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This is to certify that the

thesis entitled

A Survey of Michigan Agricultural Producers'
Attitudes, Perceptions, and Behaviors
Regarding Deer Drop Depredation to Fruit,
Vegetables, and Field Crops

presented by

Peter A. Fritzell, Jr.

has been accepted towards fulfillment
of the requirements for

M.S. Fish. & Wildl.

degree in

 

 

 

Date August 26, 1998

0-7639 MS U is an Affirmative Action/Equal Opportunity Institution

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1/96 mus-p14

 

 

A SURVEY OF MICHIGAN AGRICULTURAL PRODUCERS’
ATTITUDES, PERCEPTIONS, AND BEHAVIORS
REGARDING
DEER CROP DEPREDATION To FRUIT, VEGETABLES, AND FIELD CROPS
By

Peter Algren Fritzell, Jr.

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF SCIENCE

Department of Fisheries and Wildlife

1998

ABSTRACT
A SURVEY OF MICHIGAN AGRICULTURAL PRODUCERS’
ATTITUDES, PERCEPTIONS, AND BEHAVIORS
REGARDING
DEER CROP DEPREDATION TO FRUIT, VEGETABLES, AND FIELD CROPS
By

Peter A. Fritzell, Jr.

During the last 20 years several states have seen dramatic changes in the size of
their white-tailed deer (Odocoileu_s virgi_r_u'_a_n§) populations and also more frequent
debates about how the deer resource should be managed. One central area of conflict
between stakeholders involved in deer management is the issue of crop depredation and
the management of deer, people, and habitat to minimize such depredation. Between
April and June, 1995 agricultural producers from six regions of Michigan were surveyed
about their attitudes and behaviors regarding deer crop depredation. Producers generally
did not believe the state agency was considering farming interests fairly in their deer
management decision making, and a majority of producers cited low deer harvests on
adjacent lands as responsible for their inability to control losses to deer. The extent of
crop losses to deer and producer responses to those losses were shown to vary regionally.
Producers with intolerable losses more frequently indicated they provided deer hunting
access to non-acquaintances, but some with intolerable losses were not encouraging the
harvest of antlerless deer. Analysis suggested that producers with prolonged intolerable
losses are more likely to engage in disruptive issue activity, but also that there are
opportunities for biologists to moderate producer attitudes and behaviors through more

frequent contact.

ACKNOWLEDGMENTS

Financial support for this project was provided by the Michigan Agricultural
Experiment Station, SAPMA. Without this support the project would not have existed. I
need to express my gratitude to the numerous Michigan State University Extension agents
and Michigan Department of Natural Resources biologists who offered their assistance by
reviewing questionnaires, providing guidance, advice, farmer contacts, and munerous
other services. Thanks also to my office mates and fellow students for their assistance
with various aspects of this project. Special thanks to Donna Minnis, Chuck Nelson, and
to Glenn Dudderar and Doug Parr for the many conversations regarding this project and
wildlife damage control. I would like to express my thanks and gratitude to R. Ben
Peyton for providing me this opportunity to extend my education and understanding of
resource management. Finally, thanks to the farmers without whom this project could not

have been accomplished.

iii

TABLE OF CONTENTS

LIST OF TABLES ..................................................................................................... vii
LIST OF FIGURES .................................................................................................... xii
INTRODUCTION ..................................................................................................... 1
Deer and Crop Damage in Michigan .............................................................. 1
Goal of Research ............................................................................................. 2
Objectives ..................... , ................................................................................. 4
LITERATURE REVIEW ............................................................................................ 8
Issues Management ......................................................................................... 8
Stages of Issue Development ............................................................ 9
Components of Issues ................................................................................... 12
How many deer are there? ................................................................. 13
Loss Assessment technology ............................................................. 13
Non-lethal control alternatives? .......................................................... 16
Who’s responsible for deer damage? .................................................. 17
Conflicting values ............................................................................... 17
Tolerance of Crop Loss and Deer ................................................................... l8
Attitudinal Response to Deer .......................................................................... 20
Graphing human tolerance of deer numbers ....................................... 23
Synopsis of Michigan’s Deer Depredation Control Permits .......................... 28
Acceptance of Damage Control Programs ..................................................... 29
Agency Credibility ......................................................................................... 31
Summary of Literature Review ...................................................................... 33
METHODS ................................................................................................................. 34
Study Site Selection ........................................................................................ 34
Interviews ....................................................................................................... 36
Questionnaire Testing and Review ................................................................. 37
Sample Selection ............................................................................................ 38
Mail Survey Implementation .......................................................................... 38
Non-response Follow-up ................................................................................ 39
Data Entry and Analysis ................................................................................. 40
Special Calculations and Data Transformations ............................................ 40
Procedures for estimating percentage losses and
dollar values of crop losses to deer ......................................... 41
Special permit favorability scales ....................................................... 44
Biologist, Agent, and Agency credibility scales ................................. 45
Fairness of perceived MDNR stakeholder weightings ....................... 46
Tolerance of losses ............................................................................. 47

iv

RESULTS ................................................................................................................... 49

Organization of section ................................................................................... 49
Non-response .................................................................................................. 49
Generalizability of results .................................................................. 58
Farmer Respondent Profile ............................................................................. 59
Levels of crop loss tolerance ........................................................................... 66
Cummulative Tolerance of Loss .......................................................... 66
Reported Crop Losses Due to Deer in 1994 .................................................... 69
1994 Row and Field Crop losses ........................................................ 69
1994 Fruit and Tree losses ................................................................. 70
Counties ranked by relative crop loss amounts .................................. 71
Tolerance of reported 1994 crop loss estimates ................................. 73
Most severe loss years and past loss attitudes .................................... 77
Quality losses ...................................................................................... 81
Crop losses to other wildlife ................................................................ 84
Loss estimation methods ..................................................................... 86
Behavioral Responses of Producers to Crop Losses ....................................... 90
Lethal control, non-lethal control, and disruptive behavior ................ 90
Past, current, and intended behaviors associated with crop loss ........ 90
Producer Perceptions
and Use of Hunting as a Crop Damage Control Method ............................... 103
Access to hunters ............................................................................... 103
Deer habitat per farm ......................................................................... 106
Number of hunters on opening day 11-15-94 .................................... 109
Perceptions of safe hunter densities ................................................... 111
Number of deer harvested on respondents’ farms in 1994 .............. 115
Harvest ratios of bucks and antlerless deer reported by respondents..115
Encouragement of antlerless harvest .................................................. 117
Adjacent antlerless harvest a problem ................................................ 119
Producer Perceptions and Use of Shooting and Block Permits ..................... 122
Permit favorability ............................................................................. 122
Satisfaction with number of permits received in 1994 ....................... 124
Specific attitudes about special permits:
recipients vs. non-recipients .................................................. 125
Open-ended producer comments about special permits .................... 130
Acceptable criteria for evaluating need for special permits .............. 132
Producers’ Perceptions and Attitudes about Deer Density ............................ 134
Perceived deer population trends ....................................................... 134
Estimated deer densities ..................................................................... 139
Cultural carrying capacity response curves ........................................ 146
Tolerance of deer densities ............................................................................. 154
Factors Influencing Producer Tolerance of Deer Density ............................... 159
Discriminant analysis of factors that predict tolerance
of October 1994 deer densities .................................... 166

Perceptions of and Attitudes about the MDNR ............................................. 167

Perceptions of the MDNR ................................................................. 167
Agency weighting of constituents’ interests ...................................... 172
Perceived fairness of current stakeholder weighting .......................... 175
Other stakeholders considered in deer damage issues ........................ 179
Perceptions of the Michigan State Univ. Extension Service .......................... 180
Contact fi'equency ............................................................................... 180
Credibility of extension agents ........................................................... 181
DISCUSSION ............................................................................................................. 182
Values, Perceptions, and Behaviors ................................................................ 183
Agency Credibility .......................................................................................... 185
Biologist competence .......................................................................... 188
Program Administration .................................................................................. 189
Proactive Opportunities .................................................................................. 190
Non-lethal depredation control ........................................................... 190
Effective shooting permit use ............................................................. 191
Efl‘ectiveness of block permits ........................................................... 192
Hunter management ............................................................................ 192
Inadequate harvests on adjacent lands ............................................. 194
Hunter preference ............................................................................ 194
Areas closed to hunting ...................................................................... 195
Public Involvement and Education ................................................................ 195
Identification of Issue Stages ......................................................................... 197
Research Needs ...................................................................... 200
Conclusion ..................................................................................................... 201
LITERATURE CITED .............................................................................................. 204
APPENDICES ........................................................................................................... 212
Appendix I Questionnaire ........................................................ 212
Appendix 11 Cover letters ......................................................... 228
Appendix III Postcard reminder ................................................... 229
Appendix IV Telephone non-response discussion guide ....................... 233

Editorial note - Throughout this manuscript the terms farmer, producer, and grower are
used interchangeably to mean a person who farms, grows, or raises an agricultural crop
for commercial sale or as feed or fonder for livestock which are sold or which produce a
product for subsequent sale.

vi

LIST OF TABLES

Table 1: A comparison of Michigan’s shooting and block permit programs .......... 29
Table 2: Profile of study counties by crop types, issue intensity, percentages of forest and
agricultural lands, and relative deer densities ........................................................... 36
Table 3: Number of interviews completed per county ............................................ 37
Table 4: Initial per county sample size .................................................................... 39
Table 5: Initial per county sample size, returns, and response rates ........................ 50
Table 6: Non-response telephone follow-up contacts .............................................. 50
Table 7: Comparison of job status between respondents and non-respondents ....... 50
Table 8: Comparison of tolerance of 1994 losses between respondents and non-
respondents by county ................................................................................................ 52
Table 9: Comparison of tolerance of 1994 deer numbers between respondents and non-
respondents by county ................................................................................................ 53
Table 10: Comparison of hunting participation between respondents and non-respondents
by county ................................................................................................. 54
Table 11: Comparison of farm size and dependence on farm income between
respondents and non-respondents by county .............................................................. 55
Table 12: Comparison of proportions of respondents and non-respondents by county who
have requested special permits from the MDNR to shoot depredating deer .............. 56
Table 13: Comparison of respondent and non-respondent perceptions of MDNR current
weightings of stakeholders in deer management decisions by county ........................ 57
Table 14: Comparison of respondent and non-respondent desired MDNR weightings of
stakeholders in deer management decisions by county ............................................... 58
Table 15: Education completed by respondents ......................................................... 60
Table 16: Number of full-time and part-time producers per county ........................... 60
Table 17: Respondent’s mean years farming in same county ..................................... 60
Table 18: Centrality of hunting as recreation to respondents ...................................... 60
Table 19: Age and Farm size of respondents by county .............................................. 61
Table 20: Number of respondents indicating memberships in various farm
organizations ................................................................................................................. 63
Table 21: Number of respondents indicating memberships in various conservation
organizations ................................................................................................................. 64
Table 22: Farmer Respondent County Demographics - Hunting participation, Farm type,
Gross income, Education .............................................................................................. 65
Table 23: Farmer respondents’ tolerance of 1994 crop losses, and associated mean
percent of gross income generated by farming ............................................................. 67
Table 24: Farmer respondent tolerance of 1994 crop losses by mean farm Size in
acres ............................................................................................................................... 68
Table 25: Farmer attitudes about 1994’s deer crop losses, by county, farm type, and job
status .............................................................................................................................. 68
Table 26: Row/field crop types grown, median per farm loss, median percent loss per
farm, estimated per farm dollar value loss to deer in 1994 ........................................... 70
Table 27: 1994 per farm losses to non-bearing fi'uit and Christmas trees ................... 72

vii

Table 28: Median percent losses and ranks of median losses by county for selected crop

types .............................................................................................................................. 72
Table 29: Per farm tolerance of losses for corn, soybeans, alfalfa, table beans, and small
grains ............................................................................................................................. 75
Table 30: Per farm tolerance of 1994 replacement costs and numbers of trees lost to
deer ................................................................................................................................. 75
Table 31: 1994 per farm estimated yield losses to bearing age fi'uit trees .................... 76
Table 32: Frequency of producers indicating year as most severe loss year ................. 80
Table 33: Relationship of 1994 tolerance of crop loss and tolerance of worst year’s
losses ............................................................................................................................... 80
Table 34: Farmer respondents perceptions of how significantly deer damage reduces the
quality of harvested crops, by county, tolerance of loss, and farm type ......................... 83
Table 35: Farmer respondents perceptions of the significance of crop losses caused by
other wildlife compared with deer .................................................................................. 85
Table 36: Methods used by producers to estimate crop losses ...................................... 89
Table 37: Farmer respondents anticipated and actual damage controls and types of
behavior done in direct response to deer damage ........................................................... 92

Table 38: Frequencies and ranks of anticipated damage controls and types of behavior
likely to be undertaken by farmer respondents if losses caused by deer increase in
severity, as indicated by producers who have and have not experienced intolerable

losses .............................................................................................................................. 97
Table 39: Percent of producers giving deer hunting access in 1994 in study

counties .......................................................................................................................... 105
Table 40: Percent of producers allowing deer hunting access by tolerance of 1994 crop
losses .............................................................................................................................. 105
Table 41: Acreage of deer habitat per farm and percent proportion of deer habitat per
farm: by county, tolerance of loss, farm type, and hunting participation ..................... 107
Table 42: Mean proportion of deer habitat per farm reported by producers of different
farm types and with varying tolerance of loss ............................................................... 108
Table 43: Respondent reported per farm 1 1-15-94 hunter densities ........................... 109
Table 44: Per farm numbers of hunters and hunter densities segmented by tolerance of
1994 losses ..................................................................................................................... 110
Table 45: Number of hunters and hunter densities segmented by type of farm

operation. ....................................................................................................................... 110
Table 46: Mean percent of farmers at, above, and below perceived safe opening day
hunter densities on November 15, 1994 ......................................................................... 112
Table 47: Average number of bucks and antlerless deer respondents reported were taken
on farms in 1994; segmented by county ....................................................... 115
Table 48: Number of antlerless deer reportedly shot on respondents’ farms in 1994 per
antlered bucks taken; segmented by tolerance of crop loss ................................. 116
Table 49: Number of antlerless deer per antlered bucks taken in 1994, reported by
respondents with intolerable crop losses and segmented by county ....................... 116
Table 50: Percent of producers reporting having encouraged the harvest of antlerless in
study counties in 1994 .................................................................................................... 118

viii

Table 51: Percent of producers encouraging antlerless harvest by tolerance of 1994 crop
losses .............................................................................................................................. 119
Table 52: Percentage of respondents in agreement with the statement, “Hunting seasons
should be designed to reduce deer numbers so that special kill permits to control crop
losses are not necessary.” .............................................................................................. 120
Table 53: Farmer respondents’ agreement with the statement, “I cannot control my crop
losses because not enough deer are harvested during the hunting season on lands adjacent
to my farm, “ by county, tolerance of loss, hunt participation, shooting and block permit
recipients ...................................................................................................................... 121
Table 54: Farmer respondents’ agreement with the statement, “I cannot control my crop
losses because not enough deer are harvested during the hunting season on lands adjacent
to my farm, “ by mean farm size and mean percent of gross income generated by

farming ......................................................................................................................... 121
Table 55: County mean favorabilities of shooting and block permits ........................ 123
Table 56: Farmer respondents’ mean favorability toward shooting permits, by hunt
participation, job status, and shooting permit recipient ............................................... 124
Table 57: Farmer respondents’ mean favorability toward block permits, by hunt
participation, job status, and block permit recipient .................................................... 124
Table 58: Percent of farmer respondents that believe they received as many shooting or
block permits as they felt they needed in 1994, by county ......................................... 125
Table 59: Producer attitudes regarding specifics of the MDNR shooting permit system by
total respondents and shooting permit recipients ......................................................... 127
Table 60: Producer attitudes regarding specifics of the MDNR block permit assistance
program by total respondents and block permit recipients ........................................... 129
Table 61: Open-ended comments made by farmer respondents regarding the shooting and
block permit programs ............................................................................................... 131
Table 62: Producer approval of selected criteria for determining eligibility for receiving
shooting and block permits ........................................................................................... 132
Table 63: Farmer respondents’ perceptions of deer population trends over the last 5
years, by county, tolerance of loss, hunting participation, and job status ..................... 138
Table 64: Farmer respondents’ beliefs about the most desirable number of deer per
square mile, by tolerance of loss, hunt participation, farm type, and job status ........... 140

Table 65: Farmer respondents’ beliefs about the lowest number of deer per square mile
they would tolerate in their county, by tolerance of loss, hunt participation, farm type, and
job status ..................................................................................................................... 140
Table 66: Farmer respondents’ beliefs about the greatest number of deer per square mile
they would tolerate in their county, by tolerance of loss, hunt participation, farm type, and
job status ................................................................................................................... .141
Table 67: Farmer respondents’ beliefs about the number deer per square mile in their
county in October, 1994, by tolerance of loss, hunt participation, farm type, and job
status ............................................................................................................................ 141
Table 68: Producer perceptions of deer densities that are desirable, minimal, and
intolerable expressed as percentages of perceived October 1994 deer densities by county
segmented by crop types within counties and DMU’S ................................................. 143

ix

Table 69: Producer perceptions of deer densities that are desirable, minimal, and
intolerable expressed as percentages of perceived October 1994 deer densities by county
and segmented by DMU and crop type ......................................................................... 145
Table 70: Tolerance of October 1994 deer densities segmented by county, tolerance of
crop loss, job status, and hunt participation. ................................................................ 157
Table 71: The relative importance of factors associated with opinions about satisfactory
deer densities ................................................................................................................. 160
Table 72: Farmer respondents’ ratings of the importance of personal recreational benefits
(e.g., viewing, hunting, feeding, etc.) in determining their tolerance of the deer population
in the county and by tolerance of loss ................................................................... . 163
Table 73: Farmer respondents’ ratings of the importance of others’ recreational benefits
(e.g., viewing, hunting, feeding, etc.) in determining their tolerance of the deer population
in the county and by tolerance of loss ................................................................... .163
Table 74: Farmer respondents’ ratings of the importance of economic benefits to the
county fiom the presence of deer in determining their tolerance of the deer population in
the county and by tolerance of loss .............................................................................. 163
Table 75: Farmer respondents’ ratings of the importance of the number of deer-related
vehicle accidents in the county in determining their tolerance of the deer population in the
county and by tolerance of loss ................................................................................. 164
Table 76: Farmer respondents’ ratings of the importance of personal crop losses in
determining their tolerance of the deer population in the county and by tolerance of

loss .............................................................................................................................. 164
Table 77: Farmer respondents’ ratings of the importance of other farmers’ crop losses in
the county in determining their tolerance of the deer population in the county and by
tolerance of loss .......................................................................................................... 164
Table 78: Farmer respondents’ ratings of the importance of personal economic benefits
from the presence of deer in the county (e.g., hunting leases, goods and services provided
to hunters and tourists) in determining their tolerance of the deer population in the county
and by tolerance of loss ............................................................................................... 165
Table 79: Summary table of discriminant analysis of factors affecting producer tolerance
of county deer populations .......................................................................................... 166
Table 80: Mean credibility assigned to local biologist by agricultural producers with
varying fiequency of contact and levels of crop loss tolerance ................................... 169
Table 81: Percentage of respondents in agreement with each statement about the
MDNR’S competence to manage deer populations and evaluate crop damage

situations ...................................................................................................................... 170
Table 82: Credibility of local MDNR biologists and the agency with producers in study
counties ........................................................................................................................ 170

Table 83: Percent of respondents in agreement with the statement: “the MDNR has
enough information on the deer population to adequately decide how many deer to
harvest in Michigan each year,” by tolerance of loss .................................................. 171
Table 84: Percent of respondents with no contact with MDNR biologists in agreement
with the statement: “the MDNR has enough information on the deer population to
adequately decide how many deer to harvest in Michigan each year” by tolerance of
loss .............................................................................................................................. 171

Table 85: Producer perceived weightings of stakeholders’ interests in MDNR deer

management objectives, by hunt participation and tolerance of loss .......................... 174
Table 86: Producer desired weightings of Stakeholders’ interests in MDNR deer
management objectives, by hunt participation and tolerance of loss .......................... 174

Table 87: Farmer respondents’ perceptions of the fairness of perceived stakeholder
weightings by the MDNR when setting deer population objectives by dependence on farm
income ...................................................................................................................... 177
Table 88: Farmer respondents’ perceptions of the fairness of perceived stakeholder
weightings by the MDNR when setting deer population objectives by county, tolerance,
hunting participation, job status, and permit recipients ............................................. 178
Table 89: Producer perceptions about other stakeholders whose interests are being
considered, and should be considered, by the MDNR when determining deer population

goals ........................................................................................................................... 179
Table 90: Farmer respondents’ reported contact frequency with MSU-E agents by

county ........................................................................................................................ 181
Table 91: Characteristics of issue stages .................................................................. 198

xi

LIST OF FIGURES

Figure 1: Number of White-tailed deer in Michigan (1938-1994) ........................... 5
Figure 2: Number of antlered and antlerless deer taken in Michigan during

1994 ........................................................................................................................... 6
Figure 3: Number of antlerless deer taken in Michigan with antlerless deer licenses and
crop damage control permits 1990-1994 ................................................................... 7
Figure 4: Representation of the stages of Issue DeveIOpment ................................. 11
Figure 5: Major components of the Minnis and Peyton Attitudinal Response

Model ........................................................................................................................ 22
Figure 6: Hypothetical preferred deer density for one stakeholder group ............... 25
Figure 7: Hypothetical preferred deer densities for two stakeholder groups ........... 26
Figure 8: Representation of cultural carrying capacity ......................................... 27

Figure 9: Counties included in a 1995 study of crop damage ................................ 35
Figure 10: Intention of producers to engage in various behaviors if losses become/remain
intolerable, analyzed by respondents’ history of losses ............................................ 94
Figure 11: Number of full-time and part-time farmers who have engaged in or will likely
engage in selected deer damage control measures and disruptive activity ............... 99

Figure 12: Number of primarily livestock, cash crop, and fruit/tree producers who have
engaged in or will likely engage in selected deer damage control measures and disruptive
activity ...................................................................................................................... 102
Figure 13: Deer Management Units (DMU) within each study county showing MDNR
deer density indices (Oct. 1994) and MDNR judgment of trend in deer numbers over past

5 years within the DMU ........................................................................................... 135
Figure 14: Cultural Carrying Capacity (CCC) response curves for farmers in each study
county ........................................................................................................................ 147
Figure 15: CCC distributions by county and similar deer densities .......................... 148
Figure 16: Oceana County CCC distributions segmented by DMU’S with similar deer
densities ...................................................................................................................... 149
Figure 17: Benzie/Leelanau CCC distributions segmented by farm type ................. 150
Figure 18: Oceana County CCC distributions segmented by farm type .................... 151
Figure 19: Calhoun County CCC distributions segmented by DMU’S with similar deer
densities ...................................................................................................................... 152
Figure 20: Montcalm County CCC distributions segmented by DMU’S with similar deer
densities ...................................................................................................................... 153
Figure 21: Tolerance of 1994 deer numbers in study counties ......................... 158
Figure 22: Perceived fairness of the amount of consideration given farming interests by
the MDNR in 1994 ........................................................................... 177

xii

INTRODUCTION

Deer and Crop Damage in Michigan

White-tailed deer (Odocoileus virginianus) populations in Michigan quadrupled
fiom approximately 500,000 animals in 1972 to over 2 million in 1989. During this
period reports of severe crop damage increased dramatically as did hunter success which
peaked with record harvests of over 400,000 deer in 1989, 90, and 91 (Langenau 1993).

In part because of large numbers of complaints about crop losses from agricultural
producers, the Michigan Department of Natural Resources (MDNR) increased available
antlerless tags between 1987 and 1991 to reduce the deer herd. The herd was successfully
reduced to approximately 1.7 million in 1992; however, reduced sightings of deer led
deer hunters and newspaper writers to complain that there were not enough deer.

Since 1992, the MDNR has found it difficult to define and maintain an acceptable
deer population goal for Michigan because of the significant political clout yielded by
both deer hunting and agriculture interests. Both interests make Si gnificant economic and
cultural contributions in Michigan. Agriculture is the second largest industry in the state
and annually contributes $37 billion into the state’s economy (Skj aerlund and Norberg
1994), while over $300 million accrues annually fi'om deer hunting in the state (Dudderar
et al. 1989). Between 1987 and 1994 several citizen action groups (UPWARD, Citizens
for Responsible Wildlife Management, Concerned Sportspersons & Business People of
NE Michigan) formed to espouse the views of hunters and farmers about the deer herd

size and/or crop losses. This substantial amount of political lobbying about deer and deer

damage has at times overwhelmed the activities of the MDNR and

other organizations involved in the issue, and the MDNR has frequently had to defend its
management Objectives and population estimates. In 1995, the issues associated with
crop depredation by deer received the attention of the State House Committee on
Agriculture and Forestry, raising the possibility that legislative action might be taken on
the behalf of farming interests. Most recently the Michigan Farm Bureau has threatened
legal action against the MDNR if deer numbers are not significantly reduced in the next

two years.

Goal of Research

Michigan’s large deer herd, large agricultural industry, and large deer hunting
public provides a myriad of issues by which to examine the concept of “cultural carrying
capacity” (Ellingwood and Spignesi 1986) as it applies to deer. Of greatest concern,
however, is the pragmatic analysis of the conflicts surrounding Michigan’s deer herd and
understanding how these conflicts can best be resolved; or in other words applying
cultural carrying capacity theory. The MDNR already attempts to adjust deer herd
management in response to stakeholder concerns about crop damage, car accidents,
harvest rates, etc., which could be taken as managing for cultural carrying capacity.
Because conflicts have continued to erupt as different management strategies have been
attempted, a better understanding of stakeholder (farmers and deer hunters) beliefs and
values concerning deer and deer management has been needed to reduce the frequency of
conflict.

The ultimate goals of this project were: to identify the level of crop damage

problems reported by farmers, to evaluate factors that might influence their acceptance of

management alternatives, to predict stakeholder response to management and fluctuating
deer numbers, and finally to identify targets for a communication plan to reduce the
amount of issue activity and improve acceptance of MDNR deer management programs.
Given that farmers, deer hunters, and the MDNR are the key stakeholders in this
issue, it appears that a better understanding of the perceptions, attitudes, and behaviors of
each of these stakeholder groups about crop damage issues is needed to reduce the
disruptive activities that follow crop damage. This study examined some of the social
and psychological components which influence farmer attitudes and behaviors regarding
deer damage, while a concurrent study examined the parallel components amongst deer

hunters (Minnis 1996).

Objectives
This project’s Objectives were to:

1. Acquire information on Michigan farmers’ tolerance of deer damage and deer
population densities and to identify factors which influence this tolerance.

Specific research hypotheses:

Hyp. #1. Tolerance will be related to...
past history of intolerable losses;
extent of current year’s losses;
dependence on farm income;
participation in deer hunting recreation;
relationship with the wildlife agency.

sup-99‘s

2. Identify factors that appear to be impacting producers’ abilities to control intolerable
levels of loss.

Specific research hypotheses:
Hyp. #1. Producer’s abilities to control losses will be related to...
a. adjacent landowners’ attitudes and behaviors about harvesting deer;
b. producers’ attitudes and behaviors about harvesting deer.

3. Determine what types of actions Michigan farmers have taken and are likely to take in
response to deer depredation.

4. Identify Michigan farmers’ attitudes about the MDNR and the current MDNR system
of issuing block and shooting permits to control depredation in conjunction with
regulated hunting.

Specific hypotheses:
Hyp. #1. Farmer attitudes about the MDNR and the current depredation control
system will be related to...
a. perceptions of the trustworthiness of the agency;
b. perceptions of the expertise of the agency;
c. perceptions of the competence of individual agency personnel.
Hyp. #2. Agency credibility will be positively correlated with tolerance of crop
loss.

5. Identify indicators of escalating issue development among producers regarding deer

crop damage.

6. Make recommendations about how information regarding these attitudes can be best
incorporated into the Michigan Department of Natural Resources’ deer management

programs.

 

 

 

 

 

 

 

 

 

 

 

 

2°°° 11 z

1% {w

1400 A A /l
2...... 3 ll fV
erm- 800 -V/ “be /

.0. “Av”

400

200

,_

1938 1947 1956 1965 1974 1983 1992

Figure 1: Number of White-tailed deer in Michigan (19384994)

Compiled fiom unpublished MDNR data.

Figure 2: Number of antlered bucks and antlerless deer taken in Michigan during 1994.

 

300.000

250.000 ~ 229.640

I Antlerless D Antlered
bucks

_§
§
T

1 12,490

1994 Merl-lamest
.§
§
1

 

 

 

 

 

 

Firearm Archery Muzzleloader Block Shooting
Permit Permn

 

   
 

Vehicle collision
1 3%
Block Permit

3%
Shooting Permit
.0496

   

Deer hunting license
84%

  

Note: Data for 1) deer taken by type of hunting license are preliminary.
2) deer taken on Shooting Permits may include some antlered deer.

3) deer killed in vehicle accidents assumes deer were killed in the
collision.

Compiled from unpublished MDNR and Michigan State Police data.

Antlerless deer harvest

 

 

 

 

 

2000

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1500
' Antlerless Permits

10000 /
50000 , '
* Block Permit

””1 Shooting Permit
1990 1991 1992 1993 1994

Figure 3: Number of antlerless deer taken in Michigan with antlerless deer licenses and crop damage control
permits 1990-1994.

  
 
 

 

0.4

Compiled from unpublished MDNR data.

LITERATURE REVIEW
In preparing this manuscript and preparing for this study I was fortunate to have
access to several excellent literature reviews regarding human tolerance of wildlife,
human tolerance of crop damage, and reviews of management strategies for reducing crop
losses (Dudderar et al. 1989, Langenau et al. 1993, Minnis 1996). Because of the
comprehensive nature of these reviews, I have chosen not to duplicate these reviews here,
but to use this chapter to propose that crop damage management be viewed as an issues
management problem involving conflicting human values and beliefs.
Issues Management
The issues surrounding deer management in Michigan over the last 10 years well
illustrate Aldo Leopold’s comment that, “You cannot conserve wildlife by itself; to build
the wildlife resource you must...rebuild the people who use it, and all the things they use
it for...” (Leopold 1953). Leopold’s statement appropriately points out the necessity of
managing people in concert with wildlife populations, and illustrates two central points of
Michigan’s deer conflicts. First, “building” is no longer an appropriate paradigm for the
deer resource; support for herd management, and therefore occasional herd reduction, is
What currently needs support, and appears to be at the heart of current conflicts. Second,
LeOPOId’s comment is short-sighted in that it does not acknowledge that different
Stakeholders may need to be “rebuilt” with different parts or by different methods;
fat‘mers and deer hunters need to be approached differently as do different types of
farmm (Decker and Brown 1982). AS the MDNR has become acutely aware, conflicts

bent/Ben multiple stakeholders, and the diverse concerns Of stakeholders can lead to

management problems unless these components of issues that cause conflict are
addressed.

The initial step towards managing contentious issues is an identification of the
components causing conflict. The components of deer management issues confronting
the MDNR today and in the future are not necessarily the same as those faced in 1987 or
1992. Thus, it is important for managers to anticipate changes in issue components in
addition to identifying current ones. In 1990, Peyton et al. listed 3 characteristics of
issues that managers should keep in mind when attempting to identify and manage

wildlife issues.

Issues and disputes are developmental. They evolve through social, psychological
and political processes. The earlier a resource manager intervenes, the better.

Public beliefs, public values and priorities, and the adequacy of existing science,
all play important roles in creating issues and must be dealt with differently by

resource managers.

There are no institutional quick fixes which make issue management and personal
involvement of managers unnecessary.

St_ages of Issue Development

Perhaps the most important of these issue characteristics is that issues develop
through stages. Knowing this, managers should be able to reduce the number of conflicts
they encounter by anticipating issues and addressing them early in their development.
Peyton (1984) describes the stages of issue development as: 1) latent; 2) emerging; 3)
active; and 4) disruptive (Figure 4). Latent issues are those that are concerns of
individuals but which are not being communicated to others. Emerging issues are those
that are being discussed by stakeholders amongst themselves, but not yet being brought to

the attention of the agency or other authority figures. Currently, producer concerns about

10

geese (Branta canadensis), crane (gig canadensis), and turkey (Meleagris gallonavo)
damage to crops are at this level in certain parts of the state. Active issues are those issues
which the stakeholders are actively communicating to the management agency. The
stakeholders are voicing demands at this stage but the manager generally remains in
control of the situation. When issues are taken to authorities other than the management
agency they are considered disruptive issues. If an issue results in legislative action or
court rulings they would be considered disruptive. The recent attention of the House
Committee on Agriculture and Forestry to the deer crop damage concerns of farmers
threatens to make farmers’ deer crop losses a disruptive issue. Though this issue level is
termed “disruptive,” it is important to note that seeking legislative action can sometimes
be a positive action and serve to improve management that has become too bureaucratic

or unresponsive to stakeholder interests.

 

 

DISRUPTIVE *

  

 

 

ACTIVE

----- -————
EMERGING

——-——- -—--—
LATENT

 

 

 

 

Figure 4: Representation of the stages of Issue Development (adapted from Peyton 1984)

12

Components of Issues

In a 1985 paper Kellert and Brown wrote, “Despite the willingness of the
public to support the conservation and protection of many species, many Americans,
while being aware of and interested in wildlife, appear to be motivated more by myth and
bias than by knowledge and informed opinion about wildlife and its management.”
Public beliefs and biases appear to be important components of the issues surrounding
deer management and crop damage and attention to stakeholder values and beliefs is
likely an important key to managing issue development.

Peyton (1984) identifies 3 major components of resource management issues
which may need addressing to resolve conflicts: 1) the adequacy of the existing science
and technology, 2) public beliefs, and 3) public values. The impact of public beliefs in
deer management issues is evident in Langenau’s (1993) response to questions about the
scarcity of deer in the fall of 1992. Langenau stated, “Farmers were said to have killed
Off the deer last fall with block permits or with out-of-season shooting kill permits last
summer.” He pointed out that neither was the case. This same complaint was made by
hunters in Alpena County in 1994 where general antlerless harvest had actually been
increased to lower deer densities in the areas’ Deer Management Units (DMU’S) (Carlson
pers. comm.) Such perceptions and beliefs appear numerous in deer management issues
and identifying and countering them has the potential for defusing issues before they

become disruptive.

13

How m_anv deerge there?

Though managers’ scientific understanding of deer and the effects of deer damage
is generally adequate, it is often the first area called into question by stakeholders as an
issue develops. For example, in 1992 deer hunters questioned whether deer herd
reduction had been necessary and whether the reduction had gone too far, and Langenau
(1993) reported that MDNR deer population and harvest estimates were questioned.
Similarly in 1994, sportsmen in Alpena County questioned the ability of the NUDNR to
estimate deer populations fi'om pellet count indices. Often it appears that the public
jumps to critiquing a single analytical tool although it may be only one of several indices
used by the agency to estimate relative deer densities.

Loss Assessment Technology

Estimates of crop loss are also viewed suspiciously and the methodology for
making such estimates is questioned. Studies and programs in other states have shown
through a variety of methods that deer browsing can have a significant impact on the
yields of agricultural crops, and result in large financial losses to producers (Craven 1983,
Tanner and Dirnmick 1983, Stoll and Mountz 1983). Still when seemingly large dollar
values of crop loss are reported by farmers, the validity of their estimates is frequently
questioned. Some deer hunters and others may have trouble comprehending how deer
can cause such loss and they want proof there is an issue of crop loss.

The reliability of farmer reports of damage was one target of hunter scrutiny in the

late 80’s and early 90’s. The same skepticism of farmer reports may also be expressed by
agency personnel who may question producer assessments loss based solely on visual

inspections of damage (not loss) and deer sightings in crops. Unfortunately, available

l4

methods of assessing losses are labor intensive and beyond the means of most farmers
and wildlife agencies. Thus, farmer estimates of intolerable losses are often all managers
have to go on when making management decisions. This might not be a problem if
individuals hadn’t been known to claim losses that didn’t exist (Dudderar, Odurn,
Carlson, Willman, Parr, pers. comm), so that deer hunters and other stakeholders could
trust that depredation control was necessary. One would like to believe that if a

standardized systematic method were available for farmers to report their losses that there

 

would be less conflict; however, even wildlife and agricultural professionals apparently
have trouble agreeing to acceptable methods and designs.

In 1988 and 1989 the Michigan State University Extension Service estimated that
some growers of certified seed beans in northeast Michigan were incurring losses in
excess of $225 per acre (Long et al. 1990). Despite having sought assistance from
wildlife professionals (U SDA-Wildlife Services, Michigan State University’s Department
of Fisheries and Wildlife) in performing this exclosure research, over 3 years, MDNR
personnel were reportedly still critical of the exclosure protocol and the resultant data
(Dudderar, Long and Parr pers. comm.) Interestingly, between 1993-1995 Braun (1996
unpublished) documented losses to alfalfa and beans in northeast Michigan of between
3% and 11%, but with apparently greater acceptance of the data by the agency. The
political climates surrounding each exclosure study were markedly different in that the

Extension work was done at a time of high deer numbers as opposed to the much reduced
density surrounding the Braun study. Other differences that may have elicited the
difl’erent agency responses might be the intended use of the data, and the person directing

the work. In the Extension work there was apparently the potential of using the data in a

15

lawsuit by farmers, while the Braun study was directed by a former MDNR employee and
the data was being used to evaluate the impact of adjacent habitat characteristics on losses
caused by deer. Despite their obvious differences, these two cases illustrate that
methodologies used to document crop losses need to be agreed to.

Though managers may debate the results of such exclosure work, this method and
Others have been broadly applied and are generally considered accurate and reliable
means of quantifying losses (Litvaitis et a1. 1994, Wisc. Co-op Wildl. Damage Control
Program 1990). In fact, exclosure estimates are used to test the reliability of other
methods of quantifying loss (Austin and Umess 1987). Thus, the science of quantifying
losses appears to be adequate, though communicating the abilities of this science to the
stakeholders needs improvement.

Non-lethal Control Alternatives?

The belief that deer damage can be eliminated with non-lethal control is another
common belief expressed by stakeholders when deer herd reduction is an issue and much
research has focused on evaluating the effectiveness of various control methods at
reducing losses: chemical repellents (Sayre and Richmond 1991, Fargione and Richmond
1993, Lewison et al. 1993, Tanner and Dimmick 1983, Ellingwood et al. 1983), electric
and non-electric fences (Jordan and Richmond 1991, McAninch et al. 1983, Owen et al.
1993), soap bars (Fargione and Richmond 1991), guard dogs (Beringer et al. 1994).

While these and other studies have shown that some methods are effective at reducing
deer browsing in certain situations, they also point out that the costs of these methods

may possibly prohibit use on larger scales, and that effectiveness declines as browsing

l6

pressure increases (Fargione and Richmond 1993). Usually some form of herd reduction
is required either through recreational hunting and/or special shooting permits.

Not only do biases and beliefs affect the attitudes of deer hunters but also those of
farmers, who may not use a control when it would be effective. A study by Purdy et al.
(1988) indicated that New York apple growers often made damage control decisions
based on incomplete or subjective information about the effectiveness of different
methods. Sizable numbers of producers with chronic deer damage in New Jersey have
been shown to have limited knowledge of hunting seasons and bag limits that, if taken
advantage of, might reduce their losses (Eriksen 1994). Apparently, science-based
information is available on controlling deer depredation, and much of it may be
appropriate to certain farming situations. Regrettably, this information does not appear to

be getting to farmers whose situations warrant particular controls.

Who’s Resmnsible for Deer Damage?

Not only do deer damage issues hinge on specific beliefs about the effectiveness
of non-lethal control measures and what types of deer control are legitimate, they also
hinge on philosophical beliefs about whether deer damage is a public responsibility given
that deer are considered a public resource. In fact people’s attitudes about who should be
financially responsible for deer damage may be more important to the crop damage issues

than any other single factor. Legally, states are allowed to “regulate” wildlife and wildlife
agencies “manage” this public resource (Gray 1993). Whether the state is then
“reSponsible” for losses caused by its charges (the deer) is a legitimate question for

farmers with intolerable losses. Islieb’s finding that only 31% of farmers would install

17

deer fencing even with a 75:25 (statezfarrner) cost-share suggests that considerations other
than just cost influence farmer willingness to engage in controls which have been
scientifically proven (Islieb 1994). Perhaps farmers do not believe that they should have

to pay to control the “state’s” deer.

Conflicting Values

Public values are also an important component of deer management issues that
need to be understood. Peyton (1985) identifies issues involving conflicting values as
perhaps the most difficult conflicts to manage. Deer hunters, whose license fees support
the majority of deer management, value the recreational opportunities provided by those
dollars. Understandably, they may become upset if they perceive that the products of
their dollars and their hunting opportunities are being given away to control crop losses.
Consequently it can be hypothesized that issues may develop if hunters are not given
opportunities to harvest animals that need to be removed to control crop losses. At the
same time farmers who value living a farming lifestyle may see that value threatened by

an over abundance of deer.

 

 

 

l8

Tolerance of Crop Loss and Deer
Not all farmers respond to deer crop losses in the same way. It has been shown
that not all farmers who have identical losses rate these losses similarly. Studies done in
New York and Ohio indicate that when losses exceed $500 producers consider the losses
to be a problem and that losses of approximately $1,500 are considered severe (Brown et
a1 - l 978, Stoll and Mountz 1983). Still a single farmer may have particular values that
cause him/her to tolerate a “severe” loss, while another farmer may find the same loss
intolerable. This tendency is born out by the large variances surrounding the means
reported in New York and Ohio studies. The median “tolerable” amount of loss in Brown
et al , (1978) was the category $l-99, with an inter-quartile range around that of $0 to
s 1 00.499. Similarly the median amount of loss considered “unreasonable” was $500-999
with an inter-quartile range of 3100-499 to $1,000-2,999. These studies did not, however,
document what type of actions producers take as damage approaches these levels, nor did
they adequately identify the factors that determine whether a producer will tolerate such
losses - For instance, a producer may consider losses a problem but decide to tolerate
them, while another producer may engage in damage control, and still another producer
may decide to petition his state legislator to lobby for decreased deer numbers.
Prompted by the belief that managers need to better understand the views of their
consfituents, several studies have examined farmer and landowner attitudes toward deer
and the human tolerance of deer populations and crop damage (Decker and Brown 1982,
Tanner and Dimmick 1983, Stoll and Mountz 1986, Morgan et al. 1990, Minnis 1996). A
few Studies have even looked at Michigan farmers attitudes about deer and attempted to

id °
entrfy t1‘ends in the extent of deer caused losses to crops with surveys by Albright

19

(1993) and Nelson (1995) being the most recent. Some general findings of these studies
were: that < 10% of agricultural producers find deer damage to be intolerable, that the
benefits derived fi'om esthetic and consumptive uses of deer sometimes compensates for
damages incurred, that those receiving a greater proportion of their income from the land
are less tolerant of damage, and that landowner willingness to permit hunting increased as
estimated losses increased. (For a more detailed review of the history of and research on
Michigan’s deer crop damage issues the reader is referred to the reports of Dudderar et al.

(1989) and Minnis (1996).)

2O

Attitudinal Response to Deer

Tolerance or intolerance is an attitudinal response to a stimuli such as crop
depredation, and disruptive issue behaviors are one type of behavioral manifestation of
such attitudinal responses to deer damage. This link of tolerance to behavior was not
investigated by the aforementioned studies despite it being a critical piece of information
for deer managers. To be able to prevent disruptive issue activity it is important that deer
managers understand how Stakeholder attitudes are developed, and how these attitudes
express themselves in terms of action. In their 1995 paper “Cultural Carrying Capacity:

modeling a notion” Minnis and Peyton proposed an Attitudinal Response Model (ARM)
to explain human response to wildlife. Their model consists of 4 major dimensions:
“actuality, perceptions of actuality, attitudinal response and behavioral response.”1 The
model poses that an individual’s behaviors regarding a wildlife population will be
determined by a linear relationship between the actual nature of the wildlife-human
interactions, the individual’s perception of those interactions, the individual’s evaluation
of the costs and benefits of those interactions, and, eventually, the adoption of a
behavioral intention (attitudinal response) in response to the wildlife-human interactions
(Figure 5).

“Attitudinal response to a wildlife population level is proposed as being modified
by ““131 and perceived wildlife-human interactions. Often, it is the perception of reality
rather than the actual incidence of wildlife-human interactions that determines the
attitudinal response” (Minnis and Peyton 1995). The crux of the ARM is the

stakeholder’s evaluation of the perceived costs and benefits of the wildlife-human

21

interaction. Minnis and Peyton describe the process as stakeholders asking themselves,
whether “the perceived cost-benefit assessment is satisfactory/desirable or
unsatisfactory.” They explain, “A satisfactory/desirable response will result in the
stakeholder taking no action to change the [wildlife-human] interaction and that
following an unsatisfactory evaluation a person will either tolerate or not tolerate the
perceived cost-benefit assessment.” Tolerance will result in no change being sought,
while intolerance will result in some effort on the part of the stakeholder to change the

wildlife-human interaction to create a satisfactory perceived cost—benefit assessment.

M
The .
and P:°mplexrty of the model is too much to review in its entirety here and the reader is referred to Minnis

Yton 1995 and Minnis I996.

ACTUALITY

 

Actual
cost-benefit
of deer

 

 

 

 

22

 

 

 

 

 

 

 

 

 

 

 

 

 

Intentions

Intention to try to create an acceptable

perceived cost-benefit of deer

 

BELIEFS ATTITU DES
cost.benefit perceived cost/benefit
of deer of deer (e.g.

tolerable/intolerable)
Preferences /
regarding
numbers of deer
Behaviors

 

 

 

Selected behavior to create an acceptable
perceived cost-benefit of deer

 

 

 

Figure 5: Major components of the Minnis and Peyton Attitudinal Response Model.

-rm~¢ M I'

23

Hypothesized behaviors resulting from a stakeholder’s intolerance might be that the
person: abandons the situation (e.g. ceases to plant a crop), personally attempts to change
the situation (e.g. alternative crops, fencing, repellents, shooting permits, non-permitted
shooting), or to get others to change the cost-benefit in favor of the stakeholder (e. g. via

agency harvest quotas, legislative mandates, compensation for losses, etc.)

Graphing human tolerance of deer numbers
Ellingwood and Spignesi (1986) defined the term Cultural Carrying Capacity

(CCC) as “the maximum number of deer that can compatibly co-exist with a local human
population.” Thus, there is logical link between tolerance and wildlife population size.
This link between deer herd density and attitudinal response is a component of the
Actuality segment of the ARM (Minnis and Peyton 1995). Minnis and Peyton (1995)
propose that CCC may be best defined as “the wildlife population level in a defined area
that produces the most manageable amount of issue activity at a particular time.” They
Propose that the relationship between issue activity and wildlife population size can be
graphically represented by plotting issue activity and wildlife population size as axes on a
Cartesian graph. Each stakeholder is proposed to have a pair of tolerance thresholds
Which bracket their preferred range of densities for a single wildlife species (Figure 6).
At the lower end is the point of “Minimum Demand”, below which there are too few
animals (deer) for the stakeholder’s satisfaction. The upper threshold is the stakeholder’s
Wildlife Acceptance Capacity (WAC), a term borrowed from Decker and Purdy (1988).
WAC is that Population level beyond which there are too many animals (deer) for the

Stakeholder’s liking. When jointly plotted each stakeholder should have a U or V-shaped

‘ -—

0-

“. 1
my

a
0"

24

curve that represents their tolerance of deer densities. The distance between one’s WAC
and point of Minimum Demand is referred to as the stakeholder’s “Latitude Of

Acceptance” (LOA) for deer populations.

25

 

 

Tolerance
of Deer

Minimum
Demand

 

 

 

Intolerable

’ {2} €13 Latitude ofAcceptancd—‘b {Z}

 

Stage
nrsnumvr:

Wildlife Acceptance
Capacity

 

 

 

 

ACTIVE

 

_--—

 

 

EMERGING

393

JUST RIGHT

LATENT

 

 

0510

Figure 6: Hypothetical preferred deer density for one stakeholder group (adapted from Minnis and Peyton 1995)

152025303540455060

70 80 90
DEER PER SQUARE MILE

26

Tolerance
of Deer

Intolerable {:3 m m {C}

________/a‘________

iiiiiiZiii-ifiii

0510l52025303540455060708090

 

 

 

Tolerable

 

 

DEER PER SQUARE MILE

Figure 7: Hypothetical preferred deer densities of two stakeholder groups

 

 

27

When combined in stakeholder groups composite curves can then be used to
illustrate differences and/or similarities in preferred deer densities between stakeholders
such as farmers and deer hunters (Figure 7). In practice it may be possible to affect a
change in a group’s LOA, thus offering potential for resolving conflicts between groups
over what is an acceptable number of deer (Figure 8). Minnis and Peyton (1995) propose
that analyzing farmer and deer hunter attitudes and behaviors using the ARM may allow

agencies to affect such changes among stakeholders.

Tolerance

 

Intolerable {:2} -{.:}._ l . -£:!} {:1} .. r

   

 

 

h-_--- _-___—_

Tolerable * #

 

 

 

 

051015202530140455060708090
DEER PER SQUARE MILE

Figure 8: Hypothetical representation of Cultural Carrying Capacity

 

 

28

Synopsis of Michigan’s Deer Depredation Control Permits

Three permit systems are currently used in Michigan to encourage the harvest of
antlerless deer in specific areas to help reduce the local deer population and control crop
losses.

Shooting Permits - In 1979, the Natural Resources Commission in Michigan adopted
Out-of-Season Shooting Permits to help control deer depredation of agricultural crops.
These permits are issued to farmers whose losses to deer are deemed significant by
MDNR biologists. The permits are issued to kill depredating antlerless deer at times
outside of the regular firearms, muzzleloader and archery deer seasons. Permits allow
antlered deer to be shot only when circumstances are deemed appropriate by MDNR
biologists. The permits are valid only for times, fields, and the number of deer designated
by the biologist. In most areas, deer shot under this permit system are to be collected by
MDNR personnel or designated persons and distributed to charitable causes. Up to three
designated shooters can be allowed to fill the permits, and there is no charge to the farmer
for the permits.

Block Permits - In 1990, another type of permit was introduced to reduce the number of
Shooting Permits issued and to use licensed deer hunters to control crop losses. Block
Permits are valid only for shooting antlerless deer during the regular fall hunting seasons.
The biologist determines how many deer should be taken, and then these permits are
issued in “blocks” often or more to farmers with documented losses. In certain situations
the biologist has the discretion to issue a block of less than 10 but no fewer than 5
permits. Farmers must purchase these bonus licenses for a cost of $3.00 each. The
licenses are then distributed by the farmer to licensed hunters for use on their farm or
adjacent lands with the permission of adjacent landowners. Hunters are allowed to keep
the deer they shoot, and there is no limit to the number of Block Permit licenses that a
hunter can fill. Licenses are also transferable between hunters so that unused tags can be
returned to the farmer and then reissued to other hunters. All regular hunting season
restrictions apply as to the type of equipment and legal shooting hours.

3&3 ar Antlerless Lotteg Licenses - Michigan also uses a lottery system to allocate a
limited number of antlerless deer hunting licenses in the majority of its deer management
units. Antlerless licenses are issued both through a general and a private lands lottery.
$31“th hunters in the private lands category are issued one license to harvest an
antlerless deer on the parcel they specified on their application.

29

Table l: A comparison of Michigan’s shooting and block permit programs.

 

 

Frequently asked questions: Shooting Permits Block Permits
First used statewide? 1979 1990

 

Purpose?

Control deer numbers to reduce crop loss at
times outside of regular deer hunting
seasons.

Reduce need for Shooting permits and
allow licensed hunters during regular fall
deer hunting seasons to help control
numbers of deer that cause damage.

 

Who issues the permits?

Local MDNR personnel who assess
flultural loss.

Local MDNR personnel who assess
agricultural loss.

 

 

Who can get the permits? Producers with “significant" crop loss. Producers with “ a history of significant”
crop loss.
Who shoots the deer? Up to 3 shooters designated by the permittee. Anyone who has a current deer hunting

license.

 

Wherecanthepermitsbeused?

Only on the permittee’s lands/fields/blocks
as designed on the permit.

 

Whatis thccostofthepermits?

On the permittee‘s land and on adjacent
private land with permission.

 

None

$3 per permit; minimum block of 10 must
be purchased, unless otherwise approved by
MDNR.

 

How many deer can be shot?

Local MDNR biologists determine the
maximum number of deer that can be
removed.

Local MDNR biologists determine the
maximum number of deer that can be
removed.

 

Who gets thedeerafieritis
shot?

Varies but the MDNR maintains the right to
determine how deer are to be used and by
whom.

The licensed hunter who shot the deer.

 

 

Can antlered bucks be shot? (Le.

deer with antlers extending more
than 3” above the skull)

 

Only in select cases when the MDNR
determines that a need exists. (eg when
excessive buck rubbing damages fruit or
Christmas trees)

 

No antlered deer can be shot with block
permits.

 

 

Michigan Department of Natural Resources, 1995c. Guidelines and procedures for issuance of 1994 deer
crop damage block permits. Interotfrce communication from R.C. Elden to Regional Wildlife Supervisors.

Wildl. Div. Lansing.

Acceptance of Damage Control Programs

Three recent studies in Pennsylvania, Michigan, and Wisconsin have looked at

stakeholder use and acceptance of special culling programs and the factors related to

attitudes about such programs to control deer-crop depredation. In Pennsylvania,

landowner attitudes about an extended antlerless season to control crop losses were

studied by Boyd and Palmer (1991). Farmer respondents to their survey indicated general

aI’l’rWal of the extended season but indicated that it was not as effective as it could be

because adjacent landowners were not supportive of killing deer to control crop losses. In

Wiseonsin, Horton and Craven (1996) examined farmer attitudes about Wisconsin’s

Shooting Permit system. They expressed doubt that the system was being used

 

mar

 

.ra IOTA-L1

 

3O

effectively by farmers but went on to postulate that the system may be worth while
because it gives producers a sense of control over the situation. Their analysis further
suggested that farmers were unwilling to shoot pregnant or nursing does, that hunters
could not be found to use shooting permits in the summer, and that shooting hour
restrictions and shooter training prevented effective use of shooting permits. Nelson and
Yuan (1991) studied farmer, hunter, and adjacent landowner attitudes about Michigan’s
Block Permit system two years after its inception as part of the program’s 3-year
evaluation. Some of their findings were that block permit recipients were more
dependent upon farm income than non-recipients and that the system appeared to be
achieving its purpose of locally increasing antlerless kill on affected farms. However,
they also found that hunters who did not or could not participate in the control program
were least supportive of the program, perhaps because they did not perceive the allocation
0f Permits to be fair. This approach of the Michigan Department of Natural Resources’
(MDNR) to deer depredation control continues to be one particular area of conflict
hem/Ben hunting and agricultural interests in Michigan. Several reports of urban deer
reduction programs have been made in the last few years which suggest factors that
immct 0n stakeholder acceptance of deer reduction programs. Common areas of conflict
are beliefs about the need for herd reduction and the method of herd reduction,
SPCCifiCally whether lethal control is justified. Acceptance of herd reduction programs
also appears related to the credibility of the management agency and the approach used by
the aScrlcy to select the management technique (Stout and Knuth 1995, McAninch and

Parker 1995, Curtis et al. 1995, Hall 1991).

 

 

 

31
Agency Credibility
Agency credibility appears important at many points throughout the issue

management process. Eberhardt et al. (1990) define two components of agency
credibility: l) competence and 2) trustworthiness. Stakeholders’ perceptions of an
agency’s competence to perform management functions and of an agency’s willingness to
act in the best interest of stakeholders can potentially make or break any management
effort. Grise (1994) wrote that, “When agency credibility is high, decisions are more
likely to be accepted as necessary and the best possible choice, even when they differ
from the personal preferences of the stakeholder...With low agency credibility,
stakeholders will... continue to question the agency’s ability to manage effectively.” In
the case of deer crop damage management, the MDNR is precariously positioned because
key values (financial security and recreation) held by farmers and deer hunters are

fundamentally opposed. It would be easy for the agency to lose the trust of hunters while
acting in the best interests of farmers, or conversely, to lose the trust of farmers while
acting in the interest of deer hunters. Smolka and Decker (1985) found in New York that
the cOnflicts over deer management appeared to revolve around beliefs about whether
there is a problem or reason to change the status quo and/or about the appropriate method
0f addressing the problem. If the agency’s credibility is high then stakeholders will more
likely trust the agency’s assessment of the existence of a problem and the most
appropriate course of action. Crop damage abatement programs by their personal nature
offer an interesting opportunity to examine the relationship between producer contact

With agency personnel (biologists, game wardens, etc.), and producer perceptions of

WW

32

agency competence. In particular it allows for an examination of the ability of local

biologists to affect attitudes about crop damage and tolerance of deer.

33

Summary of Literature Review

This literature review sought to accomplish two things: first, to propose that deer
depredation should be viewed by agencies as an issues management problem, and second,

to frame or identify those components which appear to be contributing to conflicts
concerning deer depredation management.

General Issues:

Issues can be large or small in magnitude, but all issues are significant because of their
potential to escalate from emerging issues to active and disruptive issues.

Sources of issues:

0 Gaps in scientific knowledge and understanding (Proven facts)

0 Differing beliefs about what is known (Perceived facts)

0 Differing beliefs about what should be done if facts are agreed on (Important values)

Crop Damage Issues:

Issues related to deer crop damage in Michigan appear to be both large and small in

magnitude and appear to be present at different stages of development in different areas
of the state and with different segments of the public.

HYPOthesized components of issues contributing to conflicts concerning deer crop
damge in Michigan:

Existence of actual deer-caused crop losses

Tolerance of losses in dollars, percent of crop (What is an acceptable loss?)
Perceptions of current numbers of deer

T01el'ance of deer numbers (What is an acceptable number? CCC)

Acceptance of crop damage control program (Identified need and appropriateness)
Cl'ttdibility of agency and personnel (Strengths, weaknesses, administrative ability)
LP111211 and non-lethal damage control tools (Use and preferences)

Hunting as a control tool (Use and access)

Role of hunters, farmers, and MDNR (Who’s responsibility are the deer?)

The Pragmatic orientation of this study sought to document the current state of the crop
damage 1ssue in different regions of Michigan and to determine the extent to which each

0f the above hypothesized components is contributing to the 1ssue of crop damage 1n
Michigan

METHODS
Study Site Selection
This study was part of a larger comprehensive examination of deer depredation

problems in Michigan funded by the Michigan Agricultural Experiment Station, MDNR,
and Mchigan State University Extension (MSUE). Ecological portions of the study took
place in the northeastern (Presque Isle, Alpena, Montmorency Counties) and northwestern
(Benzie and Leelanau Counties) portions of the lower peninsula, and therefore this study
examined farmer attitudes about crop depredation in those areas. To better represent the
breath and variety of deer damage situations throughout the state, four additional areas
were selected that were identified as having different types of damage problems and
different levels of public involvement in the deer damage issue after consultation with
Extension and DNR personnel and after examining the 1987 Deer Damage Committee
report, In total 7 counties were selected for study (Calhoun, Montcalm, Oceana,
BelHie/Leelanau, Presque Isle, and Menominee) (Figure 9). As much as possible,
counties were paired so as to control for the ratio of cropland to forest, the types of crops
grown, and the relative deer density (Table 2). For this reason Benzie and Leelanau
counties were combined as one region that would be somewhat comparable to Oceana
°°“my. Calhoun County’s index of deer related vehicle accidents (DRVA’s) is greatly
affected by the presence of 2 interstate highways 0-94 and I-69) which bisect the county
33“ aaccount for account for significantly more miles driven in the county relative to other
comlties. Despite the skewed DRVA index, MDNR biologists fi'om Calhoun and

Montcalm County did believe that deer densities were similar in these counties.

34

35

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

c"W/

Figures: mmmnmmdmmm

 

 

 

Table 2: Profile of study counties by crop types, issue intensity, percentages of forest and agricultural

lands, and relative deer densities.

36

 

 

 

 

 

 

 

 

 

 

 

 

 

County Representative Crop Damage Ratio of Deer/Car
Crop types‘ Issue Intensity” farmlands to Accidents
forestc per million
miles
drivend
Calhoun I Com, Low 56:24 0.97
soybeans,
grams
Montcalm‘ Com, Moderate to 53:29 2.42
soybeans, table High
beans, potatoes
Oceana 1 Fruit, Moderate to 38:54 1.61
vegetables High
Benzie/Leelanau 2 Fruit Moderate to 21 :79 1.12
High
Presque Isle 3 Table beans, High 19:74 2.45
corn, alfalfa
Menominee 3 Corn, alfalfa High 18:79 4.26

 

County pairings are denoted by common numbers following county names. ' 1993 Michigan Agricultural
Statistics, Mich. Dept. of Agriculture; 5 Pers. Commun. MDNR & MSU Extension; ° 1993 Forest Inventory,
North Central Forest Experiment Station; ‘ 1991 Michigan State Police.

Interviews

Focus groups were initially planned for the spring of 1994 to become familiar
with farmer beliefs, concerns, and values about deer crop damage and to identify
language that would be appropriate for use in a questionnaire. Instead a personal
interview format was adopted because it was more compatible with farmer schedules in
May and June. County Extension agents provided the names and telephone numbers of
cheats whom they felt would provide a representative cross section of attitudes about deer
damage in their counties. These clients were contacted by phone and asked if they would
be willing to visit with the researcher at their farm for approximately 1 hour. Times were
arranged that were convenient for the farmer, usually around the lunch, dinner and
evening hours. Interviewees were asked for permission to tape record the session for

n0te--taking purposes; however, because a number of producers appeared suspicious of

37

the proposed study and the use of such recordings, only written notes were taken after the
first few interviews. Notes from these conversations were used as a guide in developing
the content and wording of the subsequent mail questionnaire. Producers were asked to
describe their experience with deer damage, their impressions of the current size of the
deer herd, their use of deer damage control tools, their impressions of the benefits and
costs of the presence of deer, their familiarity with Michigan Department of Natural

Resources personnel, and to describe how they estimated losses.

Table 3: Number of interviews completed per county.

 

County Number of interviews completed

 

Calhoun

 

Montcalm

 

Oceana

 

Benzie/Leelanau

 

Presque Isle

 

 

OGDObOs-h

Menominee

 

 

 

Questionnaire testing and review

In early February, 1995 a pilot questionnaire was mailed to 102 MSU-E contacts
in Isabella County to test the clarity and content of questions. Respondents were asked to
complete the questionnaire and return it along with any additional written comments.
The single mailing yielded 49 returned surveys fi'om extension clients, 37 (76%) were
full-time or part-time farmers that could be used in an analysis, 24% of the returns were
from retired farmers and non-farmers. It was hypothesized that this percentage of non-
eligibles would vary depending on the extension agent’s maintenance of a farm list. The
Survey was also sent out to MSU-E and MDNR personnel for review. Adjustments to the

questionnaire were made based on the pilot results and reviewer comments.

38

Sample Selection

Mailing lists of contacts were obtained fiom MSU-E directors and agricultural
agents in the study counties. Agents were asked to clean lists as much as possible to
eliminate those individuals who did not grow crops. Farms were often family operations
and contact lists did not distinguish between different farming operations; therefore some
of our respondents were likely describing the same farming operation. To somewhat
control for this, mailings were sent only to the first name on the contact list when
individuals had the same last name and address. Unfortunately, individuals with the same
last name but difi‘erent addresses could not be assumed to have the same or different
farms. Mailings were sent to all contacts on the county lists except in Calhoun County,
which had a prohibitively large number of contacts (907). Therefore 60% of the Calhoun
County contacts were sampled. In total 2,134 individuals were selected to receive the
questionnaire (Table 4).
Mail Survey Implementation

An initial mailing of the questionnaire, cover letter, and business reply envelope
was made by 3rd. class mail on April 7, 1995 to producers in Calhoun and Montcalm
Counties (Appendices I & II). Questionnaires to producers in more northern counties
were initially mailed on April l4, l9, and 28. These initial mailings were followed
approximately 10 days later by a postcard reminder/thank you which went out to all 2,134
individuals (Appendix IV). Approximately 2 weeks later a second mailing of the
questionnaire and a modified cover letter, was sent to individuals who had not yet

r€8ponded. All mailings were completed on June 20th., 1995.

39

Table 4: Initial per county sample size.

 

 

 

 

 

 

 

 

 

 

 

County Initial sample size
Calhoun 545
Montcalm 329
Oceana 379
Benzie 100
Leelanau 263
Presque Isle 318
Menominee 200

Total 2134

 

 

Non-response Follow-up

During the period July 27, 1995 and through August 15, 1995, a non-response
telephone follow-up was performed. This was due to concerns that the timing of the
survey may have selected against active full- and part-time farmers who were in the field
and had no time to respond. Non-respondents with published telephone numbers were
sampled. This provided a sample of 280 individuals (29% of the non-respondents) who
could be called. Selected questions on important descriptive variables such as tolerance
of loss were drawn from the mail questionnaire and adapted for telephone use (Appendix
III). The sample was called repeatedly until approximately 30 non-respondents had been
contacted from each county. All calling was done between 1100 - 1300 hours or after

1700 hours.

40

Data Entry and Analysis

All data were entered in and statistically analyzed using SPSS for WINDOWS
version 6.0. The error rate of data entry was less than 0.05%, and was determined by
sampling 10% of the coded surveys for errors. The error rate was determined by the
following formula: (number of cells containing errors)/(total number of cells in the
sample).

Primarily non-parametric statistics (Wilcoxon Matched Pairs, Mann-Whitney U,
Kruskal-Wallis, Chi-square) were used to test differences because of the preponderance
of nominal data and frequently polarized response frequencies which did not meet
assumptions of normality and/or homogeneity of variance. In the few situations where
the parametric assumptions were met the more powerful parametric technique was used
to test for differences. All differences reported use a significance level of alpha=0.05.
Sample sizes are not equal for all questions because not all respondents answered all
questions. Percentages given are the valid percentages for respondents who answered the
question unless otherwise specified.

Special calculations and data transformations
Specific questions required special transformations and/or calculations for

interpretation purposes and are presented below.

41

Procedures for Estimating grcentage losses and dollwlues of crop losses to deer:
F ield/row crops

Crops were not identified by variety nor were individuals asked to report how they
marketed their crops; therefore assumptions had to be made concerning the
appropriateness of using the mean reported price per unit in estimating loss values. It was
assumed that all losses reported in the same units (i.e. bushels, tons, hundred weights,
etc.) were marketed in the same fashion or had the same equivalent value if kept on farm
even though not all producers reported prices received. Applying the mean reported price
per unit to individually reported losses did not seem appropriate because it was likely that
some producers had more marketing opportunities than others which would have meant
that the losses would have been either over or underestimated. This associated error
further made it inappropriate to total each farmer’s reported losses in a final dollar value
for all crops combined. However, it seemed appropriate to apply the mean reported price
per units to the median per farm units lost to obtain an estimate of the dollar value lost
per farm for each crop type.

To estimate the dollar value of field and row crops lost to deer in 1994, the
reported bushel, ton, or hundred weight loss to deer for each crop (Question #7) was
multiplied by the mean price received for the crop in 1994 as reported by the producers in
response to question number 21 on the questionnaire. It must be noted that because of
producer abilities to market their products in different ways (i.e. futures, different
elevators, cash market, etc.) dollar values reported may not accurately represent the value
lost by producers. However, as an approximate measure for comparison of value lost by

producers it is believed that the estimates are useful. Percentage losses of field/row crops

42

were calculated by dividing the production units of the crop lost to deer (Question #7) by
the product of the acres planted in the crop (Question #7) and the average per acre yield

of the crop.
Non-bearing fruit tree losses

To estimate the dollar value of non-bearing fruit tree losses, the reported number
of non-bearing trees damaged by deer was multiplied by the estimated cost of replacing a
single tree. The assumption was made that all trees reported as damaged by deer had to
be replaced. Though some trees may not have been replaced, trees that were not replaced
would have required extra care and pruning to restore them to usable condition. Thus,
even though a damaged tree may not have been replaced it’s extra care would still
represent a financial cost to the farmer. The cost of caring for damaged trees could not be
estimated without knowing the extent of damage to individual trees. Thus, the cost of
replacing the tree was applied to the total number of trees the respondent indicated as
damaged by deer. This method of estimating the dollar value of damage done to non-
bearing trees was also supported by conversations with orchardists who indicated that
trees that were once damaged by deer are often damaged again later, such that the tree
will likely need to be replaced at a later date. The estimated cost of replacing a single tree
was estimated using equipment and labor figures provided in 1989 MSU-E bulletins on
the costs of producing apples (Kelsey and Schwallier 1989), cherries (Kelsey et al.
1989a), and peaches (Kelsey et al. 1989b), and tree cost figures fi'om the 1995-96 price
list of Hilltop Nurseries Hartford, MI. Hilltop Nurseries was used because they are a

major provider of trees to the Michigan fruit industry, and because their prices

43

were reportedly representative of the general nursery market (J 1111 Nugent, Sutton’s Bay
Agricultural Experiment Station director, pers. comm). Michigan State University
Extension recommends that replants be at least 5/ 8” in diameter (N ugent and
Bardenhagen, pers. comm), therefore tree costs of this size were used assuming a bulk
order of 100 trees. It is possible for producers to reduce the per tree price by purchasing
still larger quantities; however, it is less likely that such orders will be made after the
initial block establishment. The stated cost of replacing trees in the bulletins was divided
by the number of trees to obtain a cost per tree figure for equipment and labor. Tree,
equipment, and labor costs were adjusted to 1994 dollars using the 1982 base year
producer price index for all commodities. Percentage losses to non-bearing fi'uit trees
were estimated by dividing the reported number of trees damaged by deer (Question #10)
by the product of the acres planted in the crop and the average number of trees planted per
acre (Question #10). No allowance was made for future income lost due to delays in
bringing trees into production. Production delays can have a significant impact on
producers’ profitability and would best be addressed by an agricultural economist. Losses
calculated here represent only the replacement costs of trees.
W

Bearing age fi'uit tree losses were calculated by multiplying the reported yield lost
(Question #10) by the 1994 mean fi'uit price reported by producers in response to question
number 21 on the questionnaire. Percentage production losses were estimated for hearing
age fruit trees by dividing the estimated number of pounds lost to deer in 1994 by the

product of average yield per acre and the number of acres in production (Question #10).

44

Christmas tree losses
Christmas trees that were reported damaged by deer were assumed to be
unmarketable and therefore were priced at the mean price of a Christmas tree in 1994 as
reported by producers in response to question number 21. A better estimate of loss would
have been possible if the species and age of the damaged trees were known, however, this
information was not collected on the questionnaire. Consequently the reported dollar

value losses for Christmas trees should be viewed with caution.

Smial grmit favorability scales:

Because of space farmers were not asked explicitly to indicate their approval or
disapproval of shooting and block permits; however, a measure of the favorability toward
each of the two types of permits among farmers was obtained using summated scales
created from attitudinal items about each permit type. The scale of favorability towards
shooting permits was constructed from 5 items (50a, b, d, f, h) (Queston #50) . A
reliability analysis using Cronbach’s alpha (alpha = 0.71) was performed which indicated
that this combination of items was more appropriate for measuring the construct of
favorability than other combinations of the items probing shooting permit attitudes.

These five items were measured on a 5 point Likert scale where 1 = strongly agree, 3 =
undecided, and 5 = strongly disagree. Items were recoded 2 to -2 to reflect the positive or
negative favorability of the response items and then summed and the mean taken. A scale
of favorability towards block permits was similarly constructed fi'orn 4 items (50i, j, l, 11)
(Question #50) and had a reliability coefficient of alpha = 0.70. Even though the

reliability coefficients indicate that the variables included in the scales measure a

45

construct better than the other variables considered, the validity of this measurement of
favorability towards these two permit systems should be viewed cautiously as factors not

measured may also determine whether producers are favorable approving of the permit

systems.

Biologist, agentn and agency credibility scales:

Summated scales were also constructed to approximate the credibility that the
MDNR and MSU-Extension have with farmers. Trust and competence were the two
aspects of credibility that were measured. It is assumed that if an individual trusts an
agency and believes an agency competent that the agency is then deemed credible by the
individual. It is possible, however, that the measurement items are invalid and these
scales should be viewed cautiously. Likert scales used were 1= strongly agree, 3=
undecided, and 5= strongly disagree. These were recoded to reflect positive and negative
favorability of the response items. Response scores were summed and averaged to obtain
an index of credibility where -2 was the lowest possible credibility score, 0 was
undecided, and +2 was the greatest possible credibility score. A three item scale
consisting of items 61d,e, and f (Cronbach’s alpha = 0.8305) was constructed to evaluate
the credibility of local MDNR biologists among farmers. A two item scale consisting of
items 61b and c (Cronbach’s alpha = 0.7841) was constructed to obtain an indication of
the credibility of the MDNR agency among farmers. A three item scale consisting of
items 64a, b, and c (Cronbach’s alpha=0.9257) probed producer perceptions of the

credibility of local county extension agents.

46

Larges; (fierceived MDNR sta_keholder weightfigg

Question 62 on the questionnaire assessed farmers’ perceptions regarding the
relative amount of consideration the MDNR was awarding to farmer and hunter interests
when setting deer management objectives. It also asked farmers to indicate what weight
they preferred to be assigned to farmer and hunter interests by the MDNR. To obtain an
index of producer attitudes about the fairness of MDNR weightings, producer perceptions
of the current MDNR weightings were compared to desired MDNR weightings to
produce a ratio. It was assumed that producers who perceived the current MDNR
weightings as fair would desire no change in weightings for the future, and that producers
who perceived the current weighting of farmers as unfair would desire an increase in the
farmer weighting for the future. It was also assumed that producers who desired a
decrease in the farmer weighting considered the current weighting of farmer interests as
“more than fair” to agriculttual interests. Thus, a measure of the perceived fairness of
MDNR weightings of stakeholders interests regarding deer population objectives was
created by first dividing perceived and most desired farmer weights by hunter weights,

and then subtracting the ratio of “desired” weights from the ratio of “perceived” weights.

47

As an equation:

Perceived fairness of current MDNR weighting of farmer interests

(PCP/PCH) - (DFF/DFH)

where:

Perceived current weighting of farmer interests = PCF

Perceived current weighting of deer hunter interests = PCH

Desired future weighting of farmer interests = DFF

Desired future weighting of deer hunter interests = DFH
If the result was 21 then the current weightings were assumed to be fair or in favor of the
farmer. Ifthe result was <1 then the current weightings were assumed not to be fair or in

favor of the farmer. Thus, a nominal measure was created to assess the fairness of the

perceived MDNR weighting of stakeholder interests.

Tolerang of losses:

In this study we sought to account for some of the variance in dollar losses
tolerated by farmers in others studies, but just as importantly to make the tolerance
measures used by other studies (Brown et al. 1978, Stoll and Mountz 1983) more
operational. We felt that the words “light”, “moderate”, “tolerable”, “unreasonable”,
“moderate” and “severe” utilized by these earlier studies to describe loss severity and
producer tolerance of loss were not practical, because they did not give us any
information about intended producer responses. It was felt that these measures would be
more useful to managers if they were descriptive of the type of action that a producer
would take in response to the loss.

We therefore created a 3 tiered measure of tolerance suggested by Minnis and

Peyton (1995) that was based on the producers desire to take “corrective action” to

48

change their loss situation. It was hypothesized that losses could either be a problem or
not a problem. Problematic losses could be either tolerable or intolerable. Tolerable
losses include both producers who sustain losses which are problematic but are endured
because of offsetting benefits derived from having deer around, and also producers who
maintain tolerable losses because they prevent the losses from being more severe.
Intolerable losses are those loss amounts at which a producer must go beyond their
current efforts to maintain an acceptable level of crop loss or that threshold level of loss
which they can no longer tolerate. It is important that the reader recognize that at some
point intolerance expresses itself as action and that being able to predict this action is
more useful to an agency than is knowing that the farmer considers his losses moderate.
To enable agencies to be more proactive in managing deer damage issues, we sought to
identify those amounts of loss at which disruptive activity will occur and those at which
agencies should begin managing to prevent the issue fi'om reaching a disruptive point.
Thus, we defined tolerance as “Not a problem”, “A tolerable problem, no action to reduce

losses to be taken”, and “An intolerable problem, action to reduce losses will be taken”.

RESULTS

Organization of this section: Because of the diverse nature of the questions posed to
producers and the exploratory nature of this survey a non-traditional format is used in
presenting the results of this study. So that results are fresh in reader’s minds, major
management implications and recommendations are presented immediately following
many of the sections. Broader implications and recommendations that cut across result
sections are reserved for the Discussion. It is hoped that this format will aid the reader by
reducing the amount of time required to reference tables and figures in evaluating the
implications and recommendations of this research. Throughout this section readers are
referred to the question numbers of the questionnaire; unless otherwise noted these
question #’s are found in Appendix 1.
Non-response

A total of 48% of the 2134 mailed questionnaires were not returned. Of the 52%
who returned questionnaires only 595 met the criteria of being a full- or part-time farmer
with greater than 1 acre in a study county. This was almost 25% less than the pilot survey
conducted in Isabella County. (F ull-time farmers were defined for this survey as
individuals who spent > 50% of their working time engaged in farming activities, while
part-time farmers were defined as those individuals who spent < 50% of their working
time engaged in farming activities.) The usable response rate was therefore quite low
(3 7%). It was not known prior to the survey which contacts from the initial sample were
full- and part-time farmers. However, the non-response follow-up revealed no

differences in the proportions of full- and part-time farmers vs. non-farmers and retirees

49

50

that had and had not returned the survey (Table 7). Therefore it can reasonably be

assumed that approximately 52% of the targeted full-time and part-time farmers in the

sample had responded.

Table 5: Initial sample size, returns, and response rates by county.

 

 

 

 

 

 

 

 

 

 

 

County Returned surveys Non-deliverables 1 Initial sample size County response
. rate
Calhoun 281 38 g 545 0.55
Montcalm 170 16 l 329 0.54
Oceana 179 12 379 0.49
Benzie 47 1 100 0.47
Leelanau 163 3 , 263 0.63
Presque Isle 126 20 318 0.42
Menominee 93 3 200 0.47
Total 1059 93 2134 0.52

 

 

 

 

Table 6: Non-response telephone follow-up contacts.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

County Total called Number contacted % of non-respondents contacted
Calhoun 45 32 14.2
Montcalm 40 34 23.8
Oceana 36 29 15.4
Benzie 24 18 34.6
Leelanau 40 26 26.8
Presque Isle 58 35 20.3
Menominee 37 3 1 29.8
Totals: 280 205 20.9
Table 7: Comparison of job status between respondents and non-respondents.
11 Full-time Part-time Retired/Non-
fanners farmers farmers
% % %
Survey respondents 901 45.3 20.8 34.0 100%
Non-respondents 191 41.4 17.3 41.4 100%
x2=3.93, df2,p=0.139

 

 

 

 

 

 

 

Because counties were selected to represent a range of attitudes and crop damage

situations it was assumed that a non-response analysis should focus on the county level.

In all counties there were no differences between respondents and non-respondents in the

proportion of farmers requesting permits to kill deer, nor were there any differences

 

51

between respondents’ and non-respondents’ dependence on farm income (Tables 11 &
12). In all counties there were no differences in respondent and non-respondent attitudes
about the number of deer in the respective counties; most felt there were too many (Table
9). Finally, there were no differences of opinion between respondents and non-
respondents over the amount of consideration hunting and farming interests should
receive from the MDNR when they set deer management objectives (Table 14).
Respondents were found to differ fiom non-respondents in terms of farm size, tolerance
of 1994 crop losses, hunting participation, and perceptions of current MDNR weightings
of stakeholder interests, but each of these differences appeared in no more than a single
county. Farm sizes were larger among respondents than among non-respondents in the
Benzie/Leelanau study area (Table 11), and non-respondents were more likely to indicate
that the MDNR was either favoring farmers or weighting interests equally in the county
(Table 13). Presque Isle county non-respondents also more frequently indicated that the
agency was favoring farmers and/or weighting interests more equally than did
respondents from that county (Table 13). The percent of producers who hunted differed
between respondents and non-respondents in Montcalm county where 75% of the survey
respondents deer hunted, while 47% of the non-respondents deer hunted (Table 10).
Respondents from this county also more frequently indicated intolerance of losses to the
point of taking action than did non-respondents; however, there was no difference in
attitudes about the numbers of deer in the county (Tables 8 & 9). It should be pointed out
that respondents to the less personal mail questionnaire may have been more willing to

express an extreme view than they would have if interviewed personally over the

52

telephone as were non-respondents; thus, I am cautious in assuming that the Montcalm

County was biased in favor of the less tolerant of crop losses.

Table 8: Comparison of tolerance of 1994 crop losses between respondents and non-respondents
by county.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

n Not a problem Tolerable Intolerable
% % %

Calhoun Survey 128 32.0 39.8 28.1 100%
respondents
Non-respondents 16 56.3 3 1 .3 12.5 100%
x2=3-99. df2 ,
p=0.136

Montcalm Survey 101 30.7 35.6 33.7 100%
respondents
Non-respondents 15 20.0 80.0 0.0 100%
x1=11.77, df2,
p=0.003

Oceana Survey 1 12 30.4 20.5 49.1 100%
respondents
Non-respondents 15 33.3 20.0 46.7 100%
190.056, df2,
p=0.972

Benzie/Leelanau Survey 121 27.3 30.6 42.1 100%
respondents
Non-respondents 23 34.8 43.5 21.7 100%
xz=3.44, df2,
p=0.179

Presque Isle Survey 48 25.0 37.5 37.5 100%
respondents
Non-respondents 13 38.5 53.8 7.7 100%
x’=4-25. df2,
p=0.119

Menominee Survey 61 1 .6 23 .0 75.4 100%
respondents
Non-respondents 17 5.9 17.6 76.5 100%
x’=l.ll,df2,
p=0.575

 

 

53

Table 9: Comparison of tolerance of 1994 deer numbers between respondents and non-respondents by

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

county.
11 Too few Satisfactory Too many
% % %

Calhoun Survey 124 8.9 32.3 58.9 100%
respondents
Non-respondents 16 6.3 3 1 .3 62.5 100%
xz=o.15, df 2,
p=0.928

Montcalm Stn'vey 97 14.4 32.0 53.6 100%
respondents
Non-respondents 15 6.7 26.7 66.7 100%
x’=1.1o,df2,
p=0.576

Oceana Survey 105 14.3 35.2 50.5 100%
respondents
Non-respondents 16 12.5 3 1.3 56.3 100%
x2=o.1s, df 2,
p=0.911

Benzie/Leelanau Survey 118 10.2 37.3 52.5 100%
respondents
Non-respondents 23 0.0 47.8 52.2 100%
x2=2.89, df 2,
p=0.236

Presque Isle Survey 48 12.5 33.3 54.2 100%
respondents
Non-respondents 13 7.7 53.8 38.5 100%
x2=l .84, df 2,
p=0.397

Menominee Survey 59 0.0 6.8 93.2 100%
respondents
Non-respondents 17 0.0 17.6 82.4 100%

 

x’=1.86, df2,
p=0.374

 

 

 

 

 

 

 

 

 

54

Table 10: Comparison of hunting participation between respondents and non-respondents

by county.

 

Non-Hunt
%

Hunt
%

 

Calhoun

Survey
respondents

130

40.8

59.2

1 00%

 

Non-respondents

16

56.3

43.8

1 00%

 

xz=o.s4, df 1,
p=0.36l

 

Montcalm

Survey
respondents

97

24.7

75.3

1 00%

 

Non-respondents

15

53.3

46.7

1 00%

 

x’=3.89, df 1,
p=0.048

 

Survey
respondents

108

26.9

73.1

1 00%

 

Non-remndents

16

50.0

50.0

1 00%

 

x2=2.54, df 1,
p=0.1 10

 

Benzie/Leelanau

Survey
respondents

124

30.6

69.4

1 00%

 

Non-respondents

23

52.2

47.8

1 00%

 

x2=3.1o, df 1,
p=0.078

 

Presque Isle

Survey
resmndents

49

14.3

85.7

I 00%

 

Non-respondents

13

30.8

69.2

1 00%

 

x’=o.95, df 1,
p=0.329

 

 

Menominee

Survey
respondents

61

34.4

65.6

100%

 

Non-respondents

17

35.3

64.7

I 00%

 

 

x’=o.oo, df 1,
p=1.000

 

 

 

 

 

 

55

Table 11: Comparison of farm size and dependence on farm income between respondents and non-
respondents by county. Farm size is the sum of owned and rented acres. Dependence on farm income
represented as percentage of household gross income generated by farming.

 

I]

Mean farm size in

11

Mean % of gross

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

acres (s.d.) income from farming
(s.d.)

Calhoun Survey 82 586 (613) 11 71 (94)

respondents 5

Non-respondents 16 493 (410) 15 59 (35)

Mann-Whitney Z=-0.408, P=0.682 Z=—0.158, P=0.874
Montcalm Survey 78 880 (732) 94 68 (36)

respondents

Non-respondents 15 947 4789) 13 82 (24)

Mann-Whitney =-0.251, P=0.802 Z=-0.962, P=0.336
Oceana Survey 73 420 (430) 10 59 (33)

respondents 0

Non-respondents 15 336 (423) 15 47 (35)

Mann-Whitney Z=-l.604, P=0.109 Z=-l.193, P=0.233
Benzie/Leelanau Survey 72 311 (340) 11 56 (36)

respondents 1

Non-respondents 23 202 (228) 23 68 (33)

Mann-Whitney Z=-2.163, P=0.031 Z=-1.626, P=0.104
Presque Isle Survey 39 481 (421) 42 63 (37)

respondents

Non-respondents 13 475 (43 8) 13 69 (36)

Mann-Whitney =-0.148, P=0.882 Z=-0.830, P=0.406
Menominee Survey 54 590 (387) 57 81 (28)

respondents

Non-respondents 17 816 (615) 17 84 (23)

Mann-Whitney =-1.596, P=0.110 Z=-0.490, P=0.624

 

 

 

 

 

 

 

 

 

56

Table 12: Comparison of proportions of respondents and non-respondents by county who have requested

special permits from the MDNR to shoot depredating deer.

11 Percent who have
requested permits

79.8
93.8
1.01, df l,
.314

.15, dfl,
.693

.00, df 1,
1 .000
Benzie/Leelanau

.10, dfl,
748

.00, df 1,
1 .000
Menominee

.00, df 1,
1.000

 

Percent who have
never requested

20.2 100%
6.3 100%

57

Table 13: Comparison of respondent and non-respondent perceptions of MDNR current weightings of
stakeholders in deer management decisions by county.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

n Hunters and MDNR MDNR
Farmers favors favors
weighted Farmers Hunters
equally % %
%

Calhoun Survey respondents 99 32.3 4.0 63.6 100%
Non-respondents 12 33.3 0.0 66.7 100%
x’=o.5o, df 2,
p=0.777

Montcalm Survey respondents 86 30.2 5.8 64.0 100%
Non-respondents 13 46.2 0.0 53.8 100%
x’=1.82, df 2,
p=0.401

Oceana Survey respondents 91 31.9 9.9 58.2 100%
Non-respondents 1 1 45.5 18.2 36.4 100%
x2=2.o1, df 2, .
p=0.366

Benzie/Leelanau Survey respondents 106 30.2 8.5 61.3 100%
Non-respondents 19 57.9 0.0 42.1 100%
x’=6.23, df 2,
p=0.044

Presque Isle Survey respondents 35 22.9 2.9 74.3 100%
Non-respondents 10 20.0 30.0 50.0 100%
x2=7.14, df2,
p=0.028

Menominee Survey respondents 54 24.1 9.3 66.7 100%
Non-respondents 17 52.9 0.0 47.1 100%
x2=5.85, df 2,
p=0.053

 

 

58

Table 14: Comparison of respondent and non-respondent desired MDNR weightings of stakeholders
in deer management decisions by county.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

n Hunters and MDNR MDNR
Farmers should should
should be favor favor
weighted Farmers Hunters
equally
Calhoun Survey respondents 103 49.5 42.7 7.8 100%
Non-respondents 1 1 63.6 36.4 0.0 100%
x’=1.34. df 2,
p=0.512
Montcalm Survey respondents 87 47.1 36.8 16.1 100%
Non-respondents 14 35.7 64.3 0.0 100%
x’=4.85, df 2,
p=0.088
Oceana Survey respondents 91 37.4 41.8 20.9 100%
Non-respondents 12 66.7 33.3 0.0 100%
x’=4.92, df 2,
p=0.085
Benzie/Leelanau Survey respondents 1 10 41.8 43.6 14.5 100%
Non-respondents 2 1 61 .9 3 8. 1 0.0 100%
12:4.76, df 2,
p=0.092
Presque Isle Survey respondents 35 42.9 45.7 11.4 100%
Non-respondents 8 25.0 62.5 12.5 100%
x2=o.91, df 2,
p=0.635
Menominee Survey respondents 55 43 .6 54.5 1.8 100%
Non-respondents 15 46.7 53.3 0.0 100%
x’=o.3o, df 2,
p=0.860

 

 

 

Generalizabilig of results --
The sampling was such that results from this study should not be generalized to

the greater population of Michigan farmers. I have no knowledge of how many farmers
were not included on Extension mailing lists for any county, nor whether there are
inherent bias’ among those farmers who are on Extension mailing lists. Extension agents
believed that their lists captured the majority (>80%) of farmers in their counties. Their

appraisals suggest that the survey was inclusive enough to fairly represent the views and

59

concerns of growers of different crop types in our study counties; however, readers are
cautioned not to attribute precise frequencies to the greater population of Michigan
farmers.
Farmer Respondent Profile

The typical farmer respondent averaged 53 years old (Table 19), had a high-school
diploma and some college or technical training (Table 15), had farmed in a study county
for approximately 30 years (Table 17), earned 64% of their household gross income
farming, and had a gross household income between $25,000 and $75,000 (Table 12).
Sixty-nine percent of the respondents were full-time farmers, meaning they spent >50%
of their working time engaged in farming activities. Oceana county had the lowest
proportion of full-time farmers (60%), while Menominee county had the highest
proportion with 87% (Table 16). Farmers from the Benzie/Leelanau area had been
farming for the least ntunber of years in the respective county (mean = 26.5 years),
whereas Calhoun and Montcalm farmers had been farming for an average of 33 years
(Table 17). Most producers (70%) deer hunt themselves (Table 22), and 50% of those
that deer hunt indicated that deer hunting was more important than most other

recreational activities in which they participate (Table 18).

60

Table 15: Education completed by respondents.

11 % of
No 1 02
Less than 9th. 21 3.7
Some school 33 5.8
school 202 35.5

Some or technical school 150 26.4
AB 1 16 20.4
Ph.D MD 46 8.1
569 100%

 

Table 16: Number of full-time and part-time producer respondents per comty.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

County 11 F ull-time Part- time

Calhoun 133 64% 36% 100%
Montcalm 104 73% 27% 100%
Oceana 1 15 60% 40% 100%
Benzie/Leelanau 128 69% 3 1 % 100%
Presque Isle 52 67% 33% 100%
Menominee 63 87% 13% 100%
x2=16.53, df 5, p<0.006

 

Table 17: Respondents’ mean years farming in same county.
n

Calhoun
Montcalm
Oceana

Benzie/Leelanau
Isle
Menominee
Overall
1 df

 

Table 18: Centrality of hunting as recreation to respondents.

 

 

 

 

 

 

 

n % of
respondents

Most important recreational activity in which I participate 86 25.4
More important than most other recreational activities in which I participate 83 24.5
About as important as other recreational activities in which I participate 111 32.7
Less important than other recreational activities in which Iparticipate 42 12.4
Not at all important to me 17 5.0

Overall 339 100%

 

 

 

 

 

61

Farm sizes were calculated by adding total reported acres owned and rented
(including farmsteads and non-crop lands). Mean farm sizes differed by county, with
fruit counties (Oceana and Benzie/Leelanau) having smaller farms (mean =322 acres and
250 acres respectively) than all other counties. Presque Isle county had the smallest mean
farm size (420 acres) of the non-fruit counties, while Montcalm county had the largest

mean size (702 acres) (Table 19).

Table 19: Age and farm size of respondents by county.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

County n Mean Age in years 11 Mean farm size in acres
(Owned+Rented)
x (s.d.) x (s.d.)

Calhoun 127 53.7 (12.4) 133 477.3 (577.5)
Montcalm 102 53.0 (11.6) 104 703.0 (707.3)
Oceana 109 50.3 (11.2) 115 323.0 (376.4)
Benzie/Leelanau 122 54.2 (12.3) 128 250.7 (292.6)
Presque Isle 48 52.4 (13.9) 52 420.9 @832)
Menominee 56 50.4 (10.9) 63 557.7 (384.7)

Overall 564 52.6 (12.1) 595 441.7 (508.6)

Kniskal-Wallis x2=8.99, df 5, p=0.109 Kruskal-Wallis xz=80.90, df 5, p<0.001

 

Farms were classified as being primarily oriented towards livestock, cash crops, or
tree products if farmers indicated that z 75% of their farm sales were of one of these
categories. Farms were designated as “mixed” if the primary orientation accounted for
<75% farm sales and a second category was responsible for > 25% of their farm sales.
Twenty farms could not be categorized by this scheme because these respondents did not
indicate sales that totaled 100%. The question may have been interpreted as percent of
“income” or the three sales categories may not have captured the orientation of the farm;
for instance, if the farm was a horse boarding facility. As was expected by design,

significant differences in farm type existed across counties (Table 22).

62

The majority (59%) of producers in this study had never requested either shooting
or block permit assistance from the MDNR. Twenty-nine percent had requested shooting
permits at some time in the past and 33% had requested block permits. Of those who had
requested block permits 62% had requested them in 1994. Of those who had requested
shooting permits 36% had requested them in 1994. Among those who requested
shooting permits the mean number of years requested was 4.1 (s.d. =3.1, n=150). Among
those who requested block permits the mean number of years requested was 3.4 (s.d.
=1.9, n=l78).

Approximately 60% of the respondents were members of the Michigan Farm
Bureau, 21% were members of other farming organizations and 12% were members of
conservation organizations. The United Farmers’ Union, Michigan Horticultural Society,
and Michigan Milk Producers’ Association were the three most frequently identified farm
organizations apart from Farm Bureau. Michigan United Conservation Clubs was the
most frequently identified conservation organization to which producers belonged,
followed by Pheasants Forever and the National Rifle Association. Other organizations
identified ranged from The Nature Conservancy to local rod and gun clubs (Tables 20 &

21).

63

Table 20: Number of respondents indicating memberships in various farm
organizations.

 

Farm Organization Number of
respondents
Farm Bureau 358
United Farmers Union 21
Grange
Michigan Pork Producers
Michigan Horticultural Society
Michigan Cattlemen
National Cattlemen
Micjgan Herb Association
MASA
Soil and Water Conservation Districts
DHIA
Michigan Milk Producers Association
Michigan Crop Improvement
AAM
Micman Christmas Tree Growers
South Albion Progressive Farmers
Mason-Oceana Pomsters
MACMA
Michigan \ggetable Council
Michigan Potato Growers
PCA
Michigan Livestock Exchange
OGM

Michigan Nut Growers
Future Farmers of America
National Farm Organization
Growing U.P. Association
Michigan Soybean Association
MABC
IDFTA
Maple Syrup Producers
Holstein Association

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

64

Table 21: Number of respondents indicating memberships in various
conservation organizations.

 

 

 

 

 

 

 

 

 

Conservation Organization Number of
resmndents

Michigan United Conservation Clubs 38
Nature Conservancy 4
National Rifle Association 6
North American Hunting Club 2
Pheasants Forever 10
Ducks Unlimited
National Wildlife Federation
Suttons Bay Conservation Club

 

Miclgan Wildlife Habitat Foundation

 

Tamarack Sportsmen’s Club

 

Betsie River Res.

 

World Wildlife Fund

 

Safari Club International

 

The Wildlife Society

 

Wilder Creek Conservation Club

 

Miclgan Hunting Dog Federation

 

 

KelloggSportsmen’s Club

 

 

Rocky Mountain Elk Foundation

 

I-Iart Area Sportsmen’s Club

 

National Audubon Society

 

Ruffed Grouse Society

 

National Parks Association

 

Rails to Trails Association

 

National Trappers Association

 

Michigan Trappers Association

 

White-tails Unlimited

 

Drummond Island Sportsmen’s Club

 

National Arbor Day

 

American F arrnland Trust

 

 

Sierra Club

 

 

 

65

 

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66

Levels of Crop Loss Tolerance

Producer tolerance of crop loss (Question #13) served as both a dependent and
independent variable in this study, and levels of crop loss tolerance serve as the basis for
much segmentation throughout this thesis. This first section treats crop loss tolerance as
a dependent variable to illustrate how certain demographic factors are related to crop loss
tolerance. Later sections then use crop loss tolerance as an independent variable by which
to examine other variables. There are numerous correlations between farm type and
county by design, in addition to correlations between county, crop damage issue history,
and deer density. Where sample size allowed, I attempted to control for such
correlations; however, this was not always possible. It is important that the reader be
aware of these frequent correlations because it is possible that a combination of these
factors is responsible for differences in attitudes among producer segments.
Cumulative Tolerance of Loss

Producers were asked to evaluate their cumulative 1994 losses caused by deer by
indicating their relative tolerance of the losses; were the losses a problem and would they
take action to reduce comparable losses in the future? Those producers who earned a
greater percentage of their household gross income from farming were more likely than
producers with less dependence on farming to indicate that their 1994 losses were a
problem (Table 23). Similarly, full-time farmers more frequently indicated that their
losses were intolerable (Table 25). Menominee County producers almost unanimously
agree that 1994 losses were intolerable, while other producers from other counties were

more evenly distributed across tolerance categories (Table 25).

67

Imglications/RecommendatiOM

Fruit growers, fruit growing counties, and Menominee County appear to include
more intolerant producers than other groups. The agency may find that annual
monitoring of this balance between “Not a problem” and “Intolerable” losses is useful for
prioritizing agency funds and personnel in the future. For instance, among the producers
studied here, the data (Tables 23, 24 & 25) suggest that the agency pay particular
attention to addressing the concerns of fruit growers, full-time farmers, and farmers in
Menominee County because of the skewed distribution of frequencies. The tendency for
full-time farmers to be less tolerant should also be communicated to hunting factions so
that they understand that crop damage is a major concern of those whose livelihood
depends on farming.

Table 23: Farmer respondents’ tolerance of 1994 crop losses, and associated mean percent of gross income
generated by farming.

 

 

 

 

 

 

 

 

 

 

 

n % of producers 11 Mean % ofjross income from farming__
Not a problem 152 26.6 130 47.9 (37.5)
Tolerable 179 31.3 158 64.3 (35.9)
Intolerable 240 42.0 215 73.2 (31.0)
571 100% 503 (Knrskal-Walliflz'—'3393, df 2, p<0.001)

 

68

Table 24: Farmer respondent tolerance of 1994 crop losses by mean farm size in acres (owned and rented).

 

 

 

 

 

n Mean farm size in acres (owned and rented)
Not a problem 152 346.3 (532.3)
Tolerable 179 424.5 (469.2)
Intolerable 240 513.7 (501.6)
Total 571 441.145039)

 

 

 

 

F= 5.35, df2, p=0.005

 

 

Table 25: Farmer attitudes about 1994’s deer crop losses, by county, farm type, and job status.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

n 1994 Not 1994 Losses 1994 Losses were
a problem were a intolerable and I
% tolerable am going to take
problem action to reduce
% the losses.
%

County Calhoun 128 32.0 39.8 28.1 100%
(x’=51-99. df 10, Montcalm 101 30.7 35.6 33.7 100%
p<0.001)

Oceana 1 12 30.4 20.5 49.1 100%

Benzie/Leelanau 121 27 .3 30.6 42.1 100%

Presque Isle 48 25.0 37.5 37.5 100%

Menominee 61 1.6 23.0 75.4 100%
Farm type Livestock 1 1 1 32.4 26.1 41.4 100%
(x1=18.85, df 10, Cash crops 133 28.6 35.3 36.1 100%
p<0.042)

Fruit/trees 98 21.4 23.5 55.1 100%

Livestock mixed 24 12.5 54.2 33.3 100%

Cash crop mixed 87 21.8 34.5 43.7 100%

Fruit/trees mixed 79 22.8 34.2 43.0 100%
Job status Full-time 392 19.1 30.4 50.5 100%
(x’=48.1 1, df2, Part-time 179 43.0 33.5 23.5 100%

p<0.00 I)

 

 

 

 

 

 

 

 

69

Reported Crop Losses Due to Deer in 1994

In this section dollar values of 1994 crop losses are estimated fiom data provided
by producers (Questions #7 and #10). These estimates were made to analyze producer
tolerance levels comparable with other studies of tolerance (Brown et al. 1978, Stoll and
Mountz 1983). Dollar values estimated were not designed to be used to establish
economic parameters for qualifying for crop damage assistance programs. Calculations
used to estimate percentage and dollar losses to crops are explained in the Methods
chapter.
1994 Row and Field Crop Losses

Four-hundred and forty-two farmers indicated that they grew row or field crops
for sale or feed in 1994. The most commonly grown crops were com, alfalfa/hay, small
grains, and soybeans (T able 26). Producers reported the number of acres they planted in
each crop during 1994, their average yield per acre, the total losses they believed they had
incurred due to deer during that year, and their tolerance of the losses to each crop type.

Though producers estimated their actual 1994 losses, percent losses were
calculated by dividing reported losses (bushels, tons, etc.) by the product of average yield
(bushels, tons, etc.) and total acreage planted for each crop. Calculated percent losses
ranged from 0% to 100%. Median and second and third quartile values are reported
because outliers made means unrepresentative and inappropriate for estimating threshold
levels of tolerable and intolerable losses.

Among the different field crops grown by producers responding to our survey,
table beans and corn were the most damaged crops in terms of value lost. In terms of

percentage loss, table beans and alfalfa/hay were reportedly the most damaged by deer in

70

1994 (Table 26). The median estimated dollar value lost per farm ranged from $547 for

soybeans to $3,135 for table beans.

Table 26: Row/field crop types grown, median per farm loss, median percent loss per farm, estimated dollar
value loss to deer per farm in 1994.

 

 

 

 

 

 

 

 

 

 

 

 

 

Loss per farm
Crop n 1994 1994 median % loss Estimated 1994
median loss (25th. percentile) (75th. percentile) dollar value 1055'

Com (bu) 131 469 bu (1.0) 4.2 (10.1) $1,022.42 @
2.18/bu

Soybeans (bu) 40 100 bu (1.0) 4.0 (13.2) $ 547.00 @
5.47/bu

Table beans (ths) 26 118 cwt (3.4) 9.0 (16.0) $3,135.26 @
26.57/cwt

Alfalfa/Hay (ton) 76 10 ton (1.0) 8.5 (16.1) 3 834.60 @
83.42/ton

Small grains (bu) 75 60 bu (1.0) 4.2 (11.5) $ 176.40 @
2.94/bu

Asparagus (ton) 15 <1 ton <1 -

Potatoes (ths) 5 l cwt <1 -

 

 

 

' Mean reported price per unit received by producers (Question #21) multiplied by the median loss amount.

1994 Fruit and Tree losses

Discussions with fruit growers and extension horticulture specialists indicated that
growers are most sensitive to damages to, or losses of, young non-bearing fruit trees.
Losses and/or damage to mature bearing trees is less distinctive and more difficult to
quantify on an annual basis. Consequently growers were asked to report their losses and
tolerance for both bearing and non-bearing age fruit trees.

The number of non-bearing age trees damaged per farm varied substantially.
Assuming that all damaged trees had to be replaced, the estimated replacement cost of the
median number of trees lost per farm ranged from $766 for peaches to $1,728 for apples

(Table 27). Minimal data were collected on Christmas tree losses as this survey was not

71

designed around this crop; Christmas tree data are reported for reference but the sample

population represented only a fi'action of Christmas tree growers.

Counties ranked by relative crop loss amounts

Percentage losses differed significantly by county for 4 of 5 selected crop types
with Menominee county consistently having greater percentage losses than all other
counties (Table 28). Estimated 1994 deer densities were highest in Menominee County
(Table 28). This county also had the greatest proportion of forest to farmland (Table 2).
A few producers in this county reported having stopped growing corn for grain because of

the losses incurred to deer, and this was confirmed by the county agent.

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73

Tolerance of Remrted 1994 Crop Loss Estimates

Farmer respondents were asked how much loss was incurred in 1994 for various
crops and were then asked whether these amounts of loss were tolerable or intolerable. In
evaluating threshold levels of producer tolerance of losses it was necessary to pool all
counties to obtain adequate sample sizes. The reported values are median values of
mived 1994 losses that were considered tolerable and intolerable by producers. Since
reported losses of both tolerable and intolerable categories varied substantially, the
median values and inter-quartile range should be taken conservatively as an index of how
much loss producers consider tolerable and intolerable. These values do not represent
absolute threshold points.

Tolerance by Row/Field Crogs

Though percent and estimated values associated with tolerable losses varied
across crop types, losses of approximately 4% or of about $500 appear to represent a
problem for 50% of producers but a problem they may be willing to tolerate. Losses of
approximately 11% or of about $2,000 apparently represent an intolerable level of loss to
50% of producers (Table 29). The large inter-quartile ranges around these median values
illustrates the importance of betterunderstanding how producer characteristics affect
tolerance of different amounts of loss. Unfortunately, restricted sample sizes did not
allow further investigation in this study. These amounts are for individual crops.
Cumulative losses to all crops grown would assumably be a better predictor of producer
tolerance of lost value; however, the cumulative cost data as calculated in this study did
not allow for an accurate representation of threshold levels of net value lost. It can

assumed that if the value lost in a specific crop was intolerable, then the net value lost in

74

all crops would likely be intolerable. Unfortunately, this study was unable to determine
how many producers considered their net losses intolerable, but without incurring
intolerable losses to any individual crop. Using a different method of calculating lost
value could provide a more reliable estimate of the net value of losses producers will

tolerate.

75

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76

T olergpce by F mgr/T ree crops

Percent of non-bearing age trees reported damaged did not differ statistically
between tolerant and intolerant producers; however, median percentages and estimated
costs of the 2 groups appeared similar to tolerable and intolerable levels of loss computed
for field crops (Table 30).

Inadequate sample sizes and concerns about question validity precluded an
analysis of tolerable and intolerable 1994 losses to bearing age fruit trees, though the

following table is included to illustrate median losses as reported.

Table 31: 1994 per farm estimated yield losses to bearing age fruit trees.

 

 

 

Crop n Median loss Median % yield loss Estimated ‘94 value lost
Qbs) (25th pct.) (75th pct.) per farm‘
| Apple 29 40,000 | (0.0) 0.1 (1 .0) $4,000 |
| Cherry 60 1 12,500 | (0.0) < 0.1 (0.0) $24,750 |

 

 

 

 

' Mean producer reported price per pound (mean = S 0.10/lb apple, mean = S 0.22/lb cherry) multiplied by
median pounds lost. These estimates likely reflect the potential production lost in 1994 due to the effects of
deer browsing over the lifetime of the orchard; however, it was intended that producers report the resultant
production lost in 1994 due specifically to deer damage inflicted prior to harvest in 1994. The responses
are therefore somewhat suspect. 40,000 lbs lost of apples is approximately equivalent to 2 acres of total
loss or approximately 414 trees. Similarly 112,500 lbs. of cherries lost is approximately equivalent to 17
acres of total loss or approximately 2076 trees.

Implications/Recommendations

Loss amounts vary substantially by farm, as does the tolerance of the amount of
loss. Thus, there is a need to maintain flexibility in dealing with farmers’ crop loss
concerns and not to mandate an absolute set of loss criteria which a producer needs to
meet before qualifying for assistance programs. The agency might also communicate this
need to their hunting constituents.

The large variance in tolerance of losses represented by the inter-quartile ranges

indicates that not all producers will be affected in the same way by a similar amount or

77

percentage of crop loss. Therefore it appears important that field staff administering crop
loss assistance programs make efforts to acquaint themselves with farmers and their
individual situations both financially and otherwise to determine how best to administer
assistance.

Generally it appears that managers can assume that losses approaching 5% of total
crop are going to be intolerable to most producers. Therefore regular monitoring of
producer reports of loss may allow agencies to proactively manage components of the
anticipated issue escalation. Such a percentage figure is limited, however, and the
manager must also be cognizant of the total dollar value being lost which may not be
illustrated by a small percentage loss. For example a larger farm of 4000 acres may have
a 40 acre loss (1%) while a farm of 400 acres suffering the same acreage loss would

suffer a 10% loss. The dollar losses may be significant to both farmers.

Most Severe Loss Years and Past Loss Attitudes

It was also hypothesized that past years’ losses could impact on producers’
attitudes about their current losses to deer. Producers indicated in what year they
experienced their worst losses to deer (Question # 16), and, if not 1994, they reported
relatively how much more severe the damage was in that year (Question #17). Thirty-five
percent of the respondents were undecided about which year since 1986 they had
sustained their most severe losses to deer. The 1994 calendar year was rated most severe
for deer damage by 35% of the respondents, however, there were different patterns in the

responses by county.

78

Worst vaqplosses by county

In all counties except Presque Isle, 1994 was most frequently indicated as the year
during which losses were most severe. This finding suggests that deer damage control
programs and herd reduction implemented prior to 1994 has not been generally effective
at reducing crop losses despite liberal regulations on antlerless harvest. Assumptions that
the late 80’s were the period of most severe losses to deer were not supported by producer
reports for all counties nor by all producers within a county. Distributions of worst years
were independent between counties, confirming that perceptions of deer damage, and
likely actual losses, varies across the landscape and across time (T able 32).

Past losses by tolerance

Of the producers who reported that a year other than 1994 had been their most
severe year of losses, 8% felt those losses had not been a problem, 26% classified that
year’s losses as a tolerable problem, and 66% indicated that the losses had been
intolerable during that year. A significant positive correlation between past loss attitudes
and cumulative 1994 loss attitudes existed (Spearman =0.4813, p<0.001), suggesting that
farmers with past losses of a certain perceived magnitude will likely have future losses of
that same perceived magnitude (Table 33).
Implications/Recommendations

These data clearly illustrate that the extent of crop damage varies spatially and
temporally, therefore it is unlikely that crop damage will disappear but will always be
evident somewhere in the state. Analysis of these perceptions of most severe loss years in
conjunction with an examination of antlerless and block permit availability suggests that

managers should be aware of producers’ perceptions of the trend in damage levels, as

79

MDNR management program objectives as reflected in harvest regulations may at times
run counter to farmers’ perceptions of crop loss severity. For instance, these data suggest
that the frequency and severity of crop losses were increasing in Benzie and Leelanau
counties between 1991 and 1994; however, there was a 66% decrease in the number of
available antlerless tags over this same period, and block permits were not made available
to growers in the fall of 1994. Also Oceana county’s reporting fi'equency of crop loss
severity was relatively high during the same period, yet available antlerless tags were
down 95% in 1993 and 1994 from the four previous years (Langenau pers. comm).
Comments from producers in Benzie and Leelanau Counties indicated that they perceived
the agency’s restriction of block permits in 1994 as a disregard of their concerns about
crop damage, even though the agency increased the number of available antlerless lottery

tags in 1994.

80

 

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81

Qu_ali_ty losses
Items also probed whether the quality value of the remaining harvested crop was

greater or less than the value of the crop actually consumed by the deer (Question #12).
One third of the producers were not sure what affect deer browsing had had on the quality
value of their harvested crops. Another third indicated that the lost value in crop quality
was negligible. Though most producers believed that quality value lost was negligible,
significantly more producers who had intolerable losses in 1994 indicated that the quality
lost was greater than or equal to the yield lost (Table 34).

Mligx losses by mum

Only in Menominee and Presque Isle Counties was the dollar value of quality lost
more likely than expected to be reported as greater than the volume value of the crops lost
to deer (Table 34). According to Extension personnel when a bean or alfalfa plant is
browsed the surviving dry beans and alfalfa are impacted. Beans are reportedly
downgraded because of wrinkling and bloating when plants are browsed by deer (Long
pers. comm). Since these two crops are more frequently grown in these counties this
may explain the greater than expected reporting on important quality losses in these
counties. However, there were no significant differences in attitudes about the

significance of quality lost by the type of farm (Table 34).

82

Imglications/Recommendati0n_s

Though the frequency of significant quality losses does not appear to be high,
manager’s should be aware that quality damage is a real concern for certain crops and
may result in significant losses to the producers. Potatoes are a crop where damage to the
harvested product may downgrade the lot and consequently reduce the value to producers.
This is an example of the possible need for collaboration with MSU-E personnel who
have expertise in agricultural marketing. They could be consulted if field staff have

questions about the impact that deer might have on the marketing of harvested crops.

83

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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84

Crop losses to other wildlife

Producers were asked to evaluate the relative amount of crop loss they incurred
due to deer compared to the amount lost to other wildlife species (Question #14). One
half of the producers indicated that deer are the species causing the most significant
damage to crops on their farm. Six percent of the producers were not sure how much they
were losing to deer compared to other wildlife and about 10% did not believe that they
were losing any crops to other wildlife species. Those producers who indicated that their
1994 losses to deer were “Not a problem” were more likely than expected to indicate that
losses to other wildlife were more significant than losses to deer (Table 35).

Losses to other wildlite by coum
More producers than expected reported that deer losses were less significant than

 

 

losses to other wildlife in Calhoun and Benzie/Leelanau Counties, while most producers
from Menominee County indicated that deer losses exceeded losses to other wildlife
(Table 35). Producers in Calhoun, Benzie, and Leelanau Counties commented that
raccoons (P_r_ocyon lotor), birds (Gulls, turkeys, blackbirds) and voles (Microtus spp.)

caused frequent damage to their corn and fi'uit crops.

Implications/Recommendations
During the study producers commented that they had concerns about the

increasing numbers of other species, such as turkeys and sandhill cranes, and the potential
that these species have for damaging crops. The MDNR may wish to begin monitoring
damage complaints caused by species other than deer so that future conflicts can be

anticipated and proactively addressed before they become a widespread concern.

85

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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86

Loss estimation methods

It was anticipated that producer perceptions of the amount of loss they were
incurring to deer and subsequently their tolerance of loss would be impacted by their
method of estimating the severity of their loss (Question #15). Producers generally
reported using a combination of methods for estimating their losses. Twelve percent of
the producers indicated that they had “no idea what [their] losses were” in 1994. The
majority of producers estimated losses by visually inspecting crops for damage, while
42% of the producers reported that the number of deer seen in fields was an index of the
amount of loss they received. Relatively few producers reported they had crop inspectors,
MSU-E, or MDNR professionals make estimates in 1994.

Loss estimation methocis by tolerm

Producers whose losses were “Not a problem” were more likely to have “no idea
what their losses were” than those producers, whose losses were intolerable (Table 36).
Among producers with intolerable problems “visible damage to crops” and “deer seen in
fields” were the 2 most fiequently cited methods of estimating the amount of loss. Only
25% of producers with intolerable problems indicated that they estimated their crop
losses by comparing harvest receipts. It is possible that damage for these producers may
be so severe that they do not need to examine harvest receipts to get an idea of the
severity of the problem. It may also be that comparisons of harvest receipts are not
reliable indicators of how much a producer may have lost to deer. Apparently loss
estimation among producers is largely a matter of inference based on field observation

and experience.

87

lmplicationg/Recommendations

Most MDNR field staff are likely aware of the uncertainty associated with
estimating crop losses, but deer hunters do not understand this uncertainty and may
expect that an absolute amount of crop loss should be a requirement to obtain assistance.
As mentioned earlier the agency may need to communicate this uncertainty and the need
for flexibility to hunters to lessen the fiequency of complaints about the crop damage
assistance programs.

The inherent variability of yields in fields makes it difficult to determine the
amount of crop lost to deer in any given year in a particular field. Utah researchers
(Austin and Umess 1987) found that a method for determining ungulate damage to alfalfa
was reliable but could only document damages in excess of 20% of the crop. This
difficulty in differentiating deer damage fiom other damages may explain why farmers
who don’t consider their losses a problem are less capable of estimating how much they
might be losing. Designing better methods of quantifying damage may allow farmers to
better distinquish deer damage from other wildlife damage. This may reveal that deer are
not as large a problem as may be perceived, or such improved technology may cause
more farmers to become intolerant if deer are discovered to be responsible for more loss
than previously believed.

Combine-mounted yield monitors that record harvest per acre are now available
for use with com and other grains and may be an efficient way of determining relative
losses in portions of fields most used by deer. Unfortunately these monitors are
expensive and not all combines are outfitted to accept them. Thus, their use would

probably be restricted to larger operations. Though these monitors have potential to

88

improve loss quantification, they do so at harvest and therefore do not allow managers to
increase antlerless harvest in the immediate year if losses are deemed intolerable. All “at
harvest” determinations of losses have this same weakness and cause management to play
catch up with the deer herd the following year. Thus, it appears that methods that forecast
losses early in the growing season, though less accurate, are preferable in that they allow
for immediate rather than delayed management action. Despite this shortcoming of “at
harvest” loss assessments, an annual monitoring of losses would allow some analysis of
the effectiveness of control efforts over time if adjustments could be made for variations

in crop rotation, climate, and habitat changes.

89

 

 

 

 

 

 

 

 

 

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90

Behavioral Responses of Producers to Crop Losses

Wildlife agencies need to be able to predict what courses of action producers will
take in response to intolerable amounts of crop loss. This study attempted to provide
information about producer behaviors based on an Attitudinal Response Model (Minnis
and Peyton 1995, Minnis 1996) used to predict producer behaviors in response to crop
damage.

Lethal Control, Non-lethal Control, and Disruptive Behavior:

Producers were given the opportunity to indicate which of several controls and/or
actions they had taken in direct response to deer damage. Three variables were created to
attempt to capture the nature of these different types of action, lethal control, non-lethal
control, and disruptive behavior. Actions considered to be lethal controls included the
promotion of hunting, use of block permits, and use of shooting permits. Actions
considered as non-lethal controls included the use of fencing, repellents, harassment
devices, and buffer crops. Actions considered as disruptive behavior included seeking
action from elected officials or the media, organizing meetings to address deer crop
damage, and consulting an attorney regarding legal options. A producer was classified as
having engaged in an action type if he/she reported having engaged in one of the activities

considered under each heading.

Past, Current, And Future Behaviors Associated With Crop Losses
Of the behavioral options given on the questionnaire (Question #19), this sample

of producers most frequently (44%) indicated that they encouraged or promoted hunting

on their properties to reduce deer damage. The use of block permits (29%), shooting

9]

permits (24%), and repellents (23%) were the next 3 most frequently indicated past

behaviors done in response to deer damage (Table 37).

92

Table 37 : Farmer respondents anticipated and actual damage controls and types of behavior done in direct
response to deer damage. Percentage of total respondents checking the item.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DID DID IN I WILL LIKELY
PRIOR 1994 OR DO IF FUTURE
__ TO STILL IN LOSSES ARE
(n - 595) 1394 EFFECT IN INTOLERABLE
1994
1) INSTALL FENCING TO KEEP DEER OUT OF AN AREA 4.3% 3.2% 11.3%
' 2) USE REPELLENTS TO DISCOURAGE DEER FROM EATING A 23.0% 20.3% 22.0%
CROP
3) USE HARASSMENT DEVICES fiFRIGHT-EN DEE? AWAY 11.3% 3.7% 14.3%
4) USE SHOOTING PERMFTS 242% 10.4% 23.7%
5) USE BLOCK PERMITS 23.3% 15.3% 34.3%
3) ENCOURAGE OR PROMOTE HUNTING ON YOUR PROPERTY 43.3% 33.5% 35.3%
(OTHER THAN THE USE OF BLOCK PERMITS)
7) SEEK INFORMATION OR ADVICE FROM THE DNR. MSU- 13.5% 10.3% 20.3%
EXTENSION OR OTHER SOURCE ON HOW TO GO ABOUT
REDUCING CROP LOSSES _ fi
3) CHANGE OR SWITCH CROPS TO THOSE LESS PREFERRED BY 3.7% 3.4% 10.4%
DEER
3) PLANT BUFFER CROPS BETWEEN DEER HABITAT AND MORE 5.2% 4.0% 3.3%
VALUABLE CROPS J
10) START PURCHASING FEED INSTEAD OF QB IN ADDITION T0 7.2% 4.3% 3.1%
GROWING YOUR_OWN _ _
11) ABANDON A FIELD BECAUSE OF HIGH DEER LOSSES 3.4% 3.1% 3.7%
COMMUNICATE WITH QB SEEK ACTION FROM:
12) AN ELECTED OFFICIAL 5.2% 3.4% 3.1%
13) A REPRESENTATIVE OF THE DNR 13.3% 12.3% 241%
14) A REPRESENTATIVE OF THE MEDIA 2.2% 0.5% 5.7%
E) A REWESENTATIVE OF MSUExI 7.3% 5.5% 13.3%
13) HELP W MEETINGS To DISCUSS AND ADDRESS DEER 3.7% 2.0% 3.4%
CROP DAMAGE
17) m MEETINGS TO DISCUSS AND ADDRESS DEER CROP 17.3% 10.1% 21.3%
DAMAGE
13) PERSONALLY 95 JOINTLY CONSULT AN ATTORNEY 2.4% 3.5% 3.7%
REGARDING LEGAL OPTIONS TO REDUCE LOSSES TO DEER

 

 

 

Actions 16, l7, l8, and 12 were defined as disruptive courses of action.

93

Behaviors by tolerance

Of the total respondents (n=595), 240 individuals (42%) indicated that 1994
losses were intolerable and that they would increase efforts in the future to reduce losses
below 1994 levels, and Of these, 55 (23%) producers did not undertake control or engage
in a disruptive behavior in 1994.

For this analysis producers were segmented by their reported exposure to
intolerable losses. Producers may have never considered losses a problem, always found
losses tolerable, or experienced intolerable losses. Those who’d experienced intolerable
losses may have done so only in 1994, done so prior to and during 1994, or may have
found 1994 losses tolerable though in an earlier year they were intolerable.

Producers who had not experienced intolerable losses anticipated future use of
lethal techniques (including the encouragement of hunting) twice as often as non-lethal
techniques (Figure 10). This segmentation also revealed that among intolerant producers,
those with a longer history of intolerant losses were twice as likely as those with more
recent intolerable experiences to intend to engage in future disruptive behavior (Figure
10). Those who had intolerable losses in the past but whose current losses were tolerable
were twice as likely to indicate that they will use lethal methods rather than non-lethal
methods in the future, even though 50% of these producers tried non-lethal methods in
the past. This appears to indicate a greater favoritism for lethal control methods among
producers, and that those with a history Of losses do not appear to accept loss as a cost of
doing business. Those most likely to engage in disruptive behavior appear to be those

with repeated exposure to intolerable losses.

94

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95

Intended Behaviors bv Pa_st Belgviors

It was hypothesized that past behaviors done in response to damage would best
predict future behaviors that producers would engage in. The nature of the data and item
non-response precluded rigorous testing of this hypothesis, however, some comparisons
could be made. Only 39% Of those who had engaged in “disruptive” activity indicated
they were likely to again engage in disruptive activity if damage becomes intolerable in
the future. Of the current and past non-lethal users 62% were likely to engage in non-
lethal control in the future, while 67% Of current and past lethal users were likely to
engage in lethal control in the future. The 38% and 33% of these producers who did not
indicate that they would repeat lethal and/or non-lethal controls may not have found these
Options effective at reducing losses, or they may not have responded to the question. It is
also possible that producers skipped the question because their intended action was not
provided (i.e. a consequence of red-tape encountered in trying to follow proper channels
may result in an intention to gut-shoot deer, an Option not provided.)
Implications/Recommendations

We can assume that past controls will likely be repeated if they have been
effective or perceived as being effective. Just as important is the finding that producers
with a long history of intolerable losses will more frequently consider disruptive behavior
in the future. This suggests that managers ensure that available controls can be applied
effectively and immediately support/attend to producers with intolerable levels Of loss.

Otherwise there is potential for disruptive activity or illegal behavior.

96

Framer behavior preferences

Preferences Of producers for individual behaviors were evaluated by ranking the
frequencies Of each intended behavior. Preferences Of producers were then compared
based on their exposure to severe losses (Table 38). The promotion of hunting (1 st.) and
the use of block (2nd.) and shooting (3rd.) permits were ranked highest for both those
who had never experienced intolerable losses and those who had. An important
difference in the rankings is that producers without exposure to intolerable losses
indicated a desire to seek information and attend meetings about crop damage as
frequently as they desired to use shooting permits. On the other hand, producers who had
experienced intolerable losses followed lethal controls with seeking action from the
MDNR, using repellents, and attending meetings. Both groups infrequently considered
consulting attorneys, contacting the media, or contacting elected Officials. These

behaviors were further down the list than abandoning a field (Table 38).

97

Table 38: Frequencies and ranks Of anticipated damage controls and types Of behavior likely to be
undertaken by farmer respondents if losses caused by deer increase in severity, as indicated by
producers who have and have not experienced intolerable losses.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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INSTALL FENCING TO KEEP DEER OUT OF AN AREA 5.5 8 tie 15.2 10 1:10
USE REPELLENTS TO DISCOURAGE DEER FROM EATING A 8.8 8 tie 28.3 5
CROP
USE HARASSMENT DEVICES TO FRIGHTEN DEER AWAY 7.7 7 20.8 8
USE SHOOTING PERMITS 14.3 3 tie 37.7 3
USE BLOCK PERMITS 20.8 2 44.5 2
ENCOURAGE OR PROMOTE HUNTING ON YOUR 28.7 1 48.8 1
PROPERTY (OTHER THAN THE USE OF BLOCK PERMITS)
SEEK INFORMATION OR ADVICE—FROM THE DNR, MSU- 14.3 3 II. 25.7 7
EXTENSION OR OTHER SOURCE ON HOW TO GO ABOUT
REDUCING CROP LOSSES
CHANGE OR SWITCH CROPS TO THOSE LESS 8.8 8 tie 15.2 10 tie
PREFERRED BY DEER __
PLANT BUFFER CROPS BETWEEN DEER HABITAT AND 8.8 5 ti. 8.8 14
MORE VALUABLE CROPS
START PURCHASING FEED INSTEAD OF QB IN ADDITION 3.3 11 ti. 8.4 13 tlo

‘ TO GROWING YOUR OWN

ABANDON A FIELD BECAUSE OF HIGH DEER LOSSES 8.8 8 18.2 8

 

 

 

 

COMMUNICATE WITH QB SEEK ACTION FROM:

 

 

 

 

 

 

 

 

 

 

 

 

 

AN ELECTED OFFICIAL 4.4 10 11.5 12
A REPRESENTATIVE OF THE DNR 11.0 4 31.3 4
A REPRESENTATIVE OF THE MEDIA 3.3 11 u. 3.4 15
A REPRESENTATTVE OE MSUEXT. 3.3 5 03 15.2 10 0.
HELP 286M MEETINGs To DISCUSS AND ADDRESS 5.5 3 113 3.4 13 53
DEER CROP DAMAGE
m MEETINGS TO DISCUSS AND ADDRESS DEER 14.3 3 tlo 27.7 3
_CI_ROP DAMAGE _
PERSONALLY QB JOINTLY CONSULT AN ATTORNEY 3.3 11 11. 13.1 11
REGARDING LEGAL OPTIONS TO REDUCE LOSSES TO
DEER
n- 91 n- 181

 

 

 

 

 

98

Behaviors by iob type

Several behavioral differences between full-time and part-time farmers were also
identified. Full-time farmers were more likely tO have sought and to seek information
and advice about reducing losses (x2= 14.15, df 1, p<0.001) and to have attended or to
attend meetings to discuss and address deer crop damage concerns than part-time farmers
(x2= 7.55, df l, p<0.007). Full-time farmers were consistently more likely to have
engaged in or to anticipate engaging in lethal control, non-lethal control, and disruptive
activity than were part-time farmers (Figure 11), regardless Of whether the producer

personally hunted deer.

99

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100

Behaviors by hunting participation

Two important behavioral differences between hunting and non-hunting farmers
were identified. First, hunting farmers are slightly more likely to use lethal methods
(including the promotion Of hunting) in the future (56.7%) than are non-hunting farmers
(47.7%) (x2= 3.56, df 1, p=.05). Second, hunting farmers are more likely to have used
non-lethal controls (41.8%) in the past than were non-hunting farmers (32.6%) (x2= 3.94,
df 1, p=.05). These findings suggest that hunting farmers may be more apt to recognize
the use of hunting as a damage control tool than are non-hunting farmers. Also hunting
farmers may place greater value on fall hunting Opportunities for themselves and others,
and therefore utilize non-lethal controls to avoid shooting deer at other times Of the year.

Behaviors b arm e

The type of farm Operation also appeared to influence the types of behaviors
chosen by producers. Fruit and tree growers were more likely than other farm types to
have used lethal controls prior to 1994 (x2= 11.88, df 5, p=0.036), to have had lethal
control in place in 1994 (x2= 23.09, df 5, p<0.001), to have used non-lethal controls prior
to 1994 (x2= 83.69, df 5, p<0.001), to have had non-lethal controls in place in 1994 (x2=
84.25, df 5, p<0.001), and were more likely to use non-lethal control in the future (x2=
45.52, df 5, p<0.001) (Figure 12). Fruit/tree growers were more likely than other farm
types to have sought “information and advice on reducing losses” prior to 1994 (x2=
22.64, df 5, p<0.001) and during 1994 (x2= 21.33, df 5, p<0.001), and were also more
likely to indicate a likelihood Of seeking such information and advice in the future.

Providing non-lethal technical assistance to Mt growers might be something that an

101

agency could consider in order tO reduce tensions between stakeholders about the most

acceptable number of deer.

102

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103

Producer Perceptions and Use of Hunting as a Crop Damage Control Method

It was postulated that hunting participation would likely influence attitudes about
deer numbers and tolerance of crop losses (Stoll and Mountz 1983). It was also known
that farmers suffering crop losses tend to allow increased access to hunters, even non-
acquaintances, as the severity of loss increases (Stoll and Mountz 1983, Scott and
Townsend 1985, Morgan et al. 1990, Nelson and Schomaker 1995); however, comments
made during preliminary interviews with farmers indicated that perhaps hunting was not
being maximized, or could not be maximized, as a damage control tool by farmers
suffering losses. As a result of these comments producers were questioned about the
amount of access given to deer hunters, preferred hunter densities, and antlerless harvest
on their farm.
Access to hunters

In general farmers allow deer hunting access to their immediate family and friends
& neighbors. On average about a third of the producers also allowed non-acquaintances
to deer hunt on their farms.

Acces;_by com

Producers in Presque Isle County least frequently allowed non-acquaintances to
hunt and most frequently allowed immediate family to hunt (Table 39). Producers in
Benzie and Leelanau Counties appeared most willing to allow hunting by non-
acquaintances. A large proportion of Menominee County producers also allowed non-
acquaintances to deer hunt. Menominee County producers more frequently indicated that

they lease hunting privileges on their farms.

104

Access by tolerance

Producers whose losses were not a problem were more likely than producers with
problematic losses not to allow any hunting on their land (Table 40). Those producers
with intolerable losses were most likely to allow friends and neighbors as well as non-
acquaintances to deer hunt on their farm. Producers with intolerable losses were also
more likely to allow non-acquaintances to deer hunt for a fee; most of these were
Menominee county producers, where 25 to 38% of the producers indicated some degree
of leasing. Of the producers in other counties, 0 to 4% indicated leasing either to non-
acquaintances, friends, or neighbors.

Implications/Recommendation;

Leasing has often been suggested as a means by which a farmer can realize a
benefit from large deer populations, and some may turn to leasing in an attempt to recover
some of their losses. Unfortunately, it does not appear that this source of income makes
producers more tolerant of losses. In addition the producer must be willing to become
more involved in dealing with and monitoring hunters. As Burger and Teer (1981)
indicated, “wildlife was a nuisance to some ranchers (farmers) because it forces them to
deal with people who wish to hunt.” In preliminary interviews to this study a few
producers’ reflected this same sentiment, commenting that they did not have the time or
desire to become hunt outfitters. Thus leasing hunting priveleges is apparently of limited

worth for defusing disruptive activity for some farmers.

105

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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106

Deer habitat acres gr farm

By summing the acreages of wetland, forest, pasture, and fallow ground cover
types reported by producers (Question #34), a habitat variable was created to give an
estimate of the amount of total deer habitat per farm. (Agricultural crops such as corn
provide substantial cover and food for deer; however, the purpose of the variable was to
index how much non-agricultural habitat was available per farm.) The mean habitat per
farm was equal to 113 acres (s.d. =l40, n=595), and the mean habitat as percent of farm
size was 32% (s.d. =27, n=592).

Habitat acres by county

Montcalm and Calhoun County farms contained the smallest proportions of deer
habitat, while farms in Benzie/Leelanau, Menominee, and Presque Isle counties contained
the greatest proportions of deer habitat per farm (Table 41 ). There were no differences in
the percent of habitat on the farm between hunting and non-hunting producers.

Habitat acres by tolerance andfa_rm_tme

Producers with intolerable problems had a significantly lower proportion of deer
habitat on their farms than producers whose losses were not a problem (Table 41). Fruit
and tree producers had a greater proportion of deer habitat on their farms than producers
of cash crops and livestock (Table 41). These relationships between the proportion of
deer habitat on the farm and the variables “tolerance” and “farm type” were consistent
when entered as factors in an analysis of variance while controlling for “county of

residence” and “hunting participation”. Both “tolerance” and “farm type” had significant

107

main effects with F-values of (F = 9.696, df 2, p <0.001) and (F = 3.652, df 2, p = 0.027)

respectively (Table 42).

Table 41: Acreage of deer habitat per farm and % proportion of deer habitat per farm: by county, tolerance
of loss, farm type, and hunting participation.

 

11

Mean acres of deer

Mean % of farm in deer

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

habitat per farm (s.dQ habitat (s.d.)
County Calhoun 133 97.2 (114.7) 132 28.7 (27.3)
Montcalm 104 123.7 (135.6) 103 25.0 (24.3)
Oceana 115 75.9 (94.7) 115 30.0 (29.3)
Benzie/Leelanau 128 89.6 (116.1) 128 37.4 (25.1)
Presque Isle 52 147.0 (181.0) 52 40.9 (30.9)
Menominee 63 220.5 (206.0) 62 39.0 (25.3)
Kruskal-Wallis Knlskal-Wallis x’=30.57,
#3799, df 5, p<0.001 df 5, p<0.001
Tolerance of Not a problem 151 103.07 (117.00) 151 40 (32.2)
loss
Tolerable 178 107.79 (141.04) 178 31 (25$
Intolerable 239 129.49 (156.98) 239 28 (23.8)
Kruskal-Wallis 192.31, Kruskal-Wallis x’=11-07.
df2, p=0.3 143 df2, p=0.0039
Farm type Livestock 118 152.8 (170.6) 117 35.24 (28.89)
Cash crops 136 98.7 (125.8) 135 26.29 (24.09)
Fruit/trees 101 84.0 (113.6) 101 35.87 (28.05)
Livestock mixed 24 135.1 Q7111) 24 26.27 (23.56)
Cash crops 92 101.3 (103.7) 92 29.11 (23.69)
mixed
Fruit/trees mixed 81 148.2 (170.8) 81 34.83 (29.55)
Kruskal-Wallis Kruskal-Wallis x2=11.13,
xz=20.99, df 5, p<0.001 df 5, p=0.049
Hunting Non-hunter 172 120.62 (160.83) 171 31.2 (27.3)
participation
Hunter 397 115.58 (132.72L 395 342 (27.3)
Mann-Whitney Mann-Whitney

 

 

 

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108

 

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109

Number of hunters on opening day of deer firearm sgason (1 1-15-9‘1)

Producers were asked to estimate the number of hunters that hunted deer on the
lands they farmed on November 15, 1994 (opening day of the general firearms season)
(Question #30). After removing extreme reported hunter numbers of 100 or more, the
mean number of hunters per farm was 8.10 (s.d. = 6.86). Mean hunters per farm-acre
was calculated by dividing the number of hunters on 11-15-94 by the farm size. The
mean hunters per habitat acre was similarly calculated by dividing the number of hunters
by the sum of the total acres of wetland, forest, fallow ground, and pasture on the farm
(Question #34). The mean number of hunters per farm acre was 0.032 (s.d. = 0.043) and
the mean hunters per habitat acre was 0.146 (s.d. = 0.299).

Hunter densigz by mung;

Mean hunter densities per farm acre were highest in the fruit growing counties of
Benzie, Leelanau, and Oceana. Presque Isle County had the lowest hunter density per
farm acre. Mean hunter densities per habitat acre were highest in Oceana and Calhoun

Counties, and lowest in Presque Isle and Menominee counties (Table 43).

Table 43: Respondent reported per farm ll-15-94 hunter densities.

 

 

 

 

 

 

 

 

 

 

 

 

 

County mean farm acres / mean habitat acres /
hunter hunter
Calhoun 32.1 (29.4) 5.2 (2.5)
Montcalm 42.6 (48.0) 8.3 (5.6)
Oceana 24.4 (18.7) 4.1 (2.2)
Benzie/Leelanau 22.1 (13.9) 9.5 (6.3)
Presque Isle 52.4 (61.3) 15.6 (8.3)
Menominee 39.7 (65.3) 11.9 (8.8)
Total 31.6 (23.5) 6.8 (3.3)
KW x’=28.01, df KW x2=36.61, df 5,
5, p<0.001 p<0.001

 

110

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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111

Miter derisitv by tolerance

Those producers who considered their losses a problem had more hunters on their
properties on 11-15-94, and significantly higher hunter densities per habitat acre than
those who did not consider losses a problem (Table 44). Hunter densities per habitat acre
were highest for producers who considered their losses to be a tolerable problem (5.5
habitat acres/hunter) and were slightly lower for those whose losses were considered
intolerable (6.5 habitat acres/hunter).
Imelications/Recommendations

As shown in other studies, producers with more severe losses tend to allow more
hunting access than do producers with less severe losses. Interestingly, hunter densities
were highest among farmers who considered their losses to be a tolerable problem.
Though we must be careful about inferring causality it may be possible that their tolerable
level of losses is a function of that higher hunter density and the number of deer shot by
those hunters. This relationship is certainly worthy of additional investigation as it
suggests that farmers may be able to manage their losses by managing hunters and
hunting pressure, and agencies might suggest means of improving such management by

farmers.

Perceptions of safe hunter densities
Producers also reported the number of hunters they thought the lands they farmed

could safely support on opening day of the 1994 firearms deer season (Question #31). A

variable was created by subtracting perceived safe hunter numbers from 1994 reported

112

hunter numbers to get a measure of the proportion of producers who felt that opening day
hunter numbers on their farmlands were at, below, or above what they considered safe
levels. Extreme reports of 100 hunters or more for either the 1994 numbers or the safe
level were not included for this analysis.

Forty-one percent of the producers considered the number of hunters on their
farmlands on 11-15-94 to be below the number they considered safe for their farmlands.
Nine percent of the producers felt that the number of hunters on their farmlands exceeded
the number they considered safe for their farm, while the remaining 50% of the producers
felt they were at the maximum safe level for their farm (Table 46).

Sate hunter densities by tolerance

Mean differences varied significantly depending on the producer’s tolerance of
their 1994 losses. Significantly more producers who rated the number of hunters on 11-
15-94 below maximum safe levels considered their losses not to be a problem or to be an

intolerable problem.

Table 46: Mean percent of farmers at, above, and below perceived safe opening
day hunter densities on November 15,1994.

 

 

 

 

 

 

 

 

 

 

 

 

Percent of farmers indicating their farm was at, above, and
below perceived safe opening day (1 l-15-94) hunter densities
n=340
Above At Below
maximum % maxilnlun % maximum %
Not a problem (n=81) 4.9 48.1 46.9 100% |
Tolerable problem (n=112) 11.6 58.9 29.5 100% I
intolerable problem (n=147) 8.8 44.2 46.9 100% |
Total 8.8 50.0 41.2 100% |
, Chi-gum f= 10.75, df2, p=0.008 .

 

113

A seemingly important segment of producers is the group of farmers that had
intolerable losses, yet had hunter densities that were below what they felt they could
safely support. Upon further investigation, this group was found to be composed mostly
of full-time farmers (84%), and were well distributed across all counties. Oceana County
producers made up 27% of the group while Montcalm County farmers accounted for the
lowest proportion of the segment (10%). Forty-five percent of the segment reported
allowing non-acquaintances to hunt with permission. This was slightly more than the
percentage of producers having intolerable losses as a whole. Similarly, the segment
reported allowing access to friends, neighbors, and family more than the larger group of
producers with intolerable losses. Twenty-six percent of the segment reported having had
no contact with MDNR biologists.

Implications/Recommeidationg

Apparently producers with intolerable amounts of losses would be willing to
allow one or two more hunters on their farms to help harvest additional deer. Though this
seems encouraging, one should consider that maximizing hunter numbers may not
increase deer kill, nor even be desirable to producers or hunters. The reader should also
bare in mind that the numbers provided by producers were opening day hunter numbers,
and these may not reflect the continued hunting pressure on farms during the remainder of
the season. Also higher hunter densities will not necessarily increase the harvest of
antlerless deer. Some producers commented that they could no longer find hunters

willing to harvest antlerless deer. Some producers also expressed an interest in

114

designating deer hunters as shooters for out-of-season shooting permits but could not or
did not know of anyone willing to take on this role. Thus, it appears that this is
opportunity for deer hunters to take on a more active role in crop damage management,
perhaps through local coordination between deer hunting organizations, farmers, MSU-E,

and the MDNR.

115

Number of deer harvested on respondent’s farms in 1994

Respondents reported mean harvest rates of 9.6 bucks per square mile and 14.7
antlerless deer per square mile for their farms in 1994. Harvest rates differed significantly
by county, with the effect of frequent block permit use clearly evident in the Menominee

County antlerless harvest (Table 47).

Table 47 : Average number of bucks and antlerless deer respondents reported were taken on farms in 1994;
segmented by county.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

County Mean bucks taken per Mean antlerless taken per Bucks per Antlerless
farm acre (all seasons) farm acre (all seasons) mi2 per mi2
Calhoun 0.013 (0.014) 0.021 (0.029) 8.32 13.44
Montcalm 0.012 (0.013) 0.012 (0.013) 7.68 7.68
Oceana 0.016 (0.015) 0.017 (0.025) 10.24 10.88
Benzie/Leelanau 0.023 (0.064) 0.022 (0.069) 14.72 14.08
Presque Isle 0.006 (0.005) 0.008 (0.009) 3.84 5.12
Menominee 0.018 (0.014) 0.053 (0.037) 11.52 33.92
Total 0.015 (0.023) 0.023 (0.040) 9.6 14.72
KW {=3 1 .32, df 5, KW x2=71.88, (If 5,
p<0.001 p<0.001

 

Harvest ratios of bucks and antlerless deer remrted by resmndents.

Though the total number of deer taken by hunters is important in controlling crop
losses, so is taking the proper proportion of antlerless deer and antlered bucks. It was
encouraging to find that producers who were least tolerant of their crop losses reported
shooting a greater proportion of antlerless deer than did producers whose crop losses were
tolerable or not a problem (Table 48). This relationship held regardless of the county in
which the producer farmed. Among producers with intolerable losses, Menominee
County producers reported shooting the greatest number of antlerless deer per buck taken.

It was also encouraging to find that producers from all counties appeared to have a

116

“antlerless-oriented” harvest; a behavior consistent with trying to control their crop losses

(Table 49).

Table 48: Number of antlerless deer reportedly shot on respondents’ farms in 1994 per antlered bucks
taken; segmented by tolerance of crop losses.

 

 

 

 

 

 

 

 

 

 

County 11 Mean number of antlerless deer
shot per antlered buck taken.
Not yroblem 48 1.06 (s.d. = 1.01)
Tolerable problem 76 1.37 (s.d. = 1.15)
Intolerable problem 101 3.48450. = 8.10)
Total 225 2.25 (s.d. = 5.58)
Chi-square = 25.60, df 2, p<0.001

 

Table 49: Number of antlerless deer per antlered bucks taken in 1994, reported by respondents with
intolerable crop losses and segmented by county.

 

 

 

 

 

 

 

 

 

 

 

 

 

County 11 Mean number of antlerless deer
shot per antlered buck taken.

Calhoun 20 2.11 (s.d. = 1.96)
Montcalm 21 1.63 (s.d. = 1.46)
Oceana 12 1.56 (s.d. = 1.01)
Benzie/Leelanau 12 1.26 (s.d. = 0.80)
Presque Isle 5 2.03 (s.d. = 1.94)
Menominee 31 7.46 (s.d. = 13.78)

Total 101 3.48 (s.d. = 8.10)
F = 2.32, df 5, p = 0.049

 

117

Encouraginnent of antlerless harvest

Interviews suggested that producers felt that deer hunters were generally unwilling
to shoot antlerless deer and that this behavior would restrict the utility of manipulating
hunting seasons to control crop losses. We sought to document this behavior in our
survey of deer hunters, but we also wished to know to what extent farmers were
encouraging the harvest of antlerless deer as some producers indicated that not all
producers were using recreational hunting as a damage control tool. Horton and Craven
(1995) also indicated that farmers in Wisconsin did not recognize hunting as a specific
damage abatement technique.

Nearly 50% of the producers responding to our survey indicated that they did not
encourage the harvest of antlerless deer in 1994 (Question #32); however, it is likely that
not all producers felt a need to encourage such a harvest. The most common
encouragement’s offered by producers were either verbal requests to shoot antlerless deer
before shooting bucks or distributions of block permits (Table 50). Only 11 respondents
indicated that they provided their property tax numbers to hunters so that they might
apply for antlerless tags through the private lands lottery; however, because this technique
was not mentioned independently as an option on the questionnaire it is likely under
represented. It may also be that these permits are not salient in producers’ minds as being
damage control measures.

Attitude about antlerless harvest by county

Producers from Menominee County, a county in which MDNR personnel

estimated the deer density at between 60 and 100 deer/mi2 , were most likely to encourage

118

the harvest of antlerless deer (Table 50). Block permits were not available in Benzie and
Leelanau Counties in 1994 and could not be offered to encourage antlerless harvest. Why
Oceana County producers did not more frequently request hunters to shoot antlerless deer
is not known, but it may have something to do with the firearm antlerless season having

been closed the 2 years prior to 1994.

Table 50: Percent of respondents reporting having encouraged the harvest of antlerless in study counties in
1994.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

County 11 Did not Distributed Requested Other
encourage Block Tags hunters to shoot
antlerless deer %
% % first
%

Calhoun 133 42.1 12.8 28.6 9.8
Montcalm 104 43.3 14.4 26.0 17.3
Oceana 115 48.7 13.0 7.8 1.7
Benzie/Leelanau 128 36.7 2.3 13.3 14.1
Presque Isle 52 32.7 19.2 15.4 13.5
Menominee 63 6.3 73.0 63.5 15.9

x2=35.32, df 5, x2=157.02, df5, x’=83.69, df5, xz=16.90, df5,

p=0.001 p<0.001 p<0.001 p<0.005 ‘

 

119

@616 about antlerless harvest by tolerance

Producers with intolerable losses were more likely to encourage hunters to harvest
antlerless deer than both producers with tolerable losses and those without a loss problem
(Table 51). Oddly, 17% of producers with intolerable losses did not encourage the
harvest of antlerless deer and 43% of those with tolerable problems did not encourage

antlerless harvest (Table 51).

Table 51: Percent of producers encouraging antlerless harvest by tolerance of 1994 crop losses.

 

 

 

 

 

 

 

 

 

 

 

 

11 Did not Distribution of Requested Other
encourage block tags antlerless deer
% % be shot first %
%
Not a problem 152 64.5 1.3 6.6 5.9
Tolerable 179 42.5 12.8 19.6 12.3
Intolerable 240 16.7 32.9 38.3 15.4
x1=93.53, df2, x’=67.42, df2, xz=54.27, df2, x2=8.03, df2,
p<0.001 p<0.001 p<0.001 p=0.018

 

Implications/Recommendations

The relatively large number of producers who considered losses a problem but did
not encourage antlerless harvest should concern MDNR managers, especially those
producers whose losses were intolerable. Farmers should be made aware that by
encouraging antlerless harvest when loss problems are emerging that they can prevent

losses from becoming intolerable for themselves and other farmers in the area.

Adjacent antlerless harvest a problem
Most producers (61%) supported the idea of manipulating hunting season design

to reduce deer numbers so that special kill permits to control crop losses are not necessary

120

(Question #24) (Table 52). Even though several producers indicated that they felt that
modifying hunting seasons could help to reduce the need for special kill permits,
recreational hunting on its own cannot control all incidents of crop loss. For example,
fifty-two percent of the producers indicated that they felt that low deer harvests on

adjacent lands were a factor in their inability to control crop losses (Question #24).

Table 52: Percentage of respondents in agreement with the statement, “Hlmting seasons should be
designed to reduce deer numbers so that special kill permits to control crop losses are not necessary.”

 

Strongly Agree Undecided Disagree Strongly
Agree Disagee

 

 

Hunting seasons should be designed to reduce deer
numbers so that special kill permits to control crop
losses are not necessary. n=522

31.6% 30.1% 16.4% 12.3% 9.6%

 

 

 

 

 

 

Adz'acent antlerless harvest g problem by tolerm

Producers with intolerable losses were most likely to agree that they could not
control their losses because not enough deer were harvested during the hunting season on
lands adjacent to their farm (77%) (Table 53). Attitudes about harvests on adjacent lands
did not differ significantly by county or between hunting and non-hunting farmers (Table

53).

 

121

 

 

 

 

 

 

 

 

 

 

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8.54.... 58...... 859.5 828...... 8.3. 8.»... 59...... a

 

 

 

 

 

 

 

 

 

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122

Adz'acent antlerless hgrvest groblem by permit recipients

Greater than 70% of both block and shooting permit recipients indicated that they
were unable to control their losses for this reason, whereas non-recipients of both types
of permits were split on whether this was a reason they could not control their crop
losses (Table 53). Similarly, producers with larger farms were more likely to agree that
they cannot control losses because of low adjacent harvests than were producers with
less acreage, as were producers with a larger proportion of the household gross income

generated by farming (Table 54).

Producer Perceptions and Use of Shooting and Block Permits

Other than the promotion of hunting on the farm, Out-of-Season Shooting
permits and Crop Depredation Control Bonus Deer Hunting Licenses (Block permits)
were the most widely used form of depredation control used by farmers in this survey
(29% indicated using block permits, 24% indicated using shooting permits, whereas 44%
had promoted hunting on their farm by means other than the distribution of block
permits) (Table 37). Because of the important role of these permits in Michigan for deer
and crop damage management producers were asked to evaluate certain aspects of these
permit systems.
Permit favorabilig

Producers were not asked directly to indicate whether they approved or
disapproved of block and/or shooting permits; however, an index of producer

favorability towards the 2 types of permits was obtained through the use of a summated

123

scale (see Methods). A score of positive 2 was the greatest possible score while a score
of negative 2 was the lowest possible score. A score of zero was taken to mean
generally undecided or neutral.

The mean favorability of shooting permits was 0.029 or generally undecided.
Favorability ratings for the two permit types also differed by county with Menominee
county being significantly more favorable towards both shooting and block permits than
all other counties (Table 55). Full-time farmers (mean = 0.152) differed from part-time
farmers (mean = -0.245), and permit recipients were more favorable than non-recipients.
Also hunting participation influenced favorability ratings of shooting permits (Table 56).

The mean favorability of block permits was -0.014 slightly negative but again
generally undecided. Block permit favorability also differed between full-time (mean =
0.057) and part-time (mean = -0.l75) farmers, and between recipients (mean = 0.323)
and non-recipients of permits (mean = -0. 194). There were no differences between

hunters and non-hunters (Table 57).

Table 55: County mean favorabilities of shooting and block permits.

 

 

 

 

 

 

 

 

 

 

County Mean favorability score of Mean favorability score of
shooting permit program (s.d.) block permit program (s.d.)
n= 513 n= 502

Calhoun 0.0602 (.7459) -0.0068 (.7814)
Montcalm -0. 1070 (7777) 0.0327 (.8017)
Oceana -0.l683 (.6973) -0.0662 (.8287)
Benzie/Leelanau 0.0804 (.7722) -0. 1944 (.7082)
Presque Isle -0.2217 (.7557) -0.1163 (.7566)
Menominee 0.6436 (.7559) 0427347228)
Total 0.0288 (.7840) -0.0144 (.7870)

KW x2=53-34. df5, p<0.001 KW z2=26. 14, df 5, p<0.001

 

 

 

+2 = most favorable, 0 = undecided, -2 = least favorable

 

124

Table 56: Farmer respondents’ mean favorability toward shooting permits, by hunt participation, job

status,
and shooting permit recipient.

 

 

 

 

 

 

 

 

 

 

 

n Mean favorability of shooting
permits (s.d.)
Hunt participation Non-hunter 151 0.2768 (0.5977)
Mann-Whiting F -4.68, p<0.001 Hunter 349 -0.0819 (0.8359)
Job status Full-time 354 0.1520(0744)
Mann-Whitney F -5.07, p<0.001 Part-time 159 -0.2453 (0.804)
Shooting permit recipient Non-recipient 356 -0. 161 8 (0.762)
Mann-Whitney z= -8.31,p<0.001 Recipient 146 0.4589 (0.665)

 

Range: +2 = most favorable, 0 = undecided, -2 = least favorable

Table 57 : Farmer respondents’ mean favorability toward block permits, by hunt participation, job status,

 

 

 

 

 

 

 

 

 

 

 

and block permit recipient.
n Mean favorability of block
permits (s.d.)

Hunt participation Non-hunter 139 -0.0432 (0.5533)
Mann-Whitney F -1.36, p=0.173 Hunter 351 -0.0114 (0.8649)

Job status Full-time 348 0.0568 (0.741)
Mann-Whitney z= -3.08, p=0.002 Part-time 154 -0.1753 (0.864)
Block permit recipient Non-recipient 322 -0. 1941 (0.752)
Mann-Whitney z= -6.90, p<0.001 Recipient 147 0.3231 (0.743)

 

Range: +2 = most favorable, 0 = undecided, -2 = least favorable

Satisfaction with number of permits received in 1994

 

 

Most producers who requested either shooting or block permits in 1994 indicated
that they received as many permits as they felt they needed (Questions #41 and #48);
however, nearly half of shooting pemiit recipients and a third of block permit recipients
indicated that they had not received the permits they needed. This was particularly
prevalent in counties where the MDNR was attempting to restrict the antlerless kill to

meet DMU goals (Table 58).

Table 58: Percent of farmer respondents’ that believed they received as many shooting or block permits as
they felt they needed in 1994, by county.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Shootingpermit Block permit

County Did not Received Did not Received

receive enough receive enough

enough permits enough permits

permits % permits %

% (n=31) % (n=83)

(n=29) (n=39)
Calhoun 0.0 100.0 100% 0.0 100.0 100%
Montcalm 50.0 50.0 100% 50.0 50.0 100%
Oceana 77.8 22.2 100% 47.8 52.2 100%
Benzie/Leelanau 53.3 46.7 100% 87.5 12.5 100%
Presque Isle 50.0 50.0 100% 27 .3 72.7 100%
Menominee 38.5 61.5 100% 9.3 90.7 100%

(x2=6.l6, dg,p=o.290) (1944.76, df 5, p<0.001)

 

 

 

 

The agency may wish to monitor the proportion of dissatisfied recipients and non-
recipients as it may be indicative of potential conflicts and an indicator of resentment
against the agency. In any case this question should be further investigated with permit
holders to determine what afl‘ects their satisfaction regarding the number of permits they

receive.

Spe_<_:ific attitudes about sxcial pmits: recipients vs. non-recipients

Fourteen questions were asked to specifically tap attitudes and perceptions about
block and shooting permits which were of interest to researchers and had been suggested
by producers. These items composed question number 50 on the survey. Attitudes
regarding the shooting and block permits themselves were also related to whether the

producer had ever used the respective permits (Tables 59 & 60).

126

Shooting permits

Recipients generally agreed that shooting permits were distributed fairly and that
they were used successfully to control losses within their counties, while non-recipients
were undecided or tended to disagree. Recipients disagreed that too many male deer
were being shot with shooting permits and that too many deer killed were not being
utilized, while non-recipients were undecided or agreed. Both recipients and non-
recipients indicated that permits should not be given more readily to growers of high
value crops, indicating perhaps that if permits are warranted then it does not matter
whether the crop is apples or alfalfa. Both groups were split about whether shooting
permits were important because they made producers feel in control of the situation.
Recipients and non-recipients for the most part did not feel that neighbor’s objections to
shooting permit use affected their decision to use the permits, though non-recipients
tended to be more undecided. Assuming that non-recipients took the question as “would
neighbor’s objections influence your decision to use shooting permits”, it would appear
that most would not worry about upsetting a neighbor if they had a crop loss problem.
Of the recipients, 25% indicated that neighbors’ objections did influence their use of

shooting permits.

127

Table 59: Producer attitudes regarding specifics of the MDNR shooting permit system by total
respondents and whether respondents were ever shooting permit recipients.

 

Question 50 - SA=Strongly Agree A=Agree U=Undecided D=Disagree SD=Strongly
Disagree

 

 

SA A U D SD
a) in this county, shooting permits are distributed fairly to growers 5.6% 22.5 41.8 14.1 16.0%
whoneedthemregardlessoithe value otthe crops grown. n-538 % % %

All respondents

 

n8 376 NonoRecipients of Shooting permits 2.9 12.8 51.9 14.1 18.4

n=150 Shooting permit recipients 11.3 46.7 18.0 15.3 8.7
Mann-Whitney z--7.03, p<0.001
b) Shooting permits are successfully used to reduce crop losses in 10.2 30.3 33.6 15.0 10.9%

 

 

 

 

this county. n-532 % % % %
n- 371 Non-Recipient of Shooting permit 4.6 27.2 37.2 16.4 14.6
naiso Shooting permit recipient 22.7 39.0 24.7 12.0 2.7

Mann-Whitney its-6.83, p<0.001
c) in this county, shooting permits should be given more readily to 6.4% 18.0 20.6 34.1 21.0%

 

 

 

 

growers of high value crops than to growers of lesser value crops. % % %
n=534
n- 372 Non-Recipient of Shooting permit 5.9 17.7 20.2 33.3 22.8
n-151 Shooting permit recipients 7.3 18.5 23.2 35.1 15.9
No differences
d) Regardless of wheuier shooting permit actually reduce crop 10.5 33.5 20.8 22.5 12.7%

losses, they are still important to farmers because they at least make % % % %
farmers feel in control of the situation. n=534

 

n- 377 Non-Recipient of Shooting permit 8.5 35.3 22.8 19.4 14.1

 

 

 

 

 

n=146 Shooting permit recipients 14.4 30.8 15.8 28.8 10.3
No differences
e) My neighbors’ objections to the use of shooting permit 4.0% 14.0 22.2 41.7 18.2%
influences my decision to use them. n-523 % % % ‘
n- 364 Non-Recipients of Shooting permits 4.1 11.0 26.9 40.7 17.3
n-148 Shooting permit recipient 4.1 20.9 10.8 44.6 19.6
_Ngdilierences
!) Too many male deer are killed on shooting permits. n-536 11.0 17.0 33.4 25.9 12.7%

 

n- 373 Non-Recipient of Shooting permit 14.5 20.9 37.3 19.8 7.5

 

n-152 Shooting permit recipient 3.3 8.6 24.3 38.8 25.0
Mann-Whitney z-8.04, p<0.001
g) The venison and/or recreation I get by using shooting permit is 5.7% 10.0 23.4 31.4 29.5%

 

 

 

inportant to me. n-509 % % %
n- 351 Non-Rooipionu or Shooting permit 4.9 9.1 29.5 32.2 25.4
n=147 Shooting permit recipient —7.5 12.2 12.9 29.3 39.1

Mann-Whitney zit-1.96, p000!»
h) Too many of the deer killed on shooting permits are not utilized. 18.4 21.2 28.7 18.8 12.9%

 

 

 

n-533 % % % %
n:I 371 Non-Recipient of Shooting permit 22.4 23.2 32.1 14.6 7.8
n-151 Shooting permit recipient 9.9 18.6 19.9 29.1 24.5

 

 

 

 

 

 

 

Mann-Whitney z-6.34, p<0.001

 

128

Block permits

Block permit recipients tended to agree that the permits were distributed fairly
and were used successfully to control losses within the counties. Non-recipients were
undecided or expressed some feelings that the permits were not distributed fairly or were
not effective. Non-recipients also tended to be undecided or to disagree with the items
concerning crop value, farmer control, neighboring influence, and meat and recreational
benefit. Recipients felt that crop type should not be a basis for permit distribution, and
were not influenced by neighboring objections to permit use. Recipients were split on
whether block permits were important solely because they gave the farmer perceived
control of the situation, and were also split on whether meat and recreation acquired

through block permit use were personally important (Table 60).

129

Table 60: Producer attitudes regarding specifics of the MDNR block permit assistance program by total
respondents and whether respondents were ever block permit recipients.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Question 50 - SA=Strongly Agree A=Agree U=Undecided D=Disagree SD=Strongly
Disagree
SA A U D SD
i) In this county, block permits are distributed fairly to growers who 7.8 26.7 40.6 11.3 13.5
need them regardless of the value of the crops grown. n=524 % % % % %
All respondent
n- 339 Non-block permit recipients 2.4 17.7 51.3 10.9 17.7
n=152 Block permit recipient 17.1 44.7 18.4 13.2 6.6
Mann-Whitney z-7.53, p<0.001
]) Block permits are successfully used to reduce crop losses in this 14.2 33.6 31.1 10.8 10.2
county. % % % % %
n=527 All respondent
n: 337 Non-block permit recipient 4.5 28.2 40.7 13.9 12.8
n-155 Block permit recipient 32.9 43.2 12.3 5.2 6.5
Mann-Whitney z-9.10, p<0.001
k) In this county, block permit should be given more readily to 4.9 14.7 23.0 35.5 21.9
growers of high value crops than to growers of lesser value crops. % % % % %
n-530 All respondent
n- 340 Non-block permit redolent 2.9 14.4 25.9 34.4 22.4
No differences n-154 Block permit recipient 7.1 14.9 15.6 41.6 20.8
i) Regardless of whether block permit actually reduce crop losses, 9.3 33.1 23.2 20.5 13.9
they are still important to farmers because they at least make fanners % % % % %
feel in control of the situation. n=526 All respondent
n- 342 Non-block permit recipient 6.7 33.6 26.3 17.0 16.4
n8149 Block permit recipient 12.8 36.9 16.1 25.5 8.7
Mann-Whitney 2.4.54, p<0.001
m) My nelghbors’ objections to the use of block permit influences 2.9 8.1% 21.9 44.1 23.0
my decision to use them. n-517 All respondent % % % %
n- 333 Non-block permit recipient 3.0 9.9 27.0 39.6 20.4
n-150 Block permit recipient 3.3 5.3 8.0 55.3 28.0
Mann-Whitney z-4.03Lp<0.001
n) The venison and/or recreation I get by using block permit is 7.0 14.8 24.9 31.3 22.0
important to me. n-514 All respondent % % % % %
n- 327 Non-block permit recipient 5.2 9.5 31.5 33.0 20.8
n=152 Block permit recipient 10.5 26.3 12.5 27.6 23.0
Mann-Whitney z-1.95, 980.05

 

 

 

 

 

 

 

 

 

130

finended producer comments about specigl permits

Producers were also given the opportunity to make comments regarding the

 

shooting and block permit systems (Questions #52 and #53). Two-hundred and thirty-six
of the respondents made additional comments concerning the shooting permit system, and
237 made comments concerning the block permit system.

Shooting permits

The most frequently made comments about the shooting permit system were that
the rules were too restrictive (23%) and that the practice of issuing shooting permits
should be stopped (12%). Producers fi-om Menominee County and fruit counties more
frequently made comments that the shooting permit rules were too restrictive. Their
comments ranged from simplifying the application procedure to extending the shooting
hours and making the permits available earlier in the growing season. Producers from
Presque Isle County more frequently expressed concerns about better monitoring the use
of the permits and reducing waste and gut shooting which are perceived associated with

the shooting permits (Table 61).

Block permits

The most frequently made comments about the block permit system were that they
should be distributed more equitably amongst farmers (16%), that the practice of issuing
block permits should be stopped (12%), that the rules were too restrictive (11%), and that
no fee should be charged to the farmer (10%). When examined by county

Benzie/Leelanau producers more frequently cited making block permits available as a

131

concern in their comments (Table 61). (Block permits were not issued the year preceding

the survey in these counties.)

Table 61: Open-ended comments made by farmer respondents regarding the shooting and block permit
programs. Reported as frequencies and as percent of respondents making comments.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Shooting permits Block permits (n=23 7)
(n=236)

Frequency Percent Frequency Percent
Stop or eliminate them 29 12.3 28 11.8
Rules too restrictive 54 22.9 27 11.4
Reduce the number given 11 4.7 9 3.8
No fee mm to protect property 7 3.0 24 10.1
Require public hunting access 10 4.2 14 5.9
Verify need and damage 9 3.8 12 5.1
Allow landowner/shooter to keep deer 12 5.1 l 0.4
Require non-lethal control attempts 3 1.3 0 0
Make tags available to all (fairness) 17 7.2 37 15.6
Increase regular antlerless tags 16 6.8 20 8.4
Restrict the number per farm 2 0.8 5 2.1
Monitor use (sale, areas, shootig) 13 5.5 18 7.6
Reduce waste and whooting 17 7.2 3 1.3
Unaware of permits and obtaininL 14 5.9 11 4.6
Other cement supportive 13 5.5 23 9.7
Other comments non-supportive 9 3.8 5 2.1

 

Implications/Recommendations

The MDNR may be able to gain greater acceptance of the permit programs if
producer concerns about the permits are addressed. Re-examing the regulations and
purpose of permit programs may suggest ways to increase the effectiveness of permits
and coordination with the Law Enforcement Division may lessen producer complaints

about harassment in implementing damage control.

132

 

Acceptable criteria for evaluating need for smcial permits
Producers were asked what should be considered by the WNR when issuing

permits to a producer for killing deer to control crop losses (Question #51). More than
50% of the producers indicated that the financial dependence of the farmer on the crop,
the willingness of the farmer to allow hunting on the farm, and the extent that non-lethal
control had been attempted should be considered when issuing permits to kill deer (Table

61).

Table 62: Producer approval of selected criteria for determining eligibility for receiving shooting and
block permits.

 

 

 

 

 

n Financial Non-lethal Hunting Ability & Other
dependence control access willingness to
attempted allowed plant
elsewhere
Non- 353 55% 47% 58.9% 17.8% 2.5%
recipient of
its
Recipients of 206 65% 47.1% 51.5% 9.2% 3.9%
rrnits
559 x2=4.88, df x2=o.003, or x2=2.95, or 1, xz=7.72, or 1, x7=o.784, df
1, p=0.027 1, p=0.959 p=0.086 p=0.005 l, p=0.375

 

 

 

 

 

 

 

Implications/Recommendations

Interpretation of results concerning financial dependence as a criteria for

determining eligibility for receiving shooting and block permits needs to be done

cautiously as there is a possible validity problem. Producers may have responded

 

afl'rrmatively to the item not because they felt a certain level or amormt of dependence on
the crop should be required (this was the intent of the question), but rather because they

felt the agency should generally realize that farmers are dependent on the crops they raise.

133

There are many indications that a blanket policy of meeting absolute qualification
standards for damage control assistance is inappropriate. Damage control must be
handled on a case by case basis; however, it appears farmers would support the evaluation
of standard aspects of each situation. Though half of the producers felt that whether
hunting access is allowed should be considered when issuing permits, this does p91 mean
that half the producers would support mandatory open access to qualify for permits.
There has been resistance to this sort of requirement in Wisconsin (Horton and Craven
1995). Managers might address the issue of hunting with affected farmers and encourage

appropriate use of hunting as a control.

134

Producers’ Perceptions and Attitudes about Deer Density
Perceived deer population trends

Trengs bv cou_nry

Most producers perceived deer numbers to be increasing in their counties over the
past five years (Question #59) (Table 63). Different patterns in perceived trends were
found across counties (x2= 35.89, df 10, p<0.001). Presque Isle, Oceana, and
Benzie/Leelanau producers estimates of population trends were in line with MDNR
estimates; however, Calhoun, Menominee, and Montcalm county farmers differed from
MDNR estimates (Figure 13).

Trends by tolerance

Producers who considered their 1994 losses to be intolerable were more likely that
those with tolerable losses to perceive the deer herd as increasing regardless of county
(xz= 118.62, df 4, p<0.001). Likewise, producers with tolerable 1994 losses but
previously intolerable losses were more likely to perceive that the deer herd in their
county was decreasing than producers whose 1994 losses remained intolerable. This
supports Decker et al. (1981) findings that producer beliefs about deer population trends

are associated with the producer’s experience with crop loss (Table 63).

135

 

    

M113 M112

< deertsqrian ‘ m
061.1904 001.1904
Trendzlo - TM: ' -

 

 

 

 

 

 

Leelanau County

 

 

M61

 

 

 

 

 

DMUGO
25m
Octlm
M:

 

 

 

Benzie County

Flgure13: Deer Management Unit (DMU) wlthin each study county showing MDNR deer
datflyhdices(0ct1994)andMDNRludgmeMofhendhdeermarbersoverpast
5yearswithlnDMU's. Densltylndicesarenotestimatesoftheebsokrtenumberofdeer
lnlheDMU’s. Mapsarenottoscele.

136

 

 

 

 

 

 

 

.... .... ‘1
t;

M317
Oct. 1994

 

 

 

 

 

as
We
Oct. 1994
m
Menominee County
91am
MI.
Oct. 1994
we
own
be m
o“ 1991 «2:2.
Trend:

 

 

 

 

Oct. 1994
Presque Isle County m

Figure 13: Corlflnued

 

137

 

 

 

 

 

 

 

 

 

 

Montcalm County

 

 

 

 

 

 

 

 

 

Calhoun County

Figure 13: Continued

M150

M1904

Table 63: Farmer respondents perceptions of deer population trends over the last 5 years, by county,

138

tolerance of loss, hunting participation, and job status.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

n Increasing About the Decreasing I don’t

% same each % know
year %

%

County Calhoun 129 51.9 30.2 16.3 1.6
(xz=39.01, df 15, p<0.001) Montcalm 98 32.7 28.6 36.7 2.0
Oceana 108 50.0 30.6 18.5 0.9
Benzie/Leelanau 120 47.5 33.3 16.7 2.5
Presque Isle 49 22.4 42.9 34.7 0.0
Menominee 58 51.7 37.9 10.3 0.0
Tolerance of loss Not a problem 147 17.0 34.7 46.3 2.0
(x1=1 19.46, df 6, p<0.001) Tolerable 173 41.6 35.8 20.8 1.7
Intolerable 227 65.6 27.8 5.7 0.9
Hunting participation Non-hunter 164 53.0 31.1 13.4 2.4
(x’=11.59, df 3, p=0.008) Hunter 384 41.7 32.8 24.5 1.0
Job status Full-time 386 5 I .8 30.1 17.4 0.8
(f=29.32, df 3, p<0.001) Part-time 176 29.0 38.1 30.1 2.8

 

Note: x2 values presented in the text differ from those presented in this table, because respondents who

checked

the “I don’t know” option were excluded from the comparisons done in the text.

Trends by ('0b status and hunting participation

Approximately half (52%) of the full-time farmer respondents perceived deer

populations as increasing in their counties, whereas part-time farmers reported all 3 trends

equally (x2= 25.87, df 2, p<0.001) (Table 63). Hunting producers were more likely than

non-hunting producers to perceive the herd size as decreasing in their counties (x2=

10.09, df 2, p<0.007) (Table 63).

Implications/Recommendations

It appears that producer perceptions of deer population trends are related to their

underlying financial conditions and/or recreational values. If producers perceive that

their financial security is at risk from deer depredation they appear to express beliefs

 

139

about the deer herd in concert with that perception of risk. Similarly those producers who
value the recreation provided by deer hunting appear to express beliefs consistent with a
perception that their recreational enjoyment is at risk. This knowledge may allow MDNR
managers to target these perceptions of risk with information that places the amount of

risk in context or its proper light.

Estimated deer densities

To support and augment work by Minnis (1996) on a cultural carrying capacity
framework this questionnaire asked producers to estimate the October 1994 deer density
(deer/miz) in the portion of the county in which they did the majority of their farming and
to indicate what deer densities they would consider most acceptable and intolerable. It
was hypothesized that producers might have both a Minimum Demand for deer (a number
below which they would not find tolerable because benefits they derive fi'om deer would
cease to exist), and a maximum (Wildlife) Acceptance Capacity for deer (a number above
which additional deer would cause intolerable crop losses or otherwise incur intolerable
costs to the farmer). Minnis and Peyton (1995) labeled the range of deer densities
between these intolerable numbers as the “latitude of acceptance.” This section applies
the work of Minnis (1996) by defining the latitude of acceptance for various farmer
segments from each study county and regions within counties. Latitudes of acceptance
are plotted relative to producer perceptions about the number of deer in their portion of
the county in October 1994. Minimum Demand, Desired levels, and Wildlife Acceptance

Capacity are therefore presented as a proportion of the producer’s estimate of the number

140

of deer present in his area in October 1994. MDNR density estimates are provided for

reference although it should be noted that farmers may actually have experienced a much

higher or much lower absolute density because of distributional differences within

counties.

Table 64: Farmer respondents’ beliefs about the most desirable number of deer per square mile ,

by tolerance of loss, hunt participation, farm type, and job status.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

n Most desirable number of deer
mean deer/mile: (s.d.)
Tolerance of loss Not a problem 67 25 (30)
(Kruskal-Wallis x‘=19.65, or 2, p<0.001) Tolerable so 14 (17)
Intolerable 153 14 (15)
Hunt participation Non-hunter 87 13 (18)
(Mann-Whitney F -3.54, p<0.001) Hunter 212 18 (21)
Farm type Livestock 70 23 (25)
(Kruskal-Wallis xz=23.32, df 5, p<0.001) Cash crops 73 18 (22)
Fruit/trees 47 9 (12)
Livestock mixed 1 l 9 (6)
Cash crops mixed 44 18 (31)
Fruit/trees mixed 39 16 Q4)
Job status Full-time 212 15 (19)
(Mann-Whitney z= -1 .3 1,p=0. 190) Part-time 94 20 (28)

 

Table 65: Farmer respondents’ beliefs about the minimum number of deer per square mile they would
tolerate in their county, by tolerance of loss, hunt participation, farm type, and job status.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

n Lowest acceptable number of
deer
mean deer/mile: (s.d.)
Tolerance of loss Not a problem 65 17 (22)
(Kruskal-Wallis x’=3o.05, df2, p<0.001) Tolerable 72 10 (13)
Intolerable 123 7 (10)
Hunt participation Non-hunter 74 8 (12)
(Mann-Whitney F -3.53, p<0.001) Hunter 187 12 (16)
Farm type Livestock 64 13 (15)
(Kruskal-Wallis x2=16.72, or 5, p=0.005) Cash egos 57 11 (11)
Fruit/trees 39 6 (6)
Livestock mixed 12 5 (4)
Cash crops mixed 37 11 (24)
Fruit/trees mixed 38 11 (11)
Job status Full-time 181 10 (12)
(Mann-Whitney z= -l .46, p=0.144) Part-time 83 13 (21)

 

 

 

 

 

 

141

Table 66: Farmer respondents’ beliefs about the greatest number of deer per square mile (Wildlife
Acceptance Capacity) they would tolerate in their county, by tolerance of loss, hunt participation, farm type,

and job status.

 

Greatest acceptable number of

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

deer
mean deer/mile2 (s.d.)
Tolerance of loss Not a problem 64 34 (3 8)
(Kruskal-Wallis x2=12.39, df 2, p=0.002) Tolerable 73 26 (30)
Intolerable 137 20 (23)
Hunt participation Non-hunter 72 21 (31)
Q4ann--Whitneyz= -3.02, p=0.003) Hunter 202 26 (29)
Farm type Livestock 71 30 (32)
(Kruskal-Wallis x’=16.05, df5, p=0.007) Cash crops 62 28 (30)
Fruit/trees 43 15 (16)
Livestock mixed 11 18 (12)
Cash crops mixed 37 27 (43)
Fruit/trees mixed 38 23 (19)
Job status Full-time 194 24 (27)
(Mann-Whitney z= -0.55,p=0.580) Part-time 84 26 (34)

 

Table 67: Farmer respondents’ perceptions of the number of deer per square mile in their county in
October, 1994 , by tolerance of loss, hunt participation, farm type, and job status.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

11 Estimated mean number of
deer in county during October,
1994.
mean deer/mile: (s.d.)
Tolerance of loss Not a problem 77 21 (23)
(Knlskal-Wallis x2=46.95, df2, p<0.001) Tolerable 98 37 (47)
Intolerable 147 56 (56)
Hunt participation Non-hunter 83 50 (52)
(Mann-Whitney F -2. 10, p=0.036) Hunter 239 39 (48)
Farm type Livestock 73 63 (75L
(Kruskal-Wallis x’=13.41, df5, p=0.020) Cash crops 78 4o (51)
Fruit/trees 45 29 (3g
Livestock mixed 15 32 (28)
Cash crops mixed 54 36 (36)
Fruit/trees mixed 43 43 (39)
Job status Full-time 25 48 (57)
mam-Whitney z= 34;, 50.001) Part-time 105 32 (40)

 

 

 

142

Though these tables illustrate possible relationships between producer desired
numbers of deer and selected variables, analysis of variance controlling for the effects of
county revealed main effects only for tolerance of loss (F = 6.28, df 2, p = 0.002). Those
producers who did not perceive their 1994 losses as a problem desired higher deer
densities than did producers who considered their losses a problem regardless of the deer
density in the county in which they farmed and regardless of whether the producer hunted
deer. This appears to support the hypothesis that producers attitudes about deer numbers

are most influenced by their perceptions of the amount of loss incurred to deer.

143

Table 68: Producer perceptions of deer densities that are desirable, minimal. and intolerable (WAC) expressed as a proportion of
perceived October 1994 deer densities; segmented by crop types within DMU’s with similar deer densities (e.g. Perceived densities

considered desirable, minimal, and intolerable.)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

County Farm type 11 Mean 25th. pct 75th. pct
Presque Isle Livestock 9 Min. Demand .34 .14 .60
MDNR estimate: 11 Desired .57 .38 .88
40-45 deer/square mile 9 W.A.C. .80 .49 1.23
Cash Crops 6 Min. Demand .24 .14 .40
8 Desired .50 .37 .62
7 W.A.C. .82 .57 1.07
Trees or fruit 4 Min. Demand .64 .44 .92
2 Desired .83 .67 1.00
3 W.A.C. 1.44 1.00 2.00
Oceana Livestock 5 Min. Demand 1.29 .46 2.42
MDNR estimate: 7 Desired 1.51 .36 1.50
35 deer/square mile 7 W.A.C. 2.32 .56 2.00
Cash Crops 19 Min. Demand .65 .04 1.14
24 Desired 1.03 .16 1.66
19 W.A.C. 1.49 .20 2.00
Trees or fruit 14 Min. Demand .62 .20 .78
19 Desired .82 .29 1.20
15 W.A.C. 1.36 .40 2.00
Benzie/Leelanau (57 &59) Livestock 5 Min. Demand .79 .37 1.32
MDNR estimate: 4 Desired .69 .44 .95
10-15 deer/square mile 5 W.A.C. 1.76 .62 3.18
Cash Crops 9 Min. Demand .33 .17 .42
9 Desired .49 .25 .78
9 WAC. 1.54 .43 2.43
Trees or fruit 22 Min. Demand .37 .03 .66
24 Desired .55 .12 .99
25 W.A.C. .80 .14 1.47
Menominee (2 1 5) Livestock 1 5 Min. Demand . l 8 .03 .27
MDNR estimate: 16 Desired .33 .11 .50
80-100 deer/square mile 16 W.A.C. .51 .15 .66
Cash Crops 3 Min. Demand .25 .00 .60
3 Desired .32 .01 .60
3 WAC. .48 .02 1.00
Trees or fruit 3 Min. Demand .19 .11 .25
3 Desired .34 .27 .39
3 W.A.C. .44 .33 .56
Calhoun (Soutth 52,250,150) Livestock 7 Min. Demand .51 .22 .67
MDNR estimate: 9 Desired .91 .49 1.12
30-40 deer/square mile 9 W.A.C. 1.31 .70 1.75
Cash Crops 18 Min. Demand .22 .05 .40
24 Desired .40 .14 .64
18 W.A.C. .70 .16 1.22
Trees or fruit 4 Min. Demand .60 .43 .75
6 Desired .60 .25 .91
6 W.A.C. 1.19 .25 1.87
Calhoun (North)(139&251) Livestock 3 Min. Demand 1.80 .44 4.29
MDNR estimate: 5 Desired 1.66 .69 2.94
40+ deer/square mile 4 W.A.C. 2.91 1.44 5.00
Cash Crops 5 Min. Demand .49 .12 .89
6 Desired .58 .13 .98
5 W.A.C. 1.34 .33 2.57
Trees or fruit 2 Min. Demand 26 .12 .40
2 Desired .78 .35 1.20
2 W.A.C. 1.24 .47 2.00

 

 

 

 

 

 

 

 

Table 68: Continued

144

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

County Farm type It Mean 25th. mt. 75th. pct
Montcalm (220&133) Livestock 6 Min. Demand .26 .01 .43
MDNR estimate: 5 Desired .52 .20 .87
30-35 deer/square mile 5 W.A.C. .63 .27 1.04
Cash Crops 6 Min. Demand .65 .16 1.20
10 Desired .77 .33 1.22
6 W.A.C. 1.43 .61 2.10
Trees or fruit 4 Min. Demand .28 .03 .61
4 Desired .43 .18 .83
3 W.A.C. .47 .33 .64
Montcalm (120) Livestock 7 Min. Demand .52 .11 .70
MDNR estimate: 6 Desired .48 .30 .72
45 deer/square mile 6 W.A.C. .68 .39 .88
th Crops 12 Min. Demand .47 .29 .63
13 Desired .64 .33 .88
13 W.A.C. 1.02 .48 1.58
Trees or fruit 4 Min. Demand .09 .04 .13
4 Desired .10 .03 .22
4 W.A.C. .22 .20 .26

 

 

 

 

 

 

 

 

145

Table 69: Producer perceptions of deer densities that are desirable, minimal, and intolerable (WAC) expressed as a proportion of the
perceived October 1994 deer densities. Shown by county and segmented by DMU‘s with similar deer densities.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

County Farm type n Mean 25th. pct 75111. pct
Presque Isle Overall 19 Min. Demand .37 .18 .44
MDNR estimate: 21 Desired .57 .38 .67
40-45 deer/square mile 19 W.A.C. .91 .60 1.28
Oceana Overall 39 Min. Demand .73 .13 1.11
MDNR estimate: 51 Desired 1.03 .29 1.50
35 deer/square mile 42 W.A.C. 1.60 .39 2.00
East: 112 7 Min. Demand .78 .03 .67
12 Desired 1.03 .27 1.25
8 W.A.C. 1.73 .04 1.92
West: 113 25 Min. Demand .60 .10 .92
32 Desired .85 .29 1.38
27 W.A.C. 1.32 .40 2.00
Benzie/Leelanau (57 8:59) Overall 38 Min. Demand .43 .07 .64
MDNR estimate: 40 Desired .65 .18 .99
10—15 deer/square mile 41 W.A.C. 1.15 .28 1.55
Menominee (215) Overall 22 Min. Demand .19 .04 .27
MDNR estimate: 23 Desired .33 .11 .50
80-100 deer/81m mile 23 W.A.C. .49 .23 .64
Calhoun Overall 50 Min. Demand .46 .08 .60
67 Desired .62 .18 .86
57 W.A.C. 1.10 .30 1.43
MDNR estimate: South: 152,250,150 31 Min. Demand .32 .07 .57
30-40 deer/square mile 43 Desired .51 .16 .80
36 W.A.C. .89 .24 1.41
MDNR estimate: North: 139, 251 13 Min. Demand .75 .13 .91
40+ deer/square mile 17 Desired .87 .19 1.17
14 W.A.C. 1.62 .40 2.17
Montcalm (22019133) Overall 47 Min. Demand .54 .13 .80
49 Desired .73 .30 1.00
43 W.A.C. 1.07 .44 1.60
MDNR estimate: East: 120 24 Min. Demand .47 .14 .65
30-35 deer/square mile 24 Desired .61 .27 .79
24 W.A.C. .89 .31 1.43
MDNR estimate: South/West: 220, 133 18 Min. Demand .54 .13 .98
45 deer/square mile 21 Desired .80 .33 1.15
16 WAC. 1.14 .47 1.73

 

 

146

Cultural Cm’ g Capacihr Respon_se Curves

To graphically illustrate producer deer density preferences Cultural Carrying
Capacity response curves (Minnis 1996) were plotted for selected producer segments.
The exploratory nature of this analysis and portion of the results needs to be emphasized
and readers should evaluate this methodology for its potential and seek to refine the
methodology.

The left-hand points of the curves represent the Minimum Demand of producers,
while the right-hand points represent the Wildlife Acceptance Capacity of producers for
deer. The most desirable deer density as indicated by each segment of producers is
represented by the apex of each curve. Though each of these points is representative of a
deer density they are presented as a ratio of 1994 producer perceived densities rather than
as deer/mi2 figures. The vertical dashed line at point 1 on each curve represents the 1994
perceived deer density. MDNR density indices are provided with each graph as these
indices are the best estimates of population size for each county; however, these may be
inadequate because of differences in deer distribution which may cause some farmers to
be reacting to more or less deer than the MDNR figure.

The preponderance of county means desiring a 40% reduction in the October 1994
deer herd suggests that between 18-24 deer/mi2 might be an appropriate October
population density target for farmers, although we must recognize that there is substantial

variance around these means.

147

 

    

 

 

 

 

Tolerance '
of Deer l l
intolerable r a] l
i r

r '~/

,
r l
» l
t l
Desirable * ’
l
1 L l . l A l a 1 # ll 4 J J l . I a 1 . 1 a l . 1

 

0 0.2 0.4 0.0 0.0 1 1.2 1.4 1.0 1.0 2 2.2 2.4

MWWWW
"'34 -r- B-e-alt-

Figure 14: Cultural Carrying Capacity response curves for farmers in each study county
Horizontal axis is the average percent of the MDNR October 1994 deer density, which
producers found desirable and intolerable. The current condition in October of 1994 Is
represented by the value 1. (i.e. Only Oceana County farmers found the October 1994 density
desireable, while Menominee County farmers desired a deer density approximately 70% less
than the October 1994 density.) The vertical axis is attitude toward the deer density measured
as Intolerable and desirable. The 3 points on the curves represent producers' minimum

deer density demand, most desirable density, and deer acceptance capacity.

m
m

Intolerable

l

Desirable

Intolerable :

l

Desirable I
Intolerable :

Desirable :

Intolerable

Desirable :
Intolerable

Desirable :
Intolerable L
Desirable »

Figure 15: CCC distributions by county and similar October 1994 deer
densities. The horizontal bars included on the graphs are the interquartile

148

Menominee (DMU 215) MDNR Density Index 80-100 deer/sq. mile

 

L

l

 

*n822

*n-23

n-23

 

Index 40-45 deer/sq. mile

 

 

Presque Isle (All DMUs) MDNR Density
I

 

 

 

 

 

 

nsity Index 30-45 deer/sq. mile

 

 

Montcalm (All DMUs) MDNR De

 

 

 

 

 

 

 

 

 

 

 

 

L

 

 

0

0.2 0.4 0.6 0.8 1

ranges of each point.

1.2

1.4

1.6

1.8 2

2.2 2.4

 

 

 

 

 

149

Fruit - tree growers +

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I
i l.
T I
Tolerance
ofDeer I "815
Intolerable ” '
~ I
’ '.
I
—— l
_ I:
l
Desirable r '
r n=19 '
i r L 1 l r l l l i ! r 1 m l r l 1 L J l 4 l r l .1
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4
Cashcropfarrners -I-
. I
l
Tolerance F .
“9°" . n=19 n=19
Intolerable _
L.
I n-24
1 a J i l a l a l a T a l L 1 a 1 #1 a l i J i L i
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4

Figure 16: Oceana County CCC distributions segmented by
farmtype The horizontal bars included on the graphs are the
interquartile ranges of each point.

150

 

 

 

 

 

 

 

Tm Fruit - Tree growers +
of Deer
Intolerable L
~ I
“' l
L I
. I
— I
'.
’ I
. _ I
Desirable r n = 24 I
. 1 r A .
1

0 0.2 0.4 10.6 08 i 12 14 11.6 1.8 A 2 12.2 2.4 A

Livestock - Cash crop farmers -I-

 

 

 

 

 

 

 

 

Tolerance
ofDeer
Intolerable _
f
- I
~ I
l
Desirable *- I
- n=13 I
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4

Figure 17: Benzie/Leelanau CCC distributions segmented by
farmtype The horizontal bars included on the graphs are the
interquartile ranges of each point.

151

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Oceana East DMU 112 MDNR Density Index 35 deer/sq. mile +
Tolerance I
ofDeer I
’ l
IMOIBI‘BDIO fl = 7 |
— I
l.
e l
I
_ l
l
_ l
I
Desirable _
l I n 8 12
1 L 1 . 1 r 1 . 1 r T 1 1 a 4 r I r L r 1 r I r 1 1
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4
_Oceana West DMU 113 MDNR Density Index 35 deer/sq. mile +
Tolerance T
of Deer I
Intolerable
Desirable _
r n 8 32
1 L 1 1 i l L J I L 4; J L 1 g 1 A l 4 1
O 02 04 06 08 1 1.2 1.4 1.6 18 2 2.2 24

Figure 18: Oceana County CCC distributions segmented by DMUs with similar

October 1994 deer densities. The horizontal bars included on the graphs are the
interquartile ranges of each point.

152

South (DMUs 152. 250, 150) MDNR Density Index 3040 deer/sq. mile -I-

 

 

 

 

 

 

 

 

   

 

 

 

 

 

Tolerance I
ofDeer * .
._ 1
Intolerable = a
- n=31 I n 36
r I
t I
F I
' I
T I
_ I
. I
Desirable ~ I
. n=43 I
0 0.2 10.4 0.0 10.8 A 1 I12 91.4 1.6 18 I 2 12.2 2.4 I
North (DMUs 139, 251) MDNR Density Index 40+ deer/sq. mile 4-
Tolerance ,
ofDeer I
Intolerable — 4'
~ I n-14
T l
T I
i I
" I
I.
Desirable L
n=17 l
1 r 1 i 1 a l h_L 3 l A 1 r l a l r 1 a 1 a l ._ L t
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4

Figure 19: Calhoun County CCC distributions segmented by DMUs with similar

October 1994 deer densities. The horizontal bars Included on the graphs are the
interquartile ranges of each point.

153

South \Mst (DMUs 220, 133) MDNR Density Index 30-35 deer/sq. mile +
Tobrance

 

 

   
 

 

 

 

 

 

 

 

 

 

 

 

ofDeer , :
Intolerable r I
‘ ’ ' "=16
I I
Desirable e
» n=21 I
0402 04 06‘08 1 1.2I1416I18I2 22 24
TI nee East(WU120)MDNRDensltyIndex45deerIsq.miIe -.-
ofDeer I.
Intolerable I I.
. I.
— I
I
f I
I
I. .
r I
Desirable _ I
n=24 I
l L 1 1 I 1 L 1 L l L L J L 1 Li L
1

0 0.2 I 0.4 I 0.6 I 0.8 I I 1.2 1.4 1.6 1.8 2 2.2 2.4
Figure 20: Montcalm County CCC distributions segmented by DMUs with similar
October 1994deerdensities Thehon'zontal barslncludedonthegraphsarethe
interquartile ranges of each point.

154

Tolerance of deer densities

The majority of producers believed there were too many deer in the counties in
which they farmed (Question #56). This was generally opposite the feelings of hunter
respondents to the deer hunter survey (Minnis 1996).

Tolmpce of the deer densigz by county

Producers in Menominee county were most likely to believe that there were too
many deer in the county. Conversely, producers in Oceana, Benzie, and Leelanau
counties appeared more satisfied with the number of deer in their respective counties

(Table 70).

Tolerance of the deer densia bx crop loss tolerance

 

 

Those producers whose losses were not a problem were more likely to be satisfied
with the number of deer in the county or to think there were too few, than were the
producers who indicated that losses were a problem (Table 70).

Tolerance o the deer densi huntin tici ation

Hunting farmers more frequently than non-hunting farmers believed there were
too few deer in their county (Table 70); however, the majority of both groups believed
that there were too many deer in the counties in which they farmed.

Tolerance o the deer densi b '0!) status

Full-time farmers more frequently than part-time farmers indicated there were too
many deer in their county, while a greater proportion of part-time farmers indicated that

the number of deer in their county was satisfactory or too low (Table 70).

155

Implications/recommendations

As was expected the majority of Menominee county farmers considered their
1994 losses intolerable and believed there were too many deer in the county. This finding
was not surprising considering that MDNR indices indicated that the October 1994 deer
density in DMU 215 was approximately 80-100 deer/miz. However, greater than 50% of
the farmer respondents from each of the other study counties with considerably lower
deer numbers also believed there were too many deer in their county in October of 1994.
Generally a third of the farmers from these counties were satisfied with the number of
deer in their county. In each county studied except Calhoun, greater than one third of the
producers reported that their crop losses were intolerable, while from a quarter to a third
of the producers in each county indicated that their 1994 crop losses were not a problem.
These proportions suggest that increased levels of disruptive issue activity among farmers
may be imminent in these counties in the future.

The difference between the proportion of producers indicating that crop losses
were intolerable and that reporting there are too many deer in the county may suggest that
producers have concerns for other values such as personal safety that may be at risk
because of the current numbers of deer in the counties. These other values are likely
additive to the risks of crop losses when determining an individual’s tolerance of deer
numbers. This suggests that deer managers should not limit themselves to monitoring
producer tolerance of crop losses, but that they also continue to monitor stakeholders’ risk

perceptions concerning such things as the likelihood of deer-related vehicle accidents.

156

When determining which counties to include in this study consideration was given
to the historic and suspected intensity of crop damage issues in the counties, and selection
was made so as to provide a cross section of high and low intensity counties. Menominee
County proved to be a hot-bed of activity during the study period, while other counties
were noticeably lower in intensity. Calhoun County was suspected to have a lower
amount of issue intensity than the other counties. This was supported by the lower
proportion of Calhoun County farmers who indicated that losses and deer numbers were
intolerable, but it is notable that >50% of farmers from this county still believed that there
were too many deer in the county and that losses were a problem. Presque Isle County
was expected to be an area of higher issue intensity relative to Calhoun and Montcalm
Counties and this was supported by the tolerance of losses reported by producers. The
fruit growing regions of Benzie, Leelanau, and Oceana counties were also expected to
reflect a greater amount of issue activity relative to the non-fruit mowing counties. In
these fruit growing counties there was a greater amount of intolerance of losses than in
the non-fruit mowing counties. The relative levels of issue activity were apparently
similar to the levels hypothesized in the study; however, the proportions of intolerant
producers in each county were greater than expected based upon conversations with
MDNR and Extension personnel at the beginning of the study. The data also confirm the
regional variability of crop losses and producer responses to depredation. This inherent
variability highlights the importance of maintaining a flexible system of addressing deer
depredation concerns and for systematic monitoring of producer perceptions of

depredation.

157

Table 70: Tolerance of October 1994 deer densities semnented by job status, hunt participation, county,
and tolerance of crop losses.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

11 T00 T00 Satisfied T00 Too
few, few % many many,
take % % take
action action
°/. %
Job Status Full-time 376 2.4 3.2 27.7 18.6 48.1 100
%
Part-time 175 12.0 9.1 38.9 16.6 23.4 100 x2=51.74
°/. df4
p<0.001
Hunt Hunt 377 7.7 7.2 31.6 13.0 40.6 100
Participation %
Non-hunt 159 0.6 0.0 30.2 28.9 40.3 100 1937-45
% df4
p<0.001
County Calhoun 124 5.6 3.2 32.3 28.2 30.6 100
%
Montcalm 97 8.2 6.2 32.0 16.5 37.1 100
°/.
Oceana 105 9.5 4.8 35.2 10.5 40.0 100
°/.
Benzie/Leelanau 118 3.4 6.8 37.3 17.8 34.7 100
1%
Presque Isle 48 2.1 10.4 33.3 22.9 31.3 100
°/.
Menominee 59 0.0 0.0 6.8 8.5 84.7 100 x2=78.74
% df20
p<0.001
Tolerance of NotaProblem 147 17.0 15.6 55.1 8.8 3.4 100
loss %
Tolerable 166 2.4 1.8 39.8 35.5 20.5 100
°/.
Intolerable 225 0.4 0.4 9.8 9.8 79.6 100 #3533
% 5, df8,
p<0.001

 

 

Percent of respondents

158

 

 

         

I Too few intolerable El Too few tolerable CI Satisfactory
121 Too many tolerable I Too many intolerable
100% - - - - - -
80% -
60% -—

    

46%-————— L—A —— ——

20% «

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0%1

Calhoun
(n=124)
Montcalm
(n=97)
(n=59)

Presque Isle
(n=48)
Menominee

Benzie/Leelanau
(n=118)

Oceana (n=105)

Figure 21: Tolerance of 1994 deer numbers in study counties.

159

Factors influencing tolerance of deer density

Also of interest to this study were the factors that contribute to producer tolerance
of the deer herd size (Question #54). After recoding the variables in Table 68 to remove
those that were “Unsure” of how much importance the items had on their decision
making, a Friedman’s two-way AN OVA was used to test for a difference in the mean
ranking of each item by the respondents. Producers weighted each of the provided values
differently in forming their opinions about the acceptability of deer herd numbers (Table
71). Personal crop losses, others’ crop losses and deer-car collisions were ranked mostly
highly by the respondents. Personal recreational benefits of deer were somewhat

important to farmer respondents but less so than the costs of deer.

Table 71: The relative importance of factors associated with opinions about satisfactory deer densities.

160

 

 

 

 

 

 

 

 

 

Circle only one answer for each row. Mean Very Somewhat Slightly No! Unsure
Importance Important Important Important Important
of item‘

Personal recreational benefits fi'om deer 1.79
(e.g., viewing, hunting, feeding, etc.) 32.1% 32.4% 15.2% 18.1% 2.2%
n=552
Recreational benefits from deer 1.68
provided to others in the county. 23.9% 35.9% 20.6% 16.8% 2.7%
n=548
Personal economic benefits from the 0.80
presence of deer (e.g., hunting leases, 8.6% 16.8% 16.8% 53.8% 3.8%
goods and services provided to hunters
and tourists.)
n=546
Economic benefits to the county from 1.29
the presence of deer. 13.7% 31.4% 20.7% 29.7% 4.6%
n=542
Personal crop losses to deer. 2.29
n=552 54.7% 22.6% 13.2% 7.2% 2.2%
Other farmers’ crop losses to deer. 2.33
n=551 49.7% 32.8% 10.0% 3.4% 4.0%
The number of deer-related vehicle 2.31
accident in the county. 51.6% 30.8% 9.0% 5.8% 2.9%
n=556

 

 

 

 

 

 

 

‘ Meal importance after removing respondent that were unsure (Friedman’s two-way ANOVA x 620.92, df6, p<0.001). Scale of
importance: 3-very important, 2-somewhat importult, l-slimltly important, 0-not irnpormt

 

161

Relationship between tolerance of crop Iossespnd personal and commpnitv

values at deer

Segmenting producers by their tolerance of 1994 losses revealed the following

patterns as intolerance increases:

personal recreation becomes less of a factor in determining satisfaction with the deer
herd size (T able 72).

other’s recreational benefits decrease in importance in determining satisfaction with
the deer herd size (Table 73).

personal economic benefits related to the presence of deer become less important in
determining satisfaction with the deer herd size (Table 78).

other’s economic benefits become less important in determining satisfaction with the
deer herd size (Table 74).

personal crop losses become more important in determining satisfaction with the deer
herd size (Table 76).

other farmers crop losses become more important in determining satisfaction with the
deer herd Size (Table 77).

the frequency of deer/vehicle accidents becomes marginally less important in

determining satisfaction with the deer herd size (Table 75).

These tendencies were consistent regardless of whether the farmer was full- or part-time,

or whether the farmer personally hunted deer. As was hypothesized, when income and

162

thereby livelihood are threatened other concerns and considerations become less

important to the individual in determining their satisfaction with deer numbers.

163

 

 

 

 

 

 

 

 

 

 

 

 

 

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166

Discriminant Analysis of Factors that Predict Farmer Toleraiice of October 1994 Deer
Densities

A major objective of this study was to attempt to predict producer tolerance of
deer populations. Discriminant analysis of producer tolerance of perceived October 1994
deer densities was examined and the most appropriate model for producer tolerance of
October 1994 deer densities is presented here.

The dependent variable tolerance of deer was evaluated using the producer ratings
of importance provided in response to Question #54. The model offering the most
predictive ability for tolerance of county deer densities consisted of 3 variables:
importance of personal recreational benefits derived from deer, importance of personal
crop loss, and importance of the number of deer/vehicle related accidents in the county
(Table 79). This model accounted for 45% of the variance in the dependent variable, and
correctly classified 58% of the grouped cases. The model does need to be viewed
conservatively because the Box’s M test for equality of group covariance matrices was
significant. It would not be appropriate to place much faith in the standardized weights
provided by the model; however, it seems safe to assume that the model has selected the

most discriminating variables from those provided.

Table 79: Summary Table of Discriminant analysis of factors affecting producer tolerance of county deer populations.

 

 

 

 

 

 

 

 

 

 

Step Label Wilks’ Lambda Sig. Standardized canonical
discriminant function
coefficient

1 Importance of personal crop losses .6651 .000 .7013

2 Importance of personal recreational .5610 .000 -.5912
benefits

3 Importance of number'of deer/vehicle .5193 .000 .2810
accidents in the county

 

Wilks’ Lambda = .5193, Canonical Corr. = .6703, x’ = 330.88, df 12, p<0.001
Box’s M = 195.95, P = 7.95, df24, p < 0.001
Percent of grouped cases correctly classified: 58.13%

167

Perceptions of and Attitudes about the Michigan Department of Natural Resources

Perceptions of the MDNR

Producer attitudes about the Michigan Department of Natural Resources were
measured with eight items (Questions #60, #61, and #63). These items probed the
frequency of farmer contact with DNR biologists, the credibility of the local biologist, the
perceived expertise of the MDNR to manage deer populations, and the perceived
importance of different stakeholders in the NflDNR’s management of the deer herd.

The credibility attributed to a management agency by its constituents involves two
components. One is the perceived level of trust the constituents place in the agency to
represent their interests. The second is the assessment of the agency’s expertise or
competence to manage. The competence of the agency and its biologists was evaluated
using items that compose question #61 on the survey, while the perceived trustworthiness
of the agency had to be inferred from responses to question #62 regarding the agency’s
consideration of farmers’ interests.

Data from this study indicates both the expertise and the trustworthiness of the
MDNR are questioned by a substantial number of farmers in the seven study counties.
Farmers were either undecided or did not think that the MDNR had the expertise or
enough information to manage the state’s deer herd (Table 81). Though 34% of the
farmer respondents believed that DNR biologists could adequately determine crop losses,
another 66% of the farmers either disagreed or were undecided (Table 81).

An index of biologist credibility relating to crop damage was created using a

summated scale (see Methods) where +2 = the greatest possible credibility, -2 = the least

168

possible credibility and where 0 = generally undecided. The mean credibility rating for
local biologists statewide was 0.155 (S.D.=O.865) or generally undecided, which is not
surprising considering that nearly 50% of the farmers indicated that they had never had
contact with a local biologist.

Biologist crecfibilitLby contact time

We tested the hypothesis that mean biologist credibility ratings would differ based
on the frequency of contact with the local biologist. As contact time with the biologist
increased, mean credibility improved (Kruskal-Wallis x2 =27.7, df2, p<0.001). The
tendency for credibility of the local biologist to improve with increased contact with
biologists held even for those farmers reporting the most serious crop loss problems
(Kruskal-Wallis x2 =14.0, df2, p<0.001) (Table 80).
Implications/Recommendations

An important inference of this finding is that poor attitudes about agency
professionals - at least those associated with crop damage control programs -- are not
generally the result of personal interactions with agency personnel. In fact, it appears that
wildlife professionals are generally effective in their personal dealings with crop damage
complaints by farmers, but may be too constrained by budget and time to fully meet this
public relations need. Though we could not show a significant positive correlation
between increased contact time with local biologists and perceived agency competence it
seems intuitive that a better perception of the agency would result from more frequent
contact with its professionals, thus increased contact time with biologists may result in

increased support among farmers for other agency programs.

Table 80: Mean credibility assigned to local biologist by agricultural producers with varying frequency of

contact and levels of crop loss tolerance.

169

 

 

 

 

 

No contact (s.d.) 51 time per year A few times per Total (s.d.)
(s.d.) year (s.d.)

1994 losses were not a 0.1313 (0.7751) 0.3600 (0.6155) 0.3529 (0.9608) 0.1986 (0.6843)

roblem n=99 n=25 n=l7 n=141
1994 losses were a -0.0355 (0.7904) 0.2885 (0.8808) 0.6481 (0.7274) 0.1333 (0.8465)

tolerable problem n = 94 n = 52 n = 18 n = 164
1994 losses were -02103 (0.8967) 0.1569 (0.9401) 0.3804 (1.0289) 0.1339 (0.9873)

intolerable n=65 n=68 n=85 n=218
Total -0.0155 (0.7751) 0.2391 (0.8693) 0.4167 (0.9781) 0.1511 (0.8693)

n=258 n=145 n=120 n=523

 

 

 

 

 

 

 

Note: +2 = greatest possible credibility, -2 = least possible credibility, and 0 = undecided

Mt credibilifl bucom

These mean credibility ratings differed by county (-0.27 to 0.49) (Table 82).
Presque Isle county had the only negative credibility rating and was distinctly lower than
Menominee County which had the highest credibility rating (Table 82). Both Presque
Isle and Menominee Counties have had a considerable history of deer damage and it is
likely that differences in the nature and handling of the issues in these 2 counties is
responsible for the difference in their credibility ratings. Menominee County managers
have had a very liberal policy regarding issuance of block, shooting, and regular antlerless

permits which may contribute to its positive evaluation.

170

Table 81: Percentage of respondents in agreement with each statement about the MDNR’s competence to
manage deer populations and evaluate crop damage situations.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Strongly Agree Undecided Disagree Strongly
Agree Disagree

Crop losses are imposed on farmers by the 16.5% 28.6% 22.4% 24.5% 8.0%
DNR and hunters. n=539 ’
The DNR has the expertise to manage the 6.6% 34.2% 26.1% 18.6% 14.4%
state’s deer herd. n=547
The DNR has enough information on the deer 7.4% 31.4% 25.5% 22.7% 13.0%
population to adequately decide how many
deer to harvest in Michigan each year. n=554
DNR biologists treat farmers in this county 9.7% 32.7% 43.7% 8.6% 5.3%
professionally and with respect. n=547
Our local DNR biologists can adequately 4.8% 28.8% 38.5% 19.3% 8.6%
determine the amount of loss a farmer is
incurring to deer. n=545
Our local DNR biologists understand the 7.2% 32.1% 34.3% 18.0% 8.4%
significance of crop losses to the economic
well-being of the farmer. n=545

 

Table 82: Credibility of local MDNR biologists and the agency with producers in study counties.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

County n Mean Credibility 1) Mean agency credibility
of biologist (2 point scale)
Calhoun 123 0.0027 (sd = 0.8434) 124 0.0927 (sd = 1.0409
Montcalm 92 0.2065 (sd = 0.8543) 94 0.0904 (sd = 0.9559)
Oceana 104 0.1186 (sd=0.8614) 104 -0.l97l (sd= 1.1003)
Benzie/Leelanau 117 0.2991 (sd = 0.8251) 121 0.0661 (sd = 1.0164)
Presque Isle 43 -0.27 13 (sd = 0.8141) 44 -0.4659 (sd = 0.9906)
Menominee 57 0.4912 (sd = 0.8866) 57 0.1140 (sd = 1.2248)
Total 536 0.1549 (sd = 0.8651) 544 -0.0119 (sd = 1.0596)
KW xz=28.3, df 5, p<0.001 KW 1515.25, df 5,p=0.009

 

 

Note: Possible credibility values are .2 = non-supportive, o = undecided, 2 = supportive.

Agengz commtence

The mean competence score of the state agency was low relative to the mean local
biologist credibility score. Though not conclusive this may suggest that farmers are
capable of discriminating between the MDNR’s local agents and the state agency.

The amount of contact time that the producer had with a biologist did not have a
statistically significant effect on the mean agency competence rating; however, it seems
logical that local biologists could affect the acceptance of the agency as a whole by

working with farmers. Of the farmers who had contact with biologists more frequently,

171

those with crop loss problems were more likely than those without problems to agree that

the MDNR possessed enough information to manage the deer herd (Table 83). Of the

farmers who had no contact with biologists, those with no loss problems were more

undecided than those with problematic losses about whether the DNR has enough

information to manage the deer herd (Table 84).

Table 83: Percent of respondents with more frequent contact with MDNR biologists in agreement with the
statement: “the MDNR has enough information on the deer population to adequately decide how many deer

to harvest in Michigan each year,” by tolerance of loss.

 

 

 

 

 

 

 

 

 

 

 

 

 

Strongly Disagree Undecided Agree Strongly Agree
Disagree % % % %
%
Not aproblem (n=17) 35.3 41.2 11.8 11.8 0 100%
Tolerable (n = 18) 5.6 16.7 16.7 61.1 0 100%
Intolerable (n = 88) 12.5 22.7 19.3 35.2 10.2 100%
Overall (11 = 123) 14.6 24.4 17.9 35.8 7.3

farms, df s,p=0.016

 

Table 84: Percent of respondents with no contact with MDNR biologists in agreement with the statement:
“the MDNR has enough information on the deer population to adequately decide how many deer to harvest

in Michigan each year,” by tolerance of loss.

 

 

 

 

 

 

 

 

 

 

 

Strongly Disagree Undecided Agree Strongly
Disagree % % % Agree
% %
Not a problem (11 = 102) 12.7 19.6 33.3 32.4 2.0 100%
Tolerable (n = 95) 10.5 25.3 27.4 33.7 3.2 100%
Intolerable (n = 69) 21.7 14.5 26.1 23.2 14.5 100%
Overall (n = 266) 14.3 20.3 29.3 30.5 5.6 100%

 

 

x1=21.76, df s,p=0.005

 

Implications/Recommendations

This difference between county perceptions of the MDNR strongly suggests that

district policies regarding crop damage and individual personalities affect the perceived

credibility of the biologist and by implication the agency. This may also imply that the
efforts of individuals are appreciated by farmers and that they are perhaps

educated/informed by biologists.

 

 

172

Agency Weightimf Constituents’ Interests

Interviews with farmers suggested that they did not generally feel appropriately
represented by the MDNR in decisions involving deer management. To obtain a more
accurate picture of the extent of this perception, producers were asked to indicate how
much consideration farmers’ interests were receiving fi'om the MDNR relative to the
interests of deer hunters and other stakeholders (Question #62). Respondents were also
asked to indicate how much consideration they desired the MDNR to place on the
interests of each stakeholder group. I have duplicated the question for the reader below.
The first weightings in the left column are referenced as the “current perceived”
weightings, while the second series in right column is referenced as the “desired”

weightings.

62. Please distribute 100 points within each of the following two columns to indicate how much
importance you think the DNR gurrem Mes and should place on each of the following
interest groups when the agency sets deer population goals for Oceana county.

 

THE DNR THE DNR
W PLACES IMPORTANCE ON: $11M PLACE IMPORTANCE ON:
(current perceived weightings) (desired weightings)
_ HUNTERS __ HUNTERS
_ FARMERS FARMERS
OTHER: __ OTHER:

 

 

 

= 100 =100

173

Perceived weighting of stakehflers by tolerance

No differences in how respondents perceived the consideration currently given to
farmers and hunters existed between counties (Table 85). Producers with intolerable
losses were more likely to indicate that farmers were currently given less consideration
than producers whose losses were more tolerable. Conversely those with more severe
losses were more likely to indicate that greater proportions of consideration be given to
farming interests in the firture, and they were also more likely to indicate that lesser
proportions of consideration be given to hunters interests (Tables 85 & 86).

Perceived stakehofilder weighting by hunting participation

Hunting farmers consistently differed from non-hunting farmers on their
perceptions of the current weightings and desired weightings. For example, hunting
farmers perceived the current weighting of hunting interests to be significantly lower than

did non-hunting farmers (T able 85).

174

Table 85: Producer perceived weightings of stakeholders interests in MDNR deer management objectives, segmented by hunt
participation and tolerance of loss. Values reported are proportions of 100 possible points that represent how MDNR is perceived to
weight the interests of stakeholders when determining deer management objectives.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

11 Mean weighting of 11 Mean weighting of deer 11 Mean weighting of
farmers (s.d.) hunters (s.d.) other stakeholder
interests (s.d.)
Overall 477 31.4 (20.3) 504 63.1 (23.7) 136 32.1 (26.0)
County
Calhoun 100 30.2 (19.4) 111 64.9 (24.0) 27 39.7 (31.5)
Montcalm 87 32.3 (19.2) 90 61.2 (21.8) 24 32.2 (18.8)
Oceana 92 32.4 (20.6) 95 60.4 (24.9) 26 33.4 (27.1)
Benzie/Leelmau 107 31.4 (20.6) 113 61.9 (23.1) 40 28.1 (24.7)
Presque Isle 36 27.1 (24.0) 37 69.7 (25.8) 6 41.6 (29.9)
Menominee 55 33.6 (19.8) 58 65.1 (23.5) 13 21.1 (22.8)
x1475, df 5, 38:745. df 5, p=0.189 x’=6.37, df 5,
Kruskal-Wallis statistic p=0.447 p=0.272
Tolerance of 1055
Not a problem 124 37.4 (20.2) 125 53.6 (22.4) 35 46.9 (17.4)
Tolerable 143 32.3 (20.1) 153 63.7 (22.8) 41 42.9 (16.9)
Intolerable 199 27.2 (18.9) 216 68.0 (23.5) 57 35.9 (16.3)
38:-22.0, df2, x"=31.0,df 2, p<0.001 {-97,er p-0.007
Kruskal-Wallis statistic p<0.001
Hunt participation
Hunt 334 32.7 (20.4) 353 60.6 (24.0) 100 35.6 (26.4)
Non-hunt 133 28.1 (18.9) 141 69.9 (21.1) 34 21.4 (20.1)
2 - -2.07, MD” 2 - -3.89, p<0.001 z - -2.92, p-0.004
Mann-Whitney statistic

 

 

 

 

 

 

 

Table 86: Producer desired weightings of stakeholders interests in MDNR deer moment objectives, segmented by hunt
patieipation and tolerance of 1088. Values reported are proportions of 100 possible points that represent how farmer respondents
desire MDNR to weight the interests of stakeholders when determining deer management objectives.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

11 Mean weighting of 11 Men weighting of deer 11 Mean weighting of
farmers (s.d.) hunters (s.d.) other stakeholder
interests (s.d.)
Overall 512 58.2 (20.2) 484 41.0 (17.3) 112 15.8 (13.8)
County
Calhoun 115 62.0 (20.8) 106 40.2 (17.0) 23 13.6 (13.1)
Montcalm 92 56.7 (18.6) 86 42.5 (13.8) 22 14.1 p05)
Oceana 97 55.3 (21.3) 91 43.7(193) 19 14.0 (13.8)
Benzie/Leelanau 114 54.1 (18.4) 111 41.9 (15.7) 33 20.1 (16.1)
Presque lsle 37 62.1 (23.3) 35 38.9 (22.9) 4 11.2 (6.2)
Menominee 57 63.2 (18.1) 55 35.1 (17.3) 11 15.0 (15.0)
11-1606, df5, 11137.78, df5, p=0.169 1:499, df5,
Kruskal-Wallis statistic p-o.007 p-0.4l7
Tolerance of loss
Not a problem 127 51.3 (18.8) 126 46.9 (17.4) 26 19.8 (18.7)
Tolerable 159 56.4 (19.8) 151 42.9 (16.9) 33 13.2 (10.1)
Intolerable 214 63.5 (19.6) 197 35.9 (16.3) 50 14.8 (12.6)
x2=34.8, dr 2, iii-36.7, df 2, p<0.001 11:12, df2, p=0.536
Kruskal-Wallis statistic p<0.001
Hunt participation
Hunt 349 55.7 (18.8) 337 43.4 (16.7) 74 14.8 (11.5)
Non-hunt 151 63.4 (21.8) 139 35.2 (17.7) 35 18.4 (17.9)
2 - -3.87, p<0.001 z - 4.18, p<0.001 z - 0.56, p-0.575

Main-Whitney statistic

 

 

 

 

 

 

 

 

 

175

T enafing toward egualig

A finding with some promise for reducing conflict is the reduction in the
magnitude of the difference between the weightings of farmers and hunters. The mean
difference between the current perceived weightings of farmers and hunters was 30.7 (s.d.
= 37.4) percentage points, (i.e. current farmer weighting - current hunter weighting =
1 30 1 points). This compared to a mean difference between the desired weightings of
15.0 (s.d. = 33.5) percentage points (i.e. desired farmer weighting - desired hunter
weighting = I 15 1 points). This suggests that a more equal weighting of interests would
be preferable to producers. MDNR personnel in all study counties indicated that they
attempted to balance the interests of hunters and farmers 50:50, thus perhaps farmers can

be made more aware of the consideration that the agency is giving them.

Perceived fairness of current stakeholder weighting

It was hypothesized that the preferred (desired) weightings provided by farmers in
response to Question #62 could be used to assess whether farmers considered the current
perceived weighting as fair. The calculation used to make this assessment is found in the
Methods on page 48, and is based on the assumption that producers who perceive the
current MDNR weightings of interests as fair will desire no change in weightings for the
future. Conversely, producers who perceive the current amount of consideration given to
farmers as unfair will desire an increase in the consideration given to farmers in the
future. For the reader’s convenience the equation used to assess fairness is provided

again below.

176

Perceived fairness of current MDNR weighting of farmer interests

(PCF/PCH) - (DF F/DF H)

where: Perceived current weighting of farmer interests = PCF
Perceived current weighting of deer hunter interests = PCH
Desired future weighting of farmer interests = DFF
Desired future weighting of deer hunter interests = DFH

If the calculated result was _>_l then the current weightings were assumed to be fair or in
favor of the farmer. If the result was <1 then the current weightings were assumed not to
be fair or in favor of the farmer.

Seventy-one percent of the farmer respondents perceived the current weighting of
farmers as “not fair,” and 29% perceived the current weighting as “fair.” There were no
county differences nor differences by farm type, but there were significant differences by
hunting participation, full/part-time status, and tolerance attitude (Table 88). The
majority of both hunting (67%) and non-hunting (83%) farmers found the current
weighting unfair, however, the percentage of hunting farmers who viewed the current
weighting as fair was 2 times higher than for non-hunting farmers. Similarly, the
majority of full-time (77%) and part-time (56%) farmers found the current weighting
unfair, except that the percentage of part-timers that considered the weighting fair was
twice as large as the percentage of full-time farmers. Additionally, those who perceive
the current weighting as unfair earned a greater proportion of their household income
from farming (Table 87). Those producers whose 1994 losses were not a problem were
split on the fairness of the weighting, while the majority of those whose losses were a
tolerable (68%) or intolerable (87%) problem found the weighting unfair. This

relationship held even while controlling for farmers that hunt. The majority of both

177

permit recipients (83%) and non-recipients (62%) found the current weighting unfair,

though twice as many non-recipients found the weighting to be fair.

Table 87: Farmer respondents’ perceptions of the fairness of perceived stakeholder weightings by the
MDNR when setting deer population objectives by dependence on farm income.

11 Mean Percent

 

 

Fl Not fairly weighted for farmers El Fair or >fair for farmers I

 

 

100%

90% - l
80% -
70% -
60% .
50% -
40% -
30% ..
20% .
10% -

0% -

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Percent of respondents

 

Calhoun (n=93)
Montcalm
(n=82)
Oceana (n=87)
Benzie/Leelanau
(n=1 03)

Presque Isle
(n=33)

Menominee
(n=52)

Figure 22: Perceived fairness of the amount of consideration given
taming interests by the MDNR.

 

178

Table 88: Farmer respondents’ perceptions of the fairness of perceived stakeholder weightings by the
MDNR when setting deer population objectives by county, tolerance, hunting participation, job status,
and permit recipients.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

11 Not fairly Fair or more than
weighted for fairly weighted for
farmers farmers
% %
County Calhoun 93 71 .0 29.0
(x2=6.13, df 5, p<0.293) Montcalm 82 72.0 28.0
Oceana 87 63 .2 36.8
Benzie/Leelanau 103 69.9 30.1
Presque Isle 33 72.7 27.3
Menominee 52 82.7 17.3
Tolerance of loss Not a problem 1 13 46.9 53.1
(x2=56.30, df 2, p<0.001) Tolerable 139 68.3 31.7
Intolerable 188 87.2 12.8
Hunting participation Non-hunter 128 82.8 17.2
(x’=11.87, df 1, p<0.001) Hunter 316 66.5 33.5
Job status Full-time 315 77.1 22.9
()(2=19.90, df 1, p<0.001) Part-time 135 56.3 43.7
Shooting permit recipient Non-recipient 304 65.1 34.9
(xz=l6.34, df l, p<0.001) Recipient 133 84.2 15.8
Block permit recipient Non-recipient 280 64.6 35.4
(x’=12.32. df 1, p<0.001) Recipient 135 81.5 18.5

 

 

 

 

 

 

Implications/Recommendations

The finding that a large proportion of the respondents, even from the segments of
hunting farmers and farmers without depredation problems, thought that the consideration
perceived given to farmers by the MDNR was unfair might concern the agency. These
segments might be anticipated to be less sensitive to the fairness issue. Perceived
weightings are likely to be an on-going area of concern and conflict for both farmers and
hunters, and the agency may wish to reevaluate its public participation process to ensure
that groups feel that they have been given an opportunity for input into management

decisions.

179

Other groups conflered to be stakeholders in the deer damage issue

In an open-ended question nearly one-fifth of the respondents added groups other

than farmers and hunters that should be considered when determining deer population

goals (Table 89). Some respondents also indicated that they believed other groups are

currently being considered by the agency. Non-consumptive wildlife users received

relatively frequent consideration by respondents when the assorted classifications were

combined (Table 89). Unfortunately, one of the more frequent additions was the MDNR

as a self-serving interest.

Table 89: Producer perceptions about other stakeholders whose interests are being considered, and should

be considered, by the MDNR when determining deer population goals.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Currently considered by MDNR Should be considered by MDNR

Frequency Mean Weighgipg Frequency Mean weighting
DNR Employees, money 15 54.3 (sd = 23.8) 5 4.4 (sd = 5.1)
Environmentalists 5 44.0 (sd = 16.3) 2 5.0 (sd = 7.1)
Wildlife fans, nature lovers 8 39.1 (sd = 23.9) 4 15.0 (sd = 12.2)
Non-hunters 2 40.0 (sd = 0.0) 2 10.0 (sd = 0.0)
Recreationalgroups 3 18.3 (sd = 10.4) 4 18.2 (sd = 10.9)
General public 4 20.8 (sd = 12.5) 8 24.5 (sd = 14.5)
Loggers 1 25.0 (sd = fl 1 25 (sd = —L
Hunting related businesses 4 27.5 (sd = 15.0) 5 19.0 (sd = 13.4)
Hobby farmers l 0 (sd = --) 0 ~—
Habitat conservation 3 60.0 (sd = 17.3) 4 35.0 (sd = 34.2
Politics 2 50.0 (sd = 42.4) 0 --
Landowners 2 47.5(sd = 38.9) 3 26.7 (sd = 20.8)
Automobile drivers 5 11.2 (sd = 16.5) 5 21.0 (sd = 12.4)
Auto insurance companies 15 42.6 (sd = 23.4) 9 11.5 (sd = 9.6)

Total 70 39.6 (sd = 24.5) 52 17.9 (sd = 15.8)

 

 

 

 

180

Perceptions of the Michigan State University Extension Service

Producer attitudes about the Michigan State University Extension Service (MSU-
E) were measured with four items regarding; the fi'equency of contact with MSU-E
county representatives, MSU-E agent treatment of farmers, the familiarity of MSU-E
agents with farming, and the helpfulness of MSU-E agents for locating information about
farming problems. The final three questions were designed to provide a relative measure
of how credible county MSU-E personnel are perceived by farmers. The items are similar
to three items which probe farmer attitudes about the abilities of local DNR biologists

and provide the grounds for some comparisons of credibility between the two groups.

Contact uen

Nearly 70% of the respondents indicated that they had contact with Extension
more than a “few times per year”. Extension contact fiequency was significantly higher
in fruit counties, probably because of the bi-weekly meetings conducted by Extension
IPM agents during the summer months with fruit growers (Table 90). There was a
positive Spearman correlation (0. 3037) between the amount of contact and tree growers,
while there was a negative Spearman correlation (-0. 1999) with non-tree growers. A
cross-tabulation revealed that significantly more tree growers had fi'equent contact with

Extension than non-tree growers (x2=57.83, (If 3, p<0.001).

181

Table 90: Farmer respondents’ reported contact frequency with MSU-E agents, by county.

 

 

 

 

 

 

 

County n Never < once/year A few times/year > once/month
% % % %

Calhoun 130 23.8 22.3 49.2 4.6 100%
Montcalm 100 1 1.0 18.0 60.0 1 1.0 100%
Oceana 109 10.1 14.7 56.9 18.3 100%
Benzie/Leelanau 125 0.8 12.8 57.6 28.8 100%
Presmre Isle 49 10.2 16.3 63.3 10.2 100%
Menominee 60 11.7 13.3 61.7 13.3 100%

 

 

 

 

 

 

 

x2=64.36, df 15, p<0.001

 

 

Credibilig of Extension Agents
Producers were generally positive about the knowledge, abilities, and

professionalism of their county extension agents. Local extension agents had a mean

credibility rating of 1.24 (s.d. = 0.71) (+2 = greatest possible credibility, -2 = least

possible credibility, 0 = generally undecided). An important implication of this finding is

that given the high credibility of MSU-E, it might be possible for the MDNR to work

more closely with that agency and thereby improve its own credibility with farmers.

 

DISCUSSION

The study’s over-riding purpose was to identify factors affecting farmer tolerance
of deer numbers and crop loss that might be targeted so as to reduce conflict between
stakeholders over how the deer herd is managed. In other words, a primary goal was to
advance our understanding of how crop damage issues are perceived and dealt with by
farmers, and to suggest improvements in policy, administration and communication to
reduce deer depredation and associated problems.

Some important points need to be made about Michigan’s deer crop damage
situation: first, there is no single deer crop damage issue, rather there are many points of
contention between stakeholders and within segments of stakeholder groups. As became
clear during the study, not all farmers have the same attitudes regarding acceptable
numbers of deer; likewise, actions taken to control damage varied within segments.
Second, the state of issues associated with deer crop damage varies both temporally and
spatially across the counties studied. An awareness of the inherent diversity of crop
damage issues and situations is critical for effective management and suggests two
important approaches towards managing crop damage issues; first, readiness, and
second, flexibility. Having flexible agency protocols for addressing crop damage
situations as they arise should do much to diffuse potentially disruptive situations;
unfortunately, flexibility also leads to perceptions of inconsistency by other stakeholders
such as deer hunters. This is the ultimate paradox facing wildlife agencies. To
overcome this paradox agencies may need to have proactive, involved, and well-frmded

information and education (1&E) programs.

182

183

Without such professional support, reducing the amount of disruptive issue activity over
deer depredation and other issues may be extremely difficult if not impossible. What
follows, in terms of recommendations to agencies, assumes that there is or will be
adequate backing for I & E programming targeted at farmers, deer hunters, and other

stakeholders affected by deer management.

Values, Perceptions, and Behaviors

As was hypothesized, value differences are central to the issues surrounding deer
depredation. It was found in this study that farmers are not that different from deer
hunters in that they have multiple values associated with the deer resource including
consumptive and non-consumptive recreational values, economic values, health and
safety values, and financial security values both for themselves and others. However, as
crop loss threatens financial security producers’ values of deer take on different
priorities. Even though 70% of the respondents personally hunted deer, and most of
these rated deer hunting as an important personal recreational activity, when livelihood
was threatened by crop losses, having large numbers of deer to hunt became less of a
concern than being financially secure. Clearly the dominant value of producers is
earning a living, and this value supercedes deer hunting when priorities are assigned.
Conversely, when referring to deer, the dominant value for hunters appears to be having
a quality personal recreational hunting experience. These are pronounced differences in
value priorities when crop losses are a problem but less so when losses are insignificant

or tolerable.

184

Even though tolerances of deer populations (Minnis 1996) and crop losses appear
to increase if producers are themselves avid deer hunters, this study confirms that loss
amounts and dependence on the farm income are pivotal factors in determining producer
tolerance of deer (Nelson and Yuan 1991, Nelson and Schomaker 1995). Producers
earning a greater percentage of their household income fi'om the farm and those who
were full-time farmers consistently were less tolerant of losses and were more likely to
have applied for and used special kill permits.

When there is conflict over whether deer numbers should be reduced to protect
farmers from crop losses and these value differences emerge, an agency might be able to
target misperceptions and to remind stakeholder groups that they share common values
such as financial security and recreation. In addition the agency may need to educate
deer hunters who do not perceive that producers’ financial security is at risk.

Alternatively, agencies may target the behaviors of both groups which impede the
ability to mduce losses caused by deer. For instance, 17% of the respondents with
intolerable losses did not encourage the harvest of antlerless deer by their hunters. Other
studies have shown that farmers do not maximize the effectiveness of hunting as a
damage control tool (Eriksen 1994, Nelson and Schomaker 1995), and that farmers resist
using fencing even if cost shared with the agency (Islieb 1994).

Still farmers with intolerable losses appear to be doing a reasonable job of
targeting antlerless deer, especially in Menominee County. Unfortunately, their efforts
may not be sufficient to control their losses if adjacent landowners are not contributing
to the harvest of antlerless deer or allow no hunting whatsoever. The finding that 52%

of the respondents indicated that low deer harvests on adjacent lands were contributing

185

to their crop losses appears to indicate that hunter access to private lands and hunter
behavior may need to be addressed by the agency. The recent failure of Michigan’s
firearms deer hunters to harvest more than 1/2 of the agency’s 1996 antlerless harvest
goal speaks to the size of the problem (Michigan Out-of-Doors, January 1997). A
season such as Wisconsin’s 1996 Earn-a-Buck season offers one method of forcing
hunter behavior change. Alternatively, allowing greater opportunities for antlerless
harvest by non-farm landowners is suggested by Nelson and Schomaker (1995) whose
finding was that non-farm landowners were more likely than farmer owners to harvest
antlerless deer.

The remainder of this chapter will further examine factors which appear to be
focal points of contention among stakeholders: agency credibility, biologist competence,
program administration and stakeholder involvement and education. In each case an
attempt is made to describe how deer managers can proactively target these factors and

thereby diffuse the potentially disruptive nature of these issues.

Agency Credibility

The influence of agency credibility on farmers has the potential to affect not only
acceptance of depredation assistance programs but other natural resource management
programs as well. It is desireable that stakeholders believe that management agencies
are competent and trustworthy.

Anti-government attitudes present a challenge to professional wildlife
management. These attitudes appear to center on the perception of government as an

independent regulatory agency rather than as a democratic public service organization.

186

This is reflected by the 2.5% of respondents who believed that the MDNR was catering
to itself when setting deer management goals. These perceptions of government as a
self-perpetuating regulatory agency versus a service organization appear to be linked to
perceptions of the fairness of the consideration given to all stakeholders by the
government. Currently it appears that farmers do not trust the MDNR to fairly consider
the interests of agriculture in their deer management decisions, as 71% of the
respondents apparently believe that the current weighting of farmer and hunter interests
by the MDNR was not fair. When an agency is perceived as unfairly catering too much
to the interests of a single group (i.e. deer hunters, farmers, animal rightists, etc.)
stakeholders lose trust in the agency and may more frequently pursue disruptive courses
of action to get their interests considered (i.e. lawsuits, legislative action). Actions
caused by such perceptions of unfairness may in extreme cases lead to a stripping of
agency authority to manage natural resources as was the case in Colorado with furbearers
in 1995 and 96 (Lipsher 1996). Such action is perhaps less likely in Michigan given the
recent passage of a ballot initiative giving the MDNR and the state’s Natural Resource
Commission greater control over wildlife management in the state.

The finding that farmers in this study generally did not consider the current
weighting of stakeholder interests by the MDNR as fair is a major problem facing deer
management in Michigan at this time.

Disruptive issue activity among farmers appears to be largely a result of
accumulated frustration and the inability of producers to control losses even after having
sought assistance fi'om the agency. In this study, producers with chronic intolerable

levels of loss were twice as likely as producers with less deer damage experience to

187

anticipate taking disruptive courses of action if intolerable losses continue. It was
hypothesized that producers might be becoming more tolerant of crop losses over time as
they learned to control losses and no longer contacted the MDNR for assistance. The
opposite is apparently the case, as producers appear to remain intolerant and may stop
contacting the agency because they perceive it won’t help their situation. Producers may
learn to cope with damage, but they may also cope with deer damage by taking
undesirable and perhaps even illegal actions to reduce damage after prolonged losses
without substantive assistance fi'om the agency. The perceived slowness of the MDNR
to respond to their concerns by reducing the deer herd appears to have caused producers
in Menominee, Presque Isle, and Saginaw counties to explore other avenues for making
themselves heard (Erdman, Long, and Reeves pers. comm.) Testimony at legislative
hearings and threatened court action appear to have come about because of perceived
delays or lack of action on the part of the MDNR.

Such perceptions also suggest an emerging issue concerning the competence of
the agency and whether it can adequately control the harvest so that deer population
goals can be met. This was reflected by the respondents who were generally divided
about whether the MDNR had the expertise and information to manage the state’s deer
herd. The current deer herd size and its tremendous reproductive potential are such that
the number of producer complaints could quickly increase if significant reductions in the
herd are not made fairly soon. This is doubly alarming if hunter values and behaviors
continue to favor shooting bucks and if hunter access to private lands becomes more

restricted. Without being able to control hunter behavior and hunter access to private

188

lands it does not appear that agencies have the ability to quickly “turn off the tap” when
deer numbers become problematic.

This suggests three immediate courses of action for agencies. First, gain the trust
of stakeholders and involve them in the management process, and second, attempt to
remove obstacles that prevent immediate action from being taken to reduce crop damage
in the year when it becomes intolerable to farmers. At least intolerable losses must not
be allowed to go unaddressed. Finally agencies should attempt to maintain deer
populations at what they believe to be appropriate density levels.

Biologist Comflnce

Contact fiequency influenced perceptions about the professionalism, knowledge,
and expertise of local biologists as they are related to managing crop damage. Thus,
local biologists appear to have the potential for affecting producer attitudes about the
agency as a whole by moderating beliefs about the competence and trustworthiness of
the agency. The tendency of biologist credibility to increase as contact with producers
becomes more fi'equent, provides a clear opportunity to improve producers’ perceptions
of the agency.

Given that farmers control 28% of the state’s land area statewide, and a
significantly greater percentage in southern Michigan, such farmer attention might be an
agency priority because of the potential impact on a number of species, especially if
management is to be accomplished on an ecosystem scale. Since 48% of the
respondents and 19% of those reporting chronic intolerable losses never had any contact
with their local MDNR biologist, it may be effective to seek opportunities to increase the

exposure of the biologist to area farmers.

189

Sixty percent of respondents were not entirely sure that biologists could
understand the significance of crop losses to their financial well-being. To be better able
to evaluate and understand farmers’ concerns about losses, managers might attempt to
learn as much as possible about crop production and marketing. It may also be
appropriate for biologists to receive some interpersonal training to improve farmer --
biologist interactions and to ensure that the agency is represented uniformly between
counties.

Program Administration

Several issues uncovered by this study involved the administration of the
agency’s crop damage assistance programs. These issues provide concrete targets for
reducing issue activity.

Agency administration of the crop damage program was questioned by some
farmers who felt that applying for assistance was unduly difficult and that once shooting
permits were obtained that their use was overly restricted or too confined to make them
effective. Farmers frequently expressed dislike for the paperwork involved with the
application procedure for block permits. Streamlining the application and reporting
procedure for these permits while providing rationale for the imposed regulations may
case some tension among farmers. Producers also disliked having to pay for permits
especially when hunters benefit by keeping the deer. Farmers also commented that they
were constrained by the regulations surrounding shooting permits, and some complained
of harassment by law enforcement personnel when they attempted to use their permits.

Publicly acknowledging farmer contributions to deer management may also alter

perceptions of the agency’s priorities. Allowing a farmer a fiee deer hunting license on

190

their own property, or permitting them to keep for their own consumption the first deer
taken on a shooting permit might ease objections of having to pay for block permits.
Ohio, Indiana, Wisconsin, and New Jersey have such regulations in place and could be
contacted to see how these have been received.

Though producers largely favored the manipulation of hunting seasons to control
deer damage, managers would still need to find effective seasons acceptable to both
producers and hunters. Several producers indicated that longer and/or more fiequent
seasons (early goose, late goose, spring turkey, etc.) meant they were bothered more
fi'equently by hunters looking for permission. Additional or longer deer seasons may not

be acceptable to all farmers.

Proactive Opportunities

There are several opportunities for the MDNR to encomage behaviors during the
latent and emerging phases of issue development that may forestall the issues ever
reaching the active or disruptive stages.
Non-lethal dgmed_§._tion control

Encomaging producers to be proactive and to adopt non-lethal damage controls
may decrease some conflicts over the shooting of deer to protect crops; however, general
use of non-lethal control should not be expected. Islieb’s (1994) finding that producers
may not fence fields despite significant cost-sharing by the agency suggests that
producers strongly object to having to pay anything to manage deer that feed on their
crops. In situations such as new orchard blocks, some producers adept fencing when

shown the long term costzbenefit of doing so (Long, pers. comm.)

191

Effective shooting firmit use

Though Horton and Craven’s (1995) work indicates that shooting permits are of
limited effectiveness as generally practiced by farmers in Wisconsin when restricted by
traditional hunting season shooting hours; appropriate use of shooting permits may be
effective at reducing current year losses and aid in controlling subsequent losses when
combined with block permits and normal hunting. Farmer comments frequently
indicated that shooting hours need to be relaxed for shooting permits and that shooting
permits need to be issued earlier in the growing season. Earlier use of shooting permits
would ensure that deer that are actually doing the damage are taken and prevent losses
fiom occurring.

Farmers resist earlier use of shooting permits as there are some who do not wish
to shoot pregnant or nursing females and fawns (Horton and Craven 1995). Hunter and
general public resistance is likely as well; however, this resistance might be moderated
by providing the knowledge that only deer causing losses are shot and “innocent” deer
will not be targeted later on. Shooting permits might be made more effective if
producers are allowed to occasionally shoot after dark, as studies have shown that less
than 50% of the deer that may use a crop field in a night will be present at dusk
(Montgomery 1963, Larson et al. 1978). Communications with USDA-APHIS-WS
professionals also indicates that after initial attempts to shoot deer fi'om baited blinds
during daylight hours, shooters found deer visiting baits and becoming visibly active
later at night (Parr pers. comm.). Provided that neighbors concerns about such shooting

can be addressed, it might benefit producers to be able to shoot during night-time hours.

192

Potentially equally effective and less controversial would be an integrated program of

occasional night shooting combined with regular night harassment.

Effectiveness of block pgrmits
The findings of Nelson and Yuan (1990) as confirmed by this study indicate that

farmer use of block permits increases the number of deer taken per farm acre on affected
farms, and therefore can be assumed to reduce the subsequent year’s crop losses
somewhat when the permits are used liberally. However, Sitar (1996), suggests that
because of factors associated with deer migration that block permits used during
Michigan’s general firearms season may not target the deer doing the damage, and that
an earlier use of block permits would be more appropriate. Unfortunately, it is not likely
that bow harvest can be increased substantially to improve the use of these permits. An
early firearms antlerless season, as will be attempted in the fall of 1997 in Deer
Management Unit 215 in Menominee County, may be an effective means of increasing
antlerless harvest and use of block permits. However, a large scale implementation of
such a season should be approached cautiously, as bowhunters may oppose an
infiingement on their season and firearm hunters have expressed desires to maintain the

traditional November 15th. opener (Hauge 1997, Michigan Out-of-Doors April 1997).

Hunter Ma_n_agement

Encouraging farmer management of hunters to maximize on-farm harvest of
antlerless deer could help to prevent losses fiom becoming intolerable on some farms.

Forty-three percent of farmers with tolerable losses and 17% of farmers with intolerable

193

losses did not encourage the harvest of antlerless deer on their farm. Farmers may not
understand the need to harvest antlerless deer or they may not feel comfortable imposing
restrictions on those that hunt their lands. Eriksen (1994) found that farmers with
chronic deer damage kept poor records of deer harvested on their pr0perties, and those
that did revealed that the harvest was too buck-oriented to effect a reduction in deer
numbers on the farm.

Several farmers in Menominee county have found that by leasing hunting
privileges they are able to recoup some of their losses to deer. However, if leasees are
not encouraged to shoot antlerless deer, leasing may do little to resolve chronic
intolerable losses. Farmers should also recognize that seasonal leases will result in dead
periods or days during which there are no hunters on the property and thus no potential
harvest of deer. Since there are reliable safe hunters who cannot afford to pay a fee, but
who are willing to fill their antlerless tags and/or block permits on the farmer’s property
to help the farmer; farmers might consider leasing their land during the first week of the
firearms season but not charging a fee the remainder of the season. Farmers may also
recruit bowhunters and black powder hunters to hunt in the early and late portions of the
season. This is not an exhaustive list of ways that farmers might better manage the
hunting on their farms, but it should be clear that agencies have opportunities for
working with farmers to promote such on-farm management.

There are also regulations that restrict the ability of farmers to efficiently use
regular antlerless permits to control deer numbers smrounding their farms. Cmrent
Michigan law prohibits an individual with the opportunity to fill one of his party’s

antlerless tags fi'om actually doing so. This restriction discourages individuals from

194

aiding other hunters in filling available antlerless tags and makes it more difficult for a
producer to use regular antlerless tags exclusively as a damage control technique.
Considering that the agency is frequently unable to achieve its antlerless harvest goals,
the MDNR might consider adopting a restricted party hunting regulation such as
Wisconsin’s, which would allow hunters of the same party within non-assisted auditory

range of each other to fill each other’s antlerless tags.

Inadguate harvests on Qjacent lands
Fifty-two percent of the respondents indicated that they believed that low deer

harvests on adjacent lands were a factor in their inability to control crop losses. F rorn
the data gathered and the comments producers returned on the survey, there are
apparently two factors that may contribute low adjacent land harvests: 1) hunter

preference for antlered bucks and 2) limited or restricted access.

Hunter preference

In response to a question on our deer hunter survey 8% of deer hunter
respondents indicated that they were opposed to the harvest of antlerless deer and 17%
indicated that though they supported the choice of others to harvest antlerless deer, they
would not do so themselves. One tract of land in Presque Isle county was identified in
the platbook as the “N 0 Does Hunting Club. Overcoming this preference of hunters for
shooting only antlered deer appears critical for controlling deer numbers and therein crop
losses. The MDNR proposed Deer Management Assistance Program (Reeves, pers.

comm.) also offers promise of reducing damage concerns on private lands. Under this

195

proposal large landowners or groups of adjacent landowners who agree to deer
management objectives would be issued appropriate numbers of tags to regulate buck

and doe harvest on their combined ownerships.

Areas closed to hunting

Some producers commented that areas near their farms provided refuges to deer
during the hunting season. The Audubon Society’s Baker Sanctuary and Marshall public
school forest lands were mentioned as refuges in Calhoun county, as were residential and
lakefront developments in Leelanau county and Lake Michigan lakefiont developments
in Oceana county. The potential impact and significance of these refuges should not be
underestimated. Agencies may be able to aid farmers by communicating to these
landowners and organizations the effect that their policies regarding hunting are having

on producers’ abilities to control crop losses.

Public Involvement and Education

Producers with intolerable levels of loss were more likely to have engaged in
disruptive activity and appear more likely to engage in it in the future and thus, are
important receivers of I&E efforts. However, the recurrent and diverse nature of crop
damage issues makes it just as important to communicate with those producers for
whom crop damage is yet tolerable. Likewise, the confirmation that crop damage and
crop damage issue activity vary across time and across the state points to the need for

agencies to allocate resources to maintain crop damage assistance programs, ongoing

196

communication, education and public involvement campaigns, and to establish adaptable
crop damage assistance protocols.

Though 70% of the farmer respondents personally hunted deer, only 12%
belonged to conservation organizations. Managers should not expect to effectively reach
farmers through media channels used to communicate with hunters. Since 60% of 0m
respondents belonged to the Michigan Farm Bureau and greater than 60% of the farmers
had regular contacts with the Michigan State University Extension service, managers
might consider using these and other agencies such as the Natural Resource
Conservation Service as vehicles for communicating with farmers.

In a short survey we sent to potential issue managers, most MSU-E agents were
not familiar with the MDNR biologists in their respective counties. They also expressed
an interest in having a more involved relationship with the MDNR. Though it does not
appear that MSU-E agents wish to take on large time consuming duties, it did appear
that agents would be willing to aid the MDNR in managing crop damage issues as much
as practicable while fulfilling their normal functions. Thus, potential exists for agencies
to make use of Extension’s interactions with farmers to improve their own credibility
with farmers and to improve farmer understanding of wildlife management objectives.

For example, brochures describing the MDNR’s services to farmers might be *
distributed to farmers via MSU-E during their farm visits. The type of information
included might be such administrative things as annual application deadlines qualifying
criteria for permits, and contact persons. Additionally, the information might include
current estimates of herd density, area management objectives, and other information

deemed appropriate by biologists.

197

Also, in cases where the MDNR biologist is uncomfortable judging whether the
damage to crops is sufficient to warrant issuing permits, it may be appropriate to ask the
county MSU-E agent to also evaluate the farmer’s loss. This may especially be helpful
in the case of specialty crops with which the MDNR biologist is less familiar. Similarly,
if the biologist and farmer cannot agree on the extent of the producer’s losses and/or the

action that should be taken, they might ask the MSU-E agent to serve as an intermediary.

Identification of issue stages

One premise of this research was that issues, including deer crop damage issues,
are developmental. Findings of the study support this premise and suggest cues and
methods that the agency may use to monitor the emergence and development of crop
damage issues. The fact that deer densities fluctuate and that amounts of crop loss are
related to deer densities assures that issues will continue to arise in the future. The
following table (Table 91) is the author’s attempt to define the stages of crop damage

issue development bearing in mind the inherent variability of producer situations.

198

Table 91: Characteristics of issue stages (Crop loss amounts, Deer densities, Intended behaviors,
Management strategies for issue reduction.)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Issue Stage
Latent Emerg'mg Active Disruptive
Tolerance Not a problem Tolerable loss Intolerable loss Intolerable loss
indicator
Loss amount Farmer does not Crop losses Crop losses of Crop losses of the
recognize loss as approaching $500 or approximately $2,000 intolerable level for
occurring 4% of crop or 11% of crop more than 1 year
Tolerance Satisfactory Too many deer may Too many deer take Too many deer take
indicator ntnnbers of deer or may not take action to reduce # action to reduce #
action to reduce #
Density <15 deer/square 20-30 deer/square >35 deer/square mile >35 deer/square mile
rangg mile mile (refuges likely?)
Intended None or no change Producer promotion Producer promotion Producers demanding
behavior of hunting, seeking of hunting, requests action fi'om DNR,
advice from DNR and for permits, use of threatening legal
MSU-E, requests for repellents, seeking action, calling state
special permits action from DNR representatives
Management Communicate with Communicate with Personally contact Personally contact
strategies farmers via media, farmers via media, farmers, encourage farmers,continue all
acknowledge encourage farmers to farmers to manage previous strategies,
farmer’s role in manage their hunters, their hunters, relax seek
management, inform increase private lands shooting permit creative/experimental
farmers about tags, issue shooting restrictions, issue alternatives, involve
agency services permits early in year block tags adjacent landowners
On-going: Monitoring stakeholder tolerance, loss amounts, deer density, issue components.

 

Communicate herd status and objectives to stakeholders. Incorporate stakeholder preferences
into management objectives.

 

The inherent flaw in this situation analysis is that it considers issue development

at the level of the individual producer, while issues are normally considered at the

societal level. Still, all issues start with individuals, and individuals have the ability to

rally others to their causes. Unfortunately this study could not determine the number of

producers required to place a crop damage issue at each of these stages; however, even a

societal categorization may not have utility in that the state of the issue might be

determined as much by the verbosity of a single producer as by the number of producers

experiencing intolerable levels of loss. The evolution of the Michigan Farm Bureau’s

 

199

threatened lawsuit appears to be the result of a few farmers with chronic losses
drumming up enough support among other less vocal farmers, and not solely a result of a
majority of farmers having intolerable levels of loss (Reeves pers. comm.) However, it
appeared that enough counties had to be having damage problems before the Michigan
Farm Bureau would pursue action at the state level.

Tying the development of issues to deer densities is similarly flawed in that all
crop types are not equally impacted by the same numbers of deer. Still, when taken as a
composite there appears to be promise in monitoring each of these indices together.

The crux of predicting potential disruptive activity appears to be monitoring changes in
the relative proportions of producers across each of the continuums (rows), specifically
the variables tolerance of loss, tolerance of deer numbers, and perceived value of losses
(Table 91 ). Thus, to be of use these indices need to be monitored on a regular basis as
are indices of harvest and natural mortality.

Such periodic monitoring incorporated into agency operations will allow
managers to be aware of when different issue stages are approaching. Regular
monitoring will also help to make managers aware of when farmers may perceive that
management is not considering their interests (i.e. Non-issuance of block permits in
Benzie/Leelanau counties in 1994). Crop damage is a localized phenomena and as such
requires some case by case management; however, monitoring the population of farmers
in the area with the tolerance items used in this study will let a manager know when
broader management is required. For example, examining charts such as Figures 21 and
22 may help detect when issues have grown beyond county borders and have approached

a critical mass statewide. Monitoring will also allow proactive informational strategies

200

to be directed to specific segments of farmers even though issues have not yet been
communicated to the agency, and this may in turn help to reduce the need for block and

shooting permits among the farming population.

Research Needs

The response curves hypothesized by Minnis and Peyton (1995) and developed
here (Figures 1620) show promise as quantitative monitoring and analysis tools for
defining Cultural Carrying Capacity. Currently the variances around means are
relatively large and further research might examine methods of improving this
instrument. Assuming that variances can be narrowed, charts such as these might have
applications as monitoring and management tools.

Further investigation might examine the validity of producer’s perceptions that
adjacent landowners’ attitudes regarding deer hunting are impacting crop depredation.
Adjacent landowners’ attitudes about deer hunting and deer hunter attitudes about the
taking of antlerless deer apparently played an important role leading up to Wisconsin’s
decision to require hunters to Eam—a-Buck in 1996 (Hauge 1997). Early identification of
factors such as these that impact levels of crop depredation may allow the MDNR to
adopt regulations that better address factors influencing crop damage problems.

A comprehensive evaluation of farmers’ efforts to control deer damage, would be
beneficial and would allow managers to better aid producers in managing their own
problems with currently available tools. In this survey, indications were that a fair

number of producers with intolerable losses were not taking firll advantage of the deer

201

damage control measures available to them, and Horton and Craven (1996) suggested
similar weaknesses in producers’ applications of deer damage controls.

Finally, agency credibility appears to have a significant impact on the attitudes of
producers regarding deer damage and deer management. The finding that quality
personal contact appears to moderate attitudes about the agency, suggests that
widespread efforts to cut agency budgets and downsize staffs may be counterproductive.
Because of the importance of credibility to all agency programs, it appears important that
researchers test methods of enhancing the credibility of agencies with stakeholders,
especially ways that credibility can be maintained without significantly increasing

staffing.

Conclusion

The diversity of crop depredation issues cannot be reduced into a single
management prescription. Acknowledgment of this variability by maintaining a
structured yet flexible approach toward managing crop damage situations is perhaps the
most important step in managing crop damage issues. No miracle cure for deer crop
depredation is likely to result from this or any other study; however, it appears that crop
loss conflicts can be reduced by focusing energies in 3 key areas: agency credibility,
issue monitoring, and application of controls.

A trusted and competent management agency is a prerequisite to any effective
management program and credibility is no less significant in managing depredation
issues. To avoid disruptive issue development among producers, an agency cannot allow

itself to be perceived as only being interested in perpetuating game or generating

202

revenue. To change this perception agencies will be judged by their actions and
therefore may need to involve themselves more directly with their constituents on a
regular basis. Monitoring indices of producer tolerance and components of issues will
alert managers as to when they need to work with their constituents as well as what
issues need addressing. Such monitoring should reduce the amount of reactive crisis
management that agencies do, and may reduce the fiequency of threats to an agency’s
credibility.

Two final objectives for reducing depredation conflicts are: maximizing efficient
application of available controls and gaining greater acceptance of those controls by
stakeholders. The first objective might be accomplished by removing barriers to use and
effective application of available controls. For instance, requiring hunters to shoot a
larger proportion of antlerless deer in an area; however, agencies typically give up
control of >25% of the potential antlerless harvest by permitting hunters to choose what
they shoot (Hauge 1997, Minnis 1996, Maedke and Anderson 1994). Management that
changes hunter behavior has been shown to be unpopular even though it is necessary
(Hauge 1997), therefore the second objective might be accomplished through the never
ending process of educating and involving stakeholders. Part of the education of
stakeholders should involve convincing them of the need to undertake control activities
and to convince them that the chosen method of control is the most appropriate.

These are not easy tasks, in fact the undertaking is extremely arduous and
complex given the paradoxical position of wildlife agencies and the diverse and often
conflicting dominant values of their stakeholders. Still it is the author’s belief that by

improving credibility, increasing stakeholder understanding and acceptance of deer

203

management and damage control, and by monitoring issue development managers can
reduce the fiequency and magnitude of deer depredation, the amount of farmer mistrust

of wildlife agencies, and the threat of disruptive issue activity.

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APPENDICES

APPENDIX I

 

APPENDIX I

1995 OPINION SURVEY OF MICHIGAN FARMERS
ABOUT DEER AND DEER MANAGEMENT

 

INSTRUCTIONS FOR COMPLETING “[1118 SURVEY:
Please mark hoses clearly, as shown here: D U
A farmer is anyone who attempted to produce a crop (apples. cherries, corn. beans. Christmas
trees, etc.) or an animal product (beef. pork. poultry, mills. etc.) for profit during 1994.
An “antlerless” deer is a deer without antlers or with antlers less than 3 inches in length.
“DNR” means the Michigan Department of Natural Resources.
“Shooting Permits” are permits issued by the DNR for shooting deer outside of the regular
deer hunting seasons.
“Blockl'ermits’ are special licensessold tolarmen in blocltsol's ormore by theDNR lorehootiag
antlerless deer duringthe regular deer huntlnlseaaoua.

 

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i. Pleasechcckoneofthe followingthatbeetdeecn’besyou.

o m-MFMFMBWWWAMIMWMUWWMFMJ

u PART-m rmnrrmuonsuormmvoccwmommuesmwsormmmerm.)
0 mo rm mou rm . . (Thank you for your cooperation, please place the
questionnaire in the enclosed envelope and return.)

2. in 1994 about how many total acres (including homestcnds. fecdlots, woodlots. fields. buildings. etc.) of your fanning
operation were:
mm___acees
I) Item FROM mart sum—eaten

3. Plusehidkacdiepemenugeofyomamudfarmmleswtedbyucbcmpupmduct

Crop or Product 96 of Farm

 

212

 

4. Howmanyacresdidyoufann'uilmmOceanacounty. Pleasenddothcrcumtiesandncreagesyoufamed'mlm.

 

 

For the remainder of this questionnaire please refer only to that portion of your farm that falls
within the borders of OCEANA county.

 

 

S. Weneedanapproaimateiocaticnofyomprimaryfarmingopcratioo souiecananalyaemoundinglanduseanddeer
disuibmicns. OnthkmapplceucbcbdflpafimofOcanacmmyinwhkbyoudomeMofyowfamhg

-MapofCountypiacedbcre-

6- WmmmMmI-fomhorhd (mmmwamwnmm
mm?

Elves DNO:OOTOIO

213

 

W

7. if you grew row I field crops in 1994, please provide your best estimate of the following information.
’11on don‘t knowor aren’t sure how much you lost to deer place a question mark “‘P“ in the box.

 

 

 

 

 

 

 

 

 

Crop Type 1994 1994 Ave. yield per Lost to deer The level of my 1994 losses to deer
Acre acre. in 1994! was...
s l-Not a problem.
Please Include (Estimated 2% problem but tolerable
the type of unit Bushels, 3-A problem and I intend to increase
(buJac, tonslac, Tons. my efforts to reduce the losses below
cwtnlac. etc.) M...) 1994 levels.
(Circle onh one response per crop}
conn' 1 2 3
' screams' ' 1 2 a
Weenies" Jun 1 2 a
m 1 2 a
urns—aria 'v‘ 1 2 3
mm 1 2 a

 

 

1 2 3

 

 

 

 

 

 

 

 

 

8. PMsmpuemefdMgwnmbymfidmgmeMisuuMdmcfibywmwfiewmm

1994.

 

(Forexampiez'in1994.myworst|oesestodeeronasingleiieidwereona1flacmfieldof
Mieredeerreducedmyyieidfl'omthatiieidbyflfl

 

N1”.WWLMW-NAWEFELDMMA (h) _ACREFELD“ (cop)

 

museum MYYELDFRWWTFEDIYM 5.

 

9. Did you grow fruit trees, nursery products or Christmas trees commerchlly in Oceana county in

1994?

Bras [3110 =mroe12

214

 

I. S TREE 0 T DEERIN

l0. If you grew fruit. Christmas or other trees in 1994, please provide your best estimate of the following information.
‘11on don’t know or aren't sure how much you lost to deer place a question mark “P in the box.

Acres my 1994 losses to
per d ll of Farm 1! total deer was...
acre Trees lbs. lost 1=Not a problem.
damaged due to 2-A problem but tolerable
by deer deer 3=A problem and I intend to
in 1994.‘ browsing increase my efforts to reduce
in 1994.' the losses below 1994 levels.

 

 

it on intendtoincrease our deer damn control efl’ortsin I995. ilease=>GO TOI12
llnoglease=>GOT0111

ii. li'yourI994Iossestodeerweretolerable.pleasegiveyombestestimateoftheleveloflossthatwouldbe
intolenble;thstis.causeyoutotake(furdicr)actioutoreducelosses.

of Trees

 

215

12. inadditiontotomlyieldbahowsignificantlydoesdeudamagemducediemflnofyomhuveueduops?
luvdwlostmaopqunluyduetodeeris...(01eckONLYone)
D GREATER‘IHANTHEVALIENTHEYELDLOSTTORER.
D mromvuusor‘n-EYIELDLMTODEER
D LESSTMNWVMND‘EYELDLOSTTOEER.
D “LOSTVALIENCRNWBM
D remorm

l3. ConsideringALLcrops(row,field.fiuits.trces.ctc.)howdoyoumte1994’stotal lossestodeer?
The level ofmy l9941westodeerwas... (Cheek ONLY one)
0 NOTAPROBLEM.
D ammiwmmmmmermmmmmmseea
D AWMIWWWWMTOMDELWIEWIWW.

l4. Comparedtodcer,howwouldyoudescn’bethecroplossesyouincurredtoallotherwildlifesuchasbenvcr,
mmblackbudamicecmn 1994? (CheekONLYoue)
D Lossesmoeeaansmmmmesroomm
D mmmmwwmammmm
D mmmmmmmmmmm
n lunarmmmtoesesmonflm
c] runoraule

15.0nwhathsveyoubuedyoiueni1natesofi994cropiossestodea’? (CheckALLtlintapply)

n imveuooeamruvwssesm
nmmeaoroeenseeueirans
unemmeoameroenors
messesnvomrmunew
Bowmanormsraecemmmaecoaos
es'ramesuaoeevonenrensous

0mm

0mm

0mm
Darren-museums)

16. Oftbeyearslisted below,which yearwasyourloestodcerthehighestinOceanacoimtyHCheckONLYoue)
01986 01987 01988 01989 01990 01991 01992 01993 01994

=ooros1s
DUndecidedaoorous

 

l7. Comparedtoyour 1994 lossestodeerinOceanscmmty,howmuchmorewasthelossfortbeyearyou
mdicatedabove? (Pleasespeeify)
Ownemasitosrmsu
Danom ummntosrenses
011018111:

 

216

ll. Howwouidyouratetheleveloi’lossyouindicatedintheabochuestion?
Nucleus! ofeylossestodeerdwinglheyearcheekedabove in #16 m... (Check ONLY one)
D mam
D ammiwmmmmmrommm.
D AWWIWWMWWMLOBSES

l9. lnthcfollowingtable.indicatewbcthcryouhavedoneand/orareiikelytodoanyot'the followinginflm
mtodeerdamageinOceanacotmty.

PLEASE RESPOND TO ALL 1 COLUMNS

ALL that

USE N BLOCK FEW)

WWNONTOWWWCROPLOSSES

 

217

MW
20. Whichcropgeneratedthemostrevcnueforyourfarm'm 1994?
2!. Whatwasthepriceperrmit(e.g.$lbusheL$ltou.Slet)forthiscropin1994?!

 

per

22. Forthiscrop.pleaseindicateinthetablebelowdiedamageconuoltechniquesyouusedbyfieldml994
including fields receivingnopmtecdomyourestimateofdiecosu(ifany)ofdietechniquesused.and
the efl’cctsonyields. lfyouhavemondian3fieldsindliscrop.pleaseprovideinfonuationfadie3

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

fields receiving the most control.
Costs of Damage Control Teehfiues
Used on Fields (1! an
Techniques used in 1994 installation Year [”4 19" Yield lalorIetiou
essi installed Noe-labor Labor
Mausdbydear. (ltany) Maintenance hours Pleaseprovideboththe
0' lid! M Mull:
MALI-Mural!) Dread-s (re. balms. tons/acre.
etc.

0 uoeournou

a museum 1994 Without

Fence «cumin-es).-- Ave.

0 wovenwrre (so rem _ yield per conuols.

...... acre: how much

0 manners.) _ yield

a W __ would you

oevnces ....... __ _ or did you
_ Ci snowstorm ........ lose?

0 swarm ..................

U O‘I'HER:0bannM)

0 uocournocs

0 WWW 1994 Without

mannerisms)... Ave.

0 wovenvms (to ram .... _ _ _ yield per controls.

n REPELLENTS ............... acre: how much

0 mm.-- _ _ __ yield

0 mm ......... would you

D neocit reruns .... .... _ _ _ _ or did you

D Ultimate-denote) lose?

0 uocoums

D museum 1994 Witt-tart

reuee aoauonewules)- Ave.

0 woveuwlteteorem yield controls,

...... acre: how much

0 yield

mums............. ...... would you

n W _ or did you
_ m ....... IO”?

0 sitcoms rem ........

c1 stoexremsrs ..............

g Milka”:

 

 

 

 

 

 

 

 

 

 

 

23. Anthaedhawstmbmamodflcdwimtheeeficwsthnmnmmfleaedmtheprccedingubk?

(Place waif!)

218

v _. __ o I. m. I . his a .1111. n : . l ! JL'II an ’1‘;

24. Please indicate to what extent you agree or disagree with the following statements by circling the appropriate
response.

 

SA-Strougiy Agree A-Agree U-Undeeided D-Disagree SD-Strougly Disagree
(Circle ONLY one response for each statement

 

i cannot control my crop losses because not enough deer are 3* A U
harvested duangthe huntirlseason on lands adjacent to my farm.

 

 

SD
—
SD

 

 

 

 

Hunting seasons should be designed to reduce deer numbers so “i A U T
that special kill permits to control crop losses are not necessary.

 

25. Doyoubuntdeeryourselfl Elves Duo-soonest

26. Howimportantisdeerhuntingmyoucompuledtoothertypesofrecreationinwhicbyouputicipamsuchasother
typmofhuntingfishiugcampiugjoggingbowlingcuupedfivemn?
MWS." (CheckONLYooe)
D “WWMEWMWNWIWR
U mmmmmmmmmsumimu
D WKWMMMWWMMNWIWATE
U mmmmmmmmmumimm
D WATALLMANT‘IOK

27. PleasccheckmwhoueallowedtohtmtdeerouyomfarminOceanacotmty.
D ”WWW .oorosss
D MENDIORMYW‘IEFAKY
D FRIEIOSANDIEM
D mmsmaemm
D MYLNDISWTOWWWMTOW.WDWAIKW
D WWWWMYAFEEMLMMYW
D WWSMDPAYAFEEGLEASEMYW

 

F ounsuunpumrmsemormsquuamhimaco'i’osso j

28. lfhunterspsidyoufortheprivilegeof deerhuutingouyourfarmin l994,howmuchdidyoureceive'u1totalfrom
deerhuntersusingyourOceanecotmtyfarminlmfl orOthcrpeymeut:
29. Onwhatbasiswereyoupaidbyhms? (e.g.,.mually,daily.etc.)
Pleaseexpla‘m:

 

30. AbomhowmyhmtashmmddcumhndswammmOceancumtymmeopmmgdayofmefireums

deerscasouin1994?
Hanna [3100mm

3|. AboutbowmanydeerhuntersdoyouthinltthelandsyoufarminOceuracotmtycansafelysupportouopeningdsy

ofthefireannsdccrseason?
_W DI‘MWW

219

 

32. Didyoudoanyot’thefollowingtoencouragethosewhohuntedyour landstoharvestuflglmdcerin l994?
(CheckALLthatapply)
Diwmmmmflammu1m.
D lDlSTRDUTEDILOCKPEm
D lREOWSWDMTWEERBEWEFMEm
meleasedsecrme)

 

33. ApproximlyhowmanydeawuetkeumyomfamhndsinOcumwmtydufingmofme l994dcer
huntingseasons?
museum _mmessoeea 0100mm

 

34.?1ashdiathownmnyacmofdielmdswaumedml994Wwemmh
ofthefollowingnon-cropcovertypes.

 

W
W

 

SHOOBEQ PERMII§ are permits issued by the DNR for shooting deer M of the regular
deer
hunting seasons.
BLOCK PERMfl are special permit sold to farmers in blocks of 5 or more by the DNR for
shooting
antlerless deer durinlthe refiner deer hunting seasons.

 

35. mummmmmwammmmommmmmrmmm canny?
DYES 030.0010.“

36.1-lsveyoueverrequestederomtheDNR?
Ores Duo-source“

 

 

37. Abouthowmanyyearsdidyoummmootingpermit? runs

38. Abouthowmanyyearsdidyoumfizgshootingpermits? YEARS
39- WMWWMW?

ores Duoaooroua

40. Howmany Shooting pennitdidyoureceiveinl994? Fem

 

4i.Didyoureceiveasmsnyasyouthoughtyouueeded? Elves 010
42. Howmanyofthoee Shootingpermitwereliliedinl994? PERIITSWEREFLLED.

 

220

 

43. HaveyoueverrequestedWfiomtheDNR?
DYE! DWQGOTOIII
44. AboutbowmanyyesrsdidyoumBlockpermits?
4S. Abouthowmanyyearsdidyoummockpennit?

46. Didyourequestwm 1994?
three Duoaoorosss

 

 

47. How many Block permit did you receive in 1994? __ stock PM

48.Didyoureceiveasmanyasyouthoughtyouneeded? Oven Duo
49. HowmanyoftboseBlockpermitwerefilledin I994?

 

ILWKPERHTSWEEFILED.

50. Please indicate inthe folbwingtblawhedwywapeewdtayuwidithefolbwingmtabomm
mmwmemmhmmwcimlhsanmn

 

SA-StrouglyAgl'us A-Agru U-Uudecided D-Dissgree SD-Strougly Disagree

 

 

 

 

 

 

 

 

 

 

 

Shooting rennin: (Circle ONLY one tor each
statement)

In this county. shooting permits are distrbuted fairly to growers who need TA ANT— 0 ‘0

themreggrglessofthevalueoithecropsgrown. _

Shooting permits are successfully used to reduce crop losses in this ‘A A U D '10—

county.

in this county. shooting permits should be given more readily to growers 3A A U r70—

of high value crops than to growers of lesser value crops.

Regardless of whether shooting permit actually reduce crop losses. they 'A A U T7

are still important to farmers because they at least make farmers feel in

control of the situation. 4__

My neighbors' objections to the use of shooting permits influences my 8A A U 0 "'8?"

decision to use them.

Too many male deer are killed on shooting permits. 3A A U T

The venison and/or recreation i get by using shooting permit is important ‘A A U T10—

10 me.

Too many of the deer killed on shootingpermits are not utilized. 3A A U T

 

 

 

 

 

SA-StronglyAgree A-Agree U-Undeeided D-Dlsagree SD-StronglyDisagree

 

 

 

 

 

 

 

 

 

 

 

Bloch mm: (Circle ONLY one tor each
statement

In this county. block permits are distributed fairly to growers who need 9A A U 0 ”7°"
them regardless of the value of the crops grown. __
Block permits are successfully used to reduce crop losses in this county. 3“ A U L T
in this county. block permits should be given more readily to growers of 3A A U U T
high value crops than to growers of lesser value crops. _
Regardless of whether block permits actually reduce crop losses. they are i A U f *0
still important to farmers because they at least make farmers feel in

control of the situation. _

My neighbors' objections to the use oi block permits influences my A U 0 '75—
decision to use them. J

The venison and/or recreation l getbyuaingblock permit is importantto A U U 15"
me.

 

 

 

 

221

Si. Please indicatewhetheranyofthe followingmconsideredbytheDNRwheo issuiugpermitstoafarmert’or
killingdeertoconu'olcroplossescauscdbydeer.(CheckALLthstapply)
D MFMNOIALOEPENOENOEOFWFAMRONTHEOROP
Ci mmmromwnmsamnmmwromutossesmmm
D nermwsmssmmmmmmoumum
Dmmmmwwrmmammmvnmmmmmfln

Donat-

$2. PhaudescnbemychsngesyouwwldlikemseemadcinmemmnsyminOceanacounty.

 

$3. leedum‘bemychmguyoumfliikebmemademdemmOmamty.

 

S4.thnwaamyuuopmionmwhahamedurpopuluimsianCEANAmtytacceptblehow
iruponantiseachot'thefollowingconsiderationstoyou?

 

 

 

 

 

 

 

 

 

 

 

 

 

Circle ONLY one answer for each row. a n "vita” I In” W W ”M m
= 9

Personal recreational benefits from deer
(e.g., viewing. hunting. feedingLetc.) “a" ”“3 m "°' ”
Recreational benefits from deer provided to
others in the county. veav sore surnrr nor ”
Personal economic benefits from the
presence of deer (e.g., hunting leases. goods m m m "m "
and services provided to hunters and
_tgurists.)
Economic benefits to the county from the W m _uor u
presence of deer.
Personal crop losses to deer. fill WW W; 0
Other farmers' crop losses to deer. VERY 30" W nor 0
maggot deer-related vehicle accidents veer m m nor u

 

 

 

 

 

 

222

SS. FortheponionofOceanacumtywhaeyoudodienujofityofyourfamingwhuisyourbestestimateoi’ what
dieaverugcnumberofdeerpersquaremilewasinctoberofl994?
IELEVETI‘EREWEREM ADD NERPERWNENMAREANWRNIS“.
U INAVENOIIAWNAm

 

 

56- Cmmsmwmmmmmmofhawmmmmwbwwflm
describeyourreactiontothenumberot’deerpersquaremiiethatyouindicatedinquestionss above?
(CheekONLYone)

0 roosew.mornrreroromvrooosos¢neuammr.
0 roarewsurioonorerrmrooommencanourrr.
a rmssnsrreowrmnemoroeen
Broom.n1moouorerrerorooomvnmanown.
D room.mrerreroromvrooososemanwrrr.

57. lnthepostionofOceanacountywhereyoudothemajorityofyotnfanuingwhatnumberofdeerpersquare mile
' Wflhmwm(wmmywb°femwiuflfiflnfl
newsroemerumenrotmwounue _e-Ireasaimur.
D rwweuooenmm

58. Cmsidahgbommposifiwawmhehnputwmflmdmebalcommitymlmumdimme
mmmmbuofdeupusqmmibmnyouwouflmmhthumofm
countywhereyoudothemajorityofyomfarming (AnswerBOTHMbelow)

m an Iwornouoreemuarommremum—oeenmmua
D imveuooeamrsceven

m . rmouornewuuamroceaaremmm—n-tmaatmue
c] luveuooeamrsoeven

$9. Howwouldyoudescribethetrendindeer numbersmmmthatportiouofOceanacomty

whereyoudothemeiorityofyourfarming? (CheckONLYone)
0W DWDEMEADNYEM Buccaneers Dlmm

223

VI N S

60. in any given year, how often do you typically have contact with local DNR biologist(s)? (Check ONLY one)

Uneven
Dussmoncereavua
Darewraesream
Unanimouceream

61. Please indicatetowhatextentyouagreeordisagreewithtbefollowing statementsbycirclingtheappropriate

response.

 

SA-Strongly Agree A-Agree U-Undecided D-Disagree

(Circle ONLY one response {or each statement)

SD-Strongly Disagree

 

Crop losses are imposed on farmers by the DNR and hunters.

A

U

 

The DNR has the expertise to manage the state' s deer herd.

8A
is

A

U

’6

 

The DNR has enough information on the deer population to
adequately decide how many deer to harvest in Michigan each

”8!.

A

U

 

DNR biologist treat farmers in this county professionally and with
respect

9'

 

Our local DNR biologists can adequately determine the amount of
loss a farmer is incurring to deer.

fl

 

 

Our local DNR biologists understand the significance of crop

 

lossestotheeconomicwell—beinLofthefarmer.

 

 

 

“l
818181618181

 

 

 

224

62. Please distribute i00 points within each of the following two coiurnns to indicate how much importance you think

meDNkmmandmgu-moneachofthe following interestgroupswhcntheagencysetdeer
population goals for Oceana county.

 

For example. if you think that the DNR currently places importance only on hunters’ interest
in setting deer population goals in OCEANA county. place 100 points next to hunters and 0
points next to farmers in the column on the left below. Or. if you think that hunter and farmer
interests are equally important to the DNR. give each group 60 points. if another interest
group is important to the DNR. write in the group and weight them accordingly. Follow this
same procedure for the column on the right to show us where you feel the DNR my be
placing Importance.

 

 

 

 

 

 

n-reom menus
cunnermv Purses WTANCE on: snow: PLACE marries on:
__ m __ W
mum _ rm
onen___ ones;
.100 .100

63. inanygivmyec.bowfiequafllydoywtypicallthwntflwi¢0canaMyMSU-Extnfim
representatives? (CheckONLYone)
D lever!
B Lessmosrcemm
Darewwmvm
0 mmoucerentsormr

64. Please Mkatmwhumwamadimwififiefoibwingmbyeheihgmem
response.

SA-Strougly Agree A-Agree U-Undecided D-Disagree SD-Strongly Disagree
(Circle ONLY one response for each statement

 

 

 

 

 

 

The Oceana county extension agent(s) treat farmers professionally and 3A A U T
in respect

The Oceana county extension agent(s) are knowledgeable about 3A A U T
farmfig. J

The Oceana county extension agent(s) are helpful for locating 'A A U 0 80
information about farmigg problems.

 

 

 

 

 

 

225

W

 

We need the following information to compare our sample of farmers with the statewide population of farmers.
As with the other information you have provided, this information will also remain confidential.

 

 

6S.AMwhapaeentofymuhouseholdmincmnewugaiemdbyfamingmi994? 16

“.mmmwmmumnrmmmmamammp

(Check ONLY one)

D tessmsssss Cl steam-mass D nauseous.“ Cl mascots.“

Cl monomers.” D mnooossssse El seo.oooosn.sss Cl cesareanwrsroocoo
67. What is your age? YEARS

68. HowhnghswyoubeenfummginOcanacmnuy’l—m

69. Whatwasyourhighest level ofscboolingMordeyeereceived? (CheekONLYone)
0 uoscrm
c1 usesnwrsmorune
0 continuum
El rsorrscrromornomoaeourvmm
D aorrecottsoeoarecreecarscuoor.
D mummmmnanml
D mummssmmmmus.mo.rau.nsi

70. Wouldyoubewiilingtoparticipate inafoiiowerpmveyMtheefl'ectivenessandmediodsofthedunageconuol
youhaveused? (CheckONLYone)
Elves Duo Dimveuoruseomvcomm

7]. Please indicate ifyoucurrentiybelongtoanyofthefollowingorganintions. (CheckALLthatapply)
CJ swarm
D mrmmnous: .
D mmmmmmmteawwvmmm)

 

neasesrecrv: - -
D WATION mm (e.g., MUCC. PheasthorevuJ'lmiolml Wildlife Pedermion)

 

assessment:

 

226

 

We welcome any additional comment you may have that will help us better understand
how farmers view deer and deer-caused crop damage. Feel free to add your comment
here.

W
Picasepiaccdiequestionnaireinthestmpedenvelopeprovidedandmailto:

Peter A. Fritseii

i3 Natural Resources Building
Department of Fisheries & Wildlife
Michigan State University

East Lansing. MI 48824-1222

227

APPENDIX II

APPENDIX II

MICHIGAN STATE UNIVERSITY

DEPARTMENT OF FISHERIES AND WILDLIFE
EAST LANSING, MI 48824-1222

13 NATURAL RESOURCES BUILDING

(5l7) 432-1491

FAX: (517)432-1699

 

May 12, 1995

Dear Sir/Madame:

Recently I sent you a survey regarding your opinions about deer and deer management. If you have
already completed and returned the questionnaire, thank you for your assistance and please disregard
this letter. if you did not, please reconsider taking the time to complete the questionnaire. I have
enclosed another copy in case you may have misplaced the first.

I realize how busy you are at this time of year, and this survey may seem an untimely burden;
however, let me assure you that the time you spend completing this survey will not be wasted. Your
response is very important and will aid Michigan State University Extension Agents, the Michigan
Agricultural Experiment Station and the Michigan Department of Natural Resources in better
understanding how deer and deer management affect Michigan agriculture.

Even if you are not a farmer or have no opinion about deer, your response is important. If you do
not farm, please complete the first page of the questionnaire, then fold and return the survey in the
enclosed envelope.

If the land you farm is not within the county specified in the questionnaire, please don’t throw the
survey away, I need to know about mistakes in our mailing lists; please fill out the first page and
return the survey.

The handwritten number on the back cover of the survey is not being associated with the answers
that you give in the survey; the number is only there so that I do not send additional mailings to
people who have responded to the survey. In no way will your name be associated with the
information you provide on the questionnaire. Your responses will be kept completely
confidential.

if you are unsure about how to answer a question or if you would like more information about the
study that Michigan State University is conducting please, don’t hesitate to call me toll-free
at (1-800-433-3741).

Please help by completing this questionnaire.

Sincerely,
Peter A. Fritzeli, Jr.
Research Assistant

228

APPENDIX HI

APPENDIX III

Telephone Non-Response Follow-up Questions

 

 

 

 

 

 

 

 

 

 

 

 

Phone Number: Name of Person:
lid number: )
Date of Call Contact Message Time of Comments
Made Left Call
Targeted county: Oceana Benzie Leelanau Calhoun Montcalm Presque Isle Menominee
Hello, could I speak with Mr./Mrs. ?

 

This is Peter Fritzell calling from Michigan State University. Recently I sent a survey to
farmers in XXXXXXXX county trying to get their opinions about deer management.
Could you tell me if you/he/she received this survey from Michigan State University?
[I YES, THE SURVEY WAS RECEIVED. D NO, THE SURVEY WAS NOT
RECEIVED.

Did you/he/she return the survey? [:1 YES => STOPE] NO => CONTINUE

About 50% of the individuals I sent surveys to responded. So that I can identify the
weaknesses of this survey can you tell me why you/he/she did not return the survey.

E] I AM NOT A FARMER => STOP
E] I AM A RETIRED FARMER => STOP
[:I I DID NOT FARM IN XXXXXXXX COUNTY => STOP

E] | WAS TOO BUSY FARMING WHEN I RECEIVED THE SURVEY
D SOMEONE ELSE FROM OUR FARM RETURNED A SURVEY
D I AM NOT INTERESTED IN DEER. => CONTINUE

D I DON’T HAVE ANY DEER PROBLEMS

[I I DON’T TRUST MSU WITH SUCH INFORMATION

[I I DON'T TRUST THE DNR WITH SUCH INFORMATION

Thank you.

229

 

Would you/he/she be willing to answer Q questions about deer and deer management
either now or at another time? [3 YES [3 NO

Is this a good time? D YES [:I NO

If this is not a good time can I call back at a more convenient time? D YES D NO

 

 

DAY TIME DON'T WISH TO TALK
Yes -- Okay. First... No - Okay, That’s not a problem.
Thank you for speaking with me. I’m sorry
to have taken your time.
U U STOP

1. Which of the following best describes your participation in farming in 1994?

D FULL-TIME (FARMING IS YOUR PRIMARY OCCUPATION, IN WHICH YOU SPEND >50% OF YOUR
WORKING TIME.)

D PART-TIME (FARMING IS NOT YOUR PRIMARY OCCUPATION, YOU SPEND <50°lo OF YOUR
WORKING TIME FARMING.)

C] RETIRED FARMER

C] NOT A FARMER

2. How many acres did you farm in XXXXXXXX county in 1994?

ACRES El I DID NOT FARM ANY LAND IN XXXXXXXX
COUNTY IN 1994.

 

3. In 1994 about how many total acres of your farming operation were...

 

 

 

 

| OWNED ACRES]
IRENTED ACRES]
TOTAL ACRES

 

4. Approximately what percentage of your annual farm sales is represented by...

 

 

 

 

 

 

DAIRY, LIVESTOCK, POULTRY OR RELATED %
PRODUCTS

TREES, FRUIT OR RELATED TREE PRODUCTS %
CASH CROPS (VEGETABLES AND FIELD %
CROPS)

 

5. Take a moment to consider the crop losses you may have incurred in 1994 due to the
presence of deer. Considering ALL crops (row, field, fruits, trees, etc.) how do you rate
1994’s total losses to deer?

230

Were your 1994 losses... (Check Only One)

[3 NOT A PROBLEM.

D A PROBLEM. BUT NOT SO MUCH THAT YOU INCREASED YOUR EFFORTS TO REDUCE THE
LOSSES IN 1995.

D A PROBLEM THAT CAUSED YOU TO TAKE ACTION TO REDUCE OR PREVENT SIMILAR LOSSES
FROM OCCURRING IN 1995.

6. Have you ever requested Shooting permits from the DNR? [:1 YES D NO
7. Have you ever requested Block permits fi'om the DNR? [:1 YES D NO
8. Do you yourself hunt deer? CI YES D NO

9. Take a moment to consider both the positive and negative impacts deer had on yourself
and the local community in 1994; consider such things as recreational benefits, economic
benefits, deer vehicle accidents, crop losses, etc. to yourself and others. Considering all
these things which of the following statements most accurately describes your opinion of
the size 1994’s deer herd.

There were/was... (Check Only One)

C] TOO FEW DEER IN XXXXXXXX COUNTY IN 1994. AND I HAVE TAKEN ACTION TO INCREASE
THE HERD IN 1995.
CI [99 FEW DEEB IN XXXXXXXX COUNTY IN 1994. BUT NOT SO FEW TO DO ANYTHING ABOUT IT IN
1995.
D A §ATI§EA§ I QB! NUMBER OF DEER IN 1994.
CI TOO MANY QEER IN XXXXXXXX COUNTY IN 1994. BUT NOT SO MANY TO DO ANYTHING ABOUT IT
IN 1995.

C] TOO MANY DEER IN XXXXXXXX COUNTY IN 1994. AND I HAVE TAKEN ACTION TO REDUCE THE

HERD IN 1995.

Okay, just 3 more questions.

10. Relative to the interests of hunters, how much importance do you believe the DNR
currently places on the interests of farmers when setting deer population goals for
XXXXXXXX county? (Check Only One)

Does the DNR currently place...
[:1 AN EQUAL AMOUNT OF IMPORTANCE ON THE INTERESTS OF FARMERS AND HUNTERS.

CI MORE IMP RTAN N THE I R F FARM THAN ON THE INTERESTS OF HUNTERS.
D L IMP RT F R THAN ON THE INTERESTS OF HUNTERS.

231

Relative to the interests of hunters, how much importance should the DNR place on the
interests of farmers?

The DNR should place...

[:I AN EQUAL AMOUNT OF IMPORTANCE ON THE INTERESTS OF FARMERS AND HUNTERS.

C] MORE IMPORTANCE ON THE INTERESTS QF FARMERS THAN ON THE INTERESTS OF HUNTERS.
C] S IMPORTAN N THE INT T F FARMER THAN ON THE INTERESTS OF HUNTERS.

11. In what year were you born? [:1 Refused to answer

I have one final question.

12. My early analysis of the responses I have received indicates that peoples attitudes
about deer are related to their family’s dependence on farm income. Could you tell me

approximately what percentage of your gross household income was generated by farming
in 1994?

% E] Refused to answer

 

That’s all.
Do you have any questions?

Thank you, I really appreciate the time you’ve given me. Don’t let me keep you from your
work anymore. If any questions do come to mind please don’t hesitate to call. The

number is 1-800-433-3741. => Stop

232

APPENDIX IV

APPENDIX IV

Postcard Reminder Text

Dear Michigan Farmer,

Recently I sent you a survey regarding your opinions about deer, crop
damage and deer management. If you have already completed this survey,
thank you for your assistance. If not, please complete and return it as soon

as possible.

PLEASE HELP" We need your assistance to ensure that Michigan
agricultural interests and concerns are adequately understood by Michigan’s
wildlife managers.

Thanks for your help!
Sincerely,

Peter A. Fritzell, Jr.
Research Assistant
Michigan State University
(800) 433-3741

233

 

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