Influence of Information and Crop Management Practices on Productivity among Smallholder Potato Farmers in North Rift Kenya
Abstract
Potato (Solanum tuberosum L) tuber, is a major food whose demand is increasing worldwide. Its value-chain in Kenya generates employment for approximately 800,000 farmers and 3.3M citizens. Nonetheless, in spite of dissemination of appropriate technologies, innovations, and management practices (TIMPs), Kenya’s productivity has persisted at lows of 9-15t/Ha compared to Netherland’s 36-42t/Ha. Implementation of field-specific decision support system (DSS) has been proposed as a possible intervention. However, no studies exist showing the influence of prevailing information management (IM) practices on crop management practices. Therefore, in the context of precision agriculture (PA) and the theory of the firm, this study sought to assess, subject to farming duration, the influence of information sources on crop spacing and resultant effect on productivity among smallholder potato farmers in Kenya’s North-Rift highlands. Using stratified random sampling, a survey was conducted on 353 households of whom potato was the main crop and whose farms were located at least 2300masl. Descriptive statistics, linear regression and post-estimation data analysis techniques were employed. Extension services, radio, farmer groups, Internet and telephone usage stood at 62%, 45%, 18%, 6% and 3% respectively while 11% possessed an email address. Regardless of farming duration, in decreasing order, radio and Internet (implicitly) showed positive aggregate influence while farmer groups and extension services showed negative aggregate influence. Notably, all information sources were significantly associated with ‘not known’ seed spacing response with corresponding significant negative effect on productivity. The results demonstrate poor IM and imprecise crop management practices thereby validating the necessity for entrenchment field-specific DSS
References
Anderson, J. R. (2007). Background paper for the World Development Report 2008. Washington, DC: Agricultural and Rural Development Department, World Bank.
Arnott, D., & Pervan, G. (2005). A Critical Analysis of Decision Support Systems Research. Journal of Infromation Technology, 20(2), 67 - 85.
Baba, V. V., & HakemZadeh, F. (2012). Towards a Theory of evidence Based Decision Making. Management Decision, 50(5), 832 - 867.
Bhanumurthy, K. V. (2002). Arguing A Case for the Cobb-Douglas Production Function. Review of Commerce Studies, 75 - 91.
Country Government of Uasin Gishu. (2018). County Intergrated Development Plan (CIDP) 2018 - 2022. Eldoret: Country Government of Uasin Gishu.
FAO. (2018). The future of food and agriculture – Alternative pathways to 2050. Rome. Retrieved 12 23, 2021, from https://www.fao.org/: https://www.fao.org/global-perspectives-studies/food-agriculture-projections-to-2050/en/
FAO. (2020). http://www.fao.org/faostat/en/#data/QC/visualize. Retrieved from http://www.fao.org/: http://www.fao.org/faostat/en/#data/QC/visualize
Ferroni, M., & Zhou, Y. (2012). Achievements and Challenges in Agricultural Extension in India. Global Journal of Emerging Market Economies, 4(3) 319–346.
Gujarati, N. D., & Porter, C. D. (2009). Basic Econometrics (5th ed.). New York: McGraw-Hill/ Irwin.
KIPPRA. (2019). Education and Training Budget Brief. Naiorbi: KIPPRA. Retrieved from https://www.unicef.org/esa/sites/unicef.org.esa/files/2019-03/UNICEF-Kenya-2018-Education-Budget-Brief.pdf
Malmgren, H. B. (1961). Information, Expectations and the Theory of the Firm. The Quarterly Journal of Economics, 75(3), 399-421. Retrieved from http://www.jstor.org/stable/1885131
Mansouri, S. A., Gallear, D., & Askariazad, M. (2012). Decision Support for Build-to-Order Supply Chain Management Through Multiobjective Optimization. International Journal of Production Economics, 135(1), 24 - 36. doi:http://dx.doi.org/10.1016/j.ijpe.2010.11.016
McBratney, A., Whelan, B., & Ancev, T. (2005). Future Directions of Precision Agriculture. 7th International Conference on Precision Agriculture. 6, pp. 7 - 23. Minneapolis, USA: Springer Science+Business Media Inc.
Mittal, S., Gandhi, S., & Tripathi, G. (2010). Socio-economic Impact of Mobile Phone on Indian Agriculture. (p. Working Paper no. 246). New Delhi: International Council for Research on International Economic Relations.
MoALF. (2016). The National Potato Strategy. Nairobi: Agricultural Information Resource Center.
MoALF. (2018, April 15). Climate Risk Profile for Elgeyo Marakwet County, Kenya County Climate Risk Profile Series. Nairobi: The Kenya Ministry of Agriculture, Livestock and Fisheries (MoALF). Retrieved from https://cgspace.cgiar.org/: https://cgspace.cgiar.org/bitstream/handle/10568/96285
Nenkari, H., Gakuru, M., Mulagoli, I., & Kabutha, C. (2010). http://iaald2010.agropolis.fr/proceedings/poster/NENKARI-2010-Extension_services_through_Mobile_telephony_and_internet-IAALD-Congress-158.pdf. Retrieved from http://iaald2010.agropolis.fr/: http://iaald2010.agropolis.fr/proceedings/poster/NENKARI-2010-
Extension_services_through_Mobile_telephony_and_internet-IAALD-Congress-158.pdf
Obare, A. G., Nyagaka, D. O., Omiti, J. M., & Nguyo, W. (2010). Technical Efficiency in Resource Use: Evidence from Smallholder Irish Potato Farmers in Nyandarua North District, Kenya. African Journal of Agricultural Research Vol. 5(11), pp. 1179-1186, 4 June, 2010, 5(11), 1179 - 1186. Retrieved from http://www.academicjournals.org/AJAR
Raabe, K. (2008). Reforming the agricultural extension system in India—What do we know about what works where and why? Department Strategy and Governance Division, IFPRI. (p. IFPRI Discussion Paper 00775. ). Washington, DC.: Department Strategy and Governance Division, IFPRI.
Stafford, J. V. (2000). Implementing Precision Agriculture in the 21st Centrury. Journal of Agricultural Engineering Research, 76, 267 - 275.
Taechatanasat, P., & Armstrong, L. (2014). Decision Support System Data for Farmer Decision Making. Proceedings of Asian Federation for Information Technology in Agriculture, (pp. 472 - 486). Perth, W.A. Australia. Retrieved from https://ro.ecu.edu.au/ecuworkspost2013/855
Turban, E., Aronson, J. E., & LIang, T. P. (2005). Decision Support Systems and Intelligent Systems (7th ed.). New Jersey, USA: Pearson Prentice Hall.
Vos, J. (1992). A Case History: Hundred Years of Potato Production in Europe With Special Reference to the Netherlands. American Potato Journal, 69, 731 - 751. doi:10.1007/BF02853816
Wang'ombe, J. G., & Djik, M. P. (2013). Low Potato Yields in Kenya: Do Conventional Input Innovations Account for the Yields Disparity? Agriculture & Food Security 2013, 2 - 14. Retrieved from http://www.agricultureandfoodsecurity.com/content/2/1/14
Whelan, B. M., McBratney, A. B., & Boydell, B. C. (1997). The Impact of Precision Agriculture. Proceedings of the ABARE Outlook Conference, 'The Future of Cropping in NW NSW, (p. 5). Moree, UK.
Yamane, T. (1967). Statistics: An Introductory Analysis (2nd ed.). New York: Harper and Row.
Zhang, N., Wang, M., & Wang, N. (2002). Precision Agriculture - a Worldwide Overview. Computers and Electronics in Agriculture, 36, 113 - 132.
