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AN INTEGRATED MANAGEMENT AND ASSESSMENT SYSTEM FOR CONSERVING BIODIVERSITY IN SPATIALLY EXTENSIVE PASTORAL ECOSYSTEMS OF EAST AFRICA - ASSESSMENT TEAM FORMATION
MID-TERM REPORT
U.S. A.I.D. Small Ruminant Collaborative Research Support Program
March 4, 1997
Michael B. Coughenour Natural Resource Ecology Laboratory
Part 1. ProgressProblem ModelBased upon discussions the AT has had in Colorado, and at our first workshop in Nairobi, we are revising our problem model as follows.1) Patterns of land use in pastoral areas of East Africa are rapidly changing, mainly towards systems of decreasing spatial scale, and reduced options for large-scale livestock movement. In Kenya, there was conversion of former Maasai communal grazing lands into group ranches in the 1960's. Now, these group ranches, and other communal grazing lands in Maasai Land and elsewhere are being subdivided and privatized. In many cases land-tenure rights have been granted in small parcels to individual families. They may attempt to use the parcel as a private ranch, or increasingly, they are using the value of the land holding as collateral to take out loans. As spatial scale decreases, intensity of use usually increases. Above a certain livestock density, wildlife apparently cannot exist. A second trend in land use is the increasing adoption of small-scale farming by pastoral people. In some cases the pastoralists are being encouraged to do so. Increasingly, pastoral systems are mixed systems of livestock and small agricultural plots. Our original problem model emphasized spatially extensive pastoral ecosystems. Now, we are broadening the problem model to include a wide range of land tenure arrangements ranging in spatial scale from: (a) private ranches (eg. Kajiado, Kenya, Laikipia), to (b) group ranches (eg. Kajiado, Loita), c) restricted communal land use (eg. Serengeti-Ngrongoro), to (d) traditional communal land use (eg. Loliondo, Turkana), to (e) parks which exclude people entirely. 2) Many of the formerly wildlife rich pastoral areas of East Africa are now depauperate or wildlife poor, due to warfare and increased access to advanced weaponry. This is especially true in Uganda, and may also be true in Ethiopia, Somalia, Sudan, and Eritrea. For example, game was essentially extirpated from NW Uganda during the Amin years. Now there is continued rebel activity coming from southern Sudan. In Karamoja, most of the game has been eliminated due to the arming of the people with automatic weapons left behind by Amin's exiled forces. A few "community" game reserves do exist. Game declines in Turkana can also be attributed to access to weapons, and a weak conservation ethic among these people. Other reasons for wildlife decline include increasing human populations, and increased use of marginal lands by agriculturalists than most pastoralists. Our original problem model was aimed primarily at wildlife rich areas such as Ngorongoro. The problem model is being revised to allow for the fact that many in many cases the wildlife-pastoral interaction is more of an issue of rehabilitation, than of conservation. We have not definitively decided to pursue research in wildlife poor areas, however. Security issues are a concern, as are the possibilities for accomplishing anything in such areas in a reasonable amount of time. The possibility for using the DSS in rehabilitation efforts is not to be ruled out, however. 3) Comments made by a representative of a Maasai NGO at our recent workshop suggest more attention should be given to assessing the value of indigenous knowledge, both in livestock husbandry and in natural resources conservation. The Maasai argue that they have coexisted with wildlife for centuries, and are good stewards of the land. Conservation practices evolved to a high level. Our original problem model was designed to elicit involvement by the stakeholders in the sense of helping co-design the DSS, and participating in it's utilization. We aimed to use the DSS to represent stakeholder goals, objectives, and to model what they perceive it was like in more traditional times. We must expand the problem model to also assess ecosystem responses to indigenous natural resource conservation practices. Additionally, the Maasai feel that "capacity building" is not the issue
- they already have "capacity". Similarly, "participation" is insufficient,
what is needed is increased "empowerment". This exemplifies the cultural
conflicts and the political problems that lie at the core of land-use conflicts.
Pastoralists are losing traditional grazing rights and land to other forms
of land use including conservation areas and agriculture. Our problem model
cannot address these political battles directly. The product we are aiming
to develop will not give political power to pastoralists. However, a new
goal of our problem model is to "empower with information". We believe
that the DSS will be useful for clearly illustrating the causes and effects
of land use changes, according to the goals and objectives of the pastoralists,
as well as other land use interests. Information about these causes and
effects has led to no-win situations, as neither side is capable of expressing
their goals in objective terms, much less coming to a successful resolution.
At present there are no data to support the viewpoints of either side.
A lack of information also works to the advantage of political aspirants,
who may intentionally or unintentionally distort the truth for their own
purposes. We aim to provide information that can be used in win-win solutions.
For this, all stakeholders must feel confident in the validity and completeness
of the information being used in determining the solutions.
Goals and Specific ObjectivesOur ultimate goal remains essentially the same - "to improve prospects for developing productive and sustainable pastoral systems which also conserve biodiversity, wildlife, and ecosystem services". Another way of stating this goal is to "establish an appropriate and sustainable balance between food production and natural resource conservation in East Africa."As noted above, a new goal of our project is to "empower with information". We will seek ways to make information more useful for making decisions about policies, management practices, and land uses. To achieve these goals, our immediate objective remains the same. Essentially
it is to develop a decision support system that can be used to objectively
assess livestock-environment interactions.
Assessment Team Members and Institutional ParticipantsAs shown in Table 1, our assessment team has grown to achieve the balance of expertise needed to meet our project goals and objectives (see Part 4, "Composition of Assessment Team").We have made some important institutional connections, which have yet to be formalized (see Part 4, "Consortial Approach"). We are receiving positive indications from the International Livestock Research Institute (ILRI), Kenya Agricultural Research Institute (KARI), African Wildlife Foundation, University of Nairobi, Sokoine University, The Institute of Resource Assessment of University of Dar es Salaam, the Tanzania Wildlife Division, the Kenya Wildlife Service, the Inuyat e-Maa, and USAID REDSO. We are also exploring links with the Uganda Tourism and Wildlife Project of the Ministry of Tourism and Antiquities, Tanzania National Parks, IUCN, the Kenya Pastoralist Forum, Uganda Wildlife Authority, FAO, US National Park Service, USGS, USDA-APHIS, Makerere University (Uganda), Moi University (Kenya), and Mweka Wildlife College (Tanzania). Table 1. Team Composition
Project DescriptionDevelopment of the DSS FrameworkWe have had several AT meetings in Colorado to refine the conceptual framework for the Decision Support System (DSS).
The original conceptual model (Figure 2 in our proposal), has been modified as shown in Figure 1. This diagram shows that the DSS is comprised of a process of empirical and data-based assessment procedures, linked to computer-based procedures, and landuse planning and analyses. Research on biodiversity and other topics and GIS/RS data and analyses will be conducted both within the scope of our project, and by other parallel projects funded externally. These results are used the computer-based components of the DSS, including the spatial simulation model SAVANNA. Based upon model results, and participatory involvement from stakeholders at the community level, an assessment is made. The results of the assessment may be used in an implementation, not by our project, but by the end users, including land managers, and policy analysts. The results of the implementation would ultimately be evaluated at the community level, and the assessment would be modified accordingly.
A detail of the interaction between stakeholders and the DSS is shown in Figure 2. The goal of this process is to inform stakeholders of impacts scenarios consistent with their goals and objectives. Scenarios are created and encoded as both spatial and aspatial data that can be evaluated by the DSS, Model, or other analyses tools. The scenarios are played out in the DSS and the results are returned to the stakeholders. Multiple scenarios can be compared and evaluated relative to different objectives. A process must be established to identify appropriate indicators and criteria by which to judge the scenario results.
The DSS would be used in an iterative process of conflict resolution and risk analysis (Figure 3), the goal of which is to converge on a solution that all stakeholders can accept. The DSS would be used to show the conflicts, risks, costs, and benefits to each stakeholder of multiple versions of their proposed solutions. The solutions would then be revised with the aim of converging on a solution that is most acceptable to all stakeholders. The interaction of AT activities with the DSS is shown in Figure 4. The goal is to ensure that the DSS accurately quantifies the costs and benefits of alternatives to each stakeholder using scientific methods. The components enclosed by the dashed line comprise the DSS. Through the use of models, analyses and integration, preferred management changes can be put forth. We will not make recommendations, however, as these may compromise our neutral status. Instead, we will provide information, and the local authorities will make the decisions and perform the implementation. Inventory and monitoring studies are vital to the DSS, both as information inputs, and as measurements of results. The scientific component of the process is contained in the loop from hypotheses, to process experiments, and models. Components of a DSS We have identified the following major components of a DSS: A) People tools 1) Interaction with customers
B) Data acquisition, storage, dissemination C) Information analysis tools 1. Model - SAVANNA
D) Monitoring and Assessment E) Training F) Research G) Regional networking Regional-level Assessments and Networking Many contacts have been established in the East African region. A recent workshop in Nairobi was coupled with individual meetings with organizational representatives. These contacts will propagate, to eventually form a regional network. Discussions among AT members have provided different suggestions for implementing the network. Foremost is the use of electronic communication, especially electronic bulletin boards, the world-wide-web, and email. An interactive web site will be created. We will likely have a regional office, which could be located at KARI or ILRI, for example. We have also discussed the role of workshops, having objectives of comparing research among sites, of performing regional level analyses and priority setting, and for informing each other of information sources, new technologies, and research findings. Workshops A workshop entitled DEVELOPING A DECISION SUPPORT SYSTEM FOR INTEGRATED ASSESSMENT OF PASTORAL-WILDLIFE INTERACTIONS IN EAST AFRICA was successfully held February 17-19, 1997 at the International Livestock Research Institute (ILRI), Nairobi, Kenya. A major purpose of our first workshop was to form the international team to oversee development of a system to assess livestock-wildlife interactions in extensive pastoral ecosystems of East Africa. During this workshop, we introduced our project goals, explained how we intend to meet these goals, and elicited insight and advice from participants on what the assessment system must consider to be relevant to the needs of pastoralist, resource managers, and policy makers. Participants shared their experiences with pastoral-wildlife systems, and specified the types of information that would be useful from their perspectives. We then focused on the conceptual development of the decision support system. Other activities included the identification and evaluation of research sites, and breakout groups which focused on the identification of goals, objectives, activities, constraints, inputs, and assumptions for modeling, biodiversity, range ecology, human ecology, and disease. Participants included range and wildlife ecologists, wildlife disease experts, anthropologists and social scientists, and experts and stakeholders in pastoral-wildlife interactions (Table 2). Many contacts were established in Kenya, Tanzania, and Uganda. As a result of the meetings significant institutional partnerships may be formed with the International Livestock Research Center (ILRI), the Kenya Agricultural Research Institute (KARI), the African Wildlife Foundation (AWS), the University of Nairobi, and Sokoine University in Tanzania. A workshop report is in progress.
Table 2. List of Workshop Participants and Contacts Workshop Participants 1. Barrow, Ed, Community Conservation Officer, African Wildlife Foundation
Contacts/Meetings 1. Abate, Augusta, Assistant Director of Animal Production, Kenya Agricultural
Research Institute, Nairobi
As can be seen by the list of participants and contacts, there was a large emphasis on wildlife, range ecology, pastoralists, and disease. In the second workshop, we will devote more attention to the subjects of our other Colorado AT members including human nutrition, ecology, and household economics (K. Galvin, A. Magennis), and livestock nutrition and production (D. Swift, L. Rittenhouse). Site Selection The DSS will be applied to a select few (3-5) intensive study sites
during the first phase of the project. Potential sites were suggested at
the February workshop in Nairobi. First, a list of site selection criteria
was generated, which is to be used for ranking purposes (Table 3). The
list of suggested sites in shown in Table 4 along with a preliminary ranking
based on a sum of anonymous rankings by workshop participants. This is
by no means a final selection, but provides useful information for a final
decision.
Table 3. Site Selection Criteria 1. Potential or actual sustainable livestock production
Table 4. Sites Considered and Preliminary Ranking Site Score Rank 1. Uaso Ngiro River Basin (Samburu, North-Central Kenya) 51 4
Anticipated Results and Dissemination of ResultsThe anticipated results and the methods for their dissemination are unchanged from the original proposal. Results and preliminary efforts at dissemination are described elsewhere in this report.Evaluation of the Assessment Process and Potential ImpactWe have begun to identify an advisory panel (AP) for our project. The AP will be formed and asked to evaluate our progress in May, possibly in conjunction with our second workshop. We also hope to obtain feedback from the SR-CRSP AP and external advisory panel on our progress to date based on this mid-term report. The presentation of our proposal, to be given at the meeting in July, will also provide one last opportunity for response to external review.Part 2: Results Oriented MatrixNote: This is a very preliminary first attempt at a results matrix. The outputs are very general at this point, and more focused outputs are under development (preliminary objectives were identified during the February workshop). It would be premature to estimate times to completion at this point.Objective: Establish an appropriate and sustainable balance between
food production and natural resource conservation in East Africa.
Part 3: Responses to Criteria for SelectionInterdisciplinary ApproachAn interactive and integrative process for exploring areas of research concern has been implemented. This process is based upon a framework for overcoming disciplinary boundaries. In our case, the framework is the model-based decision support system which we are aiming to develop. The DSS represents a systems approach to an interdisciplinary problem, in that we must formally identify components of the system, and how they interact. Many of the interactions are embodied in an ecological simulation model which incorporates knowledge about vegetation, landscape, wildlife, and pastoral ecology. Submodels will be developed to address livestock and wildlife disease, and rural and household economics. Other tools will be developed to aid in policy analysis.We have included persons from all of the disciplines on our assessment team. The team has met regularly to discuss the content and function of the DSS. Although the core model for the DSS already exists, there is still a considerable amount of design work that must be accomplished to develop the final DSS. The process we have undertaken is one of collaborative design, where interdisciplinary team members focus on specific components of the system and how the system must function to provide useful information. This has involved meeting as an interdisciplinary team on a regular basis. As we have met in Colorado, we have evaluated system components and their interactions. Our first workshop in Nairobi gave us an opportunity to similarly interact with team members in East Africa in a collaborative design process. Composition of Assessment TeamThe composition of our assessment team has grown since we submitted our proposal (Table 1). Our project must be extremely interdisciplinary. To accomplish our objectives, our team must include a balance of expertise in; systems ecology, plant ecology, wildlife ecology, rangeland ecology, land use, geography, geographic information systems, remote sensing, wildlife and livestock disease, livestock and wildlife nutrition, livestock production, human nutrition, pastoral ecology, indigenous pastoralism, social anthropology, household and rural economics, national economics, community-based and participatory conservation, and policy. The original team members represented most, but not all of these areas of expertise. The team was deficient in economics, policy, indigenous pastoralism, East African livestock production systems, community and participatory conservation, and wildlife. We have added team members in each of these disciplines (Table 1).Policy RelevanceThe DSS is being designed with the intent of using it for policy analysis. We have added team members with some experience in economic policy analysis (Davis, Thornton, Swallow). Other members of our team have expertise in national and international wildlife and conservation policies (Barrow, Dublin, Moehlman). We will draw upon these experts to identify policies and their effects on pastoral livestock production systems, and wildlife conservation systems in East Africa. The DSS will be structured to represent in a causal fashion the responses to policy at local through regional levels, in terms of effects on pastoralist production and well being, and in terms of wildlife and biodiversity status. This will entail modeling economic responses at community and household levels, and ecological responses at landscape and regional spatial scales, and their interactions.We are devising means to elicit the cooperation of NARs and civil agencies in addressing policy issues. We have made contacts in the Kenya Agricultural Research Institute, some of which follow from earlier SR-CRSP programs. We aim to work with KARI in establishing agricultural research priorities which benefit wildlife and biodiversity as well as livestock productivity. We are also working on establishing relations with agricultural universities such as Sokoine University in Tanzania, University of Nairobi Range Science Department, and Moi University in Kenya. These links will be instrumental in working out programs of undergraduate and graduate education which produce individuals trained to design ecologically sustainable pastoral livestock and wildlife conservation systems. Our efforts will require cooperation with government agencies involved in natural resource assessment and land-use planning. For example, Tanzania National Parks (TANAPA) must be involved as they are the central agency responsible for drafting national parks management plans. The Kenya Wildlife Service (KWS), the Wildlife Division of Tanzania (of the Ministry of Natural Resources and Tourism), and the Uganda Wildlife Authority (of the Ministry of Tourism, Wildlife and Antiquities) must be involved as they monitor natural resources, and make policy recommendations at national levels. We have made contacts in all three agencies, including AT membership. At local levels, other authorities must be involved, such as the Ngorongoro Conservation Area Authority (NCAA) which sets land use policies in the Ngorongoro Conservation Area, one of our proposed study sites. An NCAA representative will attend our next workshop and will likely become an AT member. The objectives of our project also require cooperation from non-agricultural
universities such as University of Nairobi and University of Dar es Salaam
where there is expertise in biology, conservation, and natural resource
management. The Biology, Botany, and Zoology Departments house experts
in plant and animal ecology and biodiversity, who we are including on our
AT. The Institute for Resource Assessment (IRA) at University of Dar es
Salaam has considerable expertise in natural resource assessment and we
have received a letter of interest and support from the IRA Director. Collaboration
with these institutions will produce policy impacts through education of
students and transfer of information and assessment capacity to faculty.
Demand DrivenA primary objective of our first workshop, held in Nairobi, was to elicit input from potential end users of the DSS. We also aimed to obtain inputs from other potential end users through communications, and through meetings in the region. We consulted with an expert in identifying stakeholder goals and conflicts (R. Woodmansee, now on the AT) who has developed a computer-based Structured Analysis Methodology (SAM) which we will implement at our next workshop. The SAM is a procedure for stakeholders to identify their goals and objectives, and potential conflicts. It has been applied to ecosystem management projects in Colorado, including one which is considered exemplary in the nation (Owl Mountain Project). A second expert in providing decision support for end users (T. Hobbs) was consulted. Hobbs has developed a GIS-based system for conservation planning in Colorado in which he used a collaborative design process to develop the system through a series of workshops involving potential end users. To some extent, we have modeled our first workshop after the same approach, as we elicited inputs from potential end users about what they perceive such a DSS could provide to them, which would be useful in solving the problems that they commonly encounter in their work. End users included wildlife managers, county planners, and real estate developers. In our case, potential end users and stakeholders include representatives of pastoral peoples, private and group ranches, ecotourism, government land use planning, and conservation biology.Consortia ApproachElsewhere in this report we make it clear that we are collaborating with NARS (eg. KARI, Sokoine Univ.), an IARC (ILRI), GO's (eg. TANAPA, KWS), as well as NGO's (eg. Inuyaat e-Maa), and PVO's (eg. private ranchers in Kajiado, ecotourism firms). We are also aiming to establish partnerships with U.S. agencies such as the National Park Service, USGS, BLM, and USDA/APHIS.Impacts and BenefitsPotential impacts and benefits of our research will be provided to pastoralists, wildlife and biodiversity conservationists, government planning and resource monitoring agencies, the ecotourism industry (which might include pastoralists and ranchers). Many of the impacts of our work will be more difficult to quantify than traditional measures of agricultural production increases or economic returns. For example, the benefits of wildlife conservation may benefit ecotourism in the short run, which may be quantified in terms of foreign currency generation, and subsequent inputs to local and national economies. The benefits of wildlife, increased biodiversity, and ecological sustainability are difficult to quantify economically. Therefore we will measure impacts in terms of decreased risk of species losses, habitat degradation, or biophysical measures of environmental degradation. Benefits to pastoralists are also difficult to quantify in traditional agricultural terms. These bulk of these benefits may flow directly to pastoralists, without markets, or by way of remote markets. We aim to quantify impacts on pastoralists in terms of direct and indirect flows of livestock products to humans, and human nutritional status.At national and regional levels, impacts will be quantified by scaling up household and local benefits with GIS-based maps of pastoral and wildlife distributions. Responses will be quantified based upon estimated effects of adoption of the DSS by local, national, and regional resource managers and planners. Extension efforts will take the form of a multilevel technology transfer
process. The DSS is, fundamentally, a high technology tool which will require
relatively highly educated personnel to directly operate. We envision a
core level of technicians and scientists operating the DSS at universities
and government agencies, as well as within our project. These experts could
train technicians to run lower level analyses on the DSS in a second level
of extension, perhaps comprised of workers at field offices, national park
offices, and NGO's. A third level would not involve direct use of the DSS,
but communication of results to pastoralists, landowners, and communication
of their concerns in turn, back to the higher level DSS operators and developers.
We plan to hold demonstration community-level "meetings" in conjunction
with our next workshop, possibly in Loliondo (a Maasai pastoral area) and
in Kajiado (where there is increasing privatization of group ranches).
Mode of Operation
Our mode of operation is a coordinated effort of team members to develop
both a DSS and an assessment protocol which is compatible, and integrative
across disciplines. This depends upon a central core of team members from
Colorado State and Colorado Universities who work well together, and outreach
to constituent and more peripheral AT members in East Africa. Systems ecologists
are at the very core, since they have the responsibility of synthetic analyses
and modeling. Liaisons to stakeholders and end users such as Maasai pastoralists
will be the charge of either anthropologists, or educated pastoral representatives,
with assistance from other team members. Direct liaisons to regional stakeholders
such as government planning agencies and conservation NGO's will be primarily
through our East African AT members, with less frequent contacts from Colorado
AT members via communications, workshops and networking. In the outer ring
are regional representatives from GO's, NGO's, Universities, and private
concerns, who are not active AT members, but collaborative partners.
Our core AT meets on a semi-regular basis in Colorado. We are in close email contact with AT members in Kenya and Tanzania. Regional workshops and networking activities are the primary mode of interaction between core AT members and regional AT members. We have had one successful workshop, and are planning another for May. During our first workshop, we had an in depth discussion of AT roles.
Fundamentally, we recognize four major types of roles 1) scientific, 2)
team-building, and 3) administration/organization, 4) education. Within
each, we see "problem sub-teams", each with a coordinator. The problem
sub-teams may be organized along lines such as "range ecology", "human
ecology", "livestock nutrition and production", "disease", and "modeling/DSS".
We also see "site-level teams", composed of core AT members and experts
on the specific research sites. The composition of the sub-teams would
not be unidisciplinary, but each would be a multi disciplinary mixture
of AT members working together on common issues. In this way we ensure
cross-fertilization of ideas, and solid linkages among the different aspects
of the project.
Benefits to U.S.Benefits to U.S. livestock agriculture, and wildlife management concerns are emerging during our assessment process. Livestock-wildlife interactions are problematic in the U.S. just as they are in East Africa. Problem areas in both the U.S. and East Africa include negative effects of livestock grazing on wildlife habitats, game "damage" or losses incurred from wildlife grazing on private lands, and transmission of diseases such as brucellosis from wildlife to livestock. The model-based decision support system we aim to develop will be generally applicable to these problems, whether they be in Africa, or in the U.S.The core model of our DSS, SAVANNA, is currently being developed for use in National Park Service and USGS Biological Resource Division (formerly NBS) funded research in National Parks and Recreation Areas in the Rocky Mountain region. Further development of the model and applications to East Africa under US AID funding will consequently benefit the U.S. Applications in the U.S. include: elk and bison management problems Rocky Mountain, Yellowstone, and Wind Cave National Parks; interactions between wild horses and bighorn sheep on the Pryor Mountain Wild Horse Range; and carrying capacity for bison and elk in Grand Teton National Park and the National Elk Refuge. A project is pending to use the model to aid management of bison and brucellosis in Yellowstone and Grand Teton N.P.s. A considerable value is placed on East African wildlife by U.S. citizens.
This includes benefits in the form of opportunities for tourism and education.
Our research will therefore bring benefits to U.S. citizens by helping
ensure that East African livestock development efforts also help conserve
wildlife and biodiversity.
Integration with Other Funding SourcesWe are taking measures to identify situations where our work can utilize outputs of prior research, or where our research can be coupled to ongoing research funded by other sources. In such situations, the combined efforts of our work and the work that is ongoing will provide a higher level of output than if our work was conducted as a sole effort. One example would be the Serengeti-Ngorongoro region, where there has been considerable effort expended to develop GIS data bases, and in conducting ecological and anthropological studies. A second example would come from our collaboration with ILRI, which has invested heavily in the development and maintenance of a GIS data base for the countries, the region, and Africa. ILRI studies on East African livestock production systems will also be utilized. We have recently learned that the Kenya DSRS (formerly KREMU) is willing to make it's spatially extensive livestock, and wildlife data base available.We are encouraging AT members to seek funding from other sources for work which can be effectively coupled to the SR-CRSP efforts. For example, AT members and the Colorado State University NREL currently have two proposals submitted to NSF to conduct closely related research in the Serengeti-Ngorongoro region of Tanzania. Our project will effectively leverage a considerable of amount of effort that has been expended in the development of the SAVANNA Modeling System (SMS). Currently, the SMS is largely supported by the US NPS and the BRD of USGS. Linkages with other SR-CRSP projects will provide further cost effectiveness.
For instance, the proposed Central Asian project headed by E. Laca also
aims to make extensive use of GIS-based analyses, and will develop a more
aggregated approach to modeling primary production that in SAVANNA. Through
this linkage we can also avoid duplication of efforts. There would also
be excellent potential for teaming up with the proposed East African project
headed by L. Coppock. This project is proposing work along entirely different,
but highly complementary lines.
Part 4. Constraints on Assessment Process(1) We believe it will be difficult, if not impossible to cover the entire East African Region given the resources available. With current and projected resources, we feel it may be possible to establish 3-4 study sites in 3-4 countries. Regional level work involving the other 6-7 countries would be possible in a modeling or data-based networking approach, but site level research in such a large area will be difficult if not impossible.(2) It is awkward to try to establish working relations with organizations from which we would be leveraging resources. While the benefits to our project and US AID are clear, the reciprocal benefits provided by US AID to the collaborators are less clear to us. What are they? (This is not to be confused with ultimate benefits to the end users of developing a DSS). How can we best present these benefits in our negotiations? The aims of cost effectiveness and leveraging of research funded by other agencies and donors is clearly advantageous to our project. But how can we show what our project will provide in return? Many of our potential collaborators are looking to us for some support and envision that we are running a fiscally large project, while in fact, this is would be a mid-sized project spread out thinly over several subprojects and sites. This is a serious concern since it is stated that the amounts of funds leveraged will be used as a criteria for funding of our proposal. What are the guidelines for defining what kinds of resources can be counted? Some projects may fall upon situations where it is relatively easy to accrue leveraged funds, while others may not, due to situations and the sizes of budgets of collaborating agencies rather than actual levels of collaboration. Some projects may make a more concerted effort to count as many "in kind" contributions as possible, while others may be more conservative. What kind of accounting is there for claims of "in kind" salary contributions from regional organizations? Some guidance would be greatly appreciated. (3) What kind of Memorandum of Understanding is used to arrange such partnerships? What terms and agreements would be included in the MOU? |
