• TABLE OF CONTENTS
HIDE
 Title Page
 Acknowledgement
 List of Tables
 List of Figures
 Abstract
 Literature review
 Methods
 Results
 Discussion
 Appendix A: Monitoring datafor...
 Appendix B: Interview guides
 Appendix C: Documents reviewed
 References
 Biographical sketch














Group Title: Effective Monitoring Framework for community based natural resource management
Title: An Effective Monitoring Framework for community based natural resource management
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 Material Information
Title: An Effective Monitoring Framework for community based natural resource management a case study of the ADMADE program in Zambia
Physical Description: Book
Language: English
Creator: Lyons, Andrew
Publisher: State University System of Florida
Place of Publication: Florida
Florida
Publication Date: 2000
Copyright Date: 2000
 Subjects
Subject: Wildlife Ecology and Conservation thesis, M.S   ( lcsh )
Dissertations, Academic -- Wildlife Ecology and Conservation -- UF   ( lcsh )
Genre: government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )
 Notes
Summary: ABSTRACT: Monitoring is an important element of community based natural resource management (CBNRM) which has emerged as one of the dominant conservation models for the tropics. Monitoring provides the basis for adaptive management, ecological and social impact studies, and ensuring accountability. This study presents the Effective Monitoring Framework, a conceptual model which describes the various components of effective monitoring in terms of design, implementation, application, and sustainability. The framework is based upon an iterative action research model and emphasizes the importance of internal feedback loops to integrate monitoring results back into the design and implementation of a monitoring program. The framework provides a template which can be used to help describe, evaluate, and plan resource monitoring systems in the context of CBNRM. The ADMADE conservation program in Zambia has ten years of experience in working with rural communities to monitor wildlife and provides an excellent case study to test the Effective Monitoring Framework. The framework provided an organized structure to describe comprehensively ADMADE's large and multi-tiered monitoring program, as well as analyze its strengths and weaknesses. Using the Effective Monitoring Framework as a analytical guide, this study helped identify the major bottlenecks in ADMADE's monitoring program and address system weaknesses through two interventions: an upgrade of the master monitoring database and a new course for village scouts on advanced data collection skills and analysis. More case studies of CBNRM monitoring are needed to further test and refine the Effective Monitoring Framework so that it may be applied to a greater diversity of programs using different natural resource strategies and administrative structures.
Summary: KEYWORDS: wildlife, monitoring, conservation, framework, CBNRM, Zambia, ADMADE, ICDP, safari, hunting, Africa
Thesis: Thesis (M.S.)--University of Florida, 2000.
Bibliography: Includes bibliographical references (p. 201-207).
System Details: System requirements: World Wide Web browser and PDF reader.
System Details: Mode of access: World Wide Web.
Statement of Responsibility: by Andrew Lyons.
General Note: Title from first page of PDF file.
General Note: Document formatted into pages; contains ix, 208 p.; also contains graphics.
General Note: Vita.
 Record Information
Bibliographic ID: UF00100694
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 45837562
alephbibnum - 002639569
notis - ANA6396

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Table of Contents
    Title Page
        Page i
    Acknowledgement
        Page ii
        Page iii
        Page iv
    List of Tables
        Page v
    List of Figures
        Page vi
        Page vii
    Abstract
        Page viii
        Page ix
    Literature review
        Page 1
        Page 2
        Page 3
        Page 4
        Page 5
        Page 6
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        Page 17
        Page 18
        Page 19
        Page 20
        Page 21
        Page 22
        Page 23
    Methods
        Page 24
        Page 25
        Page 26
        Page 27
        Page 28
        Page 29
        Page 30
        Page 31
        Page 32
        Page 33
        Page 34
        Page 35
        Page 36
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    Results
        Page 38
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    Discussion
        Page 121
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    Appendix A: Monitoring dataforms
        Page 179
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    Appendix B: Interview guides
        Page 187
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    Appendix C: Documents reviewed
        Page 196
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    References
        Page 201
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        Page 204
        Page 205
        Page 206
        Page 207
    Biographical sketch
        Page 208
Full Text










AN EFFECTIVE MONITORING FRAMEWORK FOR
COMMUNITY BASED NATURAL RESOURCE MANAGEMENT:
A CASE STUDY OF THE ADMADE PROGRAM IN ZAMBIA


















By

ANDREW LYONS


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2000















ACKNOWLEDGMENTS

I would like to thank the many people who made this research possible. In Zambia

I wish to thank Ackim Mwenya, Gilson Kaweche, Henry Mwima, and Dale Lewis who

were instrumental in supporting this study and helping to develop the methodology. The

entire staff of Nyamaluma Institute was extremely helpful in making my stay there pleasant

and productive. At the USAID Zambia office I would like to especially thank Dave

Soroko, Morse Nanchengwa, Kent Noel, and Walter North for sharing their knowledge

and experiences with project monitoring and supporting graduate student research in

Zambia.

My supervisory committee, Drs. Susan Jacobson and Colin Chapman, provided the

academic guidance for my research and were of great assistance during all phases of the

research, from proposal writing to preparing the final thesis.

This study could not have been possible without the generous financial support of

the Research Fellowship Program of the Wildlife Conservation Society in New York, the

Tropical Conservation and Development Program at the University of Florida, and the

USAID mission to Zambia. Many thanks to all.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ............................................................................................... ii

LIST OF TABLES .......................................................................... . . . ................v

L IS T O F F IG U R E S ........................................................................................................v i

A B S T R A C T ................................................................................................................. v iii

CHAPTERS

1 LITERATURE REVIEW ................................................................................. .. 1

P rin cip les o f C B N R M .................................................................................................. 1
M monitoring in CBNRM ....................................................................................... 10
Study Objectives and Significance .............................................................. ..............22

2 M E T H O D S ...............................................................................................................2 4

S tu d y A re a ................................................................................................................ 2 4
A D M A D E ................................................................................................................. 2 7
M methods ..................................................................................................... .............. 30

3 R E SU L T S .......................................................................................... 38

E n v iro n m en t .............................................................................................................. 3 8
D e sig n ............................................................................ ............ ................. 4 0
Im p le m e n tatio n ..........................................................................................................7 1
A applications .......................................................................................... .............. 95
Sustainability .................................................................................................. 115

4 D ISCU SSIO N .......................................................................... ..................... 121

Environm ent ............................................................................. . . .. .............. 121
D design ..................................................................................................... .............. 123
Im p lem en tatio n ....................................................................................................... 13 7
Applications ............................................................................. . . . .. .............. 158
R eco m m en d atio n s................................................................................................... 16 8

iii









C o n c lu sio n ............................................................................................................... 1 7 7

APPENDICES

A MONITORING DATAFORMS .................................... ................... 179

B IN TE R V IE W G U ID E S ........................................................................... .. 187

C D OCUM EN TS REVIEW ED .................................. ........................ .............. 196

R E F E R E N C E S ........................................................................................................... 2 0 1

BIO GRAPH ICAL SK ETCH ..................................... .......................... .............. 208







































iv
















LIST OF TABLES


Table Page

1 R research m ethods ........................................................................................ 32

2 Spatial and tem poral scales................................... ....................... .............. 67

3 S a m p lin g .......................................................................................................... .. 6 8

4 Datasets in Nyamaluma database, May 1999 .................................................75

5 Upper and lower bounds of 95% confidence intervals of population estimates in
M unyamadzi GM A from aerial surveys..................................... .............. 97

6 Quota setting indicator worksheet for Mwanya GMA, 1998 .......................... 105

7 Indicator data available for community quota setting ............... ................. 109

8 Comparison of wildlife counting methods in Zambia GMAs ........................... 129

9 C capacity building in m monitoring ........................................................................ 139

10 Potential errors and data quality controls....... ........ .................................. 158

11 K ey questions m atrix .................................... .......................... .............. 159

















LIST OF FIGURES



Figure Page

1 The project cycle (Margoluis and Salafsky, 1998)......................................... 16

2 The Effective Monitoring Framework for community based natural resource
m anagem ent program s................................... ........................ .............. 18

3 Z a m b ia ........................................................................................................... ... 2 6

4 Protected areas in Z am bia ................................... ........................ .............. 26

5 ADMADE organizational structure for a single game management area ........... 30

6 ADM ADE units visited for this research......................................... .............. 37

7 E nv iro n m ent ...................................................................................................... 3 8

8 D e sig n ............................................................................................................. ... 4 0

9 Im plem entation ............................................................................................ 70

10 Files at Lunga-Lusw ishi GM A ................................ ..................... .............. 78

11 Hunting success of hartebeest 1994-98........................................... .............. 90

12 Histogram of trophy size m easurem ents.......................................... .............. 91

13 Number of days on patrol for Kanusha and Luelo camps, 1998........................ 92

14 Timeline of safari hunting in Chanjuzi hunting block, 1998 ............................ 93

15 Map showing location of field patrols in Mumbwa Unit, 1997 ......................... 94

16 A p p licatio n s ....................................................................................................... 9 4

17 Land use planning map developed from monitoring data............................. 114









1 8 S u stain ab ility .................................................................................................... 1 1 5

19 ADMADE conceptual framework .............. ........ ................... 118

20 Safari hunting revenue collected and retained by the Wildlife Conservation
Revolving Fund in ADM ADE Areas...... ......... ................................... 120

21 Information flow, bottlenecks, and interventions in ADMADE ........................ 145

22 The AD M m ain m enu. ............................................................ .............. 150

23 D decision m odel interface ................................ ........................ .............. 151

24 The A D M filter m manager ..................................... ........................ .............. 152

25 The ADM about window ...... ................................... ............. ............. 153

26 ADM poster-sized layouts that combine maps, graphs, and tables .................... 155















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

AN EFFECTIVE MONITORING FRAMEWORK FOR
COMMUNITY BASED NATURAL RESOURCE MANAGEMENT:
A CASE STUDY OF THE ADMADE PROGRAM IN ZAMBIA

By

Andrew Lyons

August 2000

Chair: Susan Jacobson
Major Department: Wildlife Ecology and Conservation

Monitoring is an important element of community based natural resource

management (CBNRM) which has emerged as one of the dominant conservation models

for the tropics. Monitoring provides the basis for adaptive management, ecological and

social impact studies, and ensuring accountability. This study presents the Effective

Monitoring Framework, a conceptual model which describes the various components of

effective monitoring in terms of design, implementation, application, and sustainability.

The framework is based upon an iterative action research model and emphasizes the

importance of internal feedback loops to integrate monitoring results back into the design

and implementation of a monitoring program. The framework provides a template which

can be used to help describe, evaluate, and plan resource monitoring systems in the

context of CBNRM. The ADMADE conservation program in Zambia has ten years of

experience in working with rural communities to monitor wildlife and provides an

viii









excellent case study to test the Effective Monitoring Framework. The framework provided

an organized structure to describe comprehensively ADMADE's large and multi-tiered

monitoring program, as well as analyze its strengths and weaknesses. Using the Effective

Monitoring Framework as a analytical guide, this study helped identify the major

bottlenecks in ADMADE's monitoring program and address system weaknesses through

two interventions: an upgrade of the master monitoring database and a new course for

village scouts on advanced data collection skills and analysis. More case studies of

CBNRM monitoring are needed to further test and refine the Effective Monitoring

Framework so that it may be applied to a greater diversity of programs using different

natural resource strategies and administrative structures.














CHAPTER 1
LITERATURE REVIEW

Principles of CBNRM

Community based natural resource management (CBNRM) is broad rubric

encompassing a wide-array of resource management programs that share a recognition of

the importance of the participation of people who live near and are interconnected with

threatened natural resources. Similar in focus to the terms Community Based

Conservation (CBC) and Integrated Conservation and Development Project (ICDP),

CBNRM grew out of the failure and disillusionment of older protectionist styles of

management (Child, 1996a; Lewis & Carter, 1993). Colonial-era management practices

based on "fines and fences" frequently failed to achieve conservation goals because they

alienated people from their traditional resource base, thereby reducing the economic and

social value of natural resources and causing over-exploitation and mismanagement

(Child, 1996b). Protectionist practices were also limited because they only afforded

protection in legally protected areas, thereby missing the majority of wildlife and habitat

which lies outside of national parks and reserves (Gibson & Marks, 1995).

CBNRM and its variants attempt to restore the focus of natural resource

management to rural communities, whose lives are the most immediately linked to the

well-being of resources and whose cooperation is required to achieve conservation

objectives (Brandon & Wells, 1992; World Resources Institute, 1992). Although the roots

of community based management can be found in ancient pre-colonial practices, the recent








2
shift from top-down exclusionary management to community-centered conservation has

been gradual, and only really coalesced in the last two decades. The switch to CBNRM

was prompted by a gradual realization of the fundamental flaws of previous strategies,

donor pressure, national economic crises which slashed funding for protectionist policies,

democratization, and the recognition of the inherent rights of rural people (Child, 1996b;

Larson, Freudenberger, & Wyckoff-Baird, 1997; Lewis & Carter, 1993).

Although some authors argue that CBNRM programs have in general made limited

progress toward their twin goals of conserving natural resources and improving human

standards of living (Gibson & Marks, 1995; Hackel, 1999; Southgate & Clark, 1993),

many others claim success for specific projects (Bodmer, 1994; Lewis & Alpert, 1997;

Metcalfe, 1994). Reviewing the literature of CBNRM programs which consider

themselves successful, as well as those which struggle to achieve their goals, is a useful

exercise for exploring the validity of the CBNRM model and identifying factors and

principles which contribute to or hinder success.

Economic Framework

CBNRM works best when there is high potential to earn revenue from natural

resources through enterprises such as tourism (Alpert, 1996). Hence a prerequisite to

successful CBNRM is ensuring that the full economic value has been restored to

resources. Artificial government price controls on marketed resources, or subsidies for

competing land uses (e.g., agriculture) and commodities (e.g., cattle) reduce the value of

resources and hamper the success of CBNRM (Child, 1996b). However it is not enough

just to bestow a natural resource with economic value. Proprietorship plays an important

role and marketing should be open and competitive (Child, 1996a). Some sort of external








3
control is also needed, because leaving policy and conservation incentives exclusively to a

free market economy favors powerful corporations that will likely overexploit the

resource. Lohmann (1991) states that if the benefits of the resource accrue to irresponsible

stewards, such as corporations with few long-term interests, there will be little opportunity

for either conservation or community development.

Policy Framework

Government policies or their inefficient application are the root of most threats

encountered by CBNRM projects in or near protected areas (Brandon, 1998).

Communities must have legal ownership of the resource for CBNRM to work most

effectively (Brandon, 1998; Gibson & Marks, 1995; Lewis & Carter, 1993; Noss, 1997).

However granting legally recognized resource rights requires that communities form legal

entities, and the conditions of ownership of fugitive resources such as wildlife are spelled

out (Child, 1996b). Rules of tenure and resource access must be clear and widely known,

otherwise people may feel insecure and "make a run on the bank" (Brandon, 1998).

Creating this conducive legal and policy framework for CBNRM programs has been a

slow process, which still is not complete (Naughton, Hansen, Kiker, & Jones, 1998).

However due to the severity of threats on resources, most CBNRM programs can not

afford to wait for new legislation to be passed before beginning activities (Lewis,

Mwenya, & Kaweche, 1991).

Decentralization. The most effective CBNRM programs are those which have

political support from the national government (Alpert, 1996). Conservation programs

must devote significant resources to building support to guard against intrusions or

attempts from other political bodies to seize resources or reduce autonomy (Child, 1996a;









Gibson, 1999). Empowering local people to benefit from conservation requires that

resource ownership and authority to make policy are devolved from state institutions to

lower levels. However many central governments have been reluctant to devolve resource

ownership and policy making, instead decentralizing only administration and management

activities (Hackel, 1999). Resource and conservation agencies rarely trust their

constituency enough to devolve fiscal responsibility which is a meaningful part of

decentralization (Child, 1996a). Empowering communities requires weakening of

bureaucratic controls, which can be threatening to state institutions (Gibson, 1999).

Ironically, a national economic crisis may serve as a catalyst to decentralization, as can be

seen in the examples of Zambia and Zimbabwe (Child, 1996b).

Partnerships. Few institutions, be they government agencies, NGOs, or private

sector enterprises, possess all the skills and resources required to plan and manage

programs as multi-faceted as CBNRM. International conservation organizations

sometimes try to provide all required services, but have a greater impact when working as

a facilitator of partnerships, rather than as initiators and implementers (Larson et al.,

1997). Many times the roles and expectations of project partners are based on informal

agreements and good-faith. While this type of relationship might work well in the short-

term, partnerships are more likely to survive changes in leadership or institutional

structure if the relationships are based on formal memorandums of understanding and

enforceable contracts (Rocha, 1997).

Linkages Between Conservation Behavior and Benefits

The CBNRM model requires that the benefits from resource management must be

directly linked to conservation practices as transparently and as immediately as possible









for those conservation practices to become integrated into local livelihood strategies and

institutions (Child, 1996a). Handouts do not have nearly as much as an effect as benefits

which are "earned" by conservation behavior (Lewis & Phiri, 1998). To achieve

conservation through CBNRM, the unit of production should also match the unit of

management and benefit (Child, 1996a). Public goods are among the least effective

conservation incentives because everyone in the community benefits regardless of their

individual behavior (Gibson & Marks, 1995).

The benefits of regulating access to natural resources must also meet basic human

needs that were fulfilled by the former resource use. Social infrastructure projects, such as

schools and clinics, may help improve conservation attitudes, but do not address basic

needs such as food security which drive poaching (Lyons, 1998). Development projects

such as grinding mills and schools also require cash payments which may not be possible

for many households, thereby minimizing potential benefits for the vulnerable groups

which need it the most. When the benefits from foregoing resource use are non-existent or

insufficient, the incentives to conserve the resource will be weak (Gibson & Marks, 1995).

Development activities outside of protected areas do not always translate into

reduced pressure on the resources, especially if they do not address the threats to the

resource. Indeed development activities can have the reverse effect by attracting migrants

to the area (Brandon, 1998; Noss, 1997). Delays can also erode the perceived linkage

between conservation behavior and benefits (Gibson & Marks, 1995).

Distribution of Benefits

Domination by elite groups is a common threat to development programs (Larson

et al., 1997). Inequitable distribution of benefits is caused by power differentials within the









community, which are often not acknowledged in program design (Gibson & Marks,

1995). Rural communities are not homogenous entities, and there are always social

divisions based on gender, livelihood strategies, tribe, or lineage. Each group will have its

own interests, needs, and level of political and economic influence. Even traditional

authorities, which are often used by conservation programs to get a foothold in the

community, often do not represent the broader interest of all subgroups.

Equitable distribution of the benefits of CBNRM among all social groups is

desirable not only on ethical grounds but also because it has been linked to lower levels of

land degradation (IIED, 1998). Inequity in the distribution of benefits retards conservation

progress particularly for CBNRM programs, which require the cohesion of a entire

community to achieve goals (Gibson & Marks, 1995). Effective CBNRM requires that

equity exists not only in the distribution of benefits, but also in the selection of targets of

interventions such as law enforcement and restricted access policies. If a group is singled

out as the target of an action, and other groups which also impact the resource are not

affected, then the action will be perceived to be unjust. When people of influence are

allowed to circumvent regulatory mechanisms ill will also be bred (Brandon, 1998).

Concurrence with Local Practices and Culture

CBNRM must be triggered by a sense of resource depletion, whether real or

imagined (Rettig, Berkes, & Pinkerton, 1989). The community's recognition of the need

for management of the resource is a precursor to CBNRM and in fact is equally if not

more important than the specific type of management introduced (Bodmer, 1994).

CBNRM is more likely to be successful where there are amenable local practices and

traditions (Alpert, 1996). Incentives used to change behavior can not be based solely on









simplistic formulas such as the economic bottom line and caloric budgets. Resource

practices are embedded deeply in cultural traditions and social institutions. For example,

pastorialists or traditional hunters are not easily switched to agriculture, even if agriculture

is proven to be economically and ecologically more sustainable. An understanding of the

forces which drive personal identity, social order, and livelihood strategies is needed to

plan effective conservation programs (Gibson & Marks, 1995).

However Redford and Stearman (1993) state that biologists involved in

conservation often ignore indigenous people's concerns in conservation dialogues. They

sometimes claim to represent the interests of indigenous people without having the

mandate or authority to do so. When programs fail to integrate traditional management

practices and ideas, they are unlikely to benefit rural people (Lohmann, 1991).

Social Change

Community based conservation is less successful in areas of economic and social

growth (Alpert, 1996; Brandon, 1998). CBNRM is predicated on local people remaining

at a certain level of standard of living (Mano Consultancy Services, 1998). If incomes rise

above subsistence levels, then available capital will increase which may make alternative

land-use (e.g., intensive agriculture) more attractive than sustainable use of wild resources.

The level of income that is generated by CBNRM must remain appealing and meet the

needs of impacted people. If their development aspirations or standard of living increase,

then the benefits of CBNRM may no longer be attractive or be able to keep up with the

influx of people and new demands. The low levels of resource use required for

sustainability may not allow people to achieve the levels of development they desire

(Brandon, 1998).









Social Versus Biological Management

CBNRM is more about managing people than managing biological systems.

Biological systems are adapted to withstand ecological fluctuation and tend to take care of

themselves if anthropogenic disturbances can be minimized. Although both social and

ecological management are necessary, conservation programs are frequently guided by

biologists who fail to appreciate the complex socioeconomic context. Programs which

only focus on technical solutions and do not consider the interpersonal context and

institutional relationships will be undermined by a lack of motivation from community

members, reduced confidence, aversion to take risks, and non-cooperation (Child, 1996a).

Law enforcement is an important management component to all community based

conservation programs (Hackel, 1999). Increased law enforcement is the most effective

short-term means to reduce illegal resource use (Jachmann & Billiouw, 1997).

Investigations are more cost-effective than patrolling, although both are helpful

(Jachmann, 1998). There are advantages to using local residents for law enforcement,

including lower cost and performance (National Parks & Wildlife Services, 1999d).

Co-management

Rural communities rarely have the resources and skills to manage natural resources

completely on their own. Even though indigenous communities may at one time have had

effective systems for sustainable use, the social, economic, and technological foundations

of those systems are often eroded or completely gone, and present-day communities are

often less concerned and equipped to conserve their resource base. The modern variants of

traditional practices often do not meet the needs of growing populations and increasing









aspirations (Redford & Stearman, 1993). A system of co-management with government

and NGOs is more likely to prove effective (Bodmer, 1994).

The roles of communities and government agencies in a co-management

partnership need to be modified from their colonial legacy, which was often characterized

by opposition and mutual distrust. The need to build trust and confidence between parties

historically in conflict is an issue that needs to be specifically recognized and addressed by

programs (Marks, 1991). Government needs to play a supportive and regulatory role as

opposed to issuing decrees and policing (Child, 1996a).

Community-Based Organizations

Unlike some other forms of rural development, the success of CBNRM is

contingent on cooperation from all members of a community, not just a targeted group

within the community (Mano Consultancy Services, 1998). Thus to avoid the tragedy of

the commons, whereby members of a community exploit communal resources as quickly

as possible so others in the community do not exploit them first, the diverse members of

the "community" must be cohesive enough to function as a single management unit

(Hardin, 1968). Community based organizations (CBOs) are therefore integral to

CBNRM for tying together a community and developing management capacity. CBOs are

more likely to exist and be successful when scarcity or pressure on resources is apparent

and livelihoods are threatened (Brandon, 1998). Institutional structures must be cohesive

enough and legally recognized to be granted ownership and management responsibilities

(Child, 1996b). The organizational units of the community must be small enough to

regularly meet face to face, in other words not more than approximately 200 households

within 10 km radius (Child, 1996a). This principle may be difficult to follow in areas such









as Zambia where human populations in game management areas tend to be sparse and

thinly spread.

CBOs should be given all functions they are capable of performing, but new roles

and functions should not be added until a CBOs has the interest and capacity to adopt

them (Child, 1996a). It takes time to develop the capacity of CBOs and build the interest

of local people to try new social structures and economic strategies. A CBNRM program

which is truly community-based will also be community-paced (National Parks & Wildlife

Services, 1998). Problems, including misappropriation of funds, should be expected as

part of the necessary learning process for both the project staff and community leaders,

and incorporated into the project timetable (Child, 1996a). For this reason it is hard to

introduce CBNRM in a crisis situation where immediate action is needed. Unfortunately

donor time frames often do not concur with a realistic pace of progress, which has

prompted calls to lengthen the 'incubation period' of CBNRM projects (Byers, 1998).

Monitoring in CBNRM

Roles of Monitoring

Monitoring is an important element of any natural resource management project.

Monitoring provides the informational basis of adaptive management, which is often the

most effective strategy for managing natural systems characterized by high levels of human

use and natural fluctuation (Holling, 1978). Monitoring also provides mechanisms for

ensuring accountability for resource use, building local management capacity, and planning

public education (Lyons, 1999). Monitoring systems can serve to build confidence and

trust between central government departments and local management systems, facilitating

the decentralization process. Monitoring also fulfills legal mandates at national and









international levels. Unless ownership of resources have been completely turned over to

local institutions, government is ultimately responsible for monitoring to ensure that

resources are being used sustainably (Child, 1996a).

Because the majority of CBNRM programs are fueled by the consumptive use of a

natural resource, it is important that the biology of the resource is well understood to

avoid over-exploitation and population crashes. Monitoring programs can provide data

that help managers understand the impact that consumptive or non-consumptive resource

uses have on a species or ecosystem. For this to be possible, both the level of resource use

as well as biological parameters of interest (e.g., population size, reproductive success,

age and sex structures, migration patterns, feeding ecology) need to be monitored

simultaneously.

Thus although many monitoring programs are designed with a narrow focus in

mind, monitoring is potentially one of the few activities which straddles the realms of

management and science, and the social and biological fields.

Whatever the role of monitoring, articulation and consensus of the objectives has

been identified as one of the key determinants of success (Stout, 1993). Monitoring

programs should be developed at the outset of program design, and not as an add-on

(Larson et al., 1997).

Monitoring Case Studies

Although the need for monitoring to improve management and research in

conservation projects is frequently identified (Alpert, 1996), few projects have adequate

monitoring programs in operation (Kremen, Merenlender, & Murphy, 1994). This

deficiency is due to constrains such as a lack of physical resources, lack of skilled









manpower, and the perception that monitoring is among the least urgent aspects of a

project. Monitoring was seen as a burden and a donor requirement, rather than a tool

which can improve project effectiveness. There has also a tendency to avoid monitoring

and risk revealing failure because of the repercussions from donors (Larson et al., 1997).

Many CBNRM projects, particularly the earlier efforts, were designed quickly as a

response to a funding opportunity or urgent conservation threat, and did not establish

monitoring systems due to lack of time (Larson et al., 1997).

Despite the obstacles, many conservation efforts do recognize the importance of

monitoring and are able to maintain effective monitoring programs. Conservation projects

which are based upon the participation of local residents are best served by monitoring

programs that also actively involve local communities (Lewis 1993). However, when

attempting to incorporate the involvement of local residents into the monitoring process,

one must also consider the livelihoods, needs, and knowledge levels of the people

involved. Unless the monitoring system concurs with the socio-economic reality of the

primary users, local participation will likely be minimal (Bodmer, 1994).

The Makira Conservation in Development Program, a CBNRM program in the

Solomon Islands, established a community based monitoring program in 1996 to evaluate

human impacts on growth, production, and reproduction of ngali nut trees. Local

community members harvest wild ngali nuts for the project's nut oil extraction enterprise,

thereby creating an incentive to conserve the forest. With technical assistance from

Conservation International and other project partners, local people were trained in survey

methods and conducted the first survey of ngali nut trees for harvested and non-harvested

sites in three different ecological zones. Although few concrete conclusions could be









reached after only one survey, the very process of conducting the survey yielded new

insights, enthusiasm for monitoring, and an enhanced sense of self-empowerment for the

local people conducting the survey.

The protocols and methods for the survey were kept simple and low-cost to

maximize the likelihood that the survey will become a standard component of the project.

However the survey was designed and supervised largely by outside technical experts,

with community participation mainly coming in the form of field assistants and

interpretation of results, so sustainability of this type of monitoring is rather tenous (Parks,

Kohaia, & Tarihao, 1996).

The Masoala Integrated Conservation and Development Program developed a

comprehensive monitoring program as part of the establishment of a new national park in

Madagascar. The suite of variables being monitored includes measures of deforestation,

poaching levels, lemur populations, tourism management, attitudes of local people, water

quality, and resource exploitation. The methods employed include household surveys,

focus groups, harvest counts, transects, law enforcement records, reforestation plots, and

tour guide notebooks. The project has also created a spatial database of the park and

surrounding area using satellite imagery, GPS data, and digitized maps. Monitoring data

have proven useful for delineating the boundaries of the park and buffer zones to

maximize the amount of biodiversity protected and minimize the potential conflicts with

local communities. Monitoring data is also used to target management activities such as

law enforcement and public education, develop plans for timber harvesting, and guide

development activities that promote sustainable use of the park's resources. By basing the

design and implementation of the park on sound environmental and social science, the








14
viability of Masoala, both ecologically and politically, has been greatly enhanced (Kremen,

1998; Kremen, Isaia, & Lance, 1998; Kremen et al., 1999).

Although some CBNRM projects have made an effort to document their

monitoring systems, the field of monitoring is hindered by a lack of documentation from

project monitoring units. In a comprehensive case study of participatory monitoring and

evaluation programs, Estrella and Gaventa (1998) report that systematic documentation of

monitoring is rarely undertaken in practice. Most project reports focus on findings and

results of monitoring, with only a passing reference to the methodology. The few case

studies which do exist often fail to elaborate on how, under what conditions, and by which

stakeholders participatory monitoring and evaluation was developed. This is alarming not

only because it deprives conservation professionals from the experience of others in

designing effective monitoring systems, but also because the design and operation of

monitoring strongly influences the final outcomes.

Monitoring Frameworks

Developing conceptual frameworks for processes such as monitoring serves a

variety of purposes. A framework can help focus dialogue on a process, because the

underlying assumptions, terminology, factors, and causative relationships are visible and

understood. A framework can also serve as a diagnostic tool, suggesting a logical

sequence of examination questions and explanations for the behavior of different parts of

the system. Finally, a framework can lead to an implementation plan, providing a template

which can be adapted to the local characteristics of a program and site.

Conceptual frameworks are not static entities, nor is there necessarily a single best

framework for a particular process. On the contrary, our knowledge of systems can be








15
significantly advanced when alternative frameworks for the same process are contrasted or

applied to the same system. Simplicity is usually a desired quality of conceptual

frameworks, however frameworks which are more generic also tend to lose much of their

utility to frame dialogue and evaluate specific systems.

The Project Cycle Model

Not many frameworks have been developed for participatory natural resource

monitoring for conservation programs. Perhaps the most well developed framework for

conservation monitoring is the Project Cycle model developed by the Biodiversity Support

Program (Margoluis & Salafsky, 1998b). This framework (Figure 1) emphasizes the role

of monitoring in a larger context of project planning and evaluation. It also stresses the

iterative nature of monitoring and project design.

Action Research

The project cycle model and other iterative planning frameworks are based upon a

methodology called action research which was developed by a social scientist named Kurt

Lewin in the 1940s. Action research offers a problem solving methodology which has been

applied to fields as diverse as education, community development, economics, clinical

medicine, and many other human service professions. Action research presents an

alternative to the more traditional way of approaching a problem or study, where a long

period of study is undertaken before any action is taken, goals and hypotheses are

developed, and evaluation only occurs after the plan is fully implemented. Action research










































Figure 1 The project cycle (Margoluis and Salafsky, 1998)


calls for participants to become actively engaged in defining a problem or issue, defining

the criteria for success, and developing an action plan. The plan is then implemented,

monitored, and evaluated. This leads to further refinement of the plan and another

iteration of the loop. Thus all attempts to address the problem, whether they achieve the

goals or not, provide valuable knowledge of the system. This process continues until the

problem is solved to the satisfaction of the participants. Action research is therefore

characterized by ongoing tentativeness, recursion, collection of empirical evidence,








17
analysis of connotations and context, and collegial sharing. Action research is particularly

appropriate in situations where it is difficult or impossible to verify or replicate

observations, separate the observer from the observed, and isolate and control for many

confounding variables (Longstreet, 1982; Wals, Beringer, & Stapp, 1990). These are some

of the very constraints that characterize natural resource monitoring in CBNRM, hence

Action Research provides a good model upon which to build a framework for monitoring.

Effective Monitoring Framework

For this research, I developed and tested the Effective Monitoring Framework for

community based natural resource monitoring (Figure 2). This framework is similar to the

project cycle model however focuses more on the details of monitoring design and

implementation. However like the project cycle model it highlights the importance of

internal feedback loops to link analysis with design and implementation.


A discussion of the main elements of the framework follows below.

Environment

That effective monitoring can only exist within a conducive project environment

may appear self-evident, however it is important to emphasize that monitoring in CBNRM

is but one element of a much larger and more complex system. Like other project

activities, monitoring requires that the main components of the CBNRM model be present

and functioning. All CBNRM programs rely on one or more natural resources which are

valued by people based on consumptive or non-consumptive use. Without an exploitable

resource, management activities and the monitoring of those resources are not likely to be

effective. Numerous authors have also highlighted the importance of an appropriate legal























Figure 2 The Effective Monitoring Framework for community based natural resource management programs


Environment
* natural resource with economic
value
* legal and policy framework
* leadership
* community organizations


Applications
* adaptive management practices
* feedback for monitoring
implementation
* dissemination routes
* presentation of results


Design
* analysis of stakeholder information needs
* identification of monitoring goals
* inclusive participation
* indicator selection
* temporal and spatial scales
* sampling
* feasibility
* incentives

Sustainability
* perceived value in information
* participant willingness to reinvest in
monitoring
* validation of project conceptual model
* sustainable management of resource


Implementation
* identification of data collectors
* training
* observations
* supervision
* information flow
* data processing system
* timeliness
* data quality evaluation


Environment









framework that empowers local users to own and manage their resources (Child, 1996b;

Lewis & Carter, 1993; Naughton et al., 1998). Committed and capable leadership,

effective community organizations, material and human resources for training and

management, and a host of other factors outlined above all set the stage for a successful

CBNRM program with an effective monitoring system.

However monitoring may still play a valuable role even when one of the critical

pieces of the CBNRM model is not functioning. Monitoring can serve as a smoke

detector, helping to detect when something is wrong and providing the stimulus for

corrective action. However for this role to be feasible, monitoring must also be protected

from "sinking with the ship," by being as isolated as possible from potential problems

such as inadequate revenue generation, weak leadership, or resource management failures.

The ability to detect a melt down can be an important function of monitoring, however

insulated monitoring systems may also be less effective in other functions where local

participation and integration with daily activities are most important.

Design

The design of a monitoring program should both describe the conceptual basis for

monitoring and provide the blueprints for data collection, processing, analysis, and

dissemination. The need for a good design may also seem self-evident, however probably

more problems with monitoring programs can be traced to a faulty or incomplete design

than any other cause (Salafsky & Margoluis, 1999). Design begins with well-articulated

goals, which are themselves derived from a solid understanding of the project mission and

a site-specific conceptual framework of the many factors influencing a target condition

(Margoluis & Salafsky, 1998b). Participation in the design phase should include all









stakeholders who will play an eventual role in the monitoring system. A monitoring plan

should also address which indicators will be monitored, how those indicators will be

measured, the sampling scheme to be used, and the temporal and spatial scales required.

Spatial scale refers to both the total geographic area monitored as well as precision of the

spatial measurements. Likewise temporal scale consists of both the total span of time

which measurements are made in, as well as the frequency of measurements during that

period. Indicators should be SMART (Specific, Measurable, Attainable, Relevant, Time-

framed), and resonate with the intended audience (Abbot & Guijt, 1998). All of the steps

in the master plan, from data collection to dissemination of final results, must be feasible

by not exceeding available manpower and material resources. The monitoring plan should

also describe the incentives for each stakeholder's participation, which will ultimately

determine the sustainability of the monitoring program.

Implementation

Implementing monitoring requires mobilizing the necessary human and physical

resources to put the plan into action. It begins with identifying data collectors and

providing training in the proper measurement techniques. Training usually requires more

than a one-time workshop, so refresher courses and follow-up support in the field will

likely to be necessary. Once data collectors begin to make measurements, observations

need to be somehow recorded and stored for analysis. Depending on the complexity of

measurements and the volume of data, this may require a paper filing system and/or

computerized database. Checks for data quality should be embedded along the information

path, and provide immediate feedback on the effectiveness of the system. Postponing an

evaluation of data quality until the end affords no opportunity to take corrective measures









in design or implementation. The many elements of a monitoring program must work

smoothly together for the ultimate results to be disseminated in timely manner.

Applications

Some authors divide monitoring systems into 'adaptive management' and 'impact

monitoring' (Abbot & Guijt, 1998; Estrella & Gaventa, 1998; Salafsky & Margoluis,

1999). However in the present framework these divisions are simply treated as different

applications of data. Examples of adaptive management applications include setting

harvest levels, planning law enforcement, public education, ensuring accountability, and

planning community development interventions. Examples of impact monitoring

applications include determining the cost-effectiveness of law enforcement strategies,

measuring changes in wildlife populations, or evaluating the impact of project activities on

conservation attitudes and household standard of living. Monitoring for impact often

requires collecting baseline data, formulating specific hypotheses about expected changes,

and finally monitoring the system and measuring actual change. The objective of impact

monitoring is not only to assess whether a program did or did not make an impact, but

also determine why the observed outcome occurred. Thus the ultimate application of

impact monitoring is an evaluation of the conceptual framework of the project, which

describes the socio-ecological context and the expected results of each of the project's

interventions (Margoluis & Salafsky, 1998a).

Another application of monitoring is to review the monitoring system itself.

Feedback from the implementation of the monitoring plan can be used to identify problems

in the monitoring design (such as sampling regimes which are inadequate or unfeasible) or

implementation (such as insufficient supervision of data collection).









Each type of application has its own specific requirements for disseminating and

presenting results. A village committee estimating the number of scouts needed to patrol

an area requires a dissemination strategy and presentation format which is much different

than that needed by project staff who are preparing a quarterly report for an international

donor. A common constraint of many monitoring programs is trying to use the same

analyses, dissemination tools, and presentation formats for all applications.

Sustainability

For a monitoring program to be sustained, it must be relevant to the needs of the

end user and their institutions (Tobin, 1999). The information benefits must be perceived

to be valuable enough that the primary stakeholders are willing to reinvest in the

monitoring activities. If a donor or national agency is the only stakeholder which perceives

monitoring information to be valuable, then monitoring will likely cease if that donor or

agency withdraws as an active partner in the project.

Ultimately, natural resource management projects seek to attain a level of

sustainability in both resource conservation and social development. Monitoring systems

certainly can not achieve sustainability on their own, however in a conducive environment

with a well conceived monitoring design and smooth implementation, monitoring can play

an important role in both measuring sustainability as well as providing feedback for the

most effective methods to achieve it.

Study Objectives and Significance

The purpose of this study was to test the adequacy of the Effective Monitoring

Framework to describe and guide analysis of the monitoring system of an actual

community based natural resource management project, namely the ADMADE









conservation program in Zambia. By using the framework as a template to describe the

components of ADMADE's monitoring program, analyze its strengths and weaknesses,

and plan interventions, this study sought to demonstrate how the Effective Monitoring

Framework can be helpful in documenting and strengthening the monitoring component of

community based natural resource management.

Studies like this one help to refine models, such as the Effective Monitoring

Framework, and identify their limits and assumptions. The case study method employed

also records in-depth information about an actual monitoring program, so that other

frameworks may also be tested against the same real-world system and refined. This

iterative process of model making mirrors the iterative nature of monitoring, and

ultimately leads to a more comprehensive and robust collection of analytical frameworks

available for planning, structuring dialogue, and targeting interventions.














CHAPTER 2
METHODS

Study Area

Zambia is a landlocked nation in South-Central Africa, occupying some 750,000

km2 on a central elevated plateau interspersed with ancient rifted troughs and river valleys.

Three quarters of the country drains into the Indian Ocean through the Zambezi river

system, which includes the Kafue, Luano, and Luangwa rivers. The extensive river

systems, floodplains, and escarpments have allowed the formation of a wide variety of

habitats, including large wetlands, riverine ecosystems, and forested plateaus. Miombo

woodland is the dominant vegetation community, covering about 80% of the country

(Aspinwall, Bingham, Chundama, Jeffery, & Sinkamba, 1996; Wildlife Conservation

Society of Zambia, 1991).

Zambia's 10 million people are highly urbanized, with 47% living in cities primarily

along the main transportation corridors. However population density in the country as a

whole is low, with only 4.5 people per km2. After peacefully winning independence from

Britain in 1964, Zambia prospered for its first decade fueled by the mining industry.

However a long-term decline in the global prices for copper coupled with rising debt and

inefficient centralized economic policies eventually drove the economy to rock bottom in

the late 1980s. In 1991 a new government was elected and initiated structural reform.

However the economy remains crippled by a $6.7 billion foreign debt, government control

of failing industries, and annual inflation near 30%. The human population has also been









hard hit by AIDS, with an estimated 20% of the population infected. The impact of this

epidemic, which is more severe in urban areas, will be felt for decades to come (Economist

Intelligence Unit, 1999).

Zambia is richly endowed with fauna. At least 240 species of wild mammals have

been identified, including most of the large game animals (Aspinwall et al., 1996). Zambia

has not lost any of its large vertebrates, with the exception of the black rhino which was all

but extirpated by poachers in the late 1980's. Some species such as wild-dog and the

cheetah remain threatened, however elephants, which were also decimated by poaching

during the 1970s and 1980s, are slowly recovering and believed to be near carrying

capacity in much of the country. There are 733 species of birds known to live in Zambia,

including migrants (Zambian Ornithological Society, 1993). The diversity of habitats,

including several large wetlands, allow for such rich avian diversity. Although there is

relatively little endemism in fauna, Zambia hosts several important sub-species of large

mammals including Thornicroft's Giraffe (Giraffa camelopardalis thornicrofti), Cookson's

Wildebeest (Connochaetes taurinus cooksoni), Kafue Lechwe (Kobus leche kafuensis),

and Black Lechwe (Kobus leche smithemani) (Alden, Estes, Schlitter, & McBride, 1995;

Wildlife Conservation Society of Zambia, 1991).

About 10% of Zambia is protected in national parks which prohibit all human

activity except tourism (Figure 4). Another 20% falls under semi-protected game

management areas (GMAs) which are multiple-use zones which typically buffer the

national parks. GMAs permit human settlement and low-impact land uses such as small

scale agriculture and fishing, however large settlements and high-impact land uses such as









mining or commercial forestry are prohibited. All wildlife in Zambia are owned by the

state and administered by the Zambia Wildlife Authority1.


Figure 3 Zambia


Figure 4 Protected areas in Zambia


1 In 1999, the National Parks and Wildlife Services (NPWS) underwent restructuring and was renamed
the Zambia Wildlife Authority









ADMADE

The Administrative Management Design (ADMADE) program ironically has its

roots in Zambia's economic crisis of the 1970's, which was largely caused by a steady

decrease in copper prices on the world market. As the government cut back its budget,

resources for wildlife protection dwindled. The cutbacks were coupled with an unpopular

and ineffective centralized management approach which alienated local people from the

benefits of wildlife, yet forced them to bear the costs of living with wild animals. The

combination of budget constraints and backlash to traditional management approaches

developed into an atmosphere which culminated in a drastic increase in poaching (Gibson

& Marks, 1995).

The poaching epidemic of the 1970s and '80s plagued other African countries as

well (Oates, 1999) and served as a wake-up call for governments and conservation

interests to explore new approaches to wildlife management. In 1983, the Zambia National

Parks and Wildlife Service (NPWS) convened the Lupande Development Workshop,

bringing together over 40 government and community representatives, conservation

organizations, and donors. The result of this workshop was a manifesto acknowledging

the need to use a more community-friendly form of management (Lupande Development

Workshop, 1983). Subsequently, two pilot CBNRM projects were launched: the Luangwa

Integrated Resource Development Project (LIRDP) funded by the Norweigan Agency for

International Development (NORAD), and the Lupande Development Project, a National

Parks and Wildlife Service (NPWS) sponsored project which later expanded to become

ADMADE (Gibson, 1999; Lupande Development Workshop, 1983).








28
Defining ADMADE as a program is complicated because it exists in various stages

of implementation in many different areas throughout Zambia, maintains a low profile in

the field, falls under a government department but with some characteristics of an

autonomous NGO, and encompasses a wide array of stakeholders. ADMADE may be best

thought of as

* the official policy of ZWA for all wildlife management in GMAs

* a vision of a mutually beneficial relationship between wildlife and people

* a designation, granted by ZWA, for GMAs allowing residents to organize certain

structures and have access to certain services. The most significant of these structures

and services include

1) a portion of safari hunting revenue returned to a community controlled bank

account through the Wildlife Conservation Revolving Fund (WCRF);

2) authority to employ local residents as village scouts;

3) a mandate to establish a three-tiered structure of community organizations;

4) access to training programs and technical support from Nyamaluma Institute.

ADMADE's system of administration at the GMA level evolved over its first ten

years. From 1989 to 1998, each GMA in the ADMADE program maintained a committee

called the sub-authority. Members of the sub-authority were appointed by the local chief,

who also served as the sub-authority chairman. The sub-authority decided how community

revenue should be used, selected local residents for employment as village scouts, and was

responsible for interacting with NPWS staff on management issues. This system of local

governance was effective in winning the support of influential traditional rulers, a

necessary ingredient to establish the program in an area. However, it also led to many








29
problems with autocratic and non-democratic styles of governance (Alpert & DeGeorges,

1992). Initially there was another committee at the district level, called the authority,

which had oversight and veto power over the sub-authority. However authorities

gradually became inactive and were effectively phased out by the mid 1990's (Gibson &

Marks, 1995; Mano Consultancy Services, 1998).

In 1999, ADMADE began introducing a more democratic three-tiered system of

community organizations (Figure 5), building upon the 1998 Wildlife Act which vests

more power in community based organizations. Replacing the sub-authority is the

community resource board (CRB), a democratically elected body which incorporates the

local chief as an honorary patron. The CRB is advised by three management committees

also composed of residents of the GMA: the financial management committee, community

development committee, and resource management committee. To ensure equitable

representation from all geographic areas within a GMA, each GMA has been sub-divided

into village area groups (VAGs), and representation on the CRB and management

committees is equal across VAGs. Within a VAG, peer groups representing different

livelihood groups (e.g., fishermen, honey collectors, farmers) help ensure the interests of

all sub-groups within the GMA are represented (Ngulube et al., 1998). Although the

revised community structures are still quite new and have not seen the test of time, the

general move to more democratic administration has popular support and represents an

effort to redress some of the local level institutional problems that constrained progress for

achieving equitable socio-economic development for most of the 1990's (Mano

Consultancy Services, 1998).









Community Resource Board
* 9-10 members/board

Chief
* patron

Technical Committees
* 8-12 people/committee
* Financial Management (FMC)
* Resource Management (RMC)
* Community Development (CDC)


VAG Committees
* 12-16 people/committee


Peer Groups
* Resource users
* e.g., fishermen, honey
collectors, farmers, etc.
* group size varies


VAG Communities
* 500-1000 people/VAG


VAG 2 VAG 3


ADMADE Unit
Figure 5 ADMADE organizational structure for a single game management area


Methods

This research was conducted in Zambia between October 1998 and June 1999.

During this period I was based at the Nyamaluma Institute for Community Based

Resource Management2, ADMADE's training and research facility, located near Mfuwe in

the Luangwa Valley in eastern Zambia (Figure 6). Additional preliminary research was

conducted in Lusaka between July 1998 and September 1998. The following types of

research methods were used:


2 In 1999, Nyamaluma changed its name to the African College for Community Based Natural Resource
Management. However during the period in which this study was conducted it was called Nyamaluma, so
that is the name which is used throughout this paper.


0----

+,


VAG 1









* document review
* meetings and workshop participation
* interviews
* database analysis
* organizing a monitoring workshop
* field visits

Data from these methods were used to test each component of the Effective

Monitoring Framework (Table 1).

Document Review

A considerable amount of literature has been written about ADMADE.

Nyamaluma Institute has a substantial collection of manuals, workshop proceedings, trip

reports, monitoring summaries, policy papers, and newsletters. USAID/Zambia,

ADMADE's primary donor for its first ten years, contracted a number of evaluations and

studies and has a large collection of reports. Wildlife and conservation issues in Zambia

have been the topic of numerous articles from academic journals, many of which address

approaches to CBNRM and ADMADE. A list of the various reports and articles reviewed

for this study can be found in Appendix C.

Meetings and Workshop Participation

I participated in the following meetings and workshops:

* Wildlife Conservation Society Africa Program meeting, Nyamaluma, 7/98
* USAID/Zambia Performance Monitoring meetings (6), Lusaka, 7/98 8/98
* Wildlife Donor Coordinating meeting, Lusaka, 7/98
* Four community quota-setting meetings, Luangwa Valley, 10/98
* Financial Management Committee workshop, Nyamaluma, 12/98
* CBNRM District Team meeting, Environmental Support Programme, Mumbwa, 3/99













Table 1 Research methods
Framework Element Document Meetings Interviews Database Monitoring Field visits
review analysis workshop
Conducive Environment
natural resource with economic value *
legal and policy framework *
leadership 0 0 *
community organizations *
Design
analysis of stakeholder information *
needs
identification of monitoring goals *
inclusive participation 0* **
indicator selection *
temporal and spatial scales *
sampling *
feasibility *
incentives *
Implementation
identification of data collectors *
training *
observations 0 0 0 *
supervision *
information flow 0**
data processing system *
timeliness *
data quality evaluation *












Table 1 Continued
Framework Element Document Meetings Interviews Database Monitoring Field visits
review analysis workshop
Applications
adaptive management practices 0 *
feedback for monitoring *
implementation
dissemination routes 0 *
presentation of results *
Sustainability
perceived value in information 0 *
participant willingness to reinvest in *
monitoring
validations of project conceptual *
model
sustainable management of resource *









Interviews

I conducted semi-structured interviews with key stakeholders of ADMADE's

monitoring program. Interviewees were select to represent three levels of interest in

monitoring: upper-level managers (9), mid-level technicians (9), and field staff (14). The

purpose of these interviews was to ascertain information needs, familiarity with

ADMADE's monitoring activities, perceptions of monitoring, and levels of input into the

monitoring system. In the case of village scouts and unit leaders, additional questions

focused on data collection and data management issues. See Appendix B for sample

interview guides.

Upper-Level Managers
* Deputy Director, NPWS
* Chief Wildlife Research Officer, NPWS
* Technical Advisor, Nyamaluma
* Principal, Nyamaluma
* Agricultural Development Officer, USAID/Zambia
* ADMADE Project Manager, USAID/Zambia
* Project Manager, Kafue Anti Poaching Organization (KANTIPO)
* Director CBNRM, Environmental Support Program (ESP), Ministry of Environment
and Natural Resources (MENR)
* Director of National Environmental Monitoring and Information Network, MENR

Mid-Level Technicians
* GIS/database analysts, Nyamaluma
* Research Officer, Nyamaluma
* Agroforestry Officer, Nyamaluma
* Systems Analyst, NPWS
* Wildlife Biologist, Kafue Command
* Technical Advisor, Wildlife Resources Monitoring Unit, Environmental Council of Zambia
* Research Officer, South Luangwa Area Management Unit

Field Staff
* Unit Leaders: Chifunda (1), Mumbwa (1), Lunga-Luswishi Busgana (1)
* Deputy Unit Leaders: Kasonso Busanga (1), Lunga-Luswishi Busanga (1), Mumbwa
(1), Munyamadzi (1)
* Village scouts (7)










Database Analysis

All monitoring activities in ADMADE at the project level are designed and

coordinated at Nyamaluma Institute. Nyamaluma also manages the primary repository of

monitoring data, with some datasets going back as far as 1992. Nyamaluma's facilities

include a modern GIS lab, which is used for data processing, analysis and generating

outputs.

While at Nyamaluma, I was graciously given free access to the monitoring

database and GIS data. Datasets reviewed for the study include field patrol observations,

safari hunting results, crop damage, household demography, quota setting worksheets,

field staff records, scout camp facilities, hunting quotas, poacher case records, and

population trends surveys. These datasets were examined for factors affecting data quality,

such as missing data, spatial and temporal bias, sample sizes, and dispersion.

In the course of analyzing the database, I helped upgrade their information system

to a more flexible relational database management system. This exercise involved

extensive consultations with Nyamaluma's research staff to understand their needs and

mode of operation. Upgrading the database required compiling and normalizing all

existing data to a new relational data structure, and merging multiple GIS layers into

national covers for use in the new application.

I was physically present at Nyamaluma for approximately five months of the

research period. However I also worked on the database while in transit to field sites. At

Nyamaluma I observed day-to-day research and training activities, and spent a substantial

amount of time observing and interacting with research staff. Nyamaluma's research









officers also serve as extension and training staff, and I benefited immensely from their

wealth of field experience dealing with communities and monitoring issues.

Organizing a Monitoring Workshop

In May of 1999, I assisted the staff of Nyamaluma in planning and conducting a

one-week Advanced Scout Workshop for 44 village scouts and deputy unit leaders. I

helped develop the workshop objectives and outline, and created lesson plans for several

of the sessions. I also led sessions on applications of monitoring data, conducting snare

transects, waterhole and fish camp reconnaissance, and lesson planning for civic

education. I also prepared participant notes for each session in the workshop, which were

compiled into an end-of-workshop handout.

I also administered entry and exit questionnaires to the participants which were

designed to ascertain their knowledge of applications of monitoring data, workshop

expectations, level of cooperation with resource management committees, and opinions of

the workshop. In preparation for this workshop, as well as other courses at Nyamaluma, I

developed an educational framework for data analysis training, and drafted a quota setting

manual for communities. During this workshop, I also interviewed village scouts from

GMAs which I was not able to visit. The village scouts who attended the workshop were

very experienced in monitoring, and were good sources of information on monitoring

issues at the field level.

Field Visits

For most of this study, I was based at Nyamaluma Institute, however I was able to

visit several ADMADE areas while accompanying Nyamaluma inspection teams. During

these trips, I interviewed scouts, unit leaders, and deputy unit leaders. I also observed data









management practices at the unit headquarters, sat in on quota-setting exercises, and

observed Nyamaluma staff conduct other monitoring activities such as reviewing data

forms.

The list below shows the amount of time spent in each area, while the map in

Figure 6 shows the locations of the GMAs I visited.

* Lower Lumimba, Upper Lumimba, Munyamadzi, Chifunda 2 weeks, Oct. 1998
* Kasonso Busanga, Lunga Luswishi Busanga 10 days, March 1999
* Mumbwa 5 days, March 1999


X Chanjuzi
Mwanya


Kasonso-Busanga-
K3


D National parks
D ADMADE GMAs
E ADMADE GMAs visited


Figure 6 ADMADE units visited for this research















CHAPTER 3
RESULTS


Environment
______ .o ",.. ....................-.. .....
Design Impleentation Applicatiols... Sustainability





Environment
natural resource with economic value
legal and policy framework
leadership
community organizations
Figure 7 Environment


Environment

Although it was beyond the scope of this study to conduct a full-blown evaluation

of ADMADE as the broader context of the monitoring system, other authors have

conducted more thorough evaluations and concluded that although imperfect ADMADE

is functioning fairly well in a number of regards (Alpert & DeGeorges, 1992; Clarke,

2000; Mano Consultancy Services, 1998; National Parks & Wildlife Services, 1998).

Alpert and DeGeorges (1992) reported that although ADMADE has not yet succeeded in

establishing self-sustaining wildlife management practices or influenced national policy, it

had demonstrated that wildlife could be a profitable form of land use. A second mid-term

evaluation found that policy and lack of will power were still hampering progress,

particularly the flow of revenue back to the communities, and that the monitoring systems

38









were not yet strong enough to demonstrate success (ULG Consultants Ltd, 1994).

Rosenthal and Sowers (1995) produced the first evaluation to suggest that sustainability of

ADMADE was possible. They reported that the concept of community based management

had taken root within the parks department, and that sport hunting has positive economic

benefits that can be passed on to rural communities. The community development side of

the project was still weak, however village scouts were functioning well under trying

conditions. By the final USAID evaluation in 2000, Clarke (2000) reports that a strong

wildlife legislation had been adopted by the government and new democratic community

institutions were helping to improve the conversion of safari revenue into benefits for the

producer community.

ADMADE has many of the required elements for a successful CBNRM project,

including a highly lucrative natural resource (large game animals); direct linkages between

conservation behavior and economic benefits; a legal framework which does not grant

ownership yet empowers rural communities with access and management rights;

committed and competent leadership at the project level and many of the communities;

and community organizations which are gradually becoming more effective and

representative. Thus while not perfect, the design and implementation of ADMADE in

many GMAs functions well enough to serve as a conducive environment for participatory

resource monitoring.











Environment

Design Implementation Applications Sustainability






Design
analysis of stakeholder information needs
Community Resource Boards
ZWA Unit Staff
Nyamaluma Institute
ZWA Headquarters
SAID
Safari Industry
Ministry of Environemt and Natural Resources, Enivornmental Support
Programme
Environmental Council of Zambia, Wildlfe Resources Monitoring Unit
NGO Community
identification of monitoring goals
inclusive participation
community participation
ZWA participation
external partner participation
indicator selection
temporal and spatial scales
sampling
feasibility
incentives
Figure 8 Design



Design

Analysis of Stakeholder Information Needs

Community resource boards

Residents living in or near ADMADE GMAs have probably the most to gain or

lose from wildlife management in Zambia. Whereas other wildlife stakeholders are affected

by wildlife indirectly, such as by lost recreation opportunities, reduced revenue, conflicts

with esthetic and moral values, and critical performance reviews, the wildlife-related issues

faced by rural residents are very immediate and personal. These include property damage,








41
fear for personal safety, loss of vital food stocks, possible loss of life, and risk of arrest or

imprisonment. Conversely, rural residents also have much to benefit from wildlife

management, such as increased opportunities to satisfy livelihood needs, improved health

and education services, employment, community income, and better food security. Hence

it is appropriate that rural communities have been finally recognized as perhaps the most

important stakeholder in CBNRM programs such as ADMADE.

Rural communities are far from homogenous entities, and this diversity is mirrored

in a variety of information needs and interests. Community resource boards, which are the

elected representatives of the community at large, and the technical management

committees need data for management activities, such as selecting quota

recommendations, planning anti-poaching operations, and ensuring that all hunting and

fiscal regulations are adhered to. In the early years of ADMADE, many of these chores fell

almost exclusively upon the unit leader and his staff, with assistance from NPWS

headquarters and Nyamaluma Institute. However under the new ADMADE structures,

more and more of these responsibilities will fall with the various elected community

management committees. Information available for management activities includes

indicators of wildlife population trends (e.g., hunting statistics, observations on field

patrols), field patrol results, poacher case records, and Wildlife Conservation Revolving

Fund (WCRF) statements.

Planning and implementing community development projects is at least complex, if

not more so, than managing wildlife. To prioritize development needs, CRBs need

information about human demographics, household level food security, livelihood

strategies, human population growth and distribution, income flows, health and education









services, wealth distribution, markets, and intra-community dynamics. To meet these

needs ADMADE's monitoring system can provide information on human demographics

and to some extent income flows, however is less well equipped to provide other

socioeconomic data, particularly household level variables.

Catching and preventing mismanagement of funds and other project resources is

another important need of rural communities. Whether it is ammunition or food rations

taken on field patrols, or income received from the Wildlife Conservation Revolving Fund,

transparent accountability of resources is critical for the program to maintain the

confidence of the local people. To ensure accountability, CRBs need information on field

patrol supplies, license sales, hunting results, expenditures from community development

projects, and bank statements. If mismanagement should occur, CRBs need a monitoring

system that is sensitive enough to catch the problem at an early stage so that corrective

measures can be taken. Catching mismanagement also requires that a broad spectrum of

stakeholders have access to data on financial resources.

Under the 1998 Wildlife Act, CRBs will also be required to develop

comprehensive resource co-management agreements between themselves, government

agencies, and private industry. Negotiating a co-management agreement is an information-

intensive activity, requiring baseline resource inventories, resource use patterns,

management capability, and market demand for safari products. In addition to helping

negotiate co-management agreements, resource monitoring will itself be an important

component of all co-management plans.

CRBs will face other information needs when reviewing and renegotiating safari

hunting concessions with safari operators. One of the major determinants of success of









ADMADE in a GMA is the performance and integrity of the safari operator and his

professional hunters (National Parks & Wildlife Services, 1999d). Monitoring data can be

used to evaluate the past performance of a safari operator, assess the economic potential

of a hunting block, and negotiate new concession fees.

Local land-use plans have been developed for most of the ADMADE units in the

Luangwa Valley, and will be developed for the remaining areas in the near future. Land-

use plans are developed in participatory workshops, and are broad-spectrum,

comprehensive sets of proposed actions designed to resolve and prevent land-use

conflicts. Resolutions from a land-use plan may include shifting human activities away

from wildlife areas, implementing a new project such as an electric fence or road

rehabilitation to address community needs, or clarification on the roles of the various

stakeholders. Developing a land-use plan is a complex, iterative, participatory exercise,

which requires monitoring data such as wildlife habitat needs, safari hunting trends, unit

demography, community development priorities, and revenue flows.

ZWA unit staff

The information needs of Zambia Wildlife Authority field staff, which includes unit

leaders, deputy unit leaders, village scouts, and civil servant scouts, parallel the

information needs of local communities with whom they are partners in management. As

the field representatives of ZWA, these officers have the responsibility and authority to

enforce wildlife regulations, conduct anti-poaching operations, arrest poachers, and

recommend scientifically based hunting quotas. On the 'softer' side of their job, some unit

staff are active participants in formulating local policy, such as land use plans, resolving









conflicts, and public education. Each of these different types of activities requires

monitoring information to plan, execute, and evaluate.

In addition to using data to plan and review management operations, unit staff

have an interest in ADMADE's monitoring system in a way that not many other

stakeholders have: they are the source of most of the data. Village and regular scouts,

under the leadership of the unit leader and his deputies, collect all of the safari hunting,

field patrol, and poacher arrest data, and are recorders for other types of data, such as

crop damage and snaring pressure. The scouts and their supervisors need to know not

only the results of their monitoring work, but also feedback on their methodology of data

collection. One of the on-going efforts by extension staff from Nyamaluma has been to

increase the capacity of units to collect, store, and analyze the various forms of monitoring

data.

Interviews conducted for this study revealed that providing evidence for judicial

proceedings is another use of field patrol. Poacher case records and field patrol dataforms

may be important pieces of evidence when poachers are brought to court. In addition to

the prosecution of cases, dataforms may be used in the defense of scouts who are accused

of offenses such as improperly confiscating property, or injuring or killing a poacher.

At a slightly higher organizational level, wardens, who are responsible for an entire

command,3 have their own information needs. Wardens are in charge of all personnel

matters, allocation of human and material resources, and monitoring wildlife populations

in their command. Commands also get a percentage of safari hunting revenues for their

operations, so they have a vested interest in ensuring that safari hunting is being managed








45
profitably and sustainably. Some commands have biologists on staff, who are responsible

for monitoring wildlife populations in the command. Commands typically have few

resources to work with, so a biologist may rely heavily on data from ADMADE scouts, or

collaborate with unit staff in analyzing data or organizing ground transects.

Nyamaluma Institute

Nyamaluma Institute is ADMADE's center for training, research, and extension

services. Although officially a government facility, in many regards Nyamaluma functions

as a semi-autonomous NGO, providing a variety of services to ADMADE units.

Nyamaluma also serves as a liaison between ADMADE units and other stakeholders, such

as the Zambia Wildlife Authority headquarters in Chilanga, the safari industry, and the

international conservation and donor community.

Nyamaluma's information needs are as diverse as the roles it plays. To fulfil its

function as a training institute, Nyamaluma requires information about unit staff, elected

community members, retention rates, educational backgrounds, and training needs. In its

role as a source of extension and facilitation services, Nyamaluma needs all the same

information as communities and unit staff. Likewise, as the liaison between communities

and ZWA headquarters, international donors, and the safari industry, Nyamaluma requires

the same type of information as these other stakeholders.

Nyamaluma is able to fulfill so many roles partly because it functions as the central

nervous system of ADMADE's monitoring program. There are very few monitoring

activities in ADMADE that were not designed, initiated, and continuously supported by

the staff and technical resources at Nyamaluma. Nyamaluma staff also conduct special


3 Zambia is divided into nine commands









studies periodically on specific topics, such as village expansion, agricultural yields, or

community awareness and attitudes towards ADMADE.

Zambia Wildlife Authority headquarters

The Zambia Wildlife Authority office in Chilanga is the department headquarters.

This is the base for all the senior officers in ZWA, including the Director, Deputy

Director, Chief Warden, Landuse Planning Officer, and Chief Wildlife Research Officer.

The headquarters office is responsible for all policy issues and national decisions affecting

wildlife in Zambia, including approving final hunting quotas in game management areas,

budgeting and staffing, program planning, research and education, developing and

enforcing policies and regulations, collection of fees and permits, and coordination with

other agencies both domestic and foreign. ZWA is the government's legal steward of

wildlife, and they are answerable to parliament and state house concerning the state of

Zambia's wildlife estate. ZWA headquarters also has vested interest in ADMADE because

ADMADE is the department's official management policy in most non-depleted GMAs.

Safari hunting in ADMADE GMAs also provides a significant amount of revenue both for

ZWA and GRZ. Senior ZWA officers also represent Zambia in many international wildlife

fora, such as the annual CITES convention and regional conservation conferences.

As far as ADMADE is concerned, senior officers in ZWA want to know how

successfully wildlife is being conserved in the project area, and how communities are

benefiting from the program. On a more immediate level, they need information on staffing

issues and supplies for field operations. At the policy and strategic planning levels, they

need to know how government policy and private industry affect the success of safari

hunting and ADMADE, and how those policies might be altered or supplemented with









new initiatives. The decision to adopt and support ADMADE as the official government

wildlife management policy for GMAs was based in part on monitoring results from the

pilot Lupande Development Project as well as ADMADE. Projecting into the future, the

evolution of wildlife management in game management areas of Zambia will be based in

part on the experiences of ADMADE as expressed through monitoring.

In 1999 an ADMADE coordinating office was opened at ZWA headquarters

Chilanga. This office allows ADMADE to develop a presence in the day-to-day activities

of the department. The coordinating office also provides field support to the ADMADE

units surrounding Kafue National Park, and liases with other government departments and

the donor/NGO community in Lusaka. The information needs of the coordinating office

parallel those of Nyamaluma, and there is close coordination between the two branches.

The coordinating office does not presently play a role in data processing and analysis, but

once its future is stabilized, monitoring may become a larger component of its operations.

SAID

As the primary donor for the first ten years of ADMADE's existence, USAID has

its own information priorities. At a very basic level, they want to determine whether the

goals and objectives described in project documents are being achieved, and whether the

program is sustainable. In the big picture, one of USAID's interests in funding ADMADE

has been to evaluate whether CBNRM is an effective strategy for wildlife management,

and if so whether this approach can be replicated in other areas or other sectors. Thus it

needs a variety of information that will show not only whether ADMADE is achieving its

goals, but also through which strategies and under what conditions.









One of the challenges all projects with long-term donor support must face are

shifts in the donor's information needs and priorities. In the mid and late 1980s, when

ADMADE's funding agreement was developed and approved, USAID's reporting and

evaluation frameworks were generally oriented to measuring the impact of individual

projects, and biodiversity conservation was a goal in itself. In the mid 1990's, USAID

became more 'results oriented' agency wide, reflecting a larger movement in the US federal

government to improve accountability and effectiveness. Oversea missions were instructed

to develop strategic plans for the country, and streamline their project portfolios to be

more coherent and integrated around a hierarchical framework of goals and objectives.

As a result of this shift, USAID funding for ADMADE in 1998-1999 fell under

Strategic Objective One: To increase the rural income of selected groups. Under this

strategic objective, and its three intermediate results, a variety of performance indicators

are listed for which ADMADE must provide data in its quarterly and annual reports.

These indicators include the net income of rural households, access to finance, value of

commodities marketed, improved land and labor productivity, and the number of clients of

support institutions (USAID/Zambia, 1997). ADMADE, which has always had a strong

programmatic emphasis on wildlife conservation, does not fit neatly into this new branch

of USAID's strategic objectives framework, and has had to strengthen its data collection in

several areas. To measure performance towards USAID's strategic objectives, ADMADE

needs to report the number of people benefiting from community development projects,

the nature of those benefits, the effectiveness and efficiency of management activities, and

variables which impact the long-term sustainability of the program. This translates into

improving data collection on revenue flows, community awareness and support for the









program, impact of community development projects, management capacity at the local

level, wildlife population trends, and performance of the safari hunting industry.

Because donors are not involved in day to day management, USAID for the most

part only requires aggregated summaries of monitoring data, not all the details.

Furthermore, because ADMADE's impact monitoring data are combined with data from

other USAID supported projects to measure the impact of the entire SO1 project

portfolio, USAID prefers quantitative over qualitative data, in universal units such as

dollars, and in absolute values instead of simply relative measures or trends. They also

require data which is representative of the project as a whole, instead of just selected

areas, to ensure that the results are a valid measure of ADMADE's overall performance.

Safari industry

The safari hunting industry is the private sector partner with the largest role in

ADMADE. Within the safari industry, the people that have the most immediate interest in

ADMADE's monitoring system are individual safari hunters, safari operators, and

professional hunters. Safari operators are generally private individuals who have won a

concession agreement from the government to conduct safari hunting in a specific GMA.

They represent the political and business side of safari hunting. Professional hunters, on

the other hand, are highly-experienced hunters who are licensed by the government to

guide safari hunters and have been contracted by a safari operator to construct and

operate a safari hunting camp in the hunting block.

Safari operators and professional hunters in the safari business are frequently

motivated as much from a passion for wildlife and hunting as the financial rewards. They

have an interest in ensuring that hunting in Zambia is managed profitably and sustainably,









and by extension are interested in all data that are used to guide management of wildlife.

More specifically, they are interested in any information that can be used for setting

hunting quotas, to ensure maximum profit without jeopardizing the success of future

hunting seasons. Nor do they want to be in the position of selling promises for wildlife

trophies that do not exist, which can quickly ruin one's reputation in a market where the

main information source for prospective clients is word of mouth. When competing for

concessions, safari operators need data upon which to base their bid for the hunting block.

This includes measures of wildlife abundance, past hunting success, management capacity,

and characteristics of the local communities.

Both safari operators and professional hunters must recruit foreign hunters to hunt

in their area. Much of this marketing takes place during the annual Safari Club

International convention in Las Vegas. To market their hunting block to wealthy,

sophisticated, and demanding clientele, safari operators need to present evidence of the

status of wildlife and hunting success in their area. To a lesser, but growing extent, safari

hunters are also interested in the conservation benefits of hunting, and desire information

about the sustainability and ethics of hunting in a certain area. ADMADE's 'Green Bullet'

certification program is one of the newer elements of its monitoring program which

provides prospective hunters with this type of information. Green Bullet certification for a

hunting area is an indication that there is an effective partnership between the safari

operator and the local community according to ADMADE guidelines (see Appendix A).

Ministry of Environment and Natural Resources, Environmental Support Programme

Within the Ministry of Environment and Natural Resources (MENR), the

Environmental Support Programme (ESP) is a multi-faceted project aimed at increasing









environmental management capacity in Zambia. The sub-programs under ESP, each of

which is supported by a separate donor but share common goals and strategies, include the

Environmental Information Network and Monitoring System (EINMS), the Community

Environmental Management Programme (CEMP), the Project Environmental Fund (PEF),

and Institutional and Legal Framework (ILA). At least two of these component projects,

the EINMS and CEMP, have very concrete interests in the monitoring activities of

ADMADE.

The EINMS has the mandate of compiling a directory of all environmental data in

the country and developing institutional partnerships to facilitate exchange and enable

cross-sectorial analyses (Mukumbuta & Mbumwae, 1997). Wildlife is one of the key

resources identified by the ESP to be of national significance and at risk, along with forest

resources, fisheries, and clean air and water. ADMADE has one of the most complete

datasets on wildlife in the country, particularly outside national parks in the GMAs which

hosts much of Zambia's wildlife estate. Furthermore, from a methodological standpoint,

the EINMS and its institutional partners have a lot to learn from ADMADE's ten year

experience of using community residents in natural resource data collection, and

conducting analyses with GIS enabled RDBMS applications.

The CEMP program is similar to ADMADE in that it strives to involve

communities in the management of natural resources (Zulu, 1999). Two of the four CEMP

pilot areas actually overlap ADMADE units. However CEMP differs slightly from

ADMADE in that it focuses on a larger suite of resources, including forest products,

fisheries, and mining, and is being implemented through district level government. Despite

the differences in implementation strategy, CEMP stakeholders at the national, district,








52
and community levels could benefit from ADMADE's monitoring system, in both content

and methodology. Because CEMP is slated for expansion into a national program,

ADMADE areas may potentially gain as well, in developing strategies for managing non-

wildlife resources appropriate for the Zambian context, diversifying the resource base for

community development, and working more closely with local government.

Environmental Council of Zambia, Wildlife Resources Monitoring Unit

The Environmental Council of Zambia (ECZ) is a semi-autonomous government

unit that performs a variety of roles, including developing and enforcing policies and

regulations for the control of pollutants. ECZ also implements or provides support to

many environmental projects within different government ministries, including several of

the sub-programmes under ESP. Recently, a new unit within ECZ, the Wildlife Resources

Monitoring Unit (WRMU), was created with the mission to compile a database on wildlife

populations, support ZWA in monitoring activities, and serve as a third party source of

wildlife data. There is a natural opportunity for developing partnership and dialogue

between ADMADE and WRMU, as they support complimentary datasets and

methodologies.

NGO community

The Wildlife Conservation Society (WCS) of New York is ADMADE's longest

standing supporter from the international conservation community, and has many of the

same types of information needs as a donor, but with a stronger emphasis on the status of

wildlife. Unlike USAID, which sees wildlife conservation as a means to improve rural

livelihoods, WCS sees rural development as a means to conserve wildlife. They would like

detailed feedback on wildlife issues, including quantifiable data on habitat and species








53
conserved. WCS has conservation interests and objectives at the regional and continental

level as well, and would like data that can be aggregated with others to shed light on

conservation issues at larger scales. Furthermore, because WCS and other international

conservation NGOs support and plan conservation projects in many other countries, they

also want feedback on ADMADE's success as a methodology, including lessons learned

and the context for success/failure.

There are numerous wildlife NGOs, both domestic and international, working in

Zambia. WWF has activities in two of Zambia's prized wetlands, the Kafue Flats and

Bangwelu, which also support safari hunting. The Zambia IUCN office supports

biodiversity inventories and coordinates environmental research for a variety of

government units and donors. At the regional and continental level, IUCN's different

specialist groups monitor the status of threatened wildlife, of which Zambia hosts some of

the most important remaining populations. The Wildlife Conservation Society of Zambia

(unrelated to WCS New York) supports the Chongolola program, which are wildlife clubs

in schools throughout Zambia. The South Luangwa Area Management Unit (SLAMU),

formerly known as the Luangwa Integrated Rural Development Program (LIRDP) is a

program similar to ADMADE but operates in only two GMAs, Upper and Lower

Lupande. SLAMU has recently adopted selected elements of ADMADE's safari

monitoring program for its own operations. The Kafue Anti-Poaching Organization

(KANTIPO) is a young NGO comprised mostly of lodge owners supporting anti-poaching

and community development activities in and around Kafue National Park. These are just

a few of the many wildlife related NGOs and activities in Zambia, all of which have or

would like to share wildlife data and methodologies for community based monitoring.









Identification of Monitoring Goals

ADMADE's monitoring system, like the program itself, has evolved and adapted

since its inception in 1987, and even now is best characterized as a work in progress.

However monitoring has always been recognized as an integral component of community

based management, and has consistently received relatively substantial resources in terms

of training, internal and external technical support, personnel, and finances. Although there

is no single master plan for monitoring that describes all the goals and workings of

ADMADE's monitoring system, Nyamaluma Institute's research unit has produced several

internal documents describing various aspects of monitoring (National Parks & Wildlife

Services, 1990; National Parks & Wildlife Services, 1993a; National Parks & Wildlife

Services, 1993b; National Parks & Wildlife Services, 1995). These documents suggest the

following goals of the monitoring system:

* to build capacity at the local level to make informed management decisions

* to provide quantitative data to measure the effect of local participation in natural

resource management

* to meet the legal responsibility of NPWS to provide a national monitoring service of

Zambia's wildlife estate

* to make pertinent information on local resource needs more available to resident

management authorities

* to collect the information necessary to conduct participatory land use planning

workshops

* to develop monitoring methodologies within the scope of skills of locally recruited

personnel and under the supervision of officers resident in the GMA









* to provide data for the senior NPWS staff to manage personnel and field operations

Inclusive Participation

The Effective Monitoring Framework highlights the importance of inclusive

participation from all stakeholders in all aspects of the monitoring system, including

design, implementation, and analysis. The issue of participation can be perhaps best

explored by examining the role of the three main groups of stakeholders (communities,

NPWS/ZWA, and external partners) in the various stages of monitoring.

Community participation

In the early stage of ADMADE,4 participation in monitoring from the community

was limited to the use of village scouts in data collection. Village scouts constitute the

foot soldiers for ADMADE's resource monitoring program, and while their job may at

times put them at odds with fellow residents of the area, they appear to remain well

integrated into community social structures,5 at least more so than civil servant scouts.

While there has not been an experiment to see whether community members place more

confidence and trust in monitoring data if it is collected by a local village scout versus a

civil servant scout, this is an assumption made by ADMADE's monitoring model.

Community participation in data collection is extended into the realm of ownership

and control of data. Local ownership of data has always been one of the paramount

principles in ADMADE's monitoring design. This principle is translated into practice by

making sure that all data forms and summaries are returned to the unit headquarters after



4 "Early" in this sense refers to the level of implementation, and is relative to a given area. Hence some
GMAs which may have joined the ADMADE program in 1987 could still be considered in the "early"
stages of the program because they have made limited progress in establishing the various structures and
activities outlined in the program design.
5 Although some authors have questioned this, e.g., Marks 1994; Gibson and Marks 1995.









processing and analysis at Nyamaluma. Once data forms are returned to the units,

however, the collective ownership principle slips somewhat as evidence suggests that few

people in the community know what data are available, how it can be used, and that they

have a right to ask to see it.

Although village scouts have always been heavily involved in data collection,

community residents in GMAs did not play a significant role in the design of ADMADE's

monitoring system. This was due partly to a lack of resources and staff experience in

participatory monitoring design methods, and a need for project-wide standardization of

biological indicators and data collection methodologies. There was also limited time and

training opportunities to conduct the training and participatory exercises that would have

been necessary for true community based monitoring.6 However community information

needs and capability were assessed during ADMADE's early years and its precursor, the

Lupande Development Project. The designers of ADMADE's monitoring program have

always made an expressed objective to ensure monitoring methods and results are relevant

to the community and within their technical capabilities (National Parks & Wildlife

Services, 1993b).

Strengthening community participation in the data analysis phase has also always

featured in the monitoring design, even if accomplishments are more modest. One of the

early monitoring plans called for the establishment of regional data processing centers

headed by NPWS staff biologists, who would assist unit leaders in analyzing their

monitoring data and interpreting results to communities (Lewis, 1993). This strategy


6 In comparison, the CARE/Zambia Livingstone Food Security Project used PRA exercises to initially
identify community information needs and select indicators for the community self-monitoring system.
See Lyons, 1998.









aimed to lessen the dependence of unit staff and communities on the technical

backstopping from Nyamaluma, but was difficult to implement. However in mid-2000, a

similar network of support centers, called ADMADE Outposts, was established in four

areas. These outposts are staffed by extension staff from Nyamaluma and provide

facilitation and technical services to ADMADE communities, including assistance with

data processing and analysis.

Community participation in data analysis and interpretation also features in

exercises such as quota setting and land use planning. These activities have become

established in about half of the ADAMDE GMAs, and represent a significant step forward

in information-based decision making at the community level. Much of the monitoring

system has been tailored around the needs of these two specific activities, which in turn

impart an awareness and appreciation of monitoring data in local land management.

To strengthen community participation in analysis, Nyamaluma is currently

focusing on providing training to members of the three technical committees (resource

management committee, community development committee, and financial management

committee) to appreciate, understand, and use monitoring data in decision making. This

move represents an effort to broaden the number of people involved in analyzing and using

monitoring data, which until recently centered around the unit leader. Assuming the

functions and capabilities of these management committees continue to increase as

expected, their involvement in monitoring will increase and they may one day collaborate

in monitoring functions currently performed only by unit staff and Nyamaluma.









NPWS/ZWA participation

For the purposes of analyzing participation in monitoring, NPWS/ZWA can be

divided into three sub-groups: Nyamaluma staff, senior officers, and field staff. The staff

of Nyamaluma have been heavily involved in all phases of the monitoring program: design,

implementation, and analysis. They conduct virtually all of the planning, training, data

processing, and assist unit staff with analysis and interpretation.

The senior NPWS officers, namely the Director, Deputy Director, Chief Wildlife

Research Officer, Chief Warden, and Land Use Planning Officer, and Wardens are less

involved with the operation of ADMADE's monitoring system, however their information

needs have been incorporated in the design of the monitoring program. The primary role

for this group is at the level of data analysis and interpretation. In practice, this audience

has not been as well served by ADMADE's monitoring program as possible. Because of

the distance between the NPWS/ZWA headquarters, Nyamaluma, and the GMAs, senior

officers do not have many opportunities to become familiar with the datasets and their

analyses. For years, Nyamaluma sent hard copy printouts of the raw data and summaries

to the Chief Wildlife Research Officer, however this was not a format conducive to further

analysis or dissemination. Nyamaluma has made an objective to improve the data flow and

feedback between the senior NPWS/ZWA HQ, Nyamaluma, and the units. One of the

interventions of this research, a major upgrade of Nyamaluma's information system, may

help in this effort by reducing the technical barriers to data sharing.

External partner participation

ADMADE's external partners include the primary donor USAID; other

government units such as the Environmental Support Programme (ESP) in the Ministry of








59
Environment and Natural Resources, and the Environmental Council of Zambia; national

NGOs such as the Kafue Antipoaching Organization (KANTIPO) and South Luangwa

Area Management Unit (SLAMU); and international NGOs including Wildlife

Conservation Society (WCS) and World Wide Fund for Nature (WWF).

USAID has always played an active role in the development of ADMADE's

monitoring program, as they are one of the most influential stakeholders and have specific

information requirements. The recent emphasis on strengthening the monitoring of

socioeconomic benefits has been driven in part by the information needs of USAID.

USAID also supported computerization of the Wildlife Conservation Revolving Fund in

the early 1990s, which today is one of the most important datasets in ADMADE's

database. WCS and WWF also played a role in designing and refining ADMADE's

monitoring program, through the provision of technical assistance. ADMADE's long-term

technical advisor, who more than anyone is responsible for the design of the monitoring

program, is a staff member of WCS, as well as an officer of NPWS.

Other external partners have come onto the scene more recently, are less

connected with ADMADE's mission and activities, and played a negligible role in

ADMADE's monitoring program. Over time, if more linkages for sharing information and

technical resources are developed, these other partners may play a greater contribution in

supplementing or expanding the scope of natural resource monitoring in Zambia.

Appropriate Indicator Selection

From the onset of the program, ADMADE's monitoring design called for the

systematic collection of a core group of indicators, focusing primarily on wildlife

populations and management efforts. While there have been some changes over the years,









the basic content and analysis of these core datasets has not changed significantly.

Recently, a few new indicators have been added, addressing more of the social aspects of

the program, such as demography and levels of resource use. In time, as an archive of data

is gradually built up, the combination of resource monitoring data and social variables will

paint a clearer picture of the achievements of the program.

Below are descriptions of the main datasets collected in all ADMADE areas. The

datasets are categorized based on the source of the data. Except where noted, all of the

following datasets have been incorporated into the master database at Nyamaluma. See

Appendix A for sample dataforms.

1. Field patrol data (FLDPAT 1 dataform)

The field patrol dataform records patrol dates, number and classification of scouts

in the patrol, supplies taken and returned, number and location of poachers encountered,

names and origin of any poachers arrested, objects confiscated (e.g., weapons, snares,

ivory, etc.), carcasses (species, number, and cause of death), snares, fishing camps,

waterholes, poacher camps, fires, live animal sightings (each unit picks up to six key

species to monitor), and the number of hours spent in each grid (added 1999).

All field patrol observations are geo-referenced using a 5 km2 grid system. In the

early years of ADMADE, field patrol observations were recorded in an open-ended

'Comments' section. However this format frequently resulted in irrelevant details and was

impossible to analyze quantitatively or process in a computer. In 1995/96, the dataform

was redesigned for entering observations in a preset tabular format. However it was not

until 1999, when the Nyamaluma computer system was upgraded through this research,

that field patrol observations were input into the master database.









2. Safari hunting (SAFLICE, TROPHY, SAFHUNT, CLIENT dataforms)

The safari hunting dataforms record includes starting and ending dates of a hunt,

license numbers, fees paid, species desired by the client before the hunt, species actually

killed, wounded animals, locations and dates of animals killed, evidence of snare wounds

on animals (added 1999), trophy sizes (following SCI measurement conventions), sex,

number of baits (for baited species such as lion), non-hunted trophy animals seen,

disturbances to the hunt, poaching activity, client opinions of their hunt and Zambia.

The safari hunting dataset is one of the most robust datasets for two reasons.

Safari hunting datasets are generally complete, because there are typically only 10-25

hunts per season and safari hunters are legally required to be accompanied by a scout

when hunting. More importantly, the small numbers of hunters enables an assessment of

when dataforms are missing, an important aspect of data quality. Secondly, the hunting

measurements (e.g., date of the kill, trophy size, species) are not difficult for to scouts to

take, increasing confidence in the data. For these reasons, safari hunting statistics serve as

one of the more important indicators used to assess wildlife population trends. The other

main use of safari hunting data is to ensure that all hunting and fiscal regulations are

complied with.

3. Crop damage (CROPDAM, FIELD CROPDAM, GRANARY CROPDAM dataforms)

The crop damage dataforms record the name of the crop, date damage occurred,

owners name, location (village and grid number), number of plants affected (reported in

kg or buckets), size of the garden, species that caused the damage, action taken (e.g.,

shots fired), and result (animal frightened off, wounded, or killed).









Damage to crops is one of the biggest wildlife related problems facing rural

farmers in ADMADE areas. This dataset represents an effort to measure the scope of this

damage, and look for patterns in attacks. Unfortunately, it is not known what percentage

of crop damage is actually reported to wildlife scouts and recorded on paper, however a

review of the data and interview results suggest in many areas the amount of damage

reported is only a small fraction of the total crop damage. Hence this dataset can not be

used to make an accurate estimate of the total amount of damage caused by wildlife,

however it can be used for other purposes such as examining trends in crop selection and

the relative impact of different species. None of the areas I encountered had compensation

programs for wildlife damage, which would likely increase the size of the dataset.

Prior to 1998, the Crop Damage dataform was not entered into the database at

Nyamaluma. However, all existing dataforms from previous years were saved and entered

for analysis in 1998. In 1999, the Crop Damage dataform was divided into two new

dataforms Granary Crop Damage and Field Crop Damage. This division was in response

to a notable shift in attack strategies by elephants, who have learned to improve their

foraging efficiency by breaking into granaries (food storage bins areas within the village

perimeter). Increased attacks on granaries are a concern in ADMADE, and require slightly

different monitoring and preventative strategies.

4. Poacher case records (CASEREC)

The Poacher Case Record dataform records information about arrested poachers,

including occupation, origin, weapons, number and species of carcasses, and offenses.

There are also spaces for the trial date and results of the trial, however none of the

dataforms examined for this study has these areas filled in.









5. Household demography (DEMOG)

The household demography dataform records the number of people per household,

broken down by gender and age group. This is the only community-generated dataset that

is not collected by wildlife scouts. In 1998-99, Nyamaluma contracted private individuals,

mostly community health workers, from each area to conduct a door-to-door survey for

the census. This exercise was undertaken primarily to demarcate boundaries for the new

Village Area Groups, subdivision of each GMA introduced to improve equitable

representation in decision making. In addition to VAG demarcation, demography data can

be used for other purposes such as planning community development projects and

evaluating the per-capita benefits of the program. Only the GMAs in the Luangwa valley

area were surveyed in 1998, the remaining areas expected to be surveyed in 1999 or 2000.

6. Quota setting worksheets

Starting around 1996/7, units were encouraged to organizing workshops at the end

of each hunting season to discuss the hunting quota for the following season. In practice

these exercises have occurred only when extension staff from Nyamaluma were available

to facilitate the meetings, however in the future it is expected that communities will be

able to conduct these meetings on their own. The methods for assessing population trends

in the area include a mix of quantitative (e.g., hunting statistics) and qualitative (e.g., scout

opinions, feedback from the tracker) indicators. The population trend suggested by each

indicator (i.e., upward, no change, downward) is written on a flip chart for each species,

and a new recommended quota arrived at by consensus. The flip charts are then copied

onto the Quota Setting Worksheet, which is brought to Nyamaluma and entered into the

database.









7. Staff

When monitoring teams from Nyamaluma visit a GMA, they collect information

about staff in the unit, including both civil servant and local staff. Information collected

includes date of birth, education level, position, status (e.g., in camp, retired), and family

size. This data is used for analyses on staff efficiency, financial support, personnel needs,

budget reviews, training needs, and retention rates.

8. ADMADE projects

Updated on an annual basis, the dataset for community development projects

includes information on all projects financed with safari hunting revenue. Projects include

both community development projects such as clinic building as well as resource projects

such as scout quarters. Information recorded includes the type of project, when it was

started, when it was completed, the amount of money spent, and the current status. This

dataset does not include the number of beneficiaries of the project, or a measure of the

economic value of the project. Once a project is complete it is no longer monitored except

for special studies. The projects dataset had not been converted into the new database as

of May 1999.

9. Camps, assets, firearms

These datasets are also collected by teams from Nyamaluma on an annual basis,

and are used for planning support to areas and measuring changes in the operational

capacity of a unit. Fields include number of scouts at the camp, type and serial number of

firearms, camp water source, and scout ages and family size.









10. Official quotas, license prices, and daily license sales

Once a year, a committee at NPWS headquarters in Chilanga approves a final

hunting quota for each hunting block. Quotas are based on community recommendations

if available and any other relevant information such as license sales. NPWS also sets the

price for hunting licenses, and sells all hunting licenses at headquarters7. The office which

sells hunting licenses has been computerized since 1994, and all of those records have

been imported into Nyamaluma's new database. This information is useful for analyses on

topics such as the economic impacts of safari hunting at the local and national levels,

timing and distribution of revenue flows, and long-term trends in safari hunting in Zambia.

11. Green Bullet certification

The Green Bullet is a certification rating which suggests that safari hunting is being

conducted in a ecologically and socially sustainable manner. The certification is bestowed

to individual GMAs, and so requires minimum performance measures from both the local

safari company as well as community organizations. Criteria for Green Bullet certification

include the practice of distributing excess meat to community residents, adherence to

hunting policies and procedures, and effective communication between the Professional

Hunter and members of the ADAMDE community organizations. See Appendix A for the

Green Bullet dataform.

12. Discontinued dataforms

Some dataforms introduced early in the program but no longer widely used include

the Budget Cost Form, Employment Records, Skills Bank Record Form, Patrol




7 In 1999 a decentralized licensing system was pilot-tested in several GMAs, which may change the way
hunting licenses are sold and recorded.









Effort/Yield Summary Sheet, Culling Product Processing, Culling Product Marketing,

Wildlife Inventory Report, Transect Dataform, Community Attitude Survey, Village Scout

Attitude Survey, Socioeconomic Survey, and Annual Data Summary Sheet (National

Parks & Wildlife Services, 1990). Many of these dataforms were basically worksheets or

administrative templates for which alternatives were found, and hence not vital elements of

the monitoring program. However others, such as the attitude and socioeconomic surveys,

could have potentially made an important contribution to ADMADE's monitoring program

but did not take root.

13. New dataforms

In 1999, Nyamaluma introduced several new dataforms, including the Village Area

Group (VAG) Committee Establishment dataform, VAG Meeting Attendance dataform,

VAG Committee meeting report. VAG Development Needs Implementation dataform,

Social Service Provider Form, VAG Development Needs and Priorities dataform, CDC

Community Development Monitoring dataform, Self-Appraisal Monthly Work Form for

village scouts, Snare Survey dataform, and Population Trends dataform (National Parks &

Wildlife Services, 1999b). Some of these dataforms are primarily designed to assist the

new management committees, and are not designed to be analyzed project wide.

14. Other data collected

In addition to the above datasets, which are collected on a regular basis in most

ADMADE units, other data have been collected by Nyamaluma staff over the years for

focused studies. These special studies have included surveys of ADMADE awareness,

garden productivity, ground transects, infrastructure surveys, snaring pressure, behavioral

ecology of species in decline, and others.









Temporal and Spatial Scales

According to the Effective Monitoring Framework, monitoring must take place at

a spatial and temporal scale which concurs with use of the information. Short-term

management decisions require information measured at fine scales, while long-term impact

assessment requires data taken over long periods of time and larger geographic areas.

Table 2 below summarizes the temporal and spatial scales of the primary datasets collected

in ADMADE areas.

Table 2 Spatial and temporal scales
Data Set Temporal Spatial
Scale Scale
Field patrol data whenever field patrols are only patrolled areas
conducted, 5 km accuracy
weekly/monthly
Safari hunts throughout the hunting only areas hunted
season (March-Nov) 5 km accuracy
Crop damage as often as it is reported, areas close scout camps
monthly
Household demography once every five years entire GMA
(anticipated)
Population trends survey annual patrolled areas of GMA
Staff annual entire GMA
Camp conditions annual entire GMA

Sampling

The Effective Monitoring Framework also highlights the importance of using a

sampling method which allows inference to the population as a whole, and that enough

measurements are made to make inferences about the entire population. Table 3 below

summarizes the sampling method and sample size of the primary monitoring datasets.

Feasibility

The Effective Monitoring Framework requires that the design of a monitoring

system concur with the available resources in terms of manpower, training, equipment,









Table 3 Sampling
Data Set Sampling Percentage of Population Sampled
Method Design Actual (est.)
Field patrol data complete 100% unknown
Safari hunts complete 100% near 100%
Crop damage complete 100% unknown but
probably small
Household demography complete 100% 100%
Population trends survey complete as many as possible unknown
(just started)
Staff complete 100% near 100%
Camps complete 100% near 100%

financial resources, and leadership. ADMADE's monitoring program is ambitious, evident

from the quantity of datasets monitored, the size and location of areas in the program,

diversity of end users, methods used to allow quantitative measurements and analysis, and

centralized processing model. Factors which challenge the feasibility of such an ambitious

monitoring design include the state of the transportation and communication infrastructure

in ADMADE areas, the educational level of scouts and unit staff, and funding. However

the amount of data successfully collected and analyzed over the last several years suggests

that the monitoring design is feasible for many areas.

However this achievement is not uniform in all GMAs, and has come at a cost.

Nyamaluma has devoted a considerable amount of its resources, in terms of manpower,

equipment, training, and field support, to developing and supporting unit monitoring

systems. The importance of this support is clearly evident when comparing the monitoring

achievements of units which receive more field support in monitoring to those units which

receive little. Even in areas which are well served by support staff and have established

monitoring programs, it took an average of two to three years before good information

was generated on a regular basis. In addition, not all datasets have been equally successful.








69
Some dataforms, such as the scout and community attitude questionnaires, never became

well established, nor generated a lot of useful information.

Both successful and unsuccessful efforts at monitoring have provided useful

feedback for understanding issues which affect feasibility of a monitoring design. This

improved understanding has helped and will continue to help adapt monitoring strategies

to increase the effectiveness and benefits of community level participation in data

collection and analysis.

Incentives

In an effort to minimize data fabrication or falsification, ADMADE's monitoring

design does not call for direct incentives for village scouts to record data (National Parks

& Wildlife Services, 1993b). Instead, it is hoped that scouts will take an interest in

monitoring based on an appreciation of the importance of monitoring in resource

management, and because it is part of their job. In 1996 Nyamaluma sponsored a

monitoring competition and offered a small prize to recognize the best village scout

involved in monitoring in all of ADMADE, judged on the fewest number of mistakes on

data sheets. However this competition was dropped after one year because it was

cumbersome to administer and more appropriate to implement at the unit level. Unit

leaders have always been encouraged to reward scouts who demonstrate high interest and

ability in data recording with additional training opportunities and other forms of

recognition. There is also a certain incentive for monitoring safari hunts, because there is

the possibility of receiving a tip from the client. However several scouts interviewed stated

this positive incentive was overshadowed by poor accommodations at the safari camp for

scouts, and unpleasant or dishonest professional hunters.








70
There are few negative incentives for not participating in monitoring. Scouts who

choose not to record data on field patrols or not accompany safari clients are not

penalized financially or otherwise. Some unit leaders may verbally scold their scouts if

monitoring activities are not being carried out effectively, however the mix of positive and

negative incentives will depend on the leadership skills and style of the unit leader.

Negative incentives are more likely to be applied to more serious deficiencies in job

performance, such as drunkenness or failure to participate in anti-poaching operations.

Due to the multitude of factors that may disinterest a scout from monitoring (e.g., lack of

education, lack of appreciation, extra work involved), a unit leader is more likely to

concentrate on identifying and encouraging those scouts that have some interest and

aptitude in monitoring rather than apply pressure to those who are not involved.


Environment

Design Implementation Applications Sustainability


... .. ........
I...................................


Implementation
identification of data collectors
training
monitoring workshop
observations
percentage of field patrols recorded
filing system
constraints with Nyamaluma's information system
supervision
information flow
data processing system
timeliness
data quality evaluation
procedural and administrative data quality controls
processing and analytical data quality controls
Figure 9 Implementation










Implementation

Identification of Data Collectors

An important element of an effective monitoring system is the appropriate

selection of data collectors. Data collectors should be in a position to and capable of

making observations and recording data properly. Most of the monitoring data in

ADMADE is collected by village scouts. This seems an appropriate choice because village

scouts are likely to spend large amounts of time in the bush in the normal course of their

duties, are employees of the community, fall under the command structure of the NPWS

field officers, and are required to go through training courses. Scouts have also proven

that they can be competent data collectors given sufficient training and supervision. All

village scouts receive instruction in data collection during their basic training, however

only those scouts who show competence on practical trials during the course and do not

make many mistakes on dataforms become regular recorders on field patrols and safari

hunts. It is up to the unit leaders or their designated deputy to decide which scouts are

qualified recorders.

The only real alternative choice for resource monitoring data collectors are the

civil servant scouts. Civil servant scouts serve side by side with village scouts, but are

government employees and paid by NPWS, instead of safari revenue. They typically have

more formal education than village scouts, however do not go through basic training at

Nyamaluma and are not trained in the use of dataforms. Civil servant scouts are also less

attractive as data collectors because they are not local residents, which has both symbolic

connotations for trustworthiness, as well as practical implications in terms of being less

familiar with an area. According to interviews, in practice some civil servant scouts do








72
serve as monitors on both field patrols and safari hunts, having learned how to fill out the

dataforms from their fellow scouts. This arrangement seems to be generally satisfactory

among the scouts where it occurs. An exception may be the assignment as a safari hunting

monitor, which also carries the possibility of receiving a tip from the hunting client. In two

out of seven interviews, scouts complained that the unit leader was favoring civil servant

scouts for safari hunting monitoring, despite the fact that the civil servant scouts had not

received the same training as village scouts.

Training

Training is a key element in the implementation of a monitoring program, a lesson

borne out by the experiences of ADMADE. The leadership of ADMADE recognized the

importance of training from the onset, and were fortunate to have a residential training

facility, Nyamaluma Institute, available to host courses and workshops. Nyamaluma has

conducted dozens of training programs since the first village scout course in 1988, and

currently claims to offer more than 15 different courses to over 500 ADMADE

participants annually (National Parks & Wildlife Services, 1998). Courses with significant

monitoring content include

* Village Scout Basic Training
* Village Scout Advanced Training
* Unit Leader Basic Training
* Skills Training in Resource Management
* Wildlife Biologist Internships (National Parks & Wildlife Services, 1999f)

In addition to centralized training programs held at Nyamaluma, informal training

is provided during field visits for unit leaders, deputy unit leaders, and village scouts.

Feedback on dataforms and office management is a standard part of nearly every visit to

units, and since 1996-7 Nyamaluma has been sending staff to select GMAs at the end of









the hunting season to help analyze safari monitoring data and facilitate quota setting

exercises. During this study, I was not made aware of any units which had organized their

own workshops or refresher courses on monitoring. However informally unit leaders or

their deputies continually provide feedback to village scouts on their performance in

collecting data, and scouts provide feedback to each other on the proper use of dataforms.

Monitoring workshop

During this research, I assisted Nyamaluma staff in planning and conducting an

advanced course for village scouts and deputy unit leaders. About three-quarters of the

content this course focused on topics related to monitoring. This one-week workshop was

held in May 1999, and was attended by 44 scouts from all ADMADE areas. The

workshop objectives related to monitoring included to:

* review monitoring as one of the roles of village scouts
* discuss different uses of monitoring information
* review common mistakes on dataforms
* review techniques for measuring hunting trophies
* introduce a new technique for conducting snare transects
* explain how "data collection" patrols around fish camps and waterholes differ from
standard anti-poaching operations
* explain how to maintain running summaries of monitoring data using base maps
* discuss setting targets and developing work plans for monitoring and other activities

Other sessions during the workshop addressed:

* the ADMADE vision
* developing lesson plans for school groups
* new dataforms for a pilot community based licensing system
* individual participant interviews to update Nyamaluma's database on unit staff, roads,
etc.

Most of the workshop sessions were held in the classroom, but were participatory

in nature. There were two outside practical sessions, one on conducting snare transect and

another on trophy measurement. To pass the course and receive their certificates, students








74
were required to make presentations on the last day of the workshop, reviewing the topics

they had learned during the course.

Pre- and post-workshops questionnaires were administered to gauge scout

expectations of the course and knowledge of the role of monitoring in management. Pre-

workshop questionnaires revealed a general lack of understanding of the different ways

monitoring information can be used. Exit evaluations were strongly positive, but focused

mainly on satisfaction with the workshop.

In addition to this short-term intensive workshop on monitoring issues, monitoring

was covered in other courses during 1999. A workshop for unit leaders held in June

addressed monitoring supervision, and various workshops for the newly elected

community resource boards also touched on monitoring. ADMADE will need to continue

to provide training, both at Nyamaluma and in the field, to enable unit staff and

communities to collect and analyze their own monitoring data for exercises such as quota

setting, setting work targets, and resolving land use conflicts.

Observations

Table 4 shows the number of GMAs which have monitoring data entered in the

master monitoring database at Nyamaluma, broken down by year and dataset. Although

this table is not a totally complete picture of the amount of monitoring data in ADMADE,

because some units may not have submitted their dataforms to Nyamaluma, it does

accurately represent historical trends and highlights those datasets which have been most

successfully collected for the greatest number of areas.









Table 4 Datasets in Nyamaluma database, May 1999
Data Set Source Number of GMAs With Data
1994 1995 1996 1997 1998
Field patrol community 11 16 13 8 11
data (50%) (72%) (62%) (40%) (58%)
Field patrol community 0 0 3 3 10
observations (14%) (15%) (53%)
Safari hunts community 0 15 12 11 8
(68%) (57%) (55%) (42%)
Crop damage community 0 2 1 3 1
(9%) (5%) (15%) (5%)
Household community 0 0 0 0 6
demography (32%)
Quota setting community 0 0 0 8 6
worksheets (40%) (32%)
Staff Nyamaluma 11 17 12 16 18
(50%) (77%) (57%) (80%) (95%)
Camps Nyamaluma 16 20 12 13 11
(72%) (91%) (57%) (65%) (58%)
NPWS NPWS 22 22 21 20 19
Quotas (100%) (100%) (100%) (100%) (100%)

Percentage of Field Patrols Recorded

One of the important, but largely unknown, variables about ADMADE's

monitoring program is the percentage of field patrols that are actually recorded on the

field patrol dataform. This unknown ratio has implications not only for calculating

estimates of total law enforcement effort, but also evaluating the validity of any

monitoring data collected on field patrols.

In interviews for this study, scouts unanimously stated that 100% of all field

patrols are recorded on dataforms. However after reviewing the field patrols summaries it

seems unlikely that all field patrol data actually make it through the information chain to

Nyamaluma. Not only do analyses suggest low patrolling effort of 20-40 days per year per








76
scout (National Parks & Wildlife Services, 1999f),8 but some units also have lengthy gaps

where no patrolling is recorded. Hence it is suspected that either the percentage of patrols

recorded is less than 100% to begin with, or that dataforms get lost en route to

Nyamaluma.

My visit to Mumbwa GMA afforded an opportunity for the first time to empirically

study the percentage of field patrols recorded on dataforms. The Mumbwa unit

headquarters at Nalusanga keeps a field operations record book, an independent ledger for

all field operations that originate from Nalusanga camp. Although the ledger book records

just a subset of the fields of information on the ADMADE field patrol data forms, it does

record the starting and ending dates of each patrol and the number of scouts.

I compared the field patrol records in the field operations record book with

ADMADE field patrol dataforms for 1997 and 1998. For 1997, there were 17 field patrols

from Nalusanga recorded in Nyamaluma's database, and 16 original dataforms in the filing

cabinet at the unit headquarters. This implies that dataforms were not lost en route to

Nyamaluma. However the field operations ledger recorded 44 field operations during

1997, excluding operations such as escorts and checkpoints. Thus for 1997 only 39% of

the field operations were recorded on dataforms that were returned to the unit

headquarters and eventually Nyamaluma.






8 Although this estimate is probably low, it may not be far off. In the similar LIRDP project, which keeps
more detailed records of patrolling effort, Jachmann (1998) reports that effective scout-days varied from
52.5 121.7 days per year between 1988-1996. This estimate includes time spent investigating poaching
activities in settlements but excludes time spent getting to the patrol area. The main factors identified
affecting the number of effective scout days included the availability of carriers to assist with supplies and
scout salaries.









Reviewing the field patrol dataforms from 1997 reveals that all recorded patrols

were between July and December of that year. Hence the most likely explanation for only

39% of field patrols recorded is that during the first six months of the year data was either

never collected, or the dataforms were lost. However neither the unit staff nor staff at

Nyamaluma had any memory of what may have caused this gap, and the missing data still

results in a substantial underestimate of patrolling and monitoring effort.

It should also be noted that Nalusanga camp lies on the border of Mumbwa GMA

and Kafue National Park, and that scouts from Naluanga patrol in both areas. Kafue

National Park is not an ADMADE area, and there is no base map for it. Scouts are

supposed to record patrols made in either area, however it may not be entirely surprising

that not all patrols in the park, particularly day patrols, are recorded.

For 1998, there were 39 field patrols from Nalusanga recorded in the Nyamaluma

database, spread almost evenly throughout the year. However the Nalusanga Field

Operations ledger recorded 115 field operations, again excluding investigations, official

escorts, funeral drills, etc. Thus only 32% of field operations were actually recorded for

that year. The missing dataforms represent an unrecorded 122 patrols-days and 421 man-

days of patrolling effort. Thus for 1998, the number of patrols from Nalusanga recorded in

the database was only 32% of the actual, representing 56% of actual patrol-days and 67%

of total man-days.

Of the non-recorded field patrols, 69 were in Kafue National Park and nine were in

Mumbwa GMA. This pattern suggests the probable cause of the underreporting--scouts

apparently do not fill in dataforms for all patrols in Kafue, even though they do submit

dataforms for some of the patrols there. However other omissions were from patrols in the








78
GMA. The nine patrols in the GMA not recorded on dataforms represent 163 man-days of

patrolling and monitoring effort.

Filing System

Developing an organized filing system is an essential requirement for the

implementation of a community based monitoring program, however this has proven to be

a challenge for many ADMADE units. Obstacles encountered include lack of filing

equipment (Figure 10), failure to see the need to organize records, lack of understanding

how to categorize records, and failure to anticipate the volume of dataforms that will need

storage. Building capacity in managing a filing system is a basic prerequisite for

community based monitoring. Poorly organized filing has many times caused misplaced or

incomplete data sheets, resulting in lost opportunities for data analysis and management

feedback.


-~- I


Figure 10 Files at Lunga-Luswishi GMA









Nyamaluma's extension staff have tried to address the technical aspects of

managing dataforms in workshops and field visits. Staff from all units attend the same

courses at Nyamaluma, however units which receive more frequent field visits from

Nyamaluma's extension staff tend to develop functional filing systems in a shorter period

of time. The audience for data management training includes office staff in each unit, and

more importantly the unit leaders whose interest in and supervision of filing is critical for

all subsequent steps of the monitoring process.

Supervision

Supervision is an important element of the Effective Monitoring Framework, and

in the case of ADMADE exists at two levels. At the GMA level, unit leaders or their

designated deputies are supposed to supervise the data collection activities of scouts. This

includes ensuring that data forms are available and filled out on field operations, and when

completed reviewed for errors and properly filed. Research staff at Nyamaluma provided

numerous examples where months of data were lost and/or unusable because of poor

supervision within the unit. For example Munyamadzi unit staff lost all of their dataforms

for 1997. At the project level, staff from Nyamaluma must supervise the monitoring work

of the units. Communication and transportation constraints make this level of supervision

difficult for areas distant from Nyamaluma, reducing the ability of the project to catch

monitoring problems in the early stages before large amounts of data are lost or collected

improperly.

Information Flow

When discussing information flow, it is useful to categorize datasets based on their

origin. ADMADE's core datasets can be grouped as follows:










Community-collected data Nyamaluma-collected datasets Other
Field patrols Staff Hunting quotas
Safari hunting results Camps Hunting license sales
Poacher case records Unit assets
Crop damage Training records
Household demography Special studies data

According to scout interviews, most field patrol monitoring data is initially written

on blank sheets of paper, and then transferred to dataforms after the patrol is over. Scouts

interviewed stated this practice is primarily intended to avoid messing up a dataform while

on patrol. Once recorded, the dataforms are then given to the unit leader or his designated

deputy. If the scouts are based at the unit headquarters, then the dataforms will be turned

in to the office within a day or two. If the scouts are based at a distant camp, they will

wait until someone travels to the unit headquarters for salaries or supplies, or a special

request is made for the dataforms. It is not uncommon for weeks or months to pass before

field patrol and crop damage dataforms are turned in at the unit headquarters.

Communication and travel between scout camps and the unit headquarters has proven to

be one of the most challenging links in ADMADE's information flow and other aspects of

management. Long distances between camps can result in inadequate supervision of

monitoring activities, failure to maintain an adequate supply of blank dataforms, and loss

of dataforms. Providing feedback on dataforms soon after the patrol or safari hunt has

ended is an important mechanism for data quality control, a lesson also learned by other

monitoring programs (Jachmann, 1998).

At the unit headquarters, dataforms are supposed to be inspected for errors when

they are submitted, and then filed chronologically. Dataforms are then sent to Nyamaluma








81
with workshop participants, or collected by teams from Nyamaluma on field visits. There

is no mail service to Nyamaluma nor most of the units.

At Nyamaluma dataforms are entered into the computer, and summaries and

presentation maps prepared. The original dataforms, summaries, and presentation maps

are then sent back to the unit, again either with a returning workshop participant or field

team. Depending on the workload at Nyamaluma and the status of the computer system,

some dataforms may remain there for months and in a few cases years. Long processing

delays mostly affect lower priority datasets such as poacher case records, and crop

damage. With the new information system the turn around time at Nyamaluma is likely to

decrease.

In addition to producing monitoring summaries for the units, Nyamaluma is also

responsible for disseminating monitoring results to external stakeholders, including senior

NPWS officers, USAID, and WCS. Monitoring results are usually presented as

aggregated summaries, through quarterly reports, preset tabular or graphic summaries, or

technical papers.

Monitoring datasets collected by Nyamaluma staff are usually updated annually

during field visits. Occasionally questionnaires or interviews will be administered during

workshops at Nyamaluma to update specific datasets.

Data Processing System

A common challenge faced by monitoring programs is handling the large amounts

of data that can quickly accumulate (Macdonald & Smart, 1993). Hence the Effective

Monitoring Framework emphasizes the need to develop some kind of system which can

process and analyze monitoring data. For small datasets or very qualitative data, paper and









pencil methods are often adequate. Paper and pencil methods have the advantages that

they can often be more readily understood by rural people, and do not require large

amounts of outside technical support.

However, paper-based data systems are constrained in the amount of information

they can effectively store, and are not well equipped for analyzing quantitative data. Due

to the large number of datasets and GMAs participating in the program, as well as the

need to aggregate data at the project level, the designer's of ADMADE's monitoring

program elected early on to integrate computerization into data processing and analysis

(Lewis, 1995). Developed at Nyamaluma in the early days of the project, ADMADE's

database has enabled thousands of dataforms to be entered and analyzed, and tabular and

graphical summaries generated for community use and applied research. Nyamaluma

pioneered the use of GIS software for community based conservation, and digitized

dozens of Survey Department maps for the production of flipchart-sized summary maps of

monitoring data.

As pioneering as Nyamaluma's information system was, it was constrained by the

software and hardware of the early 1990s, and its performance was severely limited in a

number of regards. These are summarized below.

Difficult to operate. The old system was based on a combination of Lotus 123,

dBase IV for DOS, and ArcView GIS. Most of the tabular data was entered through

Lotus, a process which was semi-automated with macros, and summaries were produced

using Lotus and dBase. Maps and graphical summaries were designed manually in

ArcView, using imported summaries that were created in Lotus and dBase. Due to the

number of software packages involved and the number of steps in creating summaries,








83
only a very small number of staff could operate the system, and only the technical advisor

could make any significant changes to the design or structure of the data. Operating the

database was also time intensive, as only a small part of the data processing was

automated.

Restricted to single-area, single-year summaries. In the original system, the files for

each unit were saved in separate directories, and additional sub-directories created for

each year. For the tabular data, each year was saved on a separate worksheet and each

dataset was saved in a separate workbook. The spatial data were also divided into

separate coverages for each unit. While this file structure was useful in keeping data

organized, it made conducting analyses across years or across multiple GMAs tedious

almost to the point of almost being impossible. While this was not a major constraint in

providing data summaries for individual units, it made seeing the 'big picture' rather

difficult.

Little error checking. Due to the limitations of spreadsheets, error checking

depended heavily on the skills and experience of the data entry clerk. Problems such as

inconsistent units of measurement, inconsistent spelling of names, incorrect dates, and

occasional outliers limited the reliability of certain types of analyses. For example, it was

difficult to get an accurate count of all staff who worked in a unit over time because some

names appeared twice under different spellings, while others were just copied over from

the previous year without checks to confirm they were still in the unit.

Unwieldy file system. Maintaining the files in the old information system was an

administrator's nightmare. The hundreds of directories, sub-directories, and files made

copying or backing up the database challenging. A more severe constraint of the file








84
structure was the challenge of synchronizing files. Nyamaluma uses multiple computers for

entering data, making it incumbent upon the technicians to keep track of which files on

which data on which computers were the most up to date. Updating files on other

machines always carried the risk that recent data could be overwritten with older data.

Expansion difficulties. Adding new datasets into the information system was

challenging, because new files had to be integrated into both the directory system, data

structure, and multiple software formats. Creating new summaries of data was equally

difficult, because summaries had to be either done manually or by programming new Lotus

macros. This constraint on the system's expansion and flexibility not only affected staff

time, but also the number of datasets that could be entered and analyzed. Very important

datasets, including all field patrol observations, observations from safari hunts, were not

entered into the old information system at all because of the limitations of the software.

Other datasets, such as poacher case records, and crop damage, were entered but not tied

to other tables and could not be analyzed against other variables.

Tedious map production. As described previously, one of the most important

outputs of Nyamaluma's information system are the flipchart-sized maps of monitoring

data that are returned to communities. Creating these maps in ArcView, although flexible,

involves a complicated series of steps that requires a significant amount of training and

many hours of staff time.

Data never left Nyamaluma. Because it was difficult to extract data except in the

small number of preset formats, and the clunky file and software system made it

impossible to share data electronically, Nyamaluma's research unit struggled to meet the

information needs of its many external stakeholders in Zambia and abroad. Among the








85
donors and wildlife sector, Nyamaluma developed a reputation of being miserly with data,

failing to share results with even its closest institutional partners.

Timeliness

Timeliness is an important variable in the implementation of an effective

monitoring system, as many management decisions such as quota setting or targeting field

patrol effort require summaries of recent data. ADMADE must contend with built in

delays in the information flow as well as time required for data processing analysis. Due

largely to poorly developed transportation networks in rural areas, the time to process

monitoring data and return results to units has proven challenging, particularly for GMAs

outside the Luangwa Valley. This delay is compounded when scout camps are some

distance from unit headquarters, making it difficult for scouts to submit their dataforms in

a reasonable amount of time. However recent efforts to increase the capacity of units to

analyze their own data, and the possibility of using the new database system to process

data in the field with laptops, may decrease the amount of time it takes for summarizing

data. Timeliness is less of a problem with safari hunting data, which provides important

indicators for quota setting, because the dataset is fairly small, data are collected from one

location only, and indicators can be calculated in the field using pencil and paper in only a

couple of hours.

Data Quality Evaluation

Mechanisms for measuring and controlling data quality is an important element in

the implementation of a monitoring system. ADMADE has relied primarily on procedural

and human controls to ensure the quality of its data. With the new information system

described above, quantitative tools are also now available for the assessment of data









quality. Although no data quality assurance system is foolproof, these controls provide

reasonable precautions against most of the common errors that can infect data.

Procedural and Administrative Data Quality Controls

Monitoring certification for village scouts. Instruction in data recording is an

integral component of the 4-6 month basic training course all village scouts attend. Scouts

are taught the basic concepts of monitoring and use of the dataforms. While the skills for

recording data do not require a high level of education, not all village scouts demonstrate

an aptitude or interest to be monitors. At the end of their basic training, scouts are

evaluated on their ability to use dataforms, and only those who pass are 'certified' to be

monitors. Nyamaluma also holds shorter advanced scout classes from time to time, which

cover monitoring topics in more detail.

Only certified scouts are supposed to be selected for safari monitoring or

appointed as the data recorder on field patrols or crop damage. In reality, some non-

certified scouts, including civil servant scouts, may also record data on field patrols or

safari hunts. The unit leader or his designated deputy have the responsibility to weed out

those scouts who do not show competence in monitoring, and over time, only those

scouts who have the ability and interest to record data continue as recorders.

No direct incentives. To reduce the likelihood that dataforms will be falsified,

village scouts are given no material incentives for recording data. Instead it is hoped that

scouts will be motivated from an understanding and appreciation of the benefits of

collecting data. Although the policy of not providing incentives for the extra work is

unpopular among scouts, and may have other consequences, it has most likely achieved its








87
objective of minimizing falsified data and there have been no known cases were dataforms

were purposefully fabricated.

Dataform certification. The first line of defense against bad data comes at the field

level. Each dataform is supposed to be reviewed and certified by the unit leader or his

appointed deputy soon after the data are collected. Certifying data forms in the field can

catch omitted responses on forms, as well as detect certain irregularities and outliers.9 In

practice, the degree to which dataforms are certified depends in large part on the

individual unit leader or deputy assigned to monitoring, and frequency of contact with

scouts.

Spot checking during data entry. The data entry staff at Nyamaluma have a lot of

experience entering and analyzing data, and have a good feel for what is and what is not a

reasonable measurement. Many mistakes can be caught during the data entry process,

including problems with inconsistent units and outliers. For example, a hippo allegedly

shot in the hills far from water bodies would be flagged as a probable error. Data which

are suspect are not entered into the database, and common dataform mistakes are mentally

noted in preparation for the next training on monitoring.

Interpreting analyses. Previewing the results of an analysis can also highlight errors

in data. Most of the extension staff from Nyamaluma who spend a good bit of time in the

field have a pretty good intuitive feel for the major problems and accomplishments in

different areas. When summaries or graphs depict results that seem counter-intuitive, the





9 Other CBNRM projects such as LIRDP have also recognized the importance of reviewing datasheets in
the field soon after the data is collected, so that mistakes can be corrected and questions clarified while the
operation is still fresh (Jachmann 1998).








88
discrepancy may be traced either to an incorrect analysis, error in data processing, or bad

data.

Processing and Analytical Data Quality Controls

Enforced referential integrity. The new database system has a number of built-in

features that help to ensure good data. Enforced referential integrity helps to prevent

incomplete records from being entered, and makes certain that all fields containing a

lookup value (such as the id number of a species) have valid values. This prevents many

errors that formerly resulted from inconsistent spellings or impartially entered data.

Field and table validation rules. In addition to enforcing the integrity of linkages

between related tables, the new database also has the ability to validate all data being

entered against preset validation rules. For example, the date a hunt ended can not come

before the date it started (an error which in fact was encountered in the old system

because the spreadsheet was not formatted to display the year of a given date). Similarly,

table definitions specify which fields must have data, and which fields are optional. Table

validation rules also prevent duplicate records, for example there can not be two field

patrol observations entered for the same phenomenon in the same grid on the same day.

Other validation checks are done programmatically during the data entry process, such as

the check for valid trophy measurements based on the species hunted.

Statistical measures of data quality. Once data have passed through field

certification, data entry, and finally stored in the database, they can still be evaluated for

data quality. One of the advantages of using a well-designed database is that quantitative

summaries and graphs can be easily and quickly produced. The following are examples of









charts, maps, and tables of monitoring data that are built-in to the new ADMADE

database and can be used to highlight data quality concerns.

Sample size. An important and easily measured component of data quality is

sample size. Summaries which are based on only a small number of observations are less

likely to accurately reflect the population than those with a larger size. Very few

observations in ADMADE's monitoring system are based on a random sample, so

sufficient sample size becomes all the more important to minimize the bias introduced by

opportunistic sampling.

Fortunately sample size is easy to present to the user in tabular and graphical

summaries. Figure 11 is one of the interactive charts in the new database and depicts the

hunting success for hartebeest in all GMAs from 1994 to 1998. The diamond markers

represent the hunting success (calculated as the percentage of hunters who shot a

hartebeest out of those who stated they desired one at the start of their hunt), and should

be read using the scale on the left. The square markers represent sample size (the number

of hunters seeking an animal) and should be read using the scale on the right.

In this graph, we see that the sample size is between 30 and 35 hunters each year,

which is probably enough to reduce the effect of any outliers (we could also plot 95%

confidence limits for each year if we wanted even more feedback on dispersion). Hence for

the country as a whole, we can say with a fairly high degree of confidence that hunting

success for hartebeest increased between 1994 and 1998. If, on the other hand, the sample

size had only been 5-10 hunters per year seeking hartebeest, as it is for some species, then

this indicator would be a lot less significant when evaluating the sustainability of safari

hunting quotas for hartebeest.











Hunting Success

100% --------------------------------------------------------- 36
90% -----.. --------------------------- -- -------------- ------ 35
80% -- ------------------------------- --- ----------------- 34
== 70% -------- - --- ----------------------------
0 -33 CD
S 60% - ------------- --.... ...................................
S 50% --------------------------------------------------------
-- 31
S 40% --------------------------------------------------------
% 30

20% ---------------------------------------------------------------- 29
100/------------------------------------------------------------------- -2
10% .............. ........................................-. 28
0%I 27
1995 1996 1997 1998
-*-Success U Sample Size

Figure 11 -Hunting success of hartebeest 1994-98


Dispersion. The amount of dispersion in a set of measurements can suggest

whether the data have been collected properly. Histograms can quickly present the

distribution curve of the sample data, which are expected to fit certain norms.

Figure 12 shows a histogram of trophy measurements for Cape Buffalo for all

hunting blocks and all years combined. This fairly normal distribution is what we would

probably expect from a natural population of trophy specimens, and suggests that scouts

are probably making measurements properly and that individuals are probably being


selected from the population in a consistent manner.










14 Trophy Size Distribution Buffalo

12 -

10 -

c8 -

6 -

4-

2-
0 I III I I- -
21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
Trophy Measurements Inches

Figure 12 Histogram of trophy size measurements


Temporal bias. Another factor which can affect data quality is the timing of

observations. Bias can be introduced when the sampling is not consistent or representative

of the time frame of interest. Graphs and numeric summaries can be used to help detect

bias that might be introduced by irregular temporal sampling.

Figure 13 below shows the number of day on patrol for two camps in the Chifunda

Unit for 1998. A few patterns are immediately apparent from this graph. First of all,

Kanusha camp did almost no patrolling during the months of February to May--the rainy

season. Hence any data from the scouts in that camp on poaching levels, animal

abundance, or other phenomenon are likely to be biased by the lack of patrolling during

this period. Secondly, there are no patrols recorded for the months of November and

December. This can only be attributed to (1) there were no patrols during those months,

(2) not all data have been entered into the database. Assuming the later, we also note that




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