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Farmers’ preferences for attributes of rice varieties in Sierra Leone

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Abstract

Farmers’ preferences and needs are crucial in improving the development of rice varieties to increase rice production and improve food security. However, research on farmers’ preferences for crop attributes is scarce in Africa. This study contributes to bridging this gap by focusing on farmers’ preferences for rice variety attributes based on a nationwide survey conducted in Sierra Leone. Results from a Best-Worst Scaling analysis revealed that potential yield, maturity, pest and disease resistance, and seed longevity, were the most preferred attributes of a rice variety. The least preferred attributes were ease of threshing, fertiliser response, and shattering. After applying a latent class model, farmers were found to align with six distinct classes: “majority farmers”, “price sensitive”, “conservationists”, “sustainable farmers”, “output maximisers”, and “subsistence”. These classes showed differences in terms of the farmers’ characteristics (e.g. sex, education, income, farm farming experience, and farmland size) and in the importance given to extrinsic factors (e.g. access to market, extension services, and membership in farmers’ organisations). Among these classes, the “majority farmers” were more likely to have relatively more farmland, easier access to other farm resources (e.g., chemical fertilisers and market information) and are more likely to prefer potential yield. The “price sensitive” class placed high importance on seed price and consisted of farmers who were low income, and had better access to extension services and membership to farming organisations. Furthermore, preferences were distinct for “conservationists” who have environmental and sustenance concerns.

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References

  • Abdulai, A., & Huffman, W. E. (2005). The diffusion of new agricultural technologies: The case of crossbred-cow technology in Tanzania. American Journal of Agricultural Economics, 87(3), 645–659.

    Google Scholar 

  • Adesina, A. A., & Baidu-Forson, J. (1995). Farmers' perceptions and adoption of new agricultural technology: Evidence from analysis in Burkina Faso and Guinea, West Africa. Agricultural Economics, 13(1), 1–9.

    Google Scholar 

  • Adesina, A. A., & Zinnah, M. M. (1993). Technology characteristics, farmers' perceptions and adoption decisions: A Tobit model application in Sierra Leone. Agricultural Economics, 9(4), 297–311.

    Google Scholar 

  • Arouna, A., Lokossou, J., Wopereis, M., Bruce-Oliver, S., & Roy-Macauley, H. (2017). Contribution of improved rice varieties to poverty reduction and food security in sub-Saharan Africa. Global Food Security, 14, 54–60.

    Google Scholar 

  • Asfaw, S., McCarthy, N., Lipper, L., Arslan, A., & Cattaneo, A. (2016). What determines farmers’ adaptive capacity? Empirical evidence from Malawi. Food Security, 8(3), 643–664.

    Google Scholar 

  • Asrat, S., Yesuf, M., Carlsson, F., & Wale, E. (2010). Farmers' preferences for crop variety traits: Lessons for on-farm conservation and technology adoption. Ecological Economics, 69(12), 2394–2401.

    Google Scholar 

  • Bakker, W. (1970). Rice yellow mottle, a mechanically transmissible virus disease of rice in Kenya. Netherlands Journal of Plant Pathology, 76(2), 53–63.

    Google Scholar 

  • Batz, F. J., Janssen, W., & Peters, K. J. (2003). Predicting technology adoption to improve research priority—Setting. Agricultural Economics, 28(2), 151–164.

    Google Scholar 

  • Baumgartner, H., & Steenkamp, J. B. E. (2001). Response styles in marketing research: A cross-national investigation. Journal of Marketing Research, 38(2), 143–156.

    Google Scholar 

  • Burman, D., Maji, B., Singh, S., Mandal, S., Sarangi, S. K., Bandyopadhyay, B. K., Bal, A. R., Sharma, D. K., Krishnamuthy, S. L., & Singh, H. (2018). Participatory evaluation guides the development and selection of farmers’ preferred rice varieties for salt-and flood-affected coastal deltas of south and Southeast Asia. Field Crops Research, 220, 67–77.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Campbell, D., & Erdem, S. (2015). Position bias in best-worst scaling surveys: A case study on trust in institutions. American Journal of Agricultural Economics, 97(2), 526–545.

    Google Scholar 

  • Chakanda, R., van Treuren, R., Visser, B., & van den Berg, R. (2013). Analysis of genetic diversity in farmers’ rice varieties in Sierra Leone using morphological and AFLP® markers. Genetic Resources and Crop Evolution, 60(4), 1237–1250.

    Google Scholar 

  • Cohen, S., & Orme, B. (2004). What's your preference? Asking survey respondents about their preferences creates new scaling decisions. Marketing Research Magazine, 16, 33–37.

    Google Scholar 

  • Conteh, A. M., Yan, X., Fofana, I., Gegbe, B., & Isaac, T. I. (2014a). An estimation of rice output supply response in Sierra Leone: A Nerlovian model approach. International Journal of Biological, Biomolecular, Agricultural, Food and Biotechnological Engineering, 8(3), 225–231.

    Google Scholar 

  • Conteh, A. M., Yan, X., & Gborie, A. V. (2014b). Using the Nerlovian adjustment model to assess the response of farmers to price and other related factors: Evidence from Sierra Leone rice cultivation. World Academy of Science, Engineering and Technology, International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 8(3), 687–693.

    Google Scholar 

  • Conteh, A. M., Yan, X., & Mvodo, M. (2013). Evaluating the effect of farmers' training on rice production in Sierra Leone: A case study of rice cultivation in lowland ecology. Paper presented at the Proceedings of World Academy of Science, Engineering and Technology.

  • Conteh, A. M. H., Yan, X., & Sankoh, F. P. (2012). The influence of price on rice production in Sierra Leone. Agricultural Sciences, 3(04), 462.

    Google Scholar 

  • Coulibaly, J. Y., Chiputwa, B., Nakelse, T., & Kundhlande, G. (2017). Adoption of agroforestry and the impact on household food security among farmers in Malawi. Agricultural Systems, 155, 52–69.

    Google Scholar 

  • Crawford, G. W., & Shen, C. (1998). The origins of rice agriculture: Recent progress in East Asia. Antiquity, 72(278), 858–866.

    Google Scholar 

  • Dahniya, M. (1993). Linking science and the farmer: Pillars of the national agricultural research system in Sierra Leone. ISNAR, The Hague, Netherlands: Documentation http://eprints.icrisat.ac.in/12768/1/RP-%208518.pdf. .

    Google Scholar 

  • Dalton, T. J., & Guei, R. G. (2003). Productivity gains from rice genetic enhancements in West Africa: Countries and ecologies. World Development, 31(2), 359–374.

    Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319–340.

  • Fajardo Vizcayno, J., Hugo, W., & Sanz Alvarez, J. (2014). Appropriate seed varieties for small-scale farmers: Key practices for DRR implementers. FAO: Resource document http://www.fao.org/3/a-i3768e.pdf. .

    Google Scholar 

  • Finn, A., & Louviere, J. J. (1992). Determining the appropriate response to evidence of public concern: The case of food safety. Journal of Public Policy & Marketing, 12–25.

  • Flynn, T., & Marley, A. (2014). Best-worst scaling: Theory and methods. In S. Hess & A. Daly (Eds.), Handbook of choice Modelling (pp. 178–201). Cheltenham: Edward Elgar Publishing.

    Google Scholar 

  • Ghimire, R., Huang, W., & Poudel, M. (2015a). Adoption intensity of agricultural technology: Empirical evidence from smallholder maize famers in Nepal. International Journal of Agriculture Innovations and Research, 4(1), 139–146.

    Google Scholar 

  • Ghimire, R., Wen-chi, H., & Shrestha, R. B. (2015b). Factors affecting adoption of improved rice varieties among rural farm households in Central Nepal. Rice Science, 22(1), 35–43.

    Google Scholar 

  • Greene, W. H., & Hensher, D. A. (2003). A latent class model for discrete choice analysis: Contrasts with mixed logit. Transportation Research Part B: Methodological, 37(8), 681–698.

    Google Scholar 

  • Gyawali, S., Sunwar, S., Subedi, M., Tripathi, M., Joshi, K., & Witcombe, J. (2007). Collaborative breeding with farmers can be effective. Field Crops Research, 101(1), 88–95.

    Google Scholar 

  • Haughton, D., Legrand, P., & Woolford, S. (2009). Review of three latent class cluster analysis packages: Latent Gold, poLCA, and MCLUST. The American Statistician, 63(1), 81–91.

    Google Scholar 

  • Hensher, D. A., & Greene, W. H. (2010). Non-attendance and dual processing of common-metric attributes in choice analysis: A latent class specification. Empirical Economics, 39(2), 413–426.

    Google Scholar 

  • Jara-Rojas, R., Bravo-Ureta, B. E., & Díaz, J. (2012). Adoption of water conservation practices: A socioeconomic analysis of small-scale farmers in Central Chile. Agricultural Systems, 110, 54–62.

    Google Scholar 

  • Joshi, P., Joshi, L., & Birthal, P. S. (2006). Diversification and its impact on smallholders: Evidence from a study on vegetable production. Agricultural Economics Research Review, 19(2), 219–236.

    Google Scholar 

  • Kannababu, N., Rao, S., Prabhakar, B., Shyamprasad, G., Srinivasababu, K., Dhandapani, A., & Patil, J. (2016). Genetic variability for seed ageing and longevity among the advanced sweet sorghum genotypes and cultivars. Sugar Tech, 18(1), 100–104.

    CAS  Google Scholar 

  • Kijima, Y., Ito, Y., & Otsuka, K. (2012). Assessing the impact of training on lowland rice productivity in an African setting: Evidence from Uganda. World Development, 40(8), 1610–1618.

    Google Scholar 

  • Kijima, Y., Otsuka, K., & Serunkuuma, D. (2011). An inquiry into constraints on a green revolution in sub-Saharan Africa: The case of NERICA rice in Uganda. World Development, 39(1), 77–86.

    Google Scholar 

  • Laborte, A. G., Paguirigan, N. C., Moya, P. F., Nelson, A., Sparks, A. H., & Gregorio, G. B. (2015). Farmers’ preference for rice traits: Insights from farm surveys in Central Luzon, Philippines, 1966-2012. PLoS One, 10(8), e0136562.

    PubMed  PubMed Central  Google Scholar 

  • Lagarde, M. (2013). Investigating attribute non-attendance and its consequences in choice experiments with latent class models. Health Economics, 22(5), 554–567.

    PubMed  Google Scholar 

  • Liang, T., Xu, Z. J., & Chen, W. F. (2017). Advances and prospects of super rice breeding in China. Journal of Integrative Agriculture, 16(5), 984–991.

    Google Scholar 

  • Loose, S. M., & Lockshin, L. (2013). Testing the robustness of best worst scaling for cross-national segmentation with different numbers of choice sets. Food Quality and Preference, 27(2), 230–242.

    Google Scholar 

  • Louhichi, K., & YPaloma, S. G. (2014). A farm household model for Agri-food policy analysis in developing countries: Application to smallholder farmers in Sierra Leone. Food Policy, 45, 1–13.

    Google Scholar 

  • Loureiro, M. L., & Arcos, F. D. (2012). Applying best–worst scaling in a stated preference analysis of forest management programs. Journal of Forest Economics, 18(4), 381–394.

    Google Scholar 

  • Meghani, S. H., Lee, C. S., Hanlon, A. L., & Bruner, D. W. (2009). Latent class cluster analysis to understand heterogeneity in prostate cancer treatment utilities. BMC Medical Informatics and Decision Making, 9(1), 47.

    PubMed  PubMed Central  Google Scholar 

  • Mendola, M. (2007). Agricultural technology adoption and poverty reduction: A propensity-score matching analysis for rural Bangladesh. Food Policy, 32(3), 372–393.

    Google Scholar 

  • Mgumia, A. H., Mattee, A. Z., & Kundi, B. A. (2015). Characteristics of agriculture technology and application of an agricultural innovation system in Tanzania. African Journal of Science, Technology, Innovation and Development, 7(2), 73–83.

    Google Scholar 

  • Micheels, E. T., & Nolan, J. F. (2016). Examining the effects of absorptive capacity and social capital on the adoption of agricultural innovations: A Canadian prairie case study. Agricultural Systems, 145, 127–138.

    Google Scholar 

  • Morris, M. L., & Bellon, M. R. (2004). Participatory plant breeding research: Opportunities and challenges for the international crop improvement system. Euphytica, 136(1), 21–35.

    Google Scholar 

  • Mueller, S., & Rungie, C. (2009). Is there more information in best-worst choice data? Using the attitude heterogeneity structure to identify consumer segments. International Journal of Wine Business Research, 21(1), 24–40.

    Google Scholar 

  • Naseem, A., Mhlanga, S., Diagne, A., Adegbola, P. Y., & Midingoyi, G. S. K. (2013). Economic analysis of consumer choices based on rice attributes in the food markets of West Africa—The case of Benin. Food Security, 5(4), 575–589.

    Google Scholar 

  • Nwanze, K. F., Mohapatra, S., Kormawa, P., Keya, S., & Bruce-Oliver, S. (2006). Rice development in sub-Saharan Africa. Journal of the Science of Food and Agriculture, 86(5), 675–677.

    CAS  Google Scholar 

  • Onyango, A. O. (2014). Exploring options for improving rice production to reduce hunger and poverty in Kenya. World Environment, 4(4), 172–179.

    Google Scholar 

  • Pingali, P. L. (2012). Green revolution: Impacts, limits, and the path ahead. Proceedings of the National Academy of Sciences, 109(31), 12302–12308.

    CAS  Google Scholar 

  • Poku, A. G., Birner, R., & Gupta, S. (2018). Why do maize farmers in Ghana have a limited choice of improved seed varieties? An assessment of the governance challenges in seed supply. Food Security, 10(1), 27–46.

    Google Scholar 

  • Robert-Ribes, J., & Wing, P. (2004). Predicting the speed and patterns of technology take-up. Australian Venture Capital Journal, 131, 34–36.

    Google Scholar 

  • Salaudeen, M. T., Banwo, O. O., Kashina, B. D., & Alegbejo, M. D. (2010). Current status of research on rice yellow mottle Sobemovirus. Archives of Phytopathology and Plant Protection, 43(6), 562–572.

    Google Scholar 

  • Sall, S., Norman, D., & Featherstone, A. (2000). Quantitative assessment of improved rice variety adoption: The farmer’s perspective. Agricultural Systems, 66(2), 129–144.

    Google Scholar 

  • Sánchez, B. I., Kallas, Z., & Gil Roig, J. M. (2017). Farmer preference for improved corn seeds in Chiapas, Mexico: A choice experiment approach. Spanish Journal of Agricultural Research, 15(3).

  • Schut, M., van Asten, P., Okafor, C., Hicintuka, C., Mapatano, S., Nabahungu, N. L., & Dontsop-Nguezet, P. M. (2016). Sustainable intensification of agricultural systems in the Central African highlands: The need for institutional innovation. Agricultural Systems, 145, 165–176.

    Google Scholar 

  • Seck, P. A., Tollens, E., Wopereis, M. C., Diagne, A., & Bamba, I. (2010). Rising trends and variability of rice prices: Threats and opportunities for sub-Saharan Africa. Food Policy, 35(5), 403–411.

    Google Scholar 

  • Spielman, D. J., Davis, K., Negash, M., & Ayele, G. (2011). Rural innovation systems and networks: Findings from a study of Ethiopian smallholders. Agriculture and Human Values, 28(2), 195–212.

    Google Scholar 

  • Statistics Sierra Leone, Sierra Leone Integrated Household Survey (SLIHS) (2011). https://www.statistics.sl/images/StatisticsSL/Documents/sierra_leone_integrated_household_survey_2011-1.pdf. Accessed 5 June 2019.

  • Teixeira, E. I., Fischer, G., van Velthuizen, H., Walter, C., & Ewert, F. (2013). Global hot-spots of heat stress on agricultural crops due to climate change. Agricultural and Forest Meteorology, 170, 206–215. https://doi.org/10.1016/j.agrformet.2011.09.002.

    Article  Google Scholar 

  • Thurstone, L. L. (1974). A law of comparative judgment. In G. M. Maranell (Ed.), Scaling: A sourcebook for behavioral scientists (pp. 81–92). New York: Routledge.

    Google Scholar 

  • Traoré, O., Traoré, M., Fargette, D., & Konaté, G. (2006). Rice seedbeds as a source of primary infection by Rice yellow mottle virus. European Journal of Plant Pathology, 115(2), 181–186.

    Google Scholar 

  • Umberger, W. J., Stringer, R., & Mueller, S. C. (2010). Using best-worst scaling to determine market channel choice by small farmers in Indonesia. AgEcon SEARCH: Resource document https://ageconsearch.umn.edu/record/90853. .

    Google Scholar 

  • Valin, H., Sands, R. D., van der Mensbrugghe, D., Nelson, G. C., Ahammad, H., Blanc, E., & Havlik, P. (2014). The future of food demand: Understanding differences in global economic models. Agricultural Economics, 45(1), 51–67.

    Google Scholar 

  • Vermunt, J. K., & Magidson, J. (2008). LG-syntax user's guide: Manual for latent GOLD 4.5 syntax module. Belmont: Statistical Innovations.

    Google Scholar 

  • Yelome, O., Audenaert, K., Landschoot, S., Dansi, A., Vanhove, W., Silue, D., & Haesaert, G. (2018). Combining high yields and blast resistance in rice (Oryza spp.): A screening under upland and lowland conditions in Benin. Sustainability, 10(7), 2500.

    Google Scholar 

  • Yokouchi, T., & Saito, K. (2016). Factors affecting farmers’ adoption of NERICA upland rice varieties: The case of a seed producing village in Central Benin. Food Security, 8(1), 197–209.

    Google Scholar 

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Acknowledgments

We gratefully acknowledge the support from the National Natural Science Foundation of China (NNSFC-71273233) and the Zhejiang Provincial Philosophical and Social Sciences Research Grant (No.13ZJQN046YB). We are also grateful to all the District Officers and Extension Agents of the Ministry of Agriculture, Forestry and Food Security in Sierra Leone for their dedication and assistance. We appreciate the efforts and hard work of our field enumerators (Messrs. Milton Kabbia, Edward Ndoko of the Sierra Leone Agricultural Research Institute; Messr Mohamed Feika, Alpha Sesay, Brima Samking and Dr. M.K. Sesay of Njala University, Sierra Leone) and all others who contributed in one way or the other for their dedication to duty and timely execution of the survey without which this research would not have been successful. Similarly, we thank our research assistants in the Zhejiang University especially Yu Jiang and Rao Yuan for all their inputs.

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Jin, S., Mansaray, B., Jin, X. et al. Farmers’ preferences for attributes of rice varieties in Sierra Leone. Food Sec. 12, 1185–1197 (2020). https://doi.org/10.1007/s12571-020-01019-w

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