Introduction Nepal’s agricultural sector contributes approximately one third to its GDP and employs 80% of its total workforce. However, its agricultural.

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Introduction Nepal’s agricultural sector contributes approximately one third to its GDP and employs 80% of its total workforce. However, its agricultural productivity is the lowest among its regional and economic peers (South Asian Countries and Low Income Economies, respectively; Sharma and Khanal, 2010; World Bank, 2012). This lagging agricultural productivity contributes to chronic malnutrition and regional food insecurity. This is especially true in Nepal’s “Middle Hills” region (in yellow, below) where production is characterized by subsistence farming on terraced, marginally-productive rain-fed plots (Tiwari et al., 2008). Due to low production capacity and few opportunities for off-farm employment, Middle Hills’ farmers are among the most vulnerable groups in Nepal (FAO, 2007) and are the focus of this study. Conservation Agriculture Production Systems (CAPS) are an integrated approach to improving the long-term productive capacity of soil. By implementing multiple conservation agriculture (CA) practices throughout the year and adapting them to regional farming conditions, CAPS can synergistically improve soil quality and thus improve food security (SANREM CRSP, 2012). Despite the widespread use of CA practices around the world, adoption rates in the Middle Hills remains low due in part to the knowledge-heavy nature of CA and, potentially, a lack of mutual understanding of between scientists and farmers (Carr and Wilkinson, 2004; Paudel and Thapa, 2009). The primary goal of this study is to increase the likelihood of CAPS adoption by exploring preferences for CAPS among in-country scientists and tribal farmers, and identifying differences in perception and knowledge gaps. Objectives 1.Determine scientist and farmer preferences for recently-introduced CAPS using Analytic Hierarchy Process (AHP). 2.Compare preferences and perceptions of CAPS to identify knowledge gaps among village farmers and scientists. 3.Make recommendations to extension and research organizations regarding the next steps of technology transfer. Tribal farmer preferences for conservation agriculture production systems: an application of Analytic Hierarchy Process. Brinton Reed*, Catherine Chan-Halbrendt, B.B. Tamang, Narendra Chaudhary. Department of Natural Resource and Environmental Management. Methods An AHP decision hierarchy based on the overall goal of improved income was developed to evaluate differences in preferences among groups. AHP methodology requires the formulation of hierarchical levels consisting of objectives and options (see figure below). The objectives - profit*, labor savings, yield, and soil quality – were determined by conducting farmer focus groups and literature review. The following production system options were also identified**: Farmer Practice: (FP) Conventional Tillage, Finger Millet (Eleusine coracana) monocrop CAPS 1: Conventional Tillage, Cowpea (Vigna unguiculata) monocrop CAPS 2: Conventional Tillage, Cowpea and Finger Millet Intercrop CAPS 3: Minimum (Strip) Tillage, Cowpea and Finger Millet Intercrop Study Area: Three Middle Hills villages in or near the Trisuli River valley were selected based on accessibility, prevalence of rural subsistence farming on rain-fed, terraced plots, and similarities with other regional farming communities. Two organizations were selected based on their research and extension work in the area: Local Initiatives for Biodiversity Research and Development (LI-BIRD) and the Institute of Agriculture and Animal Science (IAAS). AHP was conducted with a total of 40 staff and 41 randomly selected farmers. In Thumka, Hykrang, and Khola Guan, 36%, 52% and 48% of households were surveyed, respectively. Expert Choice version was used to calculate ratio scale weights, consistency indices (CI = (λ max – n) / (n -1)) and consistency ratios (CR = CI/RI where RI = CI of a randomly generated set of reciprocals). * Profit was defined as the total value of all cultivated crops less input costs per year. ** In each treatment, corn (Zea mays) was grown in the first growing season beginning in spring. This was followed in summer by the second season crops listed for each treatment above. The third growing season began in late fall and fields were left fallow due to low rainfall. Results and Discussion Overall, soil quality (49%) was perceived as most important to the goal of improved income. This was followed by yield (25%), profit (14%), and labor savings (11%). Also in terms of improved income, CAPS 1 was weighted highest (35%) and was followed by CAPS 3 (34%), CAPS 2 (22%), and farmer practice (9%). The high preference for improved soil quality over profit and yields suggest a willingness among farmers to accept new technologies such as minimum tillage that improve soil quality over the long-term at the expense of short-term benefits. Overall, labor saving was considered least important to improved income in both organizations and villages and may not be a primary factor influencing adoption. The organizations in this study preferred CAPS 3 over all others in terms of improved income. This met expectations as CAPS 3 is the only treatment that includes both strip tillage and legume intercropping. Hyrkrang and Thumka ranked CAPS 1 much higher than other CAPS, suggesting that the perceived advantages of intercropping or strip tillage have not been communicated through the extension system or carry unidentified costs for farmers, or both. A higher preference for CAPS 2 over CAPS 3 in terms of both improved income and soil quality in these villages indicates that farmers there perceive conventional tillage as having more monetary and edaphic value than strip tillage. Compared to Thumka and Hyrkrang, Khola Guan farmers’ perceptions of CAPS treatments were much more in line with that of extension organizations. It is possible that Khola Guan has had more contact with extension personnel. It should also be noted that farmer practice was least preferred in nearly every regard, indicating a shared preference for the adoption of better farming systems. Conclusion  Despite having divergent preferences overall, scientists and farmers share a strong preference for adoption of new production systems that can improve long-term soil quality. Preferences in Khola Guan are more in line with those of scientists than preferences in other villages - this is possibly due to more exposure with extension agents.  The benefits of legume intercropping and strip tillage are unclear to village farmers and unexpectedly, labor savings may not be a primary factor motivating adoption of CAPS.  Organizations should develop a greater understanding of farmer perceptions by increasing contact with villagers and continuing on-farm experimentation to substantiate the benefits of the CAPS systems studied. This information could prove valuable to CAPS adoption efforts in the Himalayan foothills and other less developed regions worldwide. References Carr A. and R. Wilkinson (2005). “Beyond participation: boundary organizations as a new space for farmers and scientists to interact.” Society & Natural Resources 18(3): FAO (2007) “FAO/WFP Food Security Assessment Mission to Nepal.” An FAO Global information and early warning system on food and agriculture work food programme special report: 25 July Paudel, G.S. and G.B. Thapa (2004) “Impact of social, institutional and ecological factors on land management practices in mountain watersheds of Nepal.” Applied Geography 24: SANREM CRSP (2012) Conservation Agriculture Production Systems (CAPS). Available online at: Sharma K. and S.N. Khanal (2010). “A review and analysis of existing legal and policy issues related to land tenure and agriculture in Nepal.” Kathmandu University Journal of Science, Engineering and Technology 6(2): Tiwari, K.R., B.K. Sitaula, I.L.P. Nyborg, and G.S. Paudel (2008). “Determinants of Farmers’ Adoption of Improved Soil Conservation Technology in a Middle Mountain Watershed of Central Nepal.” Environmental Management 42: World Bank (2012). World DataBank. Available online at: