Farmer Participatory Research and Soil Conservation in Southeast Asian Cassava Systems

Dalton, Timothy J.; Lilja, Nina; Johnson, Nancy L.; Howeler, Reinhardt H.

doi:10.1016/j.worlddev.2011.05.011

Cited by 29 publications

(20 citation statements)

References 27 publications

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“…Firstly, the binary propensity score model was run to investigate rice farmers' preference of CSA participation and climate change adaptation response and to estimate the propensity score as the predicted probability of treatment (see equation (1)). Several previous studies also used the binary logistic model to determine key factors affecting farmers' decisions on adaptation response to climate change [6,28,29] as well as to identify influencing factors on farmers' participation decisions [11,[30][31][32]. The propensity score model is generally described as follows:…”

Section: Methodological Frameworkmentioning

confidence: 99%

The Effects of Climate Smart Agriculture and Climate Change Adaptation on the Technical Efficiency of Rice Farming—An Empirical Study in the Mekong Delta of Vietnam

Shimada

2019

Agriculture

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This study employed the propensity score matching approach to empirically assess the effects of climate smart agriculture participation and climate change adaptation response on the technical efficiency of rice production. Observational data were collected from in-depth interviews with 352 rice farm households in the Mekong Delta, Vietnam. The findings indicate that 71% of local farmers adapted their rice farming to climate change related to salinity intrusion and drought, while 29% of farmers did not. Additionally, only twenty-two rice farmers were typically chosen as participants in the climate smart agriculture pilot program by local government and institutions. The choices for adaptation response and climate smart agriculture participation are significantly influenced by agricultural extension services, belief in climate change, the area of farming land, as well as geographical locations (e.g., province and access to water sources). The results also reveal that climate change adaptation response, including climate smart agriculture participation, played a crucial role in improving technical efficiency of rice production by 13%–14% compared to no adaptation response. Regarding the individual effect of climate smart agriculture participation, participants could achieve higher technical efficiency by 5%–8% compared to non-participants.

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Section: Methodological Frameworkmentioning

confidence: 99%

The Effects of Climate Smart Agriculture and Climate Change Adaptation on the Technical Efficiency of Rice Farming—An Empirical Study in the Mekong Delta of Vietnam

Shimada

2019

Agriculture

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“…Methods listed in the literature for collecting data on the impact of participatory research range from the purposeful completion of surveys and interviews (Bunyatta et al, 2006, Dalton et al, 2011, Clark and Seidu Jasaw, 2014, Ticehurst et al, 2012, Blackstock et al, 2007, to 'softer' approaches such as document analysis (Blackstock et al, 2007) and the review of diaries (Clark and Seidu Jasaw, 2014), and system conceptualizations (Henly-Shepherd et al, 2015). While the former can provide a strong quantitative method to measure the impact of participatory science, it is also more time consuming, which can make it less feasible in the practicalities of research.…”

Section: Methods For Applying Impact Evaluationmentioning

confidence: 99%

Considering a framework to estimate the impact of co-designed science: Case study of Bayesian networks to set NRM targets

2015

Weber, T., McPhee, M.J. And Anderssen, R.S. (Eds) MODSIM2015, 21st International Congress on Modelling and Simulation

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It is now often stated that the scientific community can gain greater impact from their work if they engage intended stakeholders in co-design, implementation and evaluation of projects. However, the evidence to substantiate these claims are rarely more than anecdotal and reflective comments from scientists. Previous evaluation methods for participatory work run the risk of being over simplistic, and therefore omit important impacts, or become too complex and not user-friendly. In this paper, we provide an intermediate option, combining the research of others (detailed in Table 1), to produce 5 key dimensions to consider when evaluating the impact of participatory projects. These dimensions are 1) an increase in knowledge and awareness, 2) a change in behavior or practice, 3) active dissemination of new knowledge, 4) change in policy or planning, or some level of government endorsement, and 5) physical system improvements or "onground" change. In addition, social learning, empowerment and new social norms are important, but due to complexity, they are not yet included in this method. Each dimension can be estimated for its breadth and depth of the impact by more detailed criteria (e.g. how many people have increased their knowledge? And how much more do they know (i.e. could they explain it to someone else?)). The breadth is more of a quantitative assessment, which is generally easier to measure, while the depth, particularly if self-assessed, is more likely to be qualitative and subject to bias. We provide a grid to plot the breadth and depth impacts, and the means to combine this impact into a single visual representation on a radar plot (Figure 1). Here multiple lines represent different people's views of the same project, but they could also show the impact of different projects, or both.

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“…PAR involves the acknowledgement of different values and different forms of knowledge as valid and important and seeks to empower study participants to design projects that are most beneficial to them (Dudgeon et al 2017). In an international development context, coproduction is also used in more applied projects and for the generation of specific deliverables, such as educational programs for farmers and technologies for soil and plant management (Almekinders 2011; Dalton et al 2011; Davis et al 2012; Akpo et al 2015). Scholars have indicated that there are different types and levels of engagement between researchers, community members, and other stakeholders, as shown in Table 1 (Biggs 1989; Higginbottom and Liamputtong 2015; Meadow et al 2015).…”

Section: Figurementioning

confidence: 99%

Coproduction Challenges in the Context of Changing Rural Livelihoods

Popovici

Mazer

Erwin

et al. 2020

Contemporary Water Research

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Coproduction is a process that involves scientists and citizens engaging throughout the production of knowledge, decisions, and/or policies. This approach has been widely applied in an international context for addressing global environmental issues. It is customary for scientists to travel to rural communities, where both scientists and local knowledge holders work together and jointly design solutions to pressing problems. Such collaboration, however, often involves high costs for both residents and scientists, which can reduce project effectiveness. This study examines the challenges associated with coproduction in the context of changing rural livelihoods in beneficiary communities. We specifically conduct a self‐analysis of the coproduction process led by our own university team, where scientists designed tools for water and crop management together with community members in Peru's Caylloma province. We collected qualitative data on the coproduction challenges in five local districts in Caylloma, using focus groups and semi‐structured interviews. Our results indicate that changing socioeconomic conditions in rural communities undermined the long‐term sustainability and effectiveness of the coproduction efforts and deliverables. These included increased migration, market integration, and reliance on regional institutions for water and crop management.

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Farmer Participatory Research and Soil Conservation in Southeast Asian Cassava Systems

Cited by 29 publications

References 27 publications

The Effects of Climate Smart Agriculture and Climate Change Adaptation on the Technical Efficiency of Rice Farming—An Empirical Study in the Mekong Delta of Vietnam

The Effects of Climate Smart Agriculture and Climate Change Adaptation on the Technical Efficiency of Rice Farming—An Empirical Study in the Mekong Delta of Vietnam

Considering a framework to estimate the impact of co-designed science: Case study of Bayesian networks to set NRM targets

Coproduction Challenges in the Context of Changing Rural Livelihoods

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