THE EFFECT OF PROTECTION AND DISTANCE FROM THE FOREST EDGE

ON SOYBEAN YIELD DUE TO WHITE-TAILED DEER BROWSING

by

Joseph E. Rogerson

A thesis submitted to the Faculty of the University of Delaware in partial fulfillment of the requirements for the degree of Masters of Science in Wildlife Ecology

Spring 2005

Copyright © 2005 Joseph E. Rogerson All Rights Reserved

THE EFFECT OF PROTECTION AND DISTANCE FROM THE FOREST EDGE

ON SOYBEAN YIELD DUE TO WHITE-TAILED DEER BROWSING

by

Joseph E. Rogerson

Approved: ___________________________________________________________

Jacob L. Bowman, Ph.D. Professor in charge of thesis on behalf of the Advisory Committee Approved: ___________________________________________________________

Douglas W. Tallamy, Ph.D. Chair of the Department of Entomology and Wildlife Ecology Approved: ___________________________________________________________

Robin W. Morgan, Ph.D. Dean of the College of Agriculture and Natural Resources Approved: ___________________________________________________________

Conrado M. Gempesaw II, Ph.D. Vice Provost for Academic and International Programs

ii

ACKNOWLEDGMENTS

I express my sincere appreciation to my major professor, Dr. J. L. Bowman, and

my other committee members, Dr. B. L. Vasilas, and Dr. R. R. Roth, for their knowledge

and guidance. Without the help from my two technicians, Gina Ewald and Kate Howard,

I would not have been able to complete my research. I would especially like to

acknowledge Gina for going above and beyond what I would have expected from a

technician. I would also like to acknowledge Amy Alsfeld, Alison Banning, Dr. Jacob

Bowman, Matt DiBona, Deana Grimaldi, Jared Judy, Eric Ludwig, Regina Misiewicz,

Nathan Nazdrowicz, and Craig Rhoads for helping me harvest my plots. Without their

help I would have been unable to complete this “enjoyable” task. I especially owe thanks

to Marty Spellman for providing logistical help and information regarding my project;

no matter when or how many times I bothered him he was always willing to help me. I

would again like to thank Dr. Bowman for his constructive criticism while editing my

thesis. I would like to express my sincere gratitude to Dr. Chester Dickerson and Sally

Dickerson for allowing me to use their farm for my project and for their hospitality to my

technicians and me. Finally, I thank my parents, Dave and Linda Rogerson, whose love,

support, encouragement, and understanding has contributed greatly to my success.

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The MacIntire-Stennis Forestry Research Program provided funding for this project, as

did the Division of Wildlife within the Delaware Department of Natural Resources and

Environmental Control.

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TABLE OF CONTENTS

LIST OF TABLES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi LIST OF FIGURES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii ABSTRACT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .x Chapter 1 INTRODUCTION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 2 STUDY SITE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6 3 METHODS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Basic Study Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2003 Field Season. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2004 Field Season. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Data Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16 4 RESULTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Spatial and Temporal Patterns of Deer Browsing. . . . . . . . . . . . . . . . . . . 18 Spatial Effects on Plant Height. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Protection Effects on Plant Height. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .27 Harvest Data (Plant Development). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Harvest Data (Yield). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Population Estimate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40 5 DISCUSSION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Spatial and Temporal Distribution of Deer Browsing. . . . . . . . . . . . . . . .41 Browsing Effects on Yield and Plant Growth. . . . . . . . . . . . . . . . . . . . . . 44 Management Implications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 LITERATURE CITED. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

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LIST OF TABLES

Table 1. Monthly precipitation totals and mean minimum and maximum temperatures during the soybean growing season for 2003 and 2004, as well as the long-term average from 1971-2000 for Dover, Delaware (National Climatic Data Center 2004, 2005a, and 2005b). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

Table 2. Classification of the various vegetative and reproductive growth stages of a

soybean plant (Ritchie et al. 1997). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Table 3. ANOVA to test if the percentage of soybean plants browsed by white-tailed

deer differed by distance class within week in 2003 and 2004 near Little Creek, Delaware. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Table 4. The percentage of soybean plants browsed by white-tailed deer each week by

distance class in 2003 and 2004 near Little Creek, Delaware. The full-season and double-crop fields are separated for analyses during the 2004 field season, but not in 2003 because of similar planting dates. Only weeks 1 through 8 are shown because browse measurements were nearly 0 thereafter. . . . . . . . . . . . . . . . . . . .20

Table 5. ANOVA to test if the percentage of soybean trifoliate leaves browsed by white-

tailed deer differed by week in each distance class in 2004, near Little Creek, Delaware. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Table 6. The percentage of soybean trifoliate leaves browsed by white-tailed deer in each

distance class by week in 2004, near Little Creek, Delaware. Only weeks 1-8 (full-season fields) and 1-7 (double-crop fields) are shown because browsing rates were virtually 0 thereafter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Table 7. ANOVA to test if browsing by white-tailed deer altered soybean plant height

among distance classes in 2003 and 2004, near Little Creek, Delaware. Plant height was measured at each growth stage in 2003 and each week in 2004. . . . .28

Table 8. ANOVA to test if browsing by white-tailed deer altered soybean plant height

among treatment types in 2003 and 2004, near Little Creek, Delaware. Plant height was measured at the beginning of each growth stage in 2003 and by week in 2004. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31

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Table 9. The average full-season and double-crop soybean biomass (g), number of pods per plant, and number of beans per pod within each treatment plot in 2003, near Little Creek, Delaware. Differences among treatments was due to white-tailed deer browsing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33

Table 10. The average full-season and double-crop soybean biomass (g), number of pods

per plant, and number of beans per pod within each distance class from the forest edge in 2003, near Little Creek, Delaware. Differences among treatments was due to white-tailed deer browsing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Table 11. The cumulative monetary impact of white-tailed deer browsing on soybean

yield (bu/ac) by distance from the forest edge in 2003 and 2004, near Little Creek, Delaware. The full-season and double-crop fields were separated for analyses during the 2004 season, but not in 2003 because of similar planting dates. . . . . .39

vii

LIST OF FIGURES

Figure 1. Aerial photograph (1997) of study site near Little Creek, Delaware depicting the farm boundary (red), 2003 field sites (green), 2004 field sites (pink), and camera locations (orange) (see methods). This farm was used to identify the spatial and temporal impacts of white-tailed deer browsing on soybean yield. . . .7

Figure 2. The percentage of full-season and double-crop soybean plants browsed each

week by white-tailed deer in 2003, near Little Creek, Delaware. Only weeks 1 through 8 are shown because browse measurements were nearly 0 thereafter. . . 25

Figure 3. The percentage of soybean plants and trifoliate leaves browsed each week by

white-tailed deer in (a) full-season fields and (b) double-crop fields in 2004, near Little Creek, Delaware. Only weeks 1 through 8 are shown because browse measurements were nearly zero thereafter. Week 8 is missing from the percentage of trifoliate leaves browsed within double-crop fields because trifoliate leaves were not counted after week 7 due to senescence. . . . . . . . . . . . . . . . . . . .26

Figure 4. The average full-season and double-crop soybean plant height (cm) within each

10 m distance class from the forest edge in 2003, near Little Creek, Delaware. Variation in plant height between distance classes was due to white-tailed deer browsing. Plant height was measured at the beginning of each growth stage treatment on 5 randomly picked plants in each plot. . . . . . . . . . . . . . . . . . . . . . . 29

Figure 5. The average soybean plant height for (a) full-season soybeans and (b) double-

crop soybeans by distance class from the forest edge in 2004, near Little Creek, Delaware. Variation in plant height between distance classes was due to white-tailed deer browsing. Plant height was measured within each plot every week. Due to a later planting date, plant height was measured less in the double-crop fields. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30

Figure 6. Soybean yield for each protection treatment type during (a) 2003 and (b) 2004

near Little Creek, Delaware. Variation in yield was due to browsing by white-tailed deer. Due to a similar planting date in 2003, full-season and double-crop yields were combined. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

viii

Figure 7. Soybean yield within each distance class during (a) 2003 and (b) 2004 near Little Creek, Delaware. Variation in yield was due to white-tailed deer browsing. Due to a similar planting date in 2003, full-season and double-crop yields were combined. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

ix

ABSTRACT

Little is understood regarding the spatial and temporal distribution of white-tailed

deer (Odocoileus virginianus) browsing on soybeans (Glycine max). A better

understanding of the timing and the most effective application area for repellents is the

first step to determining their feasibility in large-scale agricultural settings. In 2003 and

2004, I investigated spatially where browsing was most intense on soybeans and

temporally when browsing had the greatest effect of yield. I examined browsing patterns

within full-season and double-crop soybeans in Little Creek, Delaware. Each of my

study fields was bordered on one side by forest. I systematically placed plots (4.6 m2) at

10 m intervals in each field. During pre-selected plant growth stages, I protected each

study plot with a fence for 7 days, which simulated a 100% effective repellent. To

examine what impact deer had on yield, I harvested a 1 m2 centralized area in each plot.

Spatially, deer browsing was most intense ≤ 20 m of the forest edge. Browsing rates

were most intense during the first 3 weeks after emergence. Yield in the unprotected

plots did not differ from any of the 1-week protection treatments so short term protection

did not increase yield. However, yield did differ between fully protected plots and

unprotected plots. Due to their high application costs and short-term effectiveness, the

use of chemical repellents does not appear to be a cost effective technique for alleviating

white-tailed deer damage to soybeans in large fields with one edge bordered by forest.

x

Chapter 1

INTRODUCTION

White-tailed deer (Odocoileus virginianus) numbers are at an all time high and

deer overabundance is causing severe human-wildlife conflicts (McCabe and McCabe

1997). In the early 1900s, the white-tailed deer was overexploited, but due to recovery

efforts their population has rebounded (McCabe and McCabe 1997). Due to the

increased deer population, deer/human conflicts have risen drastically over recent years

(Conover 2002). Conover (1997) conservatively estimated that white-tailed cause $100

million in damage to agriculture annually. Most U.S. farmers (56%) thought economic

losses due to wildlife damage were intolerable, and 33% suffered greater than $1,000 in

losses due to wildlife damage annually (Conover 1994). Conover (1997) estimated that

deer had an annual negative economic value of $2 billion, which included deer/vehicle

collisions, damage to agricultural and timber productivity, damage to common household

shrubs and gardens, and as a reservoir for Lyme disease. Even though the net annual

negative impacts of deer are staggering, the positive impacts are extensive and cannot be

ignored (Wywialowski 1994, Fagerstone and Clay 1997). Conover (1997) estimated that

deer had an annual net positive economic value of $14 billion, which included hunting

and wildlife watching.

1

Harvest is the most effective means to control deer populations, but it is either

not feasible in some areas (e.g., inside of urban areas, parks) or removes an insufficient

number of deer to reduce damage to a tolerable level (Brown et al. 2000). Therefore, the

development of other methods to reduce damage is needed. Fences have been used to

alleviate damage, but their application costs are typically too expensive for the average

agricultural producer (Tanner and Dimmick 1983, Hygnstrom and Craven 1988). Fear-

provoking stimuli such as propane cannons and remotely activated scarecrows have

shown some promise in reducing deer problems for up to 6 weeks in small fields, but

beyond 6 weeks their effectiveness is reduced (Berringer et al. 2003). Another possible

technique used to alleviate deer browsing is the application of chemical repellents, which

when applied to plants, makes them smell and/or taste bad to deer. Harris et al. (1983)

tested the efficacy of 14 repellents in reducing captive white-tailed deer damage on corn

(Zea mays) and demonstrated that the application of Hinder®, an ammonia-based product

that both smells and tastes bad to deer, was effective in reducing the amount of corn

consumed. Conover (1989) noted reduced browsing on Japanese yews (Taxus cuspidata)

after the application of Hinder®. On the other hand, Hinder® had little effect on deer

browsing on dogwood (Cornus florida) saplings (Palmer et al. 1983). Repellents have

had varying degrees of success in reducing deer browsing, so a better understanding of

the timing and scale of application could make their use more economically feasible.

Previous researchers have attempted to determine the spatial and temporal

distribution of white-tailed deer browsing on soybeans (Glycine max). Flyger and

Thoerig (1962) and DeCalesta and Schwendeman (1978) documented that deer browsing

2

on soybeans was most intense in fields that bordered forested areas, and in fields that had

more than one edge bordering forests typically received more intense browsing. Flyger

and Thoerig (1962) reported that soybean plants in small fields (< 2.5 ha) suffered more

extensive damage than those in larger fields (> 4 ha). Garrison and Lewis (1987)

investigated effects on yield based on simulated browsing (manual removal of leaflets).

They documented that 100% defoliation during the V2 to V4 growth stages had the

greatest effect on yield, whereas <67% defoliation had little effect on overall yield. In an

attempt to reduce deer browsing with chemical repellents, Tanner and Dimmick (1983)

examined the efficacy of Hinder®, which was the only product registered to control deer

browsing on soybeans. They documented that the application of Hinder® soon after the

emergence of the soybeans in small fields (2 ha) increased yield. During the 2-year

study, they found that only 29% (1982) and 22% (1983) of the treated plots were browsed

after 2 weeks, whereas 78% (1982) and 98% (1983) of the untreated plots were browsed

(Tanner and Dimmick 1983). In eastern Virginia, Lyon and Scanlon (1987b) observed

most deer (90%) within 50 m of edge cover in soybean fields. The number of deer seen

per observer-hour was related to crop phenology, being greatest during the early

vegetative stages and decreasing as the soybean plants entered reproductive growth

stages (Lyon and Scanlon 1987b). Additionally, the percentage of soybean matter found

in scat was related to plant phenology and decreased after flowering (Lyon 1984). Lyon

(1984) hypothesized that the pattern of food selection was related to the changing

nutritional quality of the soybean foliage in comparison to the quality of other available

foliage.

3

Previous researchers have attempted to determine the effects deer have on

soybeans, but their results are not applicable to the typical farm found on the Delmarva

Peninsula. While DeCalesta and Schwendeman (1978) documented that deer browsing

was most intense in small fields adjacent to forested areas, but they did not document

how far into the field browsing occurred. Lyon and Scanlon (1987b) documented that

deer usually browsed within the first 50 m of the forest edge, but without browsing

measurements they could not determine how deer used the fields after daylight hours or

the effects of deer browsing on yield. Garrison and Lewis (1987) tried to examine the

effects of deer on soybean yield but they manually removed trifoliate leaves from plants,

so the actual impact deer cause on yield is still not understood. They also did not conduct

their study prior to the V2 growth stage or after the V6. The National Crop Insurance

Association (1985) documented that damage during the reproductive growth stages has

the greatest impact on yield so understanding the impact during all growth stages is

important. Tanner and Dimmick (1983) were the first to try chemical repellents to reduce

deer browsing to soybean fields, but their experiment was conducted on uncommonly

small fields (< 2 ha).

To determine if chemical repellents are cost effective management techniques, we

must first understand the spatial and temporal distribution of browsing and how it affects

yield. We need to know how far into a field deer damage occurs. Deer browse can be

found throughout a field, but we need to know how far into a field yield-reducing damage

occurs. We need to understand temporally when browsing is most intense so we are able

to apply management strategies during the most opportune time. Research has been

4

conducted regarding the application of chemical repellents in small-scale settings (Harris

et al. 1983, Tanner and Dimmick 1983, and Conover 1987), but their application in larger

agricultural fields has usually been cost prohibitive. A better understanding of the timing

and most effective application area for repellents is the first step to determining their

feasibility. My objectives were to determine the stage of plant development that deer

browsing has the greatest effect on soybean yield and to determine the effects of spatial

and temporal variation of deer browsing on yield.

5

Chapter 2

STUDY SITE

The research farm (261 ha), located in Kent County, Delaware, 10 km south of

Little Creek on Route 9, and was owned by Dr. Chester and Sally Dickerson and

followed farming practices typical of Delaware (Figure 1). The farm was predominantly

planted in corn and soybeans during the spring and summer months, while winter wheat

(Triticum aestivum) and rye (Secale cereale) were planted in most fields during the

winter. Approximately 80% of the farm was used in crop production, and the remaining

portion was forested. The primary tree species were sweetgum (Liquidambar

styraciflua), American sycamore (Platanus occidentalis), red maple (Acer rubrum), white

oak (Quercus alba), pin oak (Quercus palustris), and American holly (Ilex opaca). Crop

fields ranged in size from 8-20 ha, and small woodlots (0.4-16 ha) were interspersed

among fields. This farm was typical of the field and forest juxtaposition found on the

Delmarva Peninsula.

Precipitation at the study site during the soybean-growing season (May-October)

averaged 63.0 cm from 1971-2000 (National Climatic Data Center 2004, 2005a, and

2005b) (Table 1). In 2003 and 2004, rainfall amounts were 89 cm and at least 61,

respectively (Table 1). During 2003, above average rainfall from May through the

beginning of July prevented the farmer of my study site from planting full-season

6

Delaware Bay Route 9

Dover Air Force Base

Figure 1. Aerial photograph (1997) of study site near Little Creek, Delaware depicting the farm boundary (red), 2003 field sites (green), 2004 field sites (pink), and camera locations (orange) (see methods). This farm was used to identify the spatial and temporal impacts of white-tailed deer browsing on soybean yield.

7

Table 1. Monthly precipitation totals and mean minimum and maximum temperatures during the soybean growing season for 2003 and 2004, as well as the long-term average from 1971-2000 for Dover, Delaware (National Climatic Data Center 2004, 2005a, and 2005b). Year May June July August September October Precipitation (cm) 2003 14.3 19.2 17.4 18.2 11.4 8.2 2004 5.21 12.8 20.01 10.9 8.6 3.9 1971-2000 10.9 9.6 10.6 12.0 11.6 8.3 Temperature (ºC)2

2003 19.7/10.8 26.1/16.8 29.9/20.2 30.1/20.9 25.9/17.1 19.6/8.8 2004 26.7/16.4 27.4/17.6 29.2/20.3 28.6/19.7 25.7/17.0 18.3/9.4 1971-2000 23.8/12.1 28.3/17.1 30.8/20.1 29.7/19.4 26.4/15.7 20.6/9.2

1Monthly totals based on incomplete time series. 1 to 9 days are missing. 2Average maximum and minimum.

8

soybeans on time. During the 2004 season, less overall rainfall occurred, but the

frequency of precipitation events was ideal for crop production. The mean maximum and

minimum monthly temperatures for both field seasons were similar to the long-term

average (Table 1). Except for the month of May, all of the months were +/- 2 ºC from the

long-term average (National Climatic Data Center 2005a, 2005b). In 2003, the average

for May was cooler than the long-term average, while in 2004 it was warmer.

During 2003 and 2004, soybeans were planted as both full-season and double-

crop soybeans. Full-season soybean fields have only one harvested crop in the field each

year, whereas double-crop soybean fields have two crops (soybeans and usually winter

wheat). Once the winter wheat is harvested, soybeans were planted in the same field.

During the 2003 field season, 2 cultivars of soybeans were planted (via seed drill) in my

study fields. One of the full-season fields, and one of the double-crop fields were planted

in a cultivar produced by Pioneer (93B85RR) and the other 2 fields were planted in a

cultivar produced by Southern States (RT4720N). During the 2004 field season, both of

the full-season fields were planted with the Asgrow AG3905RR cultivar, whereas one

double-crop field was planted in Pioneer (P94B13RR) and the other was planted in 2

cultivars, 1 from Dekalb (DKB38-52RR) and 1 from Asgrow (AG4201RR). In 2003, 1

full-season and 1 double-crop field were planted on 9 July and the other 2 fields were

planted on 13 July. In 2004, both full-season fields were planted on 15 May and the

double-crop fields were planted on 28 June. In 2003, I harvested my fields during the

first week of November on 10 October and 11 October in 2004.

9

The portion of the farm used for agricultural production had 2 soil types, Aquic

Hapludults and Typic Endoquults (USDA 1971). The Aquic Hapludult type had 3 soil

series: Woodstown loam, Sassafras sandy loam, and Mattapex silt loam (USDA 1971).

Fallsington loam was the only soil series found within the Typic Endoquult type (USDA

1971). All fields were ditched to accelerate water drainage.

10

Chapter 3

METHODS

Basic Study Design

I conducted my research in both full-season and double-crop soybean fields.

During each field season, I examined browsing in 2 full-season and 2 double crop fields.

I used both full-season and double-crop soybeans to determine if the presence of wheat

stubble in double-crop fields reduced deer browsing following emergence. To examine

the spatial impacts of browsing, I chose fields that had only one side bordered by a

forested area, from which deer would likely enter the fields. Using fields with multiple

sides would have likely confounded my spatial analyses.

Starting from the forest edge, I systematically placed plots arranged at 10 m

intervals in each field. The plots in each distance class were randomly assigned a

treatment type. I based treatments on soybean phenology (Table 2). Of the 14

recognized development stages, I chose every other stage starting with emergence. This

distribution allowed me to detect any effects of browsing throughout the soybean’s

phenology. Because of individual variation among plants, I assigned growth stage

changes when > 50% of the plants in a field were at the same stage. Each treatment was

protected with a fence for 7 days immediately after the onset of the growth stage.

11

Table 2. Classification of the various vegetative and reproductive growth stages of a soybean plant (Ritchie et al. 1997). Growth Stage Characteristics VE Emergence from soil VC Cotyledon leaves fully unroll V1 1 trifoliate leaf node present V2 2 trifoliate leaf nodes present V3 3 trifoliate leaf nodes present V4 4 trifoliate leaf nodes present V5 5 trifoliate leaf nodes present Vn Addition of each subsequent trifoliate leaf node R1 1 open flower at any node on the main stem R2 1 open flower at one of the two uppermost nodes on the main stem R3 Pod is 5 mm long at 1 of the 4 uppermost nodes on the main stem R4 Pod is 2 cm long at 1 of the 4 uppermost nodes on the main stem R5 Seed is 3 mm long in the pod at 1 of the 4 uppermost nodes on the

main stem R6 Pod with a green seed that fills the pod cavity at 1 of the 4

uppermost nodes on the main stem R7 1 normal pod on the main stem has reached its mature color

(brown or tan) R8 95% of the pods have reached their mature pod color

12

Hinder® was labeled as being effective for 7 to 14 days after application. To be

conservative, I chose to protect plots for 7 days. I protected each study plot (4.6 m2) with

a 1.22 m welded-wire fence during its treatment. To prevent bias (increased sunlight

exposure and deer browsing on plot edges) I placed a 0.5 m buffer around the centralized

measurement area. I made all plant and browse measurements in a 1.6 m2 circular area in

the center of each plot.

To investigate the temporal effects of browsing, I measured deer browsing and

plant height regularly. At the V1 growth stage, I counted the number of plants within

each plot so I could relate the number of plants browsed to the number of plants present.

I counted deer browsing as the number of plants with evidence of deer feeding in each

plot weekly. To measure browsing intensity, I counted the number of trifoliate leaves

removed from each browsed plant within the plot. I only counted browsing that occurred

since the previous week’s measurements. Previously-browsed plants turned brown at the

point of removal after approximately 1 week, so I was able to distinguish new browse

from old browse. Plant height within each plot was measured so I could relate browsing

to changes in plant height.

In the fall, I hand-harvested a circular 1 m2 centralized area in each plot and I

measured plant biomass, bean mass, number of pods/plant, and number of beans/pod in

each plot. After harvest, I dried the plant matter and beans in a plant drier for 7 days at

60ºC (Vasilas and Fuhrmann 1993). Once dried, I calculated the moisture content and

then converted bean mass to yield (bushels/acre and kg/ha) by using a moisture

conversion table (Evans et al. 1997).

13

During the evenings (1.5 hours before sunset until dark) in the summer (June-

August), I counted the number of deer browsing in the study plots and in the entire field

in an attempt to estimate deer abundance in the area. I intended to associate my deer

browse measurements with an abundance estimate. Unfortunately, the information

gained from these surveys was not useful, because I observed very few deer using the

fields before dark. Observations late in the growing season were also impeded because

of plant height. Furthermore, the plants became so tall that I could not accurately detect

all of the deer within the fields accurately.

2003 Field Season

To examine the spatial effects of browsing, I placed plots in 5 distance classes

from the forest edge (0-10 m, 11-20 m, 21-30 m, 31-40 m, and 41-50 m). I placed plots

in the middle of each distance class. Within each distance class, I randomly assigned the

plots a different treatment. To ensure that deer had enough room to freely move around

each plot, I spaced them 10 m apart. I protected plots during the VE, V1, V3, V5, R1,

R3, R5, and R7 growth stages (Table 2). I measured plant height on 5 randomly picked

plants within each plot at the beginning of each treatment growth stage.

2004 Field Season

Based on the results from 2003, I reduced the number of treatments in 2004,

which allowed me to increase the number of replicates within each treatment type.

Because browsing during the early growth stages appeared to have the greatest effect on

yield, I selected the V1, V3, and V5 stages and the same 2 controls (Table 2). After

examining the spatial arrangement of the plots during 2003, I felt that the spacing

14

between plots in each distance class was more than adequate, so I reduced this spacing to

2 m between plot edges. This spacing allowed me to have 5 replicates of each treatment

rather than just 1 which allowed me to reduce the variability observed during 2003.

Because browsing was the least within the 31-40 m and 41-50 m distance classes in 2003,

I added 6th distance class (51-60 m) in 2004 to confirm that browsing remained low and

constant beyond 30 m.

Although, I counted the number of trifoliate leaves browsed in each plot in 2003, I

did not know how many trifoliates were present so I was unable to determine the

proportion removed. In 2004, I counted the number of trifoliate leaves on 10 randomly

chosen plants in each distance class weekly. This approach allowed me to determine

browsing intensity because I was able to relate how much plant material was removed to

how much was present. I stopped counting the number of trifoliate leaves on plants once

they began to senesce because the leaves would fall off the plant when they were

manipulated for counting. Also in 2003, I noted a decrease in browsing following week 3

and I hypothesized that deer shifted from soybeans to corn as the latter became more

palatable. I identified the timing of the corn growth stages to determine if a shift

occurred between food sources. If a decrease in browsing occurred following the corn

milk stage, then I assumed deer had shifted from soybeans to corn.

I continued to measure plant height, but height was determined as the average

height of the entire plot rather than of 5 independent measurements. This change was

based on the larger sample size in 2004 and the time required to complete these

measurements. Instead of measuring height at each growth stage, I measured plant height

15

weekly when I measured deer browse so I could better relate the effect of browsing on

plant height.

The harvested area remained the same, but the data I collected to determine the

effects of deer browsing on plant development were different. I did not estimate the

number of pods/plant, the number of beans/pod, or plant biomass from each plot in 2004

because the data from 2003 proved to be only an indirect index of yield and therefore

provided no additional information. Additionally, the time required to complete these

tasks made their collection infeasible with the increased sample size in 2004.

In addition to observations, I also estimated population abundance using infrared-

triggered cameras (Jacobson et al. 1997). I systematically placed cameras over 5 baited

sites yielding a camera density of 1 per 52 ha. Although this density is greater than the 1

per 65 ha suggested by Jacobson et al. (1997), due to the juxtaposition of the woodlots on

my study site I felt that this camera density was appropriate (Figure 1).

Data Analysis

For all analyses, data from the full-season and double-crop fields were pooled in

2003 due to similar planting dates. I used a repeated measures Analysis of Variance

(ANOVA) blocking on field to test whether the percentage of plants browsed and the

percentage of trifoliate leaves browsed (2004 only) differed by week and distance. If

differences were detected by week and distance class, I used a univariate ANOVA to

investigate browsing differences by distance class within each week blocking on field

(Peterson 1985). I also investigated differences in browsing among weeks using a

univariate ANOVA. To determine the spatial effects of browsing on plant height, I used

16

an ANOVA blocking on field to determine the effect of distance from a forest edge on

plant height. To determine the effect of protection (fencing) from browsing on plant

height, I used an ANOVA blocking on field. I also used an ANOVA blocking on field

with the main effects of distance and protection to determine the effects of browsing on

plant biomass per plot, pods per plant, beans per pod, and yield. To determine if deer

browsing affected yield, I used an ANOVA blocking on field with the main effects of

distance and protection treatment (fully protected verses unprotected). I used a Fisher’s

Protected Least Significant Difference (LSD) as a means separation test to investigate

differences (α ≤ 0.05) among main affects for significant univariate ANOVA’s (Peterson

1985).

17

Chapter 4

RESULTS

I excluded one of the full-season fields from my analyses in 2003, because it was

bordered by forest on 3 sides. Initially, I felt that the field was large enough and

browsing from the edges into my plots would not be a factor but it was. My plots were

positioned so they were at least 225 m from the next nearest forest edge, but this distance

was not far enough. This field was secluded and not visible from the road so it had a high

rate of daytime deer usage. Additionally, when I conducted my counts in the evening, it

always had the most deer in it. The farmer did not apply herbicide to the first 15 m from

the forest edge so weed density within these plots resulted in high plant mortality.

Because of these factors, I was unable to accurately determine the spatial impacts of deer

browse in this field.

Spatial and Temporal Patterns of Deer Browsing

The percentage of plants browsed differed by distance class and week in 2003

(F35, 265 = 7.20, P < 0.001). Browsing rates did not differ among distance classes within

most weeks (weeks 1-4 and 7-8, Table 3 and 4). Browsing in the 5 m distance class was

greater than the other distance classes during weeks 5 and 6 (Table 4). Though not

always statistically significant, during most weeks the 5 m distance class had the most

browsed plants and the 35 m and 45 m distance classes had the least (Table 4). In 2004,

18

Table 3. ANOVA to test if the percentage of soybean plants browsed by white-tailed deer differed by distance class within week in 2003 and 2004 near Little Creek, Delaware. Week df Numerator df Denominator1 F P 2003 Field Season 1 4 120 0.76 0.551 2 4 120 1.69 0.157 3 4 110 0.65 0.631 4 4 105 0.39 0.817 5 4 130 2.76 0.030 6 4 115 6.55 < 0.001 7 4 115 0.92 0.457 8 4 125 0.70 0.593 2004 Field Season Full-season Fields 1 5 173 2.55 0.030 2 5 233 4.19 0.001 3 5 173 1.96 0.087 4 5 173 2.27 0.050 5 5 233 4.02 0.002 6 5 233 1.20 0.308 7 5 233 4.42 0.001 8 5 233 4.23 0.001 Double-crop Fields 1 5 173 3.17 0.009 2 5 233 0.81 0.545 3 5 173 1.75 0.126 4 5 173 0.94 0.453 5 5 233 1.41 0.222 6 5 233 8.78 < 0.001 7 5 233 10.52 < 0.001 8 5 233 9.82 < 0.001 1Sample size differs among weeks because plots that were protected from browsing during each week were excluded from analyses.

19

Table 4. The percentage of soybean plants browsed by white-tailed deer each week by distance class in 2003 and 2004 near Little Creek, Delaware. The full-season and double-crop fields are separated for analyses during the 2004 field season, but not in 2003 because of similar planting dates. Only weeks 1 through 8 are shown because browse measurements were nearly 0 thereafter. Distance (m) Weeks 1 2 3 4 5 6 7 8

x SE x SE x SE x SE x SE x SE x SE x SE 2003 Field Season 5 1.5 A1 0.63 17.5 A 3.83 17.5 A 4.98 4.4 A 1.67 7.3 A 2.48 12.9 A 3.54 0.1 A 0.14 0.3 A 0.25 15 2.2 A 1.20 15.7 A 4.83 20.3 A 6.24 4.1 A 2.22 2.8 B 1.20 0.9 B 0.73 0.0 A 0.00 1.4 A 1.43 25 2.0 A 1.39 15.1 A 3.32 13.3 A 5.41 4.0 A 1.64 3.3 AB 1.50 4.1 B 2.54 0.0 A 0.00 0.1 A 0.14 20 35 0.5 A 0.31 10.1 A 2.17 10.1 A 4.68 5.7 A 2.13 1.1 B 0.76 0.6 B 0.45 0.0 A 0.00 0.0 A 0.00 45 0.7 A 0.46 6.8 B 2.17 12.3 A 4.98 7.1 A 2.88 1.4 B 0.96 1.3 B 0.93 0.8 A 0.81 0.4 A 0.38 2004 Field Season Full-season Fields 5 15.2 A 4.28 3.2 BC 1.13 14.5 A 4.93 2.8 AB 1.13 3.8 A 1.29 3.4 A 1.02 5.6 A 2.02 3.1 A 1.11 15 10.6 AB 3.21 8.1 A 2.16 13.7 A 3.15 4.7 A 2.11 1.5 B 0.62 3.0 A 1.53 2.7 B 1.22 3.7 A 1.25 25 8.7 AB 2.85 4.3 B 1.24 10.0 A 3.20 2.3 AB 0.88 2.0 B 0.63 0.3 A 0.21 0.7 B 0.54 0.5 B 0.27 35 4.1 B 1.87 3.0 BC 1.24 7.4 A 2.91 0.5 B 0.26 0.6 B 0.29 3.0 A 1.39 1.0 B 0.57 1.0 B 0.55 45 3.7 B 1.81 1.7 BC 1.23 4.4 A 2.62 1.6 AB 1.03 0.5 B 0.26 1.4 A 0.67 0.2 B 0.15 0.0 B 0.00 55 4.1 B 2.79 0.2 C 0.17 4.1 A 1.38 0.0 B 0.00 0.3 B 0.24 2.1 A 1.09 0.0 B 0.00 0.6 B 0.43 Double-crop Fields 5 1.4 B 1.08 1.1 A 0.83 1.1 B 0.81 0.2 A 0.16 0.5 A 0.29 6.5 A 1.78 9.5 A 2.24 5.0 A 1.43 15 3.4 B 1.27 3.2 A 1.07 2.0 AB 0.68 0.2 A 0.11 0.4 A 0.27 1.0 B 0.48 7.3 A 2.26 0.8 B 0.44 25 5.6 AB 2.23 2.3 A 1.21 3.6 AB 2.01 0.8 A 0.41 1.5 A 0.77 0.6 B 0.34 0.2 B 0.13 0.3 B 0.27 35 3.7 B 1.73 1.9 A 1.07 1.7 B 1.12 1.1 A 0.70 1.7 A 0.70 0.5 B 0.21 0.2 B 0.18 0.2 B 0.12 45 10.3 A 3.37 0.8 A 0.53 6.4 A 2.74 0.5 A 0.29 1.9 A 0.85 0.9 B 0.48 0.8 B 0.56 0.1 B 0.13 55 11.4 A 3.43 1.8 A 0.71 5.8 AB 2.17 0.7 A 0.39 0.4 A 0.41 0.7 B 0.55 0.3 B 0.20 0.0 B 0.00 1Values with the same letter are not significantly different based on protected least significant difference (LSD) means separation tests.

20

the percentage of plants browsed in the full-season fields differed by distance class and

week (F42, 108 = 3.00, P < 0.001, Tables 3 and 4). Plants browsed differed by distance

class in all weeks except 3 and 6 (Table 3). The 5 m and 15 m distance classes had more

browsed plants than any other distance class except week 2 when only the 15 m distance

class had more plants browsed than any other class (Table 4). Though not significantly

different from other distance classes, the 5 m and 15 m had the most plants browsed

during weeks 3 and 6 (Table 4). Throughout the season, the 45 m and 55 m distance

classes were always among the 3 least browsed classes, though this relationship was not

always significant (Table 4). Browsing rates within the 2004 double-crop fields also

differed by distance class and week (F42, 108 = 4.25, P < 0.001, Table 3). The percentage

of plants browsed differed by distance class during week 1 and weeks 6-8 (Table 3). In

week 1, browsing within the 45 m and 55 m distance classes was greater than the other

classes (Table 4). During weeks 6 and 8, the 5 m class had the most plants browsed

(Table 4). The 5 m and 15 m distance classes had more plants browsed than the other

distance classes in week 7 (Table 4). No clear spatial browse pattern could be determined

during weeks 2 through 5 (Table 4).

To better assess browsing intensity, I calculated the percentage of trifoliate leaves

browsed within each plot in 2004. The percentage of trifoliates browsed differed by

distance class and week in the full-season fields (F42, 108 = 5.02, P < 0.001, Table 5).

Browsing rates differed among distance classes in all weeks, except weeks 4 and 6 (Table

5). The 5 m and/or 15 m distances classes had the most trifoliates browsed (Table 6).

Although not significantly different among distance classes, the 5 m and 15 m distance

21

Table 5. ANOVA to test if the percentage of soybean trifoliate leaves browsed by white-tailed deer differed by week in each distance class in 2004, near Little Creek, Delaware. Week df Numerator df Denominator1 F P Full Season Fields 1 5 173 2.55 0.030 2 5 233 4.20 0.001 3 5 173 2.49 0.033 4 5 173 2.12 0.065 5 5 233 4.55 0.001 6 5 233 1.50 0.191 7 5 233 4.80 < 0.001 8 5 233 4.05 0.002 Double-crop Fields 1 5 173 3.17 0.009 2 5 233 0.57 0.726 3 5 173 1.21 0.307 4 5 173 0.87 0.503 5 5 233 1.12 0.348 6 5 233 6.96 < 0.001 7 5 233 10.02 < 0.001 1Sample size differs among weeks because plots that were protected from browsing during each week were excluded from analyses.

22

Table 6. The percentage of soybean trifoliate leaves browsed by white-tailed deer in each distance class by week in 2004, near Little Creek, Delaware. Only weeks 1-8 (full-season fields) and 1-7 (double-crop fields) are shown because browsing rates were virtually 0 thereafter.

Distance (m) Weeks 1 2 3 4 5 6 7 8

x SE x SE x SE x SE x SE x SE x SE x SE Full-season Fields 5 15.2 A1 4.28 5.9 B 2.09 9.7 A 3.43 1.3 A 0.64 0.9 A 0.30 0.5 A 0.16 0.6 A 0.22 0.4 A 0.13 15 10.6 AB 3.21 11.1 A 3.14 8.8 A 2.01 1.7 A 0.82 0.3 B 0.13 0.4 A 0.18 0.2 B 0.10 0.2 AB 0.09 25 8.7 AB 2.85 5.4 B 1.57 6.4 AB 2.05 0.7 A 0.30 0.4 B 0.12 0.0 A 0.02 0.1 B 0.03 0.1 BC 0.03 23 35 4.1 B 1.87 3.2 BC 1.43 4.2 AB 1.99 0.1 A 0.07 0.1 B 0.07 0.3 A 0.14 0.1 B 0.06 0.1 BC 0.05 45 3.7 B 1.81 2.0 BC 1.39 2.0 B 1.07 0.5 A 0.32 0.1 B 0.06 0.2 A 0.09 0.0 B 0.01 0.0 C 0.00 55 4.1 B 2.79 0.2 BC 0.20 2.2 B 0.76 0.0 A 0.00 0.0 B 0.03 0.2 A 0.12 0.0 B 0.00 0.1 BC 0.04 Double-crop Fields 5 1.4 B 1.08 1.5 A 1.22 1.1 A 0.72 0.1 A 0.06 0.1 A 0.05 1.8 A 0.61 1.5 A 0.40 15 3.4 B 1.27 2.8 A 0.94 1.1 A 0.39 0.1 A 0.04 0.1 A 0.11 0.2 B 0.09 1.1 A 0.36 25 5.6 AB 2.22 2.1 A 1.12 3.1 A 1.69 0.2 A 0.08 0.3 A 0.17 0.1 B 0.06 0.0 B 0.01 35 3.7 B 1.73 1.7 A 0.99 1.1 A 0.77 0.3 A 0.17 0.3 A 0.13 0.1 B 0.03 0.0 B 0.02 45 10.3 A 3.37 0.7 A 0.40 3.8 A 1.61 0.1 A 0.06 0.4 A 0.17 0.1 B 0.07 0.1 B 0.06 55 11.4 A 3.43 1.6 A 0.63 3.0 A 1.14 0.2 A 0.09 0.1 A 0.06 0.1 B 0.08 0.0 B 0.02 1Values with the same letter are not significantly different based on protected least significant difference (LSD) means separation tests.

23

classes had more trifoliates browsed in weeks 4 and 6 than any of the other weeks (Table

6). Though not always statistically significant, the farthest distance classes (35 m to 55

m) always had the least amount of trifoliate leaves browsed (Table 6). The percentage of

trifoliates browsed also differed by distance class and week in the double-crop fields (F36,

90 = 4.23, P < 0.001, Table 5). Browsing rates differed among distance classes in weeks

1, 6, and 7 (Table 5). In week 1, the 45 m and 55 m distance classes had the greatest

percentage of trifoliate leaves browsed (Table 6). During week 6, the 5 m distance class

had the most trifoliates browsed, and in week 7, both the 5 m and 15 m distances had the

most trifoliates browsed (Table 6). No clear spatial pattern of browse intensity could be

determined during weeks 2 through 5 (Table 6).

In 2003, the percentage of plants browsed differed among weeks (F7, 972 = 25.85,

P < 0.001). The percentage of browsed plants was greatest in weeks 2 and 3 and the least

in weeks 1, 7, and 8 (Figure 2). Within the full-season fields during 2004, the percent of

plants browsed also differed by week (F7, 1732 = 18.82, P < 0.001). Weeks 1 and 3 were

similar, and had the greatest percentage of browsed plants (Figure 3a). Week 2 had the

next greatest rate of browsing, whereas weeks 4-8 had the lowest browsing rates (Figure

3a). The percentage of trifoliate leaves browsed also differed by week (F7, 1732 = 29.91, P

< 0.05) in the full-season fields. Week 1 had the greatest percentage of trifoliates

browsed, and weeks 2 and 3 had the next greatest rate (Figure 3a). Browsing rates during

the other weeks were similar and less than weeks 1-3. Within the double-crop fields

during 2004, the percent of plants browsed differed by week (F7, 1732 = 11.08, P < 0.001).

Week 1 had the most browsing, while week 3 and 7 had the next most (Figure 3b).

24

0

2

4

6

8

10

12

14

16

18

0 2 4 6 8

Weeks From Emergence

Perc

ent B

row

sed

Figure 2. The percentage of full-season and double-crop soybean plants browsed each week by white-tailed deer in 2003, near Little Creek, Delaware. Only weeks 1 through 8 are shown because browse measurements were nearly 0 thereafter.

25

(a)

0

2

4

6

8

10

12

0 2 4 6 8

Weeks From Emergence

Perc

ent B

row

sed

PlantsTrifoliate leaves

(b)

0

1

2

3

4

5

6

7

8

0 2 4 6 8

Weeks From Emergence

Perc

ent B

row

sed

PlantsTrifoliate leaves

Figure 3. The percentage of soybean plants and trifoliate leaves browsed each week by white-tailed deer in (a) full-season fields and (b) double-crop fields in 2004, near Little Creek, Delaware. Only weeks 1 through 8 are shown because browse measurements were nearly zero thereafter. Week 8 is missing from the percentage of trifoliate leaves browsed within double-crop fields because trifoliate leaves were not counted after week 7 due to senescence.

26

Weeks 2, 4-6, and 8 had the lowest rate of browsing. The percentage of trifoliate leaves

browsed also differed by week within double-crop fields (F6, 1493 = 25.34, P < 0.001).

Browsing was the most intense during week 1, while weeks 2 and 3 had the next most

browsing (Figure 3b). The remaining weeks had the lowest browsing rates.

Spatial Effects on Plant Height

In 2003, plant height differed among distance classes during every growth stage

(P < 0.05, Table 7). At the V1 and V3 growth stage, the 35 m distance class had the

shortest plants (Table 7). Beginning with the V5 growth stage and continuing for the rest

of the season, the 5 m and 15 m distance classes had shorter plants than the rest of the

classes (Figure 4). Within the full-season fields in 2004, plant height differed among

distance classes every week (Table 7). The plants in the 5 m and 15 m distance classes

were always the shortest, while the plants in the 45 m to 55 m were always within the top

3 tallest classes (Table 7, Figure 5a). Similarly, the plant heights differed among distance

classes in the double-crop fields every week (Table 7). The plants in the 5 m distance

class were significantly shorter than the plants in all other distance classes every week

except in week 2 (Table 7, Figure 5b). During week 2, only the 35 m class was taller

than the other classes (Table 7). After week 3, plants in the 35 m, 45 m, and 55 m

distance classes were the tallest every week.

Protection Effects on Plant Height

In 2003, plant height differed among protection treatments starting with the V3

growth stage treatment (Table 8). At the V3 growth stage, the fully-protected plots had

taller plants than all of the other treatments, and this difference continued for the rest of

27

Table 7. ANOVA to test if browsing by white-tailed deer altered soybean plant height among distance classes in 2003 and 2004, near Little Creek, Delaware. Plant height was measured at each growth stage in 2003 and each week in 2004. Measurement Timing df Numerator df Denominator F P 2003 Field Season V1 4 743 2.70 0.030 V3 4 743 2.51 0.041 V5 4 743 2.79 0.026 R1 4 743 10.53 < 0.001 R3 4 743 23.09 < 0.001 R5 4 743 5.53 < 0.001 R7 4 743 29.32 < 0.001 2004 Field Season Full Season Fields 1 5 293 7.43 < 0.001 2 5 293 3.20 < 0.001 3 5 293 16.68 < 0.001 4 5 293 13.27 < 0.001 5 5 293 12.69 < 0.001 6 5 293 13.22 < 0.001 7 5 293 23.39 < 0.001 8 5 293 16.03 < 0.001 Double-crop Fields 1 5 293 8.19 < 0.001 2 5 293 4.71 < 0.001 3 5 293 8.79 < 0.001 4 5 293 14.31 < 0.001 5 5 293 19.72 < 0.001 6 5 293 37.82 < 0.001 7 5 293 50.34 < 0.001

28

0

10

20

30

40

50

60

70

0 2 4 6 8

Growth Stage

Plan

t hei

ght (

cm) 0-10 m

11-20 m21-30 m31-40 m41-50 m

Figure 4. The average full-season and double-crop soybean plant height (cm) within each 10 m distance class from the forest edge in 2003, near Little Creek, Delaware. Variation in plant height between distance classes was due to white-tailed deer browsing. Plant height was measured at the beginning of each growth stage treatment on 5 randomly picked plants in each plot.

29

(a)

0

20

40

60

80

100

120

140

0 2 4 6 8 10 12

Weeks From Emergence

Pla

nt H

eigh

t (cm

) 0-10 m11-20 m21-30 m31-40 m41-50 m51-60 m

(b)

0

10

20

30

40

50

60

70

80

0 2 4 6 8 10 12

Weeks From Emergence

Plan

t Hei

ght (

cm) 0-10 m

11-20 m21-30 m31-40 m41-50 m51-60 m

Figure 5. The average soybean plant height for (a) full-season soybeans and (b) double-crop soybeans by distance class from the forest edge in 2004, near Little Creek, Delaware. Variation in plant height between distance classes was due to white-tailed deer browsing. Plant height was measured within each plot every week. Due to a later planting date, plant height was measured less in the double-crop fields.

30

Table 8. ANOVA to test if browsing by white-tailed deer altered soybean plant height among treatment types in 2003 and 2004 near Little Creek, Delaware. Plant height was measured at the beginning of each growth stage in 2003 and by week in 2004. Measurement Timing df Numerator df Denominator F P 2003 Field Season V1 9 738 0.76 0.651 V3 9 738 6.86 < 0.001 V5 9 738 7.06 < 0.001 R1 9 738 16.90 < 0.001 R3 9 738 12.97 < 0.001 R5 9 738 2.16 0.023 R7 9 738 11.92 < 0.001 2004 Field Season Full Season Fields 1 4 294 1.05 0.380 2 4 294 1.65 0.162 3 4 294 6.45 < 0.001 4 4 294 10.52 < 0.001 5 4 294 12.08 < 0.001 6 4 294 14.99 < 0.001 7 4 294 14.48 < 0.001 8 4 294 18.69 < 0.001 Double-crop Fields 1 4 294 0.44 0.781 2 4 294 0.90 0.463 3 4 294 0.68 0.605 4 4 294 0.96 0.429 5 4 294 4.11 0.003 6 4 294 3.80 0.005 7 4 294 3.13 0.015

31

the season (Table 8). Within the 2004 full-season fields, plant height differed among

protection treatments beginning in week 2 (Table 8). The fully-protected plots had taller

plants than all of the other protection treatments starting in week 2, and this difference

continued for the rest the season (Table 8). Beginning in week 5, plant height differed

among protection treatments in the double-crop fields (Table 8). The fully protected

plots had taller plants than all of the other protection treatments every week beginning in

week 5 (Table 8).

Harvest Data (Plant Development)

In 2003, plant biomass differed among protection treatments (F9, 98 = 2.00, P =

0.047). The fully protected plots had more plant biomass than all of the protection

treatments and the unprotected plots (Table 9). Though not statistically significant the

plots protected during the vegetative growth stages had more plant biomass than those

protected during the reproductive stages (Table 9). Spatially, plant biomass differed

among distance classes (F4, 98, = 13.22, P < 0.001). Plant biomass was greatest in the 35

m and 45 m distance classes and the least in the 5 m class (Table 10).

To better understand the influence of deer browse on plant development and

yield, I also counted the number of pods/plant and the average number of beans/pod. The

number of pods/plant did not differ among protection treatments (F9, 98 = 1.57, P = 0.134;

Table 9) but it did differ by distance class (F4, 98 = 3.86, P = 0.006). The plots in the 5 m

and 45 m distance classes had the greatest number of pods/plant, while the 15 m and 25

m distance classes had the least (Table 10). The number of beans/pod did not differ

among protection treatments (F9, 98 = 0.23, P = 0.99, Table 9) or by distance class (F4, 98 =

32

Table 9. The average full-season and double-crop soybean biomass (g), number of pods per plant, and number of beans per pod within each treatment plot in 2003, near Little Creek, Delaware. Differences among treatments was due to white-tailed deer browsing. Treatment Type Plant Biomass (g) Pods Per Plant Beans Per Pod x SE x SE x SE Full 165.7 A1 13.68 26.1 1.67 2.1 0.04 VE 144.8 AB 14.26 23.0 1.42 2.1 0.05 V1 145.5 AB 12.49 27.1 2.26 2.1 0.05 V3 132.5 B 12.62 27.6 1.58 2.1 0.03 V5 133.9 B 11.93 23.3 1.43 2.1 0.03 R1 125.1 B 14.49 29.8 2.26 2.1 0.06 R3 136.3 B 13.83 26.2 1.84 2.0 0.08 R5 129.3 B 16.74 25.9 1.47 2.1 0.10 R7 125.2 B 13.77 26.7 1.95 2.1 0.15 No 132.1 B 15.62 26.6 1.98 2.1 0.04 1Values with the same letter are not significantly different based on protected least significant difference (LSD) means separation tests.

33

Table 10. The average full-season and double-crop soybean biomass (g), number of pods per plant, and number of beans per pod within each distance class from the forest edge in 2003, near Little Creek, Delaware. Differences among treatments was due to white-tailed deer browsing. Distance (m) Plant Biomass (g) Pods Per Plant Beans Per Pod x SE x SE x SE 5 m 108.5 C1 8.37 27.8 A 1.25 2.0 0.04 15 m 124.5 BC 9.07 24.0 B 1.39 2.0 0.04 25 m 133.7 B 8.59 23.8 B 1.05 2.2 0.07 35 m 161.3 A 10.48 26.9 AB 0.98 2.1 0.03 45 m 157.3 A 9.64 28.6 A 1.51 2.1 0.06 1Values with the same letter are not significantly different based on protected least significant difference (LSD) means separation tests.

34

2.39, P = 0.056; Table 10). However, protection treatment and distance interacted

together to affect the number of beans/pod (F36, 98 = 1.64, P = 0.03). Because there are 50

treatment and distance combinations I did not conduct an LSD because it would not have

provided any relevant information.

Harvest Data (Yield)

In 2003, the yield did not differ among protection treatments (F9, 98 = 1.64, P =

0.116; Figure 6a). While not significant, the fully protected plots and the plots protected

during the vegetative stages had the greatest yields. Yield differed among distance

classes (F4, 98 = 7.81, P < 0.001; Figure 7a). The 35 m and 45 m distance classes had the

greatest yields and the 5 m distance class had the least (Figure 7a).

In 2004, yield within the full-season fields differed among protection treatment

types (F4, 269 = 5.94, P < 0.001). The fully protected plots had the lowest yield and

differed from all other protection treatments (Figure 6b). Yield within the V1, V3, V5,

and unprotected treatments averaged 541 kg/ha (7.4 bushels/acre) more than the fully

protected plots. In 2004, yield differed among distance classes in the full-season fields

(F5, 269 = 21.31, P < 0.001; Figure 7b). The farthest distance classes (35 m through 55 m)

had the greatest yields, while the 5 m class had the least (Figure 7b). Within the double-

crop fields, yield did not differ among protection treatments in 2004 (F4, 269 = 0.72, P =

0.577; Figure 6b), but it did differ among distance classes (F5, 269 = 41.70, P < 0.001;

Figure 7b). The 5 m distance class had the lowest yield and the farthest distance classes

(35 m through 55 m) had the greatest (Figure 7b).

35

(a)

0

500

1000

1500

2000

2500

3000

Full VE V1 V3 V5 R1 R3 R5 R7 No

Treatment Type

Yiel

d (k

g/ha

)

(b)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

Full No V1 V3 V5

Treatment Type

Yiel

d (k

g/ha

)

Double-cropFull-season

Figure 6. Soybean yield for each protection treatment type during (a) 2003 and (b) 2004 near Little Creek, Delaware. Variation in yield was due to browsing by white-tailed deer. Due to a similar planting date in 2003, full-season and double-crop yields were combined.

36

(a)

0

500

1000

1500

2000

2500

5 15 25 35 45

Distance (meters)

Yiel

d (k

g/ha

)

(b)

0500

100015002000250030003500400045005000

5 15 25 35 45 55

Distance (meters)

Yie

ld (k

g/ha

)

Double-cropFull-season

Figure 7. Soybean yield within each distance class during (a) 2003 and (b) 2004 near Little Creek, Delaware. Variation in yield was due to white-tailed deer browsing. Due to a similar planting date in 2003, full-season and double-crop yields were combined.

37

To account for the actual effect of deer browsing on soybean yield, I compared

yields from the fully protected and unprotected treatments. Yield between the two

treatments differed in 2003 (F1, 18 = 7.28, P = 0.015). Within the first 50 m of the forest

edge, deer browsing resulted in a yield loss of 464 kg/ha (6.4 bu/ac). During 2003-2004,

the projected market price for soybeans was $6.85-$7.65/bushel (Ash and Dohlman

2003). In 2003, the effects of deer browsing within the first 50 m of the forest edge

resulted in a net loss of $43.84-$48.96 per acre. In 2003, yield between the fully

protected and unprotected plots did not differ among distance classes (F4, 18 = 1.89, P =

0.155). Although not statistically significant, yield within the 5 m and 15 m distance

classes was the least, and was the greatest in the 35 m and 45 m classes (Table 11). Yield

reduction due to browsing was most severe within 30 m of the forest edge (Table 11).

Within the 2004 full-season fields, yield between the fully protected and

unprotected treatments differed (F1, 107 = 10.53, P = 0.002). Unlike in 2003, deer

browsing resulted in net yield gain in the 2004 full-season fields. Within the first 60 m of

the forest edge, browsing resulted in an increased yield of 438 kg/ha (6.0 bu/ac). During

2004-2005, the projected market price for soybeans was $4.55-$5.35/bushel (Ash and

Dohlman 2004). Within the 2004 full-season fields, deer browsing within the first 60 m

of the forest edge was responsible for an economic increase of $27.30-$32.10 per acre.

Yield between the fully protected and unprotected plots differed among distance classes

in the full-season fields, during 2004 (F5, 107 = 12.93, P < 0.001). Yield within the 5 m

and 15 m distance classes was the least and was the greatest within the 55 m class.

Increase in yield was most prominent within the first 50 m of the forest edge (Table 11).

38

Table 11. The cumulative monetary impact of white-tailed deer browsing on soybean yield (bu/ac) by distance from the forest edge in 2003 and 2004, near Little Creek, Delaware. The full-season and double-crop fields were separated for analyses during the 2004 season, but not in 2003 because of similar planting dates. Distance Yield Monetary Impact per Acre Differences Price/bushel bu/ac1 $4.00 $5.00 $6.00 $7.00 2003 Field Season 0-10 m -5.0 -$20.00 -$25.00 -$30.00 -$35.00 0-20 m -7.8 -$31.20 -$39.00 -$46.80 -$54.60 0-30 m -9.1 -$36.40 -$45.50 -$54.60 -$63.70 0-40 m -8.1 -$33.60 -$40.50 -$48.60 -$56.70 0-50 m -6.4 -$25.60 -$32.00 -$38.40 -$44.80 2004 Field Season Full Season Fields 0-10 m 2.5 $10.00 $12.50 $15.00 $17.50 0-20 m 3.4 $13.60 $17.00 $20.40 $23.80 0-30 m 3.7 $14.80 $18.50 $22.20 $25.90 0-40 m 4.9 $19.60 $24.50 $29.40 $34.30 0-50 m 7.1 $28.40 $35.50 $42.60 $49.70 0-60 m 6.0 $24.00 $30.00 $36.00 $42.00 Double-crop Fields 0-10 m -1.0 -$4.00 -$5.00 -$6.00 -$7.00 0-20 m -1.7 -$6.80 -$8.50 -$10.20 -$11.90 0-30 m -1.2 -$4.80 -$6.00 -$7.20 -$8.40 0-40 m -0.8 -$3.20 -$4.00 -$4.80 -$5.60 0-50 m -1.6 -$6.40 -$8.00 -$9.60 -$11.20 0-60 m -1.6 -$6.40 -$8.00 -$9.60 -$11.20 1fully protected yield – unprotected yield = yield difference

39

Yield between the fully protected and unprotected treatments did not differ in the

2004 double crop fields (F1, 107 = 1.37, P = 0.244). Although not statistically significant,

deer browsing resulted in a yield loss of 114 kg/ha (1.6 bu/ac). During 2004-2005, the

projected market price for soybeans was $4.55-$5.35/bushel (Ash and Dohlman 2004).

Within the 2004 double-crop fields, deer browsing within the first 60 m of the forest edge

resulted in losses of $7.28 to $8.56 per acre. Yield between the fully protected and

unprotected plots differed among distance classes within the 2004 double-crop fields (F5,

107 = 16.00, P < 0.001). Yield within the 5 m and 5 m distance classes was the least, and

in the 35 m and 55 m classes yield was the greatest (Table 11). Yield loss among

distance classes was fairly constant with distance from the forest edge (Table 11).

Population Estimate

During 2004, I used infrared-triggered cameras to obtain a deer population

estimate on my study site and the surrounding area. During the two-week survey, I

obtained 554 photographs. I had 348 antlered buck occurrences during the survey: 76

spikes and 272 branch-antlered bucks. I had 611 antlerless deer occurrences: 454 adult

does and 157 fawns. From the branch-antlered buck photographs, I identified 39

individual bucks. I determined that 162 deer were utilizing the farm during the 2004 field

season (50 bucks, 83 does, and 29 fawns).

40

Chapter 4

DISCUSSION

To effectively reduce deer damage, we must first understand when and where

deer damage is most intense on soybeans. Repellents have been shown to be effective in

reducing deer browsing in small fields but their application in a large field-type setting

has not been determined (Harris et al. 1983, Tanner and Dimmick 1983, and Conover

1987). If we are able to document the most opportune timing to apply a repellent and the

most appropriate area (i.e. along the forest edge) then their use may become more

practical in large-scale fields. Additionally, anecdotal evidence suggests that wheat

stubble in double-crop fields may inhibit browsing immediately following emergence.

Further incite into these areas will allow farmers and wildlife mangers to more effectively

manage white-tailed deer damage to soybeans.

Spatial and Temporal Distribution of Deer Browsing

I found browsing was most intense within the first 15 m from the forest edge after

which browsing rates were nearly 0. Many studies (Flyger and Thoerig 1962, DeCalesta

and Schwendeman 1978, Garison and Lewis 1987, Lyon and Scanlon 1987b)

documented that browsing was most intense along forest edges but none of the studies

attempted to quantify the actual distance into the field it occurred. Although browsing

may occur throughout a field, a better understanding of where it is most intense is the

41

first step to developing damage abatement techniques. If a repellent were only applied to

the areas most affected by browsing, then the application costs may be low enough that

use becomes economically feasible.

Understanding the spatial distribution of browsing is not the only information

managers need to resolve deer damage, but we must also know when browsing is most

intense. Some anecdotal evidence suggests that the presence of wheat stubble in double-

crop fields might inhibit deer from browsing following plant emergence. However, I did

not find a difference in browsing rates between full-season and double-crop fields during

the weeks immediately following emergence.

Temporally, browsing rates in 2003 and 2004 had a similar distribution. In both

years, browsing rates decreased sharply 3 weeks after emergence. I propose 4 hypotheses

as to why the reduction in browsing may have occurred: browsing actually did not

decrease and the biomass removed was constant, deer switched to an alternate food

source, the release of secondary compounds in response to being browsed made the

soybean plants less palatable, or the change in plant phenology made the plants less

palatable.

My first hypothesis is that browsing actually did not decrease and that the amount

of plant biomass consumed was constant throughout the season. I measured browse as

the number of trifoliate leaves removed. As soybeans grew, they added more trifoliate

leaves, and these leaves increased in size. Since the trifoliate leaves were larger, the deer

were able to eat fewer leaves and still consume the same amount of biomass. If this

hypothesis is correct, the decrease in browsing should have been more gradual than it

42

was. Additionally, Lyon (1984) examined the diet of white-tailed deer and documented

that the percentage of soybeans in the diet decreased during the season. Although

biomass likely changed less than suggested by browsing rates, I believe this explanation

alone does not explain the sudden reduction in browsing.

The second possible explanation was that deer switched food sources. Browsing

on corn would have been the most likely shift, but the drop-off in browsing did not

coincide with a palatable corn growth stage (i.e., milk stage). Additionally, in 2004 the

full-season and double-crop fields were planted on different dates, and I still saw a

reduction in browsing after week 3 in both settings. Lyon and Scanlon (1987b)

hypothesized that the reduction in browsing resulted from decreased nutritional quality of

soybeans and an increased nutritional quality of an alternate natural food source. Without

dietary analyses, determining if nutrition was the cause of reduced browsing rates is

impossible.

Plants produce secondary chemical compounds in response to herbivory (Frankel

1959, Ehrlich and Raven 1964, Karban and Baldwin 1997). Production of such

chemicals by soybeans in response to being browsed is a third explanation for decreased

browsing rates. Possibly, these compounds made the plants less palatable to deer. If

decreased palatability occurred, browsing rates should have increased within the farthest

(> 35 m) distance classes and decreased in the closest classes (< 15 m) as the season

progressed, because the deer would have been forced to travel farther into the field to find

unbrowsed and more palatable plants. Since browsing rates did not increase in the

43

farthest distance classes later in the season, I do not believe browsing was inhibited due

to the release of a secondary compound.

Finally, the change in plant phenology might have made the soybeans less

palatable to deer due to the release of secondary compounds. The reduction in browsing

that I found preceded the onset of the R1 growth stage. Lyon and Scanlon (1987b)

documented that the number of deer seen in soybean fields was less after the plants began

to flower. The change in soybean phenology is a rapid process, and the sharp decrease in

browsing rates was similarly sudden.

I conclude that the reduction in browsing was the result of a variety of factors. A

combination of increased leaf area and change in plant phenology are the most plausible

explanations for the reduction in browsing. In the absence of average trifoliate biomass

or analysis of the hormones that trigger the change in plant phenology, a resolution of the

question awaits.

Browsing Effects on Yield and Plant Growth

Yield and plant height among distance classes was negatively related to the

degree of browsing that occurred in each distance class. Yield and plant heights within

the 5 m and 15 m distance classes were less than the rest of the field in both years. This

reduction corresponded to the amount of browsing that occurred within those distance

classes. The farthest distance classes (> 35 m) suffered the least amount of browsing and

had the tallest plants and greatest yields.

One of the objectives of my study was to determine if protection during a

particular growth stage had an effect on yield. Many researchers (National Crop

44

Insurance Association 1985, Garrison and Lewis 1987) have determined that damage

during the reproductive growth stages has the greatest impact on yield. However, I found

that deer do not readily browse on soybeans during these stages. The plots protected

during the vegetative growth stages tended to have the greatest effect on yield in 2003.

In 2004, I concentrated my research on these growth stages, but I was unable to find a

stage in which protection had a significant impact on yield. Therefore, I conclude that

short-term protection does not appear to affect yield.

In 2003 and 2004, plant height differed among protection treatments, but the

difference was most notable within the 2004 full-season fields. This difference in plant

height may have had an impact on yield. Starting in week 6, the fully protected plots

were taller (> 10 cm) than any of the treatments. Although partially due to never being

browsed, competition among neighboring plants may have caused them to grow taller

rather than bushier. Pedersen and Lauer (2003) found that as row spacing increased

(lower plant density) average plant height decreased. By never being browsed, the plants

in the fully protected plots competed with each other and grew taller rather than bushier.

I hypothesize that increased plant height in the fully protected plots resulted in the

reduction of yield. Garrison and Lewis (1987) documented that moderate levels of

defoliation (< 33%) actually increased yield. The taller plants may have put too much

energy into overall biomass production and did not have enough energy left for seed

production. I do not think this same trend would be as extreme on a year-to-year basis.

Because of the excellent growing conditions during 2004, the plants were able to

compensate for moderate levels of browsing. However, during excellent growing

45

conditions, deer browsing of the intensity I observed could have a positive effect on

yield.

My study examined the effects of browsing within fields that had 1 edge bordered

by forest. Other researchers (Flyger and Thoerig 1962, Decalesta and Schwendeman

1978 and Tanner and Dimmick 1983) have documented that deer browsing within small

fields and fields with more than 1 edge bordered by forest resulted in a great reduction in

yield. My results indicate that deer damage in large fields with 1 side bordered by forest

may be more of a perceptional issue rather than economic one. When surveys were used

to determine the effects of deer browsing (Lyon and Scanlon 1987a, Wywialowski 1994,

Conover 1997) the respondents tended to over-estimate the negative impacts of deer

damage. My results indicate that the effects of deer browsing on soybeans may not be as

severe as many agricultural producers believe.

Management Implications

An Integrated Pest Management (IPM) approach should be used when trying to

reduce deer browsing on soybeans. This approach attempts to solve pest problems by

applying knowledge about the pest to minimize crop damage and involves responding

with the most effective least-risk option. Once the impact exceeds an acceptable level

then an appropriate action should be taken. The use of a repellent should be used if the

economic losses from browsing exceed an acceptable economic level. If the money spent

to apply the repellent is less than the money lost if browsing were to occur, then using the

repellent is recommended.

46

The application of a chemical repellent (i.e. Hinder®) did not appear to be a cost

effective method to reduce white-tailed deer damage in large soybean fields bordered on

1 edge by forest. I chose fields that historically received the most deer browsing, yet I

was unable to document substantial yield loss that would offset the cost of applying a

repellent. At approximately $40.00 per 3.8 L (2005), the cost of Hinder® alone does not

offset the amount of yield loss due to deer damage in 2003 or 2004. The cost to treat 1 ha

of soybeans would have been $198 to $396 for Hinder® alone. In the most extreme

instance (2003), I found that deer browsing within 50 m of the forest edge resulted in

losses of $63 to $111 per hectare at $4 and $7 per bushel, respectively. When deer

browsing negatively affected yield, I found that it was in a narrow band (20 m) along the

forest edge. To treat 0.4 ha (1 acre) of this narrow band, the field edge would have to be

270 m long. The area in which yield loss is occurring is so small compared to an entire

field that the cost of protecting this area does not appear to be economically feasible. My

study site had 2320 m and 2725 m of forest/soybean edge in 2003 and 2004, respectively.

The effects of browsing within the 20 m of the forest edge affected only 3.5 ha (8.6 acres)

and 4.1 ha (10.1 acres) in 2003 and 2004, respectively.

The second step in implementing a cost effective repellent is to find the best

timing for its application. Yield between the unprotected plots was not different from

any of the 1-week protection treatments so short term protection did not increase yield.

Because Hinder® is effective for only 7-14 days, it could not be expected to increase

yield. Additionally, I was unable to document a sufficient monetary loss between plots

protected for an entire season and unprotected plots to warrant the use of Hinder®. In

47

fact, in some situations (2004 full-season fields) moderate levels of deer browsing

actually increased yield. Since the area damaged by deer was so small and I did not

document a growth stage in which protection would offset yield loss, it does not appear

that repellents are a cost effective solution to deer damage in large fields bordered on 1

side by forest. The issue of deer damage appears to be more perceptional than economic

so efforts should be made to educate farmers on the negligible affects of deer browsing

on soybeans in large fields bordered by forest on 1 side.

48

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52