

2010, Medd et al. 2003, Meyer et al. 2007).

The ability of P. semeniperda to infect and kill cheatgrass seeds is greater under

arid conditions (199 mm mean annual precipitation (MAP)) than mesic conditions (486

mm MAP) (Beckstead et al. 2007, Meyer et al. 2007). This may be due to greater

dispersal of conidia, increased infection of undispersed cheatgrass seeds (Meyer et al.

2008b), and competition between the plant and fungus for carbohydrates in the seed,

referred to as a “race for survival” (Beckstead et al. 2007). Under mesic conditions,

cheatgrass seeds germinate and seedling growth proceeds quickly because of ample

moisture, allowing cheatgrass to utilize seed reserves faster than the fungus (Beckstead et

al. 2007). Under arid conditions, cheatgrass seeds may be either slow to germinate or

may remain dormant over the winter because of insufficient moisture; these seeds are

therefore more susceptible to P. semeniperda infection and mortality.

In general, soil-borne fungal pathogens that normally infect at or below the soil

surface are protected from environmental extremes, allowing them to persist and

continually infect target plants (Boyette et al. 1984, Jones and Hancoock 1990,

Weidemann 1988). Plant litter increases soil moisture and water availability (Fowler

70 1986), decreases incoming radiation (Facelli and Pickett 1991), and reduces temperature

fluctuations (Weaver and Rowland 1952). Cheatgrass litter was found to facilitate seed

infection by P. semeniperda due to microsite modification (Beckstead et al. 2011). Evans

and Young (1970) showed that cheatgrass litter reduced average maximum daily spring

soil surface temperatures from 28°C to 19°C and increased average minimum

temperatures from 0°C to 5°C. Such conditions are similar to optimal conditions for P.

semeniperda sporulation (Campbell et al. 1996, 2003). Thus, certain microsite conditions

may be more favorable to P. semeniperda than others, thus influencing cheatgrass

infection and mortality rates.

Integrating imazapic with P. semeniperda may improve management of one of the

most problematic annual grasses on western rangeland. Herbicides like imazapic are most

effective on recently emerged cheatgrass seedlings, while P. semeniperda can infect the

carryover seed bank and inhibit slower-germinating cheatgrass seeds (Medd and

Campbell 2005). Combining herbicides and P. semeniperda may be beneficial because

herbicides alone are not 100 percent effective, and P. semeniperda has shown promise in

exploratory research conducted in the field and greenhouse.

I conducted a greenhouse study to determine the effects of integrating imazapic

and P. semeniperda on cheatgrass emergence, density, and biomass across varying

cheatgrass seeding depths. Cheatgrass emergence was predicted to decrease with P.

semeniperda inoculation. Cheatgrass density and biomass were predicted to be lowest

when imazapic and P. semeniperda were combined compared to either treatment applied

alone or a non-treated control.

71


Experimental Design

Two P. semeniperda treatments (inoculated, non-inoculated), two imazapic

treatments (sprayed, non-sprayed), and three seeding depth treatments (0.6 cm below the

soil surface (buried), at the soil surface (surface), and within the litter layer (litter)) were

factorially arranged for a total of 12 treatments. Treatments were replicated eight times in

a randomized, complete block design. Two trials were conducted. Trial one occurred 7

February through 19 March, 2012 and trial two occurred 20 July through 31 August,

2012.

Pyrenophora semeniperda Inoculum Preparation

Pyrenophora semeniperda inoculum was prepared using a modified single

conidia isolation method (Stewart 2009). An isolate of P. semeniperda was obtained from

cropland located near Amsterdam, Montana (Miller et al., in preparation). Stromata from

cheatgrass seeds that carried this isolate were removed with sterilized tweezers, and then

placed onto damp blotter paper for 24 hours to produce conidia.

Stromata bearing conidia were placed into a 1.5 mL vial containing a 1%

Tween®/H20 solution, which was shaken vigorously for 15 seconds to release the conidia.

The solution was spread evenly using a sterile glass rod onto a Petri dish containing water

agar. Conidia were allowed to germinate at room temperature (20˚C) for 14-16 hours.

Using a dissecting scope, germinated conidia were identified based on the presence of a

clear germination tube, as long as or longer than the conidia (Miller et al., in preparation).

An X-Acto® knife dipped in ethyl alcohol (ETOH) and flame-sterilized was used to

72 transfer a single conidium onto a Petri dish containing modified alphacel medium

(MAM) (Stewart 2009).

Four-40 watt cool-white and four-40 watt black light florescent tubes were

positioned 40 cm above the Petri dishes to provide a 12 hour near visible ultraviolet

(NUV) (320-420 nm) photoperiod. Petri dishes were maintained at room temperature

(20°C). Petri dishes were checked for contaminants every two to three days, and an X-

Acto® knife dipped in ETOH and flame-sterilized was used to remove bacterial

contaminants.

After 12 days, conidia were harvested from the Petri dishes by rinsing the surface

with 5 mL of sterile deionized (DI) water, and gently scraping the surface with a rubber-

tipped glass stirring rod. Additional water was used as needed to gently scrape the surface

clean. The conidia solution was poured into a sterile glass jar. A haemocytometer was

used to quantify conidia concentration, and sterile DI water was added until a 5,000

conidia mL-1 concentration was achieved for the inoculum (Beckstead et al. 2007). The

inoculum was stored at 5˚C until seed inoculation.

Pyrenophora semeniperda Inoculum Application

Cheatgrass seeds previously collected at crop and rangeland sites in Montana

were surface sterilized to remove unwanted surface debris and microbial material by

submerging them for 60 seconds in 70% ETOH, 60 seconds in 10% bleach, 60 seconds in

70% ETOH, and then rinsing with DI water for 30 seconds. Cheatgrass seeds were

inoculated by placing them in Petri dishes containing P. semeniperda inoculum (20 seeds,

73 4 mL inoculum per Petri dish) and shaking them on a shaker table for 14 hours at 50 rpm

to allow for the absorption of P. semeniperda inoculum.

Greenhouse Conditions and Seed Planting

The study was conducted in a greenhouse at the Plant Growth Center (PGC),

Montana State University (MSU), Bozeman, Montana, USA. Greenhouse temperature

was maintained at 21.1˚C day and 12.8˚C night. Supplemental light was applied as

needed to achieve 12-hour days. A potting medium consisting of equal parts PGC Soil

Mix and Sunshine Mix #1 (the “50/50 mix,” Appendix C) was placed into 96 2.2 L pots

(15 cm dia.). All pots were seeded on the same day and all received a 0.5-1.0 cm layer of

cheatgrass litter (1.0 g pot-1) on the soil surface. Cheatgrass litter was collected from Big

Timber, MT, prior to the study and autoclaved to sterilize any cheatgrass seeds present.

Twenty cheatgrass seeds (inoculated or non-inoculated) were planted into each pot at the

appropriate seeding depth, and allowed to grow to the two-leaf stage (approximately 17

days). Upon reaching the two-leaf stage, imazapic in the form of Plateau® (BASF

Corporation 2008) was applied at 120 g a.i. ha-1 to half the pots (48) using a Generation

III Spray Booth (DeVries Manufacturing). Pots were placed back into the greenhouse and

rotated within each block every five to seven days to minimize the effect of location in

the greenhouse. Pots were watered daily throughout both trials to avoid drought stress

(i.e. visual evidence of wilting). The duration of trials one and two were 40 and 42 days,

respectively.

74 Data Collection

Cheatgrass emergence was recorded every 7 days and immediately prior to

imazapic application. Aboveground biomass of cheatgrass in each pot was harvested by

clipping plants at the soil level. Biomass was dried at 65°C for 72 hours and weighed.

Cheatgrass density per pot at the time of biomass harvest was recorded.

Statistical Analysis

To evaluate the effect of P. semeniperda and seeding depth on cheatgrass

emergence, an analysis of variance (ANOVA) was performed using a generalized linear

mixed effects model for binomially distributed data (R software, R Core Team 2012;

lme4 package, Bates et al. 2012; Appendix C). Fixed effects were P. semeniperda and

seeding depth. Random effects were trial and block. To analyze the effect of P.

semeniperda, imazapic, and seeding depth on final cheatgrass density and biomass, a

linear mixed effects model was conducted with the fixed effects of P. semeniperda,

imazapic, and seeding depth (Appendix C). Random effects were trial and block. The

multcomp package (Hothorn et al. 2008) was used to conduct means separations. Means

separations were examined for each significant main effect and interaction (α = 0.05).

Results

Cheatgrass Emergence

Pyrenophora semeniperda and seeding depth interacted to influence cheatgrass

emergence (P < 0.0001) (Table 4.1). The inoculated treatment decreased cheatgrass

emergence by 30-60% compared to the non-inoculated treatment, regardless of seeding

75 depth (Figure 4.1). The non-inoculated, buried seeded treatment had higher emergence

(69 ± 2%) than the surface and litter seeding depths (43 ± 2% and 48 ± 3%, respectively).

In the inoculated treatment, emergence was lowest for the buried seeding depth (11 ±

2%) and highest for the litter seeding depth (19 ± 2%). The surface seeding depth (13 ±

2%) resulted in emergence intermediate between the two other seeding depths.

Table 4.1. P-values from ANOVA on cheatgrass emergence.

Fixed effects df PP. semeniperda 1 < 0.0001Seeding Depth 2 < 0.0001P. semeniperda x Seeding Depth 2 < 0.0001

Cheatgrass Emergence

P. semeniperda Treatment

Non-Inoculated Inoculated

% E

mer

genc

e

0

20

40

60

80 Buried Surface Litter

a

Figure 4.1. Cheatgrass emergence as affected by Pyrenophora semeniperda and seeding depth. Error bars indicate 1 SE of the mean. Bars with different letters are statistically different at ɑ = 0.05.

d

c

c

b

a

ab

76 Cheatgrass Density

Pyrenophora semeniperda, imazapic, and seeding depth interacted to influence

cheatgrass density (P = 0.0332, Table 4.2). Density in the control, buried seeded

treatment was highest at 16 ± 1 plants pot-1 (Figure 4.2). Density was similar for the

surface and litter seeding depths (12 ± 1 and 11 ± 1, respectively). Results were similar

for the imazapic treatment; the buried seeding depth had the highest density (14 ± 1),

followed by the surface (8 ± 1) and litter (11± 1) seeding depths. The P. semeniperda and

imazapic + P. semeniperda treatments resulted in similar densities across seeding depths.

Table 4.2. P-values from ANOVA on cheatgrass density and biomass. Density Biomass

Fixed effects df P PP. semeniperda 1 < 0.0001 < 0.0001Imazapic 1 0.0022 < 0.0001Seeding Depth 2 < 0.0001 0.7887P. semeniperda x Imazapic 1 0.0133 < 0.0001P. semeniperda x Seeding Depth 2 < 0.0001 0.0450Imazapic x Seeding Depth 2 0.2727 0.2837P. semeniperda x Imazapic x Seeding Depth 2 0.0332 0.0514

The buried seeding depth resulted in similar densities for the control (16 ± 1) and

imazapic (14 ± 1) treatments, which were higher than P. semeniperda (3 ± 1) and

imazapic + P. semeniperda (4 ± 1) treatments. Densities for the surface seeding depth

were different between the control (12 ± 1) and the imazapic treatment (8 ± 1) but similar

between the P. semeniperda and imazapic + P. semeniperda treatments (3 ± 0). The litter

seeding depth behaved similarly to the buried seeding depth in that the control and

imazapic treatments were similar to each other (11 ± 1) and higher than the P.

77 semeniperda (5 ± 1) and imazapic + P. semeniperda (3 ± 0) treatments. Density was

similar across all seeding depths in the P. semeniperda and imazapic + P. semeniperda

treatments.

Treatment

Den

sity

(plan

ts p

ot-1

)

0

2

4

6

8

10

12

14

16

18 Buried Surface Litter

Imazapic,P. semeniperda

Control Imazapic

P. semeniperda

Figure 4.2. Cheatgrass density as affected by Pyrenophora semeniperda, imazapic, and seeding depth. Error bars indicate 1 SE of the mean. Bars with different lower case letters are significantly different (ɑ = 0.05) among seeding depths within a treatment. Bars with different upper case letters are significantly different (ɑ = 0.05) among treatments within a seeding depth.

Cheatgrass Biomass

Cheatgrass biomass was influenced by the interaction of imazapic and P.

semeniperda (P < 0.0001, Table 4.2). Cheatgrass biomass was highest in the control at

B a

B b

A a

Ca B

a

B b

B a

A a A

a A a

A a

A a

78 1.9 ± 0.1 g pot-1 and decreased to 0.8 ± 0.1 g pot-1 in the P. semeniperda treatment

(Figure 4.3). Biomass was lower in the imazapic (0.4 ± 0.0 g pot-1) and imazapic + P.

semeniperda (0.2 ± 0.0 g pot-1) treatments.

Figure 4.3. Cheatgrass biomass as affected by Pyrenophora semeniperda and imazapic treatments. Error bars indicate 1 SE of the mean. Bars with different letters are statistically different at ɑ = 0.05.

Cheatgrass biomass was also influenced by the interaction of P. semeniperda and

seeding depth (P = 0.0450, Table 4.2). While seeding depths within a P. semeniperda

treatment did not differ from each other, biomass decreased 70, 57, and 46% between P.

semeniperda treatments for the buried, surface, and litter seeding depths, respectively

(Figure 4.4).

c

a

a

b

79

Seeding Depth

Buried Surface Litter

Che

atgr

ass b

iom

ass (

g po

t-1)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6 Non-ioculated Inoculated

Figure 4.4. Cheatgrass biomass as affected by Pyrenophora semeniperda treatment and seeding depth. Error bars indicate 1 SE of the mean. Bars with different letters are significantly different at ɑ = 0.05.

Discussion

Pyrenophora semeniperda inoculation decreased cheatgrass emergence, density,

and biomass, leading me to accept my hypotheses that P. semeniperda would decrease

cheatgrass emergence. However, imazapic and P. semeniperda did not interact favorably,

thus leading me to reject the hypothesis that imazapic integrated with P. semeniperda

would result in decreased cheatgrass density and biomass compared to either treatment

applied alone.

b

a a

b b

a

80 Several studies have demonstrated that the use of an herbicide may increase

susceptibility to a fungal pathogen (Amsellem et al. 1990, Hodgson et al. 1988, Keen et

al. 1982, Rahe et al. 1990, Sharon et al. 1992, Wymore et al. 1987). For example,

sicklepod (Senna obtusifloia) sprayed with glyphosate was inhibited from producing

phytoalexins (antimicrobial substances) which increased its susceptibility to the pathogen

Alternaria cassiae (Sharon et al. 1991). Although my findings did not reveal a similar

relationship between imazapic and P. semeniperda, I found that P. semeniperda

effectively reduces cheatgrass emergence and density, while imazapic more effectively

reduces cheatgrass biomass.

The most favorable conditions for P. semeniperda infection and growth may

occur just below the soil surface as indicated by lower cheatgrass emergence with the

buried seeding depth relative to the surface and litter seeding depths. Greenhouse

conditions for this study were slightly lower than optimal for P. semeniperda growth and

sporulation (Campbell et al. 1996, 2003). However, the combined insulation properties of

the litter and soil may have buffered temperatures in the seed microsite enough to

encourage P. semeniperda infection. This is supported by Beckstead et al. (2011), who

found higher field-killed seed densities in high-litter patches relative to low-litter patches,

demonstrating that cheatgrass litter may modify seed microsites to favor P. semeniperda

seed infection and mortality.

Large amounts of plant litter may be present where cheatgrass control is

implemented. Although plant litter can reduce imazapic efficacy (Kyser et al. 2007), my

research (Chapters 2 and 3) suggests the reduction is minimal. Instead, plant litter may

81 favorably alter seed microsite conditions to enhance P. semeniperda infection (Beckstead

et al. 2011). Pyrenophora semeniperda infection in the field would also be augmented by

cheatgrass litter which serves as an inoculum source (Beckstead et al. 2011). For this

reason, actions to reduce plant litter to improve imazapic efficacy (i.e. prescribed fire)

(Calo et al. 2012, DiTomaso et al. 2006, Sheley et al. 2007) may not be necessary,

helping to reduce control costs associated with cheatgrass control.

Fall-applied herbicides like imazapic are most effective on recently emerged

cheatgrass seedlings, and P. semeniperda can infect the carryover seed bank and inhibit

cheatgrass germination. Findings from my study indicate that P. semeniperda could be

applied in advance of cheatgrass germination and emergence (mid- to late summer or

early fall) to affect cheatgrass emergence and density. After cheatgrass emergence,

imazapic could be applied to reduce cheatgrass biomass and even kill seedlings. Thus, P.

semeniperda can reduce the seedbank and limit future cheatgrass populations while

imazapic can control seedlings that escape pathogen-caused mortality.

Because P. semeniperda is a generalist grass pathogen, integrating imazapic with

P. semeniperda in the field raises concerns about spillover effects onto sensitive crops

and native grasses. Pathogen spillover occurs when one host species supports high

pathogen loads, causing indirect disease-mediated consequences for co-occurring host

species (Beckstead et al. 2010). Cheatgrass serves as a reservoir for P. semeniperda, so

the potential exists for P. semeniperda to infect and kill seeds of native grasses that co-

occur with cheatgrass (Beckstead et al. 2010). Beckstead et al. (2010) reported spillover

effects on five native grasses that co-occur with cheatgrass: Indian ricegrass

82 (Achnatherum hymenoides), squirreltail (Elymus elymoides), needle and thread

(Hesperostipa comate), Sandberg bluegrass (Poa secunda), and bluebunch wheatgrass.

Of these grasses, bluebunch wheatgrass, Sandberg bluegrass, and Indian ricegrass

experienced 35-80% P. semeniperda-caused seed mortality (Beckstead et al. 2010).

To mitigate spillover effects of P. semeniperda onto native grasses, P.

semeniperda application could primarily occur where pure cheatgrass monocultures exist,

especially in situations where revegetation is necessary. To prevent crop seeds or seeds

used for revegetation from becoming infected by P. semeniperda, species with low

susceptibility to P. semeniperda could be used or seeds could be treated with a fungicide

prior to seeding (Meyer et al. 2008a, Miller et al., in preparation). Spillover could also be

minimized by using P. semeniperda isolates that have the greatest host specificity

(Campbell 1996). The risks associated with spillover effects must be evaluated against

the benefits of using P. semeniperda as a biological control. Cheatgrass seed mortality

can reach > 90% due to P. semeniperda infection, which could provide seeded desirable

species the chance to establish in cheatgrass-infested rangeland (Beckstead et al. 2010).

Different rates of P. semeniperda inoculum and imazapic could influence

cheatgrass control. An optimal P. semeniperda inoculation rate would improve cheatgrass

control and minimize the risk of spillover. In my study only one inoculum and one

imazapic rate were used under controlled greenhouse conditions, and their effect was

only measured on cheatgrass. Because of varying biotic and abiotic conditions, these

same rates applied in the field may not be as successful, or may result in residual P.

semeniperda that could affect seeded desirable species. Further, extremely high P.

83 semeniperda inoculation rates could hinder cheatgrass emergence, density, and biomass

to the degree that imazapic application is unnecessary. Careful consideration of field

environmental conditions and the determination of appropriate inoculum and imazapic

rates are needed before imazapic and P. semeniperda can be integrated to control large-

scale cheatgrass infestations.

Implications

Although imazapic and P. semeniperda did not interact favorably, my findings

suggest that they offer a two-pronged approach to controlling cheatgrass: P. semeniperda

limited cheatgrass emergence and density and imazapic reduced cheatgrass biomass.

Imazapic application has produced inconsistent results (Chapters 2) and may not be the

most economical or sustainable control method available (Mangold et al., in review,

Morris et al. 2009). Fall application of synthetic herbicides has no impact on the

cheatgrass seed bank, leaving tens of thousands of seeds to germinate in the spring or

following fall. Laboratory and small-plot field research conducted to date (Beckstead et

al. 2007, 2010, 2011, Medd and Campbell 2005, Meyer et al. 2007, 2008b) has

demonstrated consistent cheatgrass mortality with P. semeniperda inoculation; however,

this approach has not been implemented in the field at a scale relevant to management.

Thus, implementing P. semeniperda with herbicides like imazapic may overcome

limitations associated with herbicide efficacy and longevity (i.e. inability to affect the

carryover seed bank).

More research is needed to test the risks, benefits and feasibility of P.

semeniperda for cheatgrass control. Field evaluations are needed to determine if spillover

84 effects are negligible, or if grass species with minimal P. semeniperda susceptibility or

fungicide applications are needed to lessen spillover risks. The logistics of mass

producing inoculum, finding an effective and economical field application method, and

determining an optimal inoculum rate also require further investigation (Medd and

Campbell 2005). Despite the additional research that is needed before commercial field-

scale application, P. semeniperda shows promise as a biological control agent for

cheatgrass. By integrating herbicides like imazapic and P. semeniperda, producers and

land managers may be able to improve management of one of the most problematic

annual grasses on western rangeland.

85

CHAPTER FIVE

SUMMARY OF FINDINGS AND DIRECTIONS FOR FUTURE RESEARCH

The invasion of cheatgrass is one of the most significant plant invasions in North

America. Most published research about cheatgrass control has emphasized the use of

herbicides, with recent focus on imazapic. However, the effects of rate and application

timing, and plant litter on imazapic efficacy have not been well documented. In addition,

integrating herbicides such as imazapic with a biological control has not been addressed

in the literature. To expand the information on cheatgrass control, I investigated how

different factors affect imazapic efficacy and persistence, as well determining whether a

favorable relationship exists between imazapic and Pyrenophora semeniperda, a seed-

killing fungal pathogen.

My first objective was to test the effect of imazapic application rate and timing,

and plant litter on cheatgrass control and desired plant species in range and Conservation

Reserve Program (CRP) lands. Results suggest that successful control of cheatgrass in

Montana can occur with imazapic; however, results are highly dependent on the annual

variability of cheatgrass populations, which is linked to seasonal precipitation. In general,

all imazapic rates reduced cheatgrass cover and biomass. Moreover, cheatgrass control

was achieved with a low imazapic rate, (70 g a.i. ha-1) which not only reduces the

potential of non-target injury, but also lessens economic costs associated with control. I

also found that early application of imazapic provides more consistent cheatgrass control,

meaning that cheatgrass can be treated once it becomes visible, giving land managers

confidence that it will be present the following growing season. Lastly, the effect of litter

86 was minimal, which suggests that decreasing plant litter will have little or no impact on

imazapic efficacy and cheatgrass control.

My second objective was to conduct a soil bioassay to determine imazapic

persistence as it relates to imazapic rate, plant litter presence, and time after herbicide

application. Findings suggest that herbicide degradation in Montana’s semiarid climate

appears to occur slowly. In fact, imazapic persisted in the soil through the following

spring. All three imazapic rates reduced cucumber and cheatgrass biomass below that of

the control. As in the field study associated with Objective 1, plant litter had a minimal

effect on cucumber or cheatgrass biomass and therefore did not appear to affect imazapic

persistence. Overall, applying imazapic in the fall not only controls cheatgrass seedlings

that emerge in the fall, but also controls spring-emerging seedlings, increasing the

likelihood of successful management.

My third objective was to determine the effects of integrating imazapic and P.

semeniperda on cheatgrass emergence, density, and biomass across varying seeding

depths. Although imazapic and P. semeniperda did not interact favorably, P.

semeniperda was found to effectively reduce cheatgrass emergence and density, while

imazapic effectively reduced biomass. The influence of P. semeniperda was heightened

just below the soil surface as indicated by lower cheatgrass emergence with the buried

seeding depth relative to the surface and litter seeding depths. This may have occurred

because of the combined insulation properties of the litter and soil, which likely led to

favorable seed microsite conditions for P. semeniperda infection. Thus, P. semeniperda

could be applied in advance of cheatgrass germination and emergence, allowing it to

87 inhibit cheatgrass emergence. After emergence, imazapic could be applied to reduce

cheatgrass biomass or even kill seedlings. Overall, P. semeniperda is able to reduce the

seedbank and limit future cheatgrass populations while imazapic can control any

seedlings that escape pathogen-caused mortality.

Future research on imazapic efficacy should incorporate climate data, specifically

precipitation, into models that are capable of forecasting cheatgrass populations. This

would give land managers the ability to prioritize areas for management. More long-term

(>2 years) research is also needed, to further investigate imazapic efficacy and

persistence. Such research should address the annual variability in cheatgrass populations

and how that influences management decisions. In addition, long-term research would

determine the ability of residual herbicides like imazapic to control cheatgrass beyond

one to two years post-treatment. Lastly, varying biotic and abiotic conditions in the field

may lessen the effectiveness of the inoculum and imazapic rates used in my study, or they

may result in residual P. semeniperda that could negatively affect seeded desirable

species.

The results of my work contribute to the knowledge of cheatgrass control as it

relates to chemical and biological control methods, specifically imazapic and P.

semeniperda. I hope that this information contributes to a more thorough understanding

of cheatgrass control in Montana’s semiarid climate, allowing greater control in the

future.

88

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100

APPENDICES

101

APPENDIX A

CHAPTER TWO STATISTICAL MODEL

102

Statistical Model

Proc Mixed for Experiment I (Rate x Litter) and Experiment II (Rate x Timing). Note that for Experiment II, “litter” is replaced with “application timing.”

model Functional Group = year litter litter*year rate rate*year litter*rate litter*rate*year/ddfm=kr; random block(year) litter*block(year) rate*block(year)

103

APPENDIX B

CHAPTER THREE STATISTICAL MODEL

104

Statistical Model

Input e.g.) Big Timber Cheatgrass 2012, i.e. “BTcheat2012”

Obs trial block rate litter trt d1 d2 d3 d4 d5 d6

1 2 1 0 no 1 12.0 7.6 42.0 82.0 104.0 12

2 2 2 0 no 1 82.0 16.5 112.0 125.8 212.0 26

3 2 3 0 no 1 26.0 34.0 214.0 196.0 172.0 22

4 2 4 0 no 1 65.0 27.7 118.0 188.0 202.0 36

5 2 1 0 yes 2 13.1 15.2 136.0 60.0 188.0 18

6 2 2 0 yes 2 24.0 14.0 134.0 166.0 198.0 42

7 2 3 0 yes 2 19.1 18.8 188.0 180.0 164.0 60

8 2 4 0 yes 2 40.0 38.0 136.0 240.0 264.0 50

9 2 1 4 no 3 3.5 3.9 7.9 3.1 18.0 18 10 2 2 4 no 3 4.0 4.0 6.0 5.2 10.2 5 11 2 3 4 no 3 3.2 5.0 3.5 10.4 64.0 38 12 2 4 4 no 3 2.8 0.9 8.0 7.7 16.7 36 13 2 1 4 yes 4 4.0 2.7 15.8 14.3 42.6 14 14 2 2 4 yes 4 4.5 8.9 7.5 8.0 28.0 34 15 2 3 4 yes 4 2.4 4.3 3.6 15.6 54.0 26 16 2 4 4 yes 4 1.9 5.2 10.0 24.1 . 52 17 2 1 8 no 5 3.6 1.9 0.7 6.8 8.5 30 18 2 2 8 no 5 0.6 2.5 4.2 2.3 5.1 24 19 2 3 8 no 5 2.5 2.0 5.5 5.8 28.6 18 20 2 4 8 no 5 5.3 2.3 3.1 6.0 40.7 36 21 2 1 8 yes 6 2.3 2.7 8.5 2.1 9.9 24 22 2 2 8 yes 6 1.8 1.6 4.6 5.0 9.7 18 23 2 3 8 yes 6 2.3 1.7 2.1 2.5 10.3 26 24 2 4 8 yes 6 1.2 3.6 16.0 6.6 16.0 36 25 2 1 12 no 7 2.8 2.7 6.0 4.1 11.7 18 26 2 2 12 no 7 1.7 2.4 2.2 4.0 5.0 30

105

Obs trial block rate litter trt d1 d2 d3 d4 d5 d6

27 2 3 12 no 7 2.0 1.6 4.4 3.1 22.0 30 28 2 4 12 no 7 5.0 4.0 1.3 5.8 8.2 24 29 2 1 12 yes 8 2.2 2.7 4.3 4.3 21.0 18 30 2 2 12 yes 8 5.0 4.0 3.6 3.4 6.0 16 31 2 3 12 yes 8 1.5 2.5 3.2 1.9 6.1 26 32 2 4 12 yes 8 1.2 1.2 2.6 3.4 3.8 36

Note: “trial,” 1 = 2011, 2 = 2012; “rate” is in oz product/A; “litter,” no = reduced litter, yes = ambient litter; d1 = 0 DPA; d2 = 15 DPA; d3 = 28 DPA; d4 = 58 DPA; d5 = 185 DPA; d6 = 287 DPA. Model Statement proc mixed data = BTcheat2012 covtest cl ic; class block rate litter; model d1 = block rate litter rate*litter /ddfm=KR outp=R; repeated/ group=rate; lsmeans rate litter rate*litter / diff adjust=Tukey; Output Note: Only output for d1 is given.

Model Information

Data Set WORK.BTCHEAT2012 Dependent Variable d1 Covariance Structure Variance Components Group Effect rate Estimation Method REML Residual Variance Method None Fixed Effects SE Method Kenward-Roger Degrees of Freedom Method Kenward-Roger

106

Class Level Information

Class Levels Values

block 4 1 2 3 4 rate 4 0 4 8 12 litter 2 no yes

Dimensions

Covariance Parameters 4 Columns in X 19 Columns in Z 0 Subjects 32 Max Obs Per Subject 1

Number of Observations

Number of Observations Read 32 Number of Observations Used 32 Number of Observations Not Used 0

Iteration History

Iteration Evaluations -2 Res Log Like Criterion

0 1 179.64218300 1 2 135.67814214 0.00398222 2 1 132.02083664 0.00943035 3 1 129.30114324 0.02428899 4 1 127.10628944 0.05751304 5 1 125.55894254 0.09156402 6 1 123.50059010 0.00197289 7 1 122.01443357 0.00197669 8 1 120.84695780 0.00135810

107

Iteration History

Iteration Evaluations -2 Res Log Like Criterion

9 1 120.78475576 0.00006106 10 1 120.78215887 0.00000019 11 1 120.78215124 0.00000000

Convergence criteria met.

Covariance Parameter Estimates

Cov Parm Group Estimate Standard

Error Z

Value Pr > Z Alpha Lower Upper

Residual rate 0 602.77 348.12 1.73 0.0417 0.05 250.24 2924.99 Residual rate 4 0.3240 0.2772 1.17 0.1212 0.05 0.1001 5.5205 Residual rate 8 3.5534 2.1775 1.63 0.0514 0.05 1.4159 19.7586 Residual rate 12 2.8119 1.6629 1.69 0.0454 0.05 1.1486 14.3832

Fit Statistics

-2 Res Log Likelihood 120.8 AIC (smaller is better) 128.8 AICC (smaller is better) 131.3 BIC (smaller is better) 134.6

Null Model Likelihood Ratio Test

DF Chi-Square Pr > ChiSq

3 58.86 <.0001

108

Information Criteria

Neg2LogLike Parms AIC AICC HQIC BIC CAIC

120.8 4 128.8 131.3 130.7 134.6 138.6

Type 3 Tests of Fixed Effects

Effect Num DF Den DF F Value Pr > F

block 3 4.04 2.68 0.1810 rate 3 9.07 4.61 0.0319 litter 1 6.13 1.87 0.2194 rate*litter 3 9.07 0.60 0.6311

Least Squares Means

Effect litter rate Estimate Standard

Error DF t Value Pr > |t|

rate 0 35.1500 8.6802 6 4.05 0.0067 rate 4 3.2875 0.2013 2.73 16.34 0.0008 rate 8 2.4500 0.6665 5.33 3.68 0.0128 rate 12 2.6750 0.5929 5.72 4.51 0.0046 litter no 13.8750 3.0859 6.13 4.50 0.0039 litter yes 7.9062 3.0859 6.13 2.56 0.0420 rate*litter no 0 46.2500 12.2757 6 3.77 0.0093 rate*litter yes 0 24.0500 12.2757 6 1.96 0.0978 rate*litter no 4 3.3750 0.2846 2.73 11.86 0.0020 rate*litter yes 4 3.2000 0.2846 2.73 11.24 0.0023 rate*litter no 8 3.0000 0.9425 5.33 3.18 0.0224 rate*litter yes 8 1.9000 0.9425 5.33 2.02 0.0964 rate*litter no 12 2.8750 0.8384 5.72 3.43 0.0151 rate*litter yes 12 2.4750 0.8384 5.72 2.95 0.0270

109

Differences of Least Squares Means

Effect litter

rate

litter

_rate

Estimate

Standard Error DF

t Value

Pr > |t|

Adjustment Adj P

rate 0 4 31.8625

8.6825 6 3.67 0.0104

Tukey-Kramer

0.0218

rate 0 8 32.7000

8.7058 6.07

3.76 0.0093

Tukey-Kramer

0.0192

rate 0 12 32.4750

8.7004 6.05

3.73 0.0096

Tukey-Kramer

0.0198

rate 4 8 0.8375 0.6962 6.46

1.20 0.2712

Tukey-Kramer

0.6401

rate 4 12 0.6125 0.6261 7.04

0.98 0.3604

Tukey-Kramer

0.7649

rate 8 12 -0.2250 0.8920 10.4

-0.25 0.8058

Tukey-Kramer

0.9940

litter no yes 5.9688 4.3641 6.13

1.37 0.2194

Tukey 0.2194

rate*litter

no 0 yes 0 22.2000

17.3604 6 1.28 0.2482

Tukey-Kramer

0.8863

rate*litter

no 0 no 4 42.8750

12.2790 6 3.49 0.0129

Tukey-Kramer

0.0803

rate*litter

no 0 yes 4 43.0500

12.2790 6 3.51 0.0127

Tukey-Kramer

0.0788

rate*litter

no 0 no 8 43.2500

12.3118 6.07

3.51 0.0124

Tukey-Kramer

0.0780

rate*litter

no 0 yes 8 44.3500

12.3118 6.07

3.60 0.0111

Tukey-Kramer

0.0689

rate*litter

no 0 no 12 43.3750

12.3043 6.05

3.53 0.0123

Tukey-Kramer

0.0767

rate*litter

no 0 yes 12 43.7750

12.3043 6.05

3.56 0.0118

Tukey-Kramer

0.0733

rate*litter

yes 0 no 4 20.6750

12.2790 6 1.68 0.1432

Tukey-Kramer

0.6975

rate*litter

yes 0 yes 4 20.8500

12.2790 6 1.70 0.1404

Tukey-Kramer

0.6899

110


Effect litter

rate

litter

_rate

Estimate

Standard Error DF

t Value

Pr > |t|

Adjustment Adj P

rate*litter

yes 0 no 8 21.0500

12.3118 6.07

1.71 0.1376

Tukey-Kramer

0.6836

rate*litter

yes 0 yes 8 22.1500

12.3118 6.07

1.80 0.1216

Tukey-Kramer

0.6353

rate*litter

yes 0 no 12 21.1750

12.3043 6.05

1.72 0.1356

Tukey-Kramer

0.6776

rate*litter

yes 0 yes 12 21.5750

12.3043 6.05

1.75 0.1296

Tukey-Kramer

0.6600

rate*litter

no 4 yes 4 0.1750 0.4025 2.73

0.43 0.6957

Tukey-Kramer

0.9997

rate*litter

no 4 no 8 0.3750 0.9846 6.46

0.38 0.7155

Tukey-Kramer

0.9999

rate*litter

no 4 yes 8 1.4750 0.9846 6.46

1.50 0.1813

Tukey-Kramer

0.7924

rate*litter

no 4 no 12 0.5000 0.8854 7.04

0.56 0.5898

Tukey-Kramer

0.9986

rate*litter

no 4 yes 12 0.9000 0.8854 7.04

1.02 0.3431

Tukey-Kramer

0.9600

rate*litter

yes 4 no 8 0.2000 0.9846 6.46

0.20 0.8453

Tukey-Kramer

1.0000

rate*litter

yes 4 yes 8 1.3000 0.9846 6.46

1.32 0.2316

Tukey-Kramer

0.8705

rate*litter

yes 4 no 12 0.3250 0.8854 7.04

0.37 0.7244

Tukey-Kramer

0.9999

rate*litter

yes 4 yes 12 0.7250 0.8854 7.04

0.82 0.4397

Tukey-Kramer

0.9872

rate*litter

no 8 yes 8 1.1000 1.3329 5.33

0.83 0.4446

Tukey-Kramer

0.9866

rate*litter

no 8 no 12 0.1250 1.2615 10.4

0.10 0.9230

Tukey-Kramer

1.0000

rate*litter

no 8 yes 12 0.5250 1.2615 10.4

0.42 0.6857

Tukey-Kramer

0.9998

111


Effect litter

rate

litter

_rate

Estimate

Standard Error DF

t Value

Pr > |t|

Adjustment Adj P

rate*litter

yes 8 no 12 -0.9750 1.2615 10.4

-0.77 0.4568

Tukey-Kramer

0.9907

rate*litter

yes 8 yes 12 -0.5750 1.2615 10.4

-0.46 0.6579

Tukey-Kramer

0.9996

rate*litter

no 12 yes 12 0.4000 1.1857 5.72

0.34 0.7479

Tukey-Kramer

1.0000

112

APPENDIX C

CHAPTER FOUR STATSTICAL MODELS AND SUPPLEMENTAL INFORMATION

113

Statistical Models

Emergence require(lme4) library(MASS) emergence<-glmer(Prop.~BFOD*Seed.Depth+(1|Trial.By.Rep), binomial,weights=Possible,data=beforespray) summary(aov(emergence)) Density require(nlme) density<-lme(Density~BFOD*HERB*PD, random=~1|Trial.By.Rep,data=BFODd) density summary(density) anova(density) Biomass require(lme4) library(MASS) biomass<-lmer(Bm.per.pot~BFOD*HERB*PD+(1|Trial.By.Rep), data=BFODd) summary(biomass) summary(aov(biomass))

Supplemental Information

Specifications of the PGC 50/50 Mix Description PGC Soil Mix Equal parts (by volume) of loam soil: washed

concrete sand:Canadian Sphagnum peat moss. AquaGro 2000 G wetting agent is blended in at

594 g per cubic meter of soil mix. Aerated steam pasteurized at 70˚C for 60 minutes.

Sunshine Mix #1 Canadian Sphagnum Peat Moss, perlite, vermiculite, starter nutrient charge, wetting agent,

and Dolomitic lime.

enhancing efficacy of herbicides to control …

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