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Impact assessment in natural resource management research

By: Poulsen, J | Centro Internacional de Mejoramiento de Maiz y Trigo (CIMMYT), Mexico DF (Mexico) | International Conference on Impacts of Agricultural Research and Development San José (Costa Rica) 4-7 Feb 2002.
Contributor(s): Douthwaite, B [coaut.] | White, D.G [coaut.] | Watson, D.J [ed.].
Material type: materialTypeLabelBookAnalytics: Show analyticsPublisher: Mexico, DF (Mexico) CIMMYT : 2003Description: p. 39-40.ISBN: 970-648-076-5.Subject(s): Ecosystems | Natural resources | Productivity | Project management | Resource management | Technology | CIMMYT | Agricultural research AGROVOCDDC classification: 338.91 Summary: This paper provides an overview of natural resource management impact assessment (IA), conducted by the Task Force on Integrated Natural Resource Management (INRM). It represents progress made during and following the INRM workshop in Cali, 28-31 August 2001. It also incorporates the results of an ongoing literature review of impact assessment studies related to natural resource management research. A Community of Practice initiative on Impact Assessment in NRM research was formally launched at the INRM workshop in Cali. The ultimate purpose of the initiative was to develop approaches and methodologies for INRM impact assessment and to integrate them into the framework of the INRM research cycle. Such methods are intended to identify more effective and efficient research and management interventions as well as enabling an assessment of the impact of agricultural research on decision-making strategies regarding the adoption or non-adoption of specific practices. However, some intermediate concern need to be addressed before appropriate methodologies can be developed for IA of INRM. In particular, we explore concepts and operational definitions with respect to:|*what types of impacts are of interest to decision makers|*what impacts need to be measured|*what types of indicators/measures are appropriate|Furthermore, we explore how results of the above would be used and operationalized. Impact assessment monitoring and evaluation must be an integral part of the INRM process. While ex-post impact assessment is still essential, a greater emphasis is needed on monitoring and evaluation. This is primarily because INRM attempts to catalyze change in complex environments using complex interventions. Perfect knowledge is not possible before a project starts and project coordinators need to learn as they go along. Hence, monitoring and evaluation constitute a key feedback mechanism. Monitoring and evaluation are therefore the basis of good, adaptive project management. It is also necessary to know what success looks like before progress in achieving that success can be evaluated. Changes in the five capitals (natural, social, physical, financial, human) would capture changes in production functions, human well-being poverty measures, and ecosystem functions, but actual indicators chosen for a specific project should be guided by what people think and want. Participatory evaluation is an essential part of INRM. Evaluation of success must include scaling up. INRM research by CG centers to produce location-specific solutions is not enough. Processes by which location- specific solutions can be scaled up both horizontally and vertically must also be developed. Both horizontal and vertical scaling up are about changing people's opinions, thinking and practices at different levels. This includes farmers, national stakeholders, and international researchers. This vital impact of INRM is often overlooked by CG center research. Innovation is a social learning process, including the use of new knowledge. There is an attribution gap. Learning itself is a social process because people construct new knowledge often in interaction with others. People socially construct technology and in the process adapt new technologies and ideas to their systems. Innovation is therefore inherently a complex process with high degrees of non-linearity. This would seem to make conventional impact assessment for INRM activities on highly aggregated development indices, such as poverty alleviation, almost impossible. INRM views innovation as a social process, in which people construct solutions to their problems. Once one accepts that users are modifying technologies and their own systems to accommodate new technologies and that these adaptations affect adoption rates and who benefits and loses, then one must also accept that technological change is an immensely complex process, with a high degree of non-linearity. Current best practice economic evaluation methods commonly used in the CGIAR system, which attempt to establish a linear link between a project's outputs and wider level impacts, struggle with this complexity. Hence IPM and INRM require different evaluation approaches that can bridge this "attribution gap." We can learn from other fields, for example evaluation of social programs. However, different skills are required, such as those offered by anthropologists, ecologists and sociologists. The central question is whether or not we should aim to bridge the attribution gap or simply accept that there is a grey area where multiple forces come together before a final impact is reached and supposedly quantified. Therefore the term "gap" may be misleading. It is more a junction where the many efforts meet. Our difficulty is attempting to quantitatively measure the (often) distinct efforts. But given the complementarity of inputs/efforts (e.g. improved seeds and fertilizers, using the agriculture example), it may be nearly impossible to distill due credit for any one effort. Moreover, given the limited financial resources with which we can analyze impact, those monies used to precisely attribute impact are probably best directed elsewhere. Thus, plausible arguments and qualitative measures of impact become more reasonable analytical approaches. IA is also essential for establishing accountability and securing continued funding. Technologies of the Green Revolution could be relatively easily scaled-up/ out, demonstrating high rates of return. However, INRM is not as easily scaled-up/out. There are problems associated with scaling-up / out of site-specific technologies. INRM involves far more complex technologies than those coming out of the Green Revolution. The benefits of improved INRM are typically accrued in the medium and long term, requiring coordinated efforts between private rural dwellers and public institutions. Conventional extension approaches, which often merely disseminate technologies, are therefore not likely to be successful. It is difficult to attribute impacts on the ground to INRM (given the array of other factors affecting local livelihoods and resource management). Scaling up / out is difficult and there are few examples of where it has been done. The difficulty is in how to attribute the scaling up / out of INRM to our work and not other variables. Some work is ongoing in this area (e.g., rules of thumb on how to deal with attribution). The difficulty of scaling up / out are two important but separate issues. Scaling out difficulties stem from the heterogeneity of geographic areas. Not only are biophysical conditions different (the same ones that affected Green Revolution efforts), but also INRM includes socio-economic-cultural issues. These latter subtleties are the crux of scaling out challenges. Areas and people are different. As for scaling up, the appropriate level of action/analysis is determined by the specific challenge. Scaling up incurs a loss of detail. The goal is to determine the correct scale of analysis. Just as the Green Revolution did not scale up all efforts (e.g. CGIAR in different parts of the globe) nor should INRM. A balance between specificity and generality is required. Collaborative research networks offer a cost efficient approach. One example would be networks for sharing germplasm and research results for a range of commodities. A key question is whether or not participatory approaches comprising monitoring and evaluation are sufficient in terms of timeliness and scientific rigor. While participatory efforts are important, there is a probability that they need to be supplemented with more structured analyses. How this balance can and should be found is an important development research question.Collection: CIMMYT Publications Collection
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Abstract only

This paper provides an overview of natural resource management impact assessment (IA), conducted by the Task Force on Integrated Natural Resource Management (INRM). It represents progress made during and following the INRM workshop in Cali, 28-31 August 2001. It also incorporates the results of an ongoing literature review of impact assessment studies related to natural resource management research. A Community of Practice initiative on Impact Assessment in NRM research was formally launched at the INRM workshop in Cali. The ultimate purpose of the initiative was to develop approaches and methodologies for INRM impact assessment and to integrate them into the framework of the INRM research cycle. Such methods are intended to identify more effective and efficient research and management interventions as well as enabling an assessment of the impact of agricultural research on decision-making strategies regarding the adoption or non-adoption of specific practices. However, some intermediate concern need to be addressed before appropriate methodologies can be developed for IA of INRM. In particular, we explore concepts and operational definitions with respect to:|*what types of impacts are of interest to decision makers|*what impacts need to be measured|*what types of indicators/measures are appropriate|Furthermore, we explore how results of the above would be used and operationalized. Impact assessment monitoring and evaluation must be an integral part of the INRM process. While ex-post impact assessment is still essential, a greater emphasis is needed on monitoring and evaluation. This is primarily because INRM attempts to catalyze change in complex environments using complex interventions. Perfect knowledge is not possible before a project starts and project coordinators need to learn as they go along. Hence, monitoring and evaluation constitute a key feedback mechanism. Monitoring and evaluation are therefore the basis of good, adaptive project management. It is also necessary to know what success looks like before progress in achieving that success can be evaluated. Changes in the five capitals (natural, social, physical, financial, human) would capture changes in production functions, human well-being poverty measures, and ecosystem functions, but actual indicators chosen for a specific project should be guided by what people think and want. Participatory evaluation is an essential part of INRM. Evaluation of success must include scaling up. INRM research by CG centers to produce location-specific solutions is not enough. Processes by which location- specific solutions can be scaled up both horizontally and vertically must also be developed. Both horizontal and vertical scaling up are about changing people's opinions, thinking and practices at different levels. This includes farmers, national stakeholders, and international researchers. This vital impact of INRM is often overlooked by CG center research. Innovation is a social learning process, including the use of new knowledge. There is an attribution gap. Learning itself is a social process because people construct new knowledge often in interaction with others. People socially construct technology and in the process adapt new technologies and ideas to their systems. Innovation is therefore inherently a complex process with high degrees of non-linearity. This would seem to make conventional impact assessment for INRM activities on highly aggregated development indices, such as poverty alleviation, almost impossible. INRM views innovation as a social process, in which people construct solutions to their problems. Once one accepts that users are modifying technologies and their own systems to accommodate new technologies and that these adaptations affect adoption rates and who benefits and loses, then one must also accept that technological change is an immensely complex process, with a high degree of non-linearity. Current best practice economic evaluation methods commonly used in the CGIAR system, which attempt to establish a linear link between a project's outputs and wider level impacts, struggle with this complexity. Hence IPM and INRM require different evaluation approaches that can bridge this "attribution gap." We can learn from other fields, for example evaluation of social programs. However, different skills are required, such as those offered by anthropologists, ecologists and sociologists. The central question is whether or not we should aim to bridge the attribution gap or simply accept that there is a grey area where multiple forces come together before a final impact is reached and supposedly quantified. Therefore the term "gap" may be misleading. It is more a junction where the many efforts meet. Our difficulty is attempting to quantitatively measure the (often) distinct efforts. But given the complementarity of inputs/efforts (e.g. improved seeds and fertilizers, using the agriculture example), it may be nearly impossible to distill due credit for any one effort. Moreover, given the limited financial resources with which we can analyze impact, those monies used to precisely attribute impact are probably best directed elsewhere. Thus, plausible arguments and qualitative measures of impact become more reasonable analytical approaches. IA is also essential for establishing accountability and securing continued funding. Technologies of the Green Revolution could be relatively easily scaled-up/ out, demonstrating high rates of return. However, INRM is not as easily scaled-up/out. There are problems associated with scaling-up / out of site-specific technologies. INRM involves far more complex technologies than those coming out of the Green Revolution. The benefits of improved INRM are typically accrued in the medium and long term, requiring coordinated efforts between private rural dwellers and public institutions. Conventional extension approaches, which often merely disseminate technologies, are therefore not likely to be successful. It is difficult to attribute impacts on the ground to INRM (given the array of other factors affecting local livelihoods and resource management). Scaling up / out is difficult and there are few examples of where it has been done. The difficulty is in how to attribute the scaling up / out of INRM to our work and not other variables. Some work is ongoing in this area (e.g., rules of thumb on how to deal with attribution). The difficulty of scaling up / out are two important but separate issues. Scaling out difficulties stem from the heterogeneity of geographic areas. Not only are biophysical conditions different (the same ones that affected Green Revolution efforts), but also INRM includes socio-economic-cultural issues. These latter subtleties are the crux of scaling out challenges. Areas and people are different. As for scaling up, the appropriate level of action/analysis is determined by the specific challenge. Scaling up incurs a loss of detail. The goal is to determine the correct scale of analysis. Just as the Green Revolution did not scale up all efforts (e.g. CGIAR in different parts of the globe) nor should INRM. A balance between specificity and generality is required. Collaborative research networks offer a cost efficient approach. One example would be networks for sharing germplasm and research results for a range of commodities. A key question is whether or not participatory approaches comprising monitoring and evaluation are sufficient in terms of timeliness and scientific rigor. While participatory efforts are important, there is a probability that they need to be supplemented with more structured analyses. How this balance can and should be found is an important development research question.

English

0310|R01CIMPU|AGRIS 0301|AL-Economics Program

Juan Carlos Mendieta

CIMMYT Publications Collection

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Si tiene cualquier pregunta, contáctenos a CIMMYT-Knowledge-Center@cgiar.org