Hostname: page-component-669899f699-vbsjw Total loading time: 0 Render date: 2025-04-26T08:08:48.925Z Has data issue: false hasContentIssue false
  • English
  • Français

Farmers’ selection criteria for sweet potato varieties in Benin: An application of Best-Worst Scaling

Published online by Cambridge University Press:  15 December 2023

Idrissou Ahoudou Open the ORCID record for Idrissou Ahoudou [Opens in a new window] ,
Dêêdi E. O. Sogbohossou Open the ORCID record for Dêêdi E. O. Sogbohossou [Opens in a new window] ,
Nicodeme V. Fassinou Hotegni Open the ORCID record for Nicodeme V. Fassinou Hotegni [Opens in a new window] ,
Charlotte O. A. Adjé Open the ORCID record for Charlotte O. A. Adjé [Opens in a new window] ,
Françoise Assogba Komlan ,
Ismail Moumouni-Moussa Open the ORCID record for Ismail Moumouni-Moussa [Opens in a new window]  and
Enoch G. Achigan-Dako Open the ORCID record for Enoch G. Achigan-Dako [Opens in a new window]
Idrissou Ahoudou
Affiliation:
Genetics, Biotechnology and Seed Science Unit (GBioS), Laboratory of Crop Production, Physiology and Plant Breeding, Faculty of Agronomic Sciences, University of Abomey-Calavi, Tri Postal Cotonou 01 BP 526, Republic of Benin
Dêêdi E. O. Sogbohossou
Affiliation:
Genetics, Biotechnology and Seed Science Unit (GBioS), Laboratory of Crop Production, Physiology and Plant Breeding, Faculty of Agronomic Sciences, University of Abomey-Calavi, Tri Postal Cotonou 01 BP 526, Republic of Benin
Nicodeme V. Fassinou Hotegni
Affiliation:
Genetics, Biotechnology and Seed Science Unit (GBioS), Laboratory of Crop Production, Physiology and Plant Breeding, Faculty of Agronomic Sciences, University of Abomey-Calavi, Tri Postal Cotonou 01 BP 526, Republic of Benin
Charlotte O. A. Adjé
Affiliation:
Genetics, Biotechnology and Seed Science Unit (GBioS), Laboratory of Crop Production, Physiology and Plant Breeding, Faculty of Agronomic Sciences, University of Abomey-Calavi, Tri Postal Cotonou 01 BP 526, Republic of Benin
Françoise Assogba Komlan
Affiliation:
Vegetable Research Program, National Institute of Agricultural Research of Benin (INRAB), Cotonou 01 BP 884, Republic of Benin
Ismail Moumouni-Moussa
Affiliation:
Laboratory of Research on Innovation for Agricultural Development (LRIDA), University of Parakou (UP), Republic of Benin
Enoch G. Achigan-Dako*
Affiliation:
Genetics, Biotechnology and Seed Science Unit (GBioS), Laboratory of Crop Production, Physiology and Plant Breeding, Faculty of Agronomic Sciences, University of Abomey-Calavi, Tri Postal Cotonou 01 BP 526, Republic of Benin
*
Corresponding author: Enoch G. Achigan-Dako; Email: e.adako@gmail.com
Get access Rights & Permissions [Opens in a new window]

Summary

Integrating farmers’ preferences into the breeding and dissemination of new genotypes is a effective approach to enhance their successful adoption by farmers. In the case of sweet potato, a staple crop in many parts of West Africa, there is a need for more research on the selection criteria used by farmers when choosing which varieties to grow. This study aims to highlight farmers’ selection criteria for sweet potato varieties in the main production areas in Benin. A total of 480 farmers from the top three sweet potato production areas were surveyed. The relative importance of various traits for sweet potato farmers was evaluated using best-worst scaling methods. Latent class analysis was applied to find groups of farmers with similar preferences. Best-Worst Scaling analysis revealed that high root yield, root size, marketability, and early maturing were the most important variety selection criteria. Latent class analysis revealed three farmers’ groups referred to as ‘Yield potential’, ‘Market value’, and ‘Plant resilience’ classes. ‘Yield potential’ farmers were more likely to be from Atlantique and Alibori departments; they significantly committed more acreage to sweet potato production. The ‘Market value’ farmers highlighted the variety of root size and commercial value as the main selection criteria and consisted of farmers with primary education levels from the Ouémé department. ‘Plant resilience’ refers to a group of Alibori farmers who prioritize environmental issues and primarily grow sweet potatoes for self-consumption. Our findings shed light on farmers’ preferences and suggested that heterogeneity in sweet potato selection criteria was highly influenced by various socio-economic factors and location.

Keywords

Best-Worst Scalingimproved cultivarslatent class analysissweet potatotechnology adoption
Type
Research Article
Information
Experimental Agriculture , Volume 59 , 2023 , e25
Check for updates
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Adekambi, S., Okello, J., Rajendran, S., Acheremu, K., Carey, E., Low, J. and Abidin, P. (2020). Effect of varietal attributes on the adoption of an orange-fleshed sweetpotato variety in Upper East and Northern Ghana. Outlook on Agriculture 49, 311–320. https://doi.org/10.1177/0030727020950324 Google Scholar
Adeola, R.G., Ogunleye, K.Y. and Adewole, W.A. (2019). Adoption intensity determinants for improved sweet potato varieties among farmers in Nigeria. International Journal of Agricultural Management and Development (Ijamad) 9, 203–211.Google Scholar
Agre, A.P., Bhattacharjee, R., Dansi, A., Becerra Lopez-Lavalle, L.A., Dansi, M. and Sanni, A. (2017). ‘Assessment of cassava (Manihot esculenta Crantz) diversity, loss of landraces and farmers preference criteria in southern Benin using farmers’ participatory approach. Genetic Resources and Crop Evolution 64, 307–320. https://doi.org/10.1007/s10722-015-0352-1 CrossRefGoogle Scholar
Akoroda, M. (2009).Sweetpotato in West Africa. The Sweetpotato 441–468. https://doi.org/10.1007/978-1-4020-9475-0_19/COVER CrossRefGoogle Scholar
Baafi, E., Blay, E.T., Ofori, K., Gracen, V.E., Manu-Aduening, J. and Carey, E.E. (2016). Breeding superior orange-fleshed sweetpotato cultivars for West Africa. Journal of Crop Improvement 30, 293–310. https://doi.org/10.1080/15427528.2016.1154492 CrossRefGoogle Scholar
Baafi, E., Manu-Aduening, J., Carey, E.E., Ofori, K., Blay, E.T. and Gracen, V.E. (2015). Constraints and breeding priorities for increased sweetpotato utilization in Ghana. Sustainable Agriculture Research 4, 1. https://doi.org/10.5539/sar.v4n4p1 Google Scholar
Barnes, A.P., Islam, M.M. and Toma, L. (2013). Heterogeneity in climate change risk perception amongst dairy farmers: a latent class clustering analysis. Applied Geography 41, 105–115. https://doi.org/10.1016/j.apgeog.2013.03.011 CrossRefGoogle Scholar
Below, T.B., Mutabazi, K.D., Kirschke, D., Franke, C., Sieber, S., Siebert, R. and Tscherning, K. (2012). ‘Can farmers’ adaptation to climate change be explained by socio-economic household-level variables? Global Environmental Change 22, 223–235. https://doi.org/10.1016/j.gloenvcha.2011.11.012 CrossRefGoogle Scholar
Bovell-Benjamin, A.C. (2010). Sweet potato utilization in human health, industry and animal feed systems, in R.C. Ray and K.I. Tomlins (eds), Sweet Potato: Post Harvest Aspects in Food, Feed and Industry. New York: Nova Science, pp. 193–224.Google Scholar
Brice Dibi, K.E., Essis, B.S., N’zué, B., Kouakou, A.M., Zohouri, G.P., Assouan, A.B. and Van Mourik, T. (2017). Participatory selection of orange-fleshed sweetpotato varieties in north and north-east Côte d’Ivoire. Open Agriculture 2, 83–90. https://doi.org/10.1515/opag-2017-0009 CrossRefGoogle Scholar
Chapman, J., Power, A., Netzel, M.E., Sultanbawa, Y., Smyth, H.E., Truong, V.K. and Cozzolino, D. (2022). Challenges and opportunities of the fourth revolution: a brief insight into the future of food. Critical Reviews in Food Science and Nutrition 62, 2845–2853. https://doi.org/10.1080/10408398.2020.1863328 CrossRefGoogle ScholarPubMed
CIP (2022). 2019 Sweetpotato Catalogue for Sub-Saharan Africa (SSA). https://research.cip.cgiar.org/sweetpotato-catalog/cip_sp_catalogue/ (accessed 19 July 2022).Google Scholar
Clark, C.A., Davis, J.A., Abad, J.A., Cuellar, W.J., Fuentes, S., Kreuze, J.F., Gibson, R.W., Mukasa, S.B., Tugume, A.K., Tairo, F.D. and Valkonen, J.P.T. (2012). Sweetpotato viruses: 15 years of progress on understanding and managing complex diseases. Plant Disease 96, 168–185. https://doi.org/10.1094/PDIS-07-11-0550 Google Scholar
Clark, C.A. and Hoy, M.W. (2006). Effects of common viruses on yield and quality of beauregard sweetpotato in Louisiana. Plant Disease 90, 83–88. https://doi.org/10.1094/PD-90-0083 CrossRefGoogle ScholarPubMed
Cohen, E. (2009). Applying best-worst scaling to wine marketing. International Journal of Wine Business Research. Edited by E. Cohen, 21(1), 8–23. https://doi.org/10.1108/17511060910948008 CrossRefGoogle Scholar
Cohen, S.H. (2003). Maximum difference scaling: and preference for segmentation. Sawtooth Software Research Paper Series 98382, 1–17.Google Scholar
Dansi, A., Vodouhè, R., Azokpota, P., Yedomonhan, H., Assogba, P., Adjatin, A., Loko, Y.L., Dossou-Aminon, I. and Akpagana, K. (2012). Diversity of the neglected and underutilized crop species of importance in Benin. The Scientific World Journal 2012, 1–19. https://doi.org/10.1100/2012/932947 CrossRefGoogle ScholarPubMed
Dawson, J.C., Murphy, K.M. and Jones, S.S. (2008). Decentralized selection and participatory approaches in plant breeding for low-input systems. Euphytica 160, 143–154. https://doi.org/10.1007/s10681-007-9533-0 CrossRefGoogle Scholar
Doussoh, A.M., Dangou, J.S., Houedjissin, S.S., Assogba, A.K. and Ahanhanzo, C. (2017). Analyse des connaissances endogènes et des déterminants de la production de la patate douce [Ipomoea batatas (L.)], une culture à haute valeur socioculturelle et économique au Bénin. International Journal of Biological and Chemical Sciences 10, 2596. https://doi.org/10.4314/ijbcs.v10i6.16 Google Scholar
Dumbrell, N.P., Kragt, M.E. and Gibson, F.L. (2016). What carbon farming activities are farmers likely to adopt? A best–worst scaling survey. Land Use Policy 54, 29–37. https://doi.org/10.1016/j.landusepol.2016.02.002 Google Scholar
Edwards-Jones, G. (2006). Modelling farmer decision-making: concepts, progress and challenges. Animal Science 82, 783–790. https://doi.org/10.1017/ASC2006112 CrossRefGoogle Scholar
Erdem, S. and Rigby, D. (2013). Investigating heterogeneity in the characterization of risks using best worst scaling. Risk Analysis 33, 1728–1748. https://doi.org/10.1111/risa.12012 CrossRefGoogle ScholarPubMed
Ezin, V., Quenum, F., Bodjrenou, R.H., Kpanougo, C.M.I., Kochoni, E.M.G., Chabi, B.I. and Ahanchede, A. (2018). Assessment of production and marketing constraints and value chain of sweet potato in the municipalities of Dangbo and Bonou. Agriculture & Food Security 7, 15. https://doi.org/10.1186/s40066-018-0164-6 CrossRefGoogle Scholar
Faostat (2022). FAOSTAT. Available at https://www.fao.org/faostat/en/#compare (accessed 7 June 2022).Google Scholar
Finn, A. and Louviere, J.J. (1992). Determining the appropriate response to evidence of public concern: the case of food safety. Journal of Public Policy & Marketing 11, 12–25. https://doi.org/10.1177/074391569201100202 CrossRefGoogle Scholar
Flynn, T.N., Louviere, J.J., Peters, T.J. and Coast, J. (2007). Best–worst scaling: what it can do for health care research and how to do it. Journal of Health Economics 26, 171–189. https://doi.org/10.1016/j.jhealeco.2006.04.002 Google Scholar
Forster, M.R. (2000). Key Concepts in Model Selection: Performance and Generalizability. Journal of Mathematical Psychology, 44(1), 205–231. https://doi.org/10.1006/jmps.1999.1284 Google Scholar
Gibson, R.W., Mpembe, I. and Mwanga, R.O.M. (2011). Benefits of participatory plant breeding (PPB) as exemplified by the first-ever officially released PPB-bred sweet potato cultivar. The Journal of Agricultural Science 149, 625–632. https://doi.org/10.1017/S0021859611000190 Google Scholar
Gobena, T.L., Asemie, M.M. and Firisa, T.B. (2022). Evaluation of released sweet potato [Ipomoea batatas (L.) Lam] varieties for yield and yield-related attributes in Semen-Bench district of Bench-Sheko-Zone, South-Western Ethiopia. Heliyon 8, e10950. https://doi.org/10.1016/j.heliyon.2022.e10950 CrossRefGoogle ScholarPubMed
Greig, L. (2009). An analysis of the key factors influencing farmer’s choice of crop, Kibamba Ward, Tanzania. Journal of Agricultural Economics 60, 699–715. https://doi.org/10.1111/j.1477-9552.2009.00215.x CrossRefGoogle Scholar
Gruneberg, W.J., Abidin, E., Ndolo, P., Pereira, C.A. and Hermann, M. (2004). Variance component estimations and allocation of resources for breeding sweetpotato under East African conditions. Plant Breeding 123, 311–315. https://doi.org/10.1111/j.1439-0523.2004.01000.x CrossRefGoogle Scholar
Hannah, C., Davies, J., Green, R., Zimmer, A., Anderson, P., Battersby, J., Baylis, K., Joshi, N. and Evans, T.P. (2022). Persistence of open-air markets in the food systems of Africa’s secondary cities. Cities 124, 103608. https://doi.org/10.1016/j.cities.2022.103608 CrossRefGoogle Scholar
Haughton, D., Legrand, P. and Woolford, S. (2009). Review of three latent class cluster analysis packages: latent gold, poLCA, and MCLUST. The American Statistician 63, 81–91. https://doi.org/10.1198/tast.2009.0016 Google Scholar
Ilukor, J., Bagamba, F. and Bashaasha, B. (2014). Application of the TOA-MD model to assess adoption potential of improved sweet potato technologies by rural poor farm households under climate change: the case of Kabale district in Uganda. Food Security 6, 359–368. https://doi.org/10.1007/s12571-014-0350-8 Google Scholar
Jaeger, S.R., Jørgensen, A.S., Aaslyng, M.D. and Bredie, W.L.P. (2008). Best–worst scaling: an introduction and initial comparison with monadic rating for preference elicitation with food products. Food Quality and Preference 19, 579–588. https://doi.org/10.1016/j.foodqual.2008.03.002 CrossRefGoogle Scholar
Jenkins, M., Shanks, C.B., Brouwer, R. and Houghtaling, B. (2018). Factors affecting farmers’ willingness and ability to adopt and retain vitamin A-rich varieties of orange-fleshed sweet potato in Mozambique. Food Security [Preprint]. https://doi.org/10.1007/s12571-018-0845-9 Google Scholar
Jin, S., Mansaray, B., Jin, X. and Li, H. (2020). Farmers’ preferences for attributes of rice varieties in Sierra Leone. Food Security 12, 1185–1197. https://doi.org/10.1007/s12571-020-01019-w CrossRefGoogle Scholar
Jogo, W., Bocher, T. and Grant, F. (2021). Factors influencing farmers’ dis-adoption and retention decisions for biofortified crops: the case of orange-fleshed sweetpotato in Mozambique. Agrekon 60, 445–459. https://doi.org/10.1080/03031853.2021.1956555 CrossRefGoogle Scholar
Jones, A.K., Jones, D.L., Edwards-Jones, G. and Cross, P. (2013). Informing decision making in agricultural greenhouse gas mitigation policy: a Best–Worst Scaling survey of expert and farmer opinion in the sheep industry. Environmental Science & Policy 29, 46–56. https://doi.org/10.1016/j.envsci.2013.02.003 CrossRefGoogle Scholar
Kaguongo, W., Ortmann, G., Wale, E., Darroch, M. and Low, J. (2012) ‘Factors influencing adoption and intensity of adoption of orange flesh sweet potato varieties: Evidence from an extension intervention in Nyanza and Western provinces, Kenya’, African journal of agricultural reseearch, 7(3). https://doi.org/10.5897/AJAR11.062 Google Scholar
Kapinga, R., Jeremiah, S., Rwiza, E.J. and Rees, D. (2003). Farmer criteria for selection of sweetpotato varieties. In Rees, D. van Oirschot, Q. and Kapinga, R. (eds), Sweetpotato post-harvest assessment: experiences from East Africa. Chatham, UK: Natural Resources Institute, pp. 9–21.Google Scholar
Karyeija, R.F., Gibson, R.W. and Valkonen, J.P.T. (1998). The significance of sweet potato feathery mottle virus in subsistence sweet potato production in Africa. Plant Disease 82, 4–15. https://doi.org/10.1094/PDIS.1998.82.1.4 Google Scholar
Khoury, C.K., Bjorkman, A.D., Dempewolf, H., Ramirez-Villegas, J., Guarino, L., Jarvis, A., Rieseberg, L.H. and Struik, P.C. (2014). Increasing homogeneity in global food supplies and the implications for food security. Proceedings of the National Academy of Sciences 111(11), 4001–4006. https://doi.org/10.1073/pnas.1313490111 CrossRefGoogle ScholarPubMed
Lagerkvist, C.J. (2013). Consumer preferences for food labelling attributes: comparing direct ranking and best–worst scaling for measurement of attribute importance, preference intensity and attribute dominance. Food Quality and Preference 29, 77–88. https://doi.org/10.1016/j.foodqual.2013.02.005 CrossRefGoogle Scholar
Lancsar, E. and Louviere, J. (2008). Conducting discrete choice experiments to inform healthcare decision making. PharmacoEconomics 26, 661–677. https://doi.org/10.2165/00019053-200826080-00004 CrossRefGoogle ScholarPubMed
Lee, J.A., Soutar, G.N. and Louviere, J. (2007). Measuring values using best-worst scaling: the LOV example. Psychology and Marketing 24, 1043–1058. https://doi.org/10.1002/mar.20197 Google Scholar
Lin, H.-I., Yu, Y.-Y., Wen, F.-I. and Liu, P.-T. (2022). Status of food security in East and Southeast Asia and challenges of climate change. Climate 10, 40. https://doi.org/10.3390/cli10030040 CrossRefGoogle Scholar
Linzer, D.A. and Lewis, J.B. (2011). poLCA: an R package for polytomous variable latent class analysis. Journal of Statistical Software 42(10), 1–29. https://doi.org/10.18637/jss.v042.i10 CrossRefGoogle Scholar
Loko, Y.L.E., Ewedje, E.-E., Orobiyi, A., Djedatin, G., Toffa, J., Gbemavo, C.D.S.J., Tchakpa, C., Gavoedo, D., Sedah, P. and Sabot, F. (2021). On-Farm management of rice diversity, varietal preference criteria, and farmers’ perceptions of the African (Oryza glaberrima Steud.) versus Asian rice (Oryza sativa L.) in the Republic of Benin (West Africa): implications for breeding and conservation. Economic Botany 75, 1–29. https://doi.org/10.1007/s12231-021-09515-6 Google Scholar
Loureiro, M.L. and Dominguez Arcos, F. (2012). Applying Best–Worst Scaling in a stated preference analysis of forest management programs. Journal of Forest Economics 18, 381–394. https://doi.org/10.1016/j.jfe.2012.06.006 Google Scholar
Louviere, J., Lings, I., Islam, T., Gudergan, S. and Flynn, T. (2013). An introduction to the application of (case 1) best–worst scaling in marketing research. International Journal of Research in Marketing 30, 292–303. https://doi.org/10.1016/j.ijresmar.2012.10.002 CrossRefGoogle Scholar
Louviere, J.J. and Woodworth, G.G. (1990) Best–Worst Scaling: A Model for Largest Difference Judgments [working paper]. Edmonton, Canada, University of Alberta, Faculty of Business. Google Scholar
Low, J., Ball, A., Magezi, S., Njoku, J., Mwanga, R., Andrade, M., Tomlins, K., Dove, R. and van Mourik, T. (2017a). Sweet potato development and delivery in sub-Saharan Africa. African Journal of Food, Agriculture, Nutrition and Development 17, 11955–11972. https://doi.org/10.18697/ajfand.HarvestPlus07 CrossRefGoogle Scholar
Low, J., Lynam, J., Lemaga, B., Crissman, C., Barker, I., Thiele, G., Namanda, S., Wheatley, C. and Andrade, M. (2009). Sweetpotato in Sub-Saharan Africa. In The Sweetpotato. Dordrecht: Springer Netherlands, pp. 359–390. https://doi.org/10.1007/978-1-4020-9475-0_16 CrossRefGoogle Scholar
Low, J.W., Arimond, M., Osman, N., Cunguara, B., Zano, F. and Tschirley, D. (2007). Ensuring the supply of and creating demand for a biofortified crop with a visible trait: lessons learned from the introduction of orange-fleshed sweet potato in drought-prone areas of Mozambique. Food and Nutrition Bulletin 28, S258–S270. https://doi.org/10.1177/15648265070282S205 Google Scholar
Low, J.W., Mwanga, R.O.M., Andrade, M., Carey, E. and Ball, A.-M. (2017b). Tackling vitamin A deficiency with biofortified sweetpotato in sub-Saharan Africa. Global Food Security 14, 23–30. https://doi.org/10.1016/j.gfs.2017.01.004 CrossRefGoogle ScholarPubMed
Low, J.W., Ortiz, R., Vandamme, E., Andrade, M., Biazin, B. and Grüneberg, W.J. (2020). Nutrient-dense orange-fleshed sweetpotato: advances in drought-tolerance breeding and understanding of management practices for sustainable next-generation cropping systems in Sub-Saharan Africa. Frontiers in Sustainable Food Systems 4. https://doi.org/10.3389/fsufs.2020.00050 Google Scholar
MAEP (2021) Evolution de la production de la patate douce. Ministère de l’Agriculture, de l’Elevage et de la Pêche (MAEP), Direction de la statistique agricole. Cotonou, Bénin.Google Scholar
Meghani, S.H., Lee, C.S., Hanlon, A.L. and Bruner, D.W. (2009). Latent class cluster analysis to understand heterogeneity in prostate cancer treatment utilities. BMC Medical Informatics and Decision Making 9, 47. https://doi.org/10.1186/1472-6947-9-47 Google Scholar
Mueller, S. and Rungie, C. (2009). Is there more information in best-worst choice data? International Journal of Wine Business Research. Edited by E. Cohen, 21(1), 24–40. https://doi.org/10.1108/17511060910948017 CrossRefGoogle Scholar
Mueller Loose, S. and 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, 230–242. https://doi.org/10.1016/j.foodqual.2012.02.002 CrossRefGoogle Scholar
Mukasa, S.B., Rubaihayo, P.R. and Valkonen, J.P.T. (2003). Incidence of viruses and virus like diseases of sweetpotato in Uganda. Plant Disease 87, 329–335. https://doi.org/10.1094/PDIS.2003.87.4.329 CrossRefGoogle ScholarPubMed
Mwanga, R.O.M., Andrade, M.I., Carey, E.E., Low, J.W., Yencho, G.C. and Grüneberg, W.J. (2017). Sweetpotato (Ipomoea batatas L.). In Genetic Improvement of Tropical Crops. Cham: Springer International Publishing, pp. 181–218. https://doi.org/10.1007/978-3-319-59819-2_6 Google Scholar
Mwanga, R.O.M., Mayanja, S., Swanckaert, J., Nakitto, M., zum Felde, T., Grüneberg, W., Mudege, N., Moyo, M., Banda, L., Tinyiro, S.E., Kisakye, S., Bamwirire, D., Anena, B., Bouniol, A., Magala, D.B., Yada, B., Carey, E., Andrade, M., Johanningsmeier, S.D., Forsythe, L., Fliedel, G. and Muzhingi, T. (2021b). Development of a food product profile for boiled and steamed sweetpotato in Uganda for effective breeding. International Journal of Food Science & Technology 56(3), 1385–1398. https://doi.org/10.1111/ijfs.14792 Google Scholar
Mwanga, R.O.M., Niringiye, C., Alajo, A., Kigozi, B., Namukula, J., Mpembe, I., Tumwegamire, S., Gibson, R.W. and Craig Yencho, G. (2011). “NASPOT 11”, a sweetpotato cultivar bred by a participatory plant-breeding approach in Uganda. HortScience 46, 317–321. https://doi.org/10.21273/HORTSCI.46.2.317 Google Scholar
Mwanga, R.O.M., Swanckaert, J., da Silva Pereira, G., Andrade, M.I., Makunde, G., Grüneberg, W.J., Kreuze, J., David, M., De Boeck, B., Carey, E., Ssali, R.T., Utoblo, O., Gemenet, D., Anyanga, M.O., Yada, B., Chelangat, D.M., Oloka, B., Mtunda, K., Chiona, M., Koussao, S., Laurie, S., Campos, H., Yencho, G.C. and Low, J.W. (2021a). Breeding progress for vitamin A, iron and zinc biofortification, drought tolerance, and sweetpotato virus disease resistance in Sweetpotato. Frontiers in Sustainable Food Systems 5. https://doi.org/10.3389/fsufs.2021.616674 CrossRefGoogle Scholar
Ngailo, S., Shimelis, H.A., Sibiya, J. and Mtunda, K. (2016). Assessment of sweetpotato farming systems, production constraints and breeding priorities in eastern Tanzania. South African Journal of Plant and Soil 33, 105–112. https://doi.org/10.1080/02571862.2015.1079933 CrossRefGoogle Scholar
Nylund, K.L., Asparouhov, T. and Muthén, B.O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal 14, 535–569. https://doi.org/10.1080/10705510701575396 CrossRefGoogle Scholar
Nylund-Gibson, K. and Choi, A.Y. (2018). Ten frequently asked questions about latent class analysis. Translational Issues in Psychological Science 4, 440–461. https://doi.org/10.1037/tps0000176 CrossRefGoogle Scholar
Okello, J.J., Swanckaert, J., Martin-Collado, D., Santos, B., Yada, B., Mwanga, R.O.M., Schurink, A., Quinn, M., Thiele, G., Heck, S., Byrne, T.J., Hareau, G.G. and Campos, H. (2022). Market intelligence and incentive-based trait ranking for plant breeding: a sweetpotato pilot in Uganda. Frontiers in Plant Science 13. https://doi.org/10.3389/fpls.2022.808597 CrossRefGoogle ScholarPubMed
Otoboni, M.E.F., Oliveira, D.J.L.S.F. de, Vargas, P.F., Pavan, B.E. and Andrade, M.I. (2020). Genetic parameters and gain from selection in sweet potato genotypes with high beta-carotene content. Crop Breeding and Applied Biotechnology 20(3). https://doi.org/10.1590/1984-70332020v20n3a42 Google Scholar
Placide, R., Shimelis, H., Laing, M. and Gahakwa, D. (2015). Farmers’ perceptions, production and productivity constraints, preferences, and breeding priorities of sweetpotato in Rwanda. HortScience 50, 36–43. https://doi.org/10.21273/HORTSCI.50.1.36 CrossRefGoogle Scholar
Rahmawati, N., Sipayung, R. and Widya, R. (2021). Analysis of yields quantity and quality of several sweet potatoes genotypes at different harvest ages. IOP Conference Series: Earth and Environmental Science 782, 042047. https://doi.org/10.1088/1755-1315/782/4/042047 Google Scholar
R core team (2022). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.r-project.org/.Google Scholar
Sakolwitayanon, H., Soni, P. and Damien, J. (2018). Attributes determining consumer preference for organic rice in Bangkok, Thailand. British Food Journal 120, 2017–2032. https://doi.org/10.1108/BFJ-12-2017-0667 CrossRefGoogle Scholar
Sanoussi, A.F., Dansi, A., Orobiyi, A., Gbaguidi, A., Agre, A.P., Dossou-Aminon, I. and Sanni, A. (2016). Ethnobotany, landraces diversity and potential vitamin A rich cultivars of sweet potato (Ipomoea batatas (L.] Lam.) in southern and central Benin. Genetic Resources and Crop Evolution 64, 1431–1449. https://doi.org/10.1007/S10722-016-0447-3 Google Scholar
Sarkodie-addo, J. (2017). Effect of vine cutting length and potassium fertilizer rates on sweet potato growth and yield components effect of vine cutting length and potassium fertilizer rates on sweet potato growth and yield components. International Journal of Agriculture and Forestry 7(September), 88–94. https://doi.org/10.5923/j.ijaf.20170704.02 Google Scholar
Sebastiani, S.K., Mgonja, A., Urio, F. and Ndondi, T. (2006). Response of sweetpotato (Ipomoea batatas) to application of nitrogen and phosphorus fertilizers in the northern highlands of Tanzania. Acta Horticulturae 703, 219–224. https://doi.org/10.17660/ActaHortic.2006.703.27 CrossRefGoogle Scholar
Shumbusha, D., Ndirigwe, J., Kankundiye, L., Musabyemungu, A. and Mwanga, R.O.M. (2015). Development of dual-purpose sweetpotato varieties through participatory breeding in Rwanda. In Potato and Sweetpotato in Africa: Transforming the Value Chains for Food and Nutrition Security. UK: CABI, pp. 79–87. https://doi.org/10.1079/9781780644202.0079 Google Scholar
Shumbusha, D., Shimelis, H., Laing, M. and Rukundo, P. (2020). Assessment of the roles and farmer-preferred traits of sweetpotato in a crop-livestock farming system in Rwanda: implications for breeding dual-purpose varieties. Open Agriculture 5, 834–843. https://doi.org/10.1515/opag-2020-0082 Google Scholar
Sohindji, F.S., Adje, O.A.C., Fassinou Hotegni, V.N., Fanou Fogny, N., Akponikpe, T., Quenum, F. and Achigan-Dako, E.G. (2022). Orange-fleshed sweetpotato production: progress and perspectives for value chain development in West Africa. JSFA Reports 2(5), 198–207. https://doi.org/10.1002/jsf2.42 CrossRefGoogle Scholar
Stanco, M., Lerro, M. and Marotta, G. (2020). Consumers’ preferences for wine attributes: a best-worst scaling analysis. Sustainability 12, 2819. https://doi.org/10.3390/su12072819 CrossRefGoogle Scholar
Tairo, F., Mneney, E. and Kullaya, A. (2008). Morphological and agronomical characterization of sweet potato [Ipomoea batatas (L.) Lam.] germplasm collection from Tanzania. African Journal of Plant Science 2, 077–085. https://doi.org/10.5897/AJPS.9000173 Google Scholar
Tairo, F., Mukasa, S.B., Jones, R.A.C., Kullaya, A., Rubaihayo, A.R. and Valkonen, J.P.T. (2005). Unravelling the genetic diversity of the three main viruses involved in Sweet Potato Virus Disease (SPVD), and its practical implications. Molecular Plant Pathology 6, 199–211. https://doi.org/10.1111/j.1364-3703.2005.00267.x Google Scholar
Tatar, O., Haward, B., Zhu, P., Griffin-Mathieu, G., Perez, S., Zimet, G. and Rosberger, Z. (2022). Using Best-Worst Scaling to investigate younger adult Canadians’ preferences for COVID-19 vaccination and public health measures: an observational study. Preventive Medicine Reports 26, 101755. https://doi.org/10.1016/j.pmedr.2022.101755 Google Scholar
Umberger, W.J., Stringer, R. and Mueller, S.C. (2010). Using best-worst scaling to determine market channel choice by small farmers in Indonesia. In Proceedings of the Agricultural and Applied Economics Association Annual Meeting, July 25-27, 2010, Denver, Colorado, USA, p. 27. https://doi.org/10.22004/ag.econ.90853.Google Scholar
Valin, H., Sands, R.D., van der Mensbrugghe, D., Nelson, G.C., Ahammad, H., Blanc, E., Bodirsky, B., Fujimori, S., Hasegawa, T., Havlik, P., Heyhoe, E., Kyle, P., Mason-D’Croz, D., Paltsev, S., Rolinski, S., Tabeau, A., van Meijl, H., von Lampe, M. and Willenbockel, D. (2014). The future of food demand: understanding differences in global economic models. Agricultural Economics 45, 51–67. https://doi.org/10.1111/agec.12089 CrossRefGoogle Scholar
Vermunt, J.K. (2002). Latent class analysis of complex sample survey data: application to dietary data. Journal of the American Statistical Association 97, 736–737.Google Scholar
Williams, G.A. and Kibowski, F. (2016). Latent class analysis and latent profile analysis. In Handbook of Methodological Approaches to Community-Based Research, pp. 143–152. https://doi.org/10.1093/med:psych/9780190243654.003.0015 Google Scholar
Xiahong, H., Yan, S., Dong, G., Fugang, W., Lei, P., Cunwu, G., Ruzhi, M., Yong, X., Chengyun, L. and Youyong, Z. (2011). Comparison of Agronomic Traits between Rice Landraces and Modern Varieties at Different Altitudes in the Paddy Fields of Yuanyang Terrace, Yunnan Province, 2, pp. 46–50. https://doi.org/10.3969/J.ISSN.1674-764X.2011.01.007 Google Scholar
Yanfu, Y., Jialan, T., Yunchu, Z. and Ruilian, Q. (1989). Breeding for early-maturing sweet potato varieties. In Mackay, K.T., Palomar, M.K. and Sanico, R.T. (eds), Sweet Potato Research and Development for Small Farmers. Laguna, The Philippines: SEAMEO-SEARCA, pp. 67–82.Google Scholar
Yeh, C.-H., Hartmann, M. and Langen, N. (2020). The role of trust in explaining food choice: combining choice experiment and attribute best–worst scaling. Foods 9, 45. https://doi.org/10.3390/foods9010045 Google Scholar
Yong’an, L., Quanwen, D., Zhiguo, C. and Deyong, Z. (2010). Effect of drought on water use efficiency, agronomic traits and yield of spring wheat landraces and modern varieties in Northwest China. African Journal of Agricultural Research 5, 1598–1608. https://doi.org/10.5897/AJAR.9000205 Google Scholar
Zawedde, B.M., Harris, C., Alajo, A., Hancock, J. and Grumet, R. (2014). Factors influencing diversity of farmers’ varieties of sweet potato in Uganda: implications for conservation. Economic Botany 68, 337–349. https://doi.org/10.1007/s12231-014-9278-3 CrossRefGoogle Scholar
Supplementary material: File

Ahoudou et al. supplementary material

Tables S1-S5

Download Ahoudou et al. supplementary material(File)
File 36.9 KB