Correo Electrónico
DNINFOA - SIA
Bibliotecas
Convocatorias
Identidad U.N.
Published
Agricultural infrastructure as the driver of emerging farmers’ income in South Africa. A stochastic frontier approach
La infraestructura agrícola como factor clave en los ingresos de los agricultores emergentes en Sudáfrica. Una aproximación de frontera estocástica
DOI:
https://doi.org/10.15446/agroncolomb.v38n2.81292Keywords:
agricultural income, smallholder farmer, infrastructure availability, infrastructure accessibility (en)renta agraria, pequeño agricultor, disponibilidad de infraestructura, accesibilidad a la infraestructura (es)
Downloads
A stochastic frontier model was applied to cross-sectional data to examine whether availability and accessibility of agricultural infrastructure for emerging farmers enhance their agricultural income through efficiency gains. Using a stratified sampling approach, the study grouped the farmers into two; those who had agricultural infrastructure and those who did not have it. Through a survey, data collected from a sample of 150 smallholder farmers in the study area were analyzed using the frontier model. The explanatory variables that were statistically significant and which influenced the agricultural income of the emerging farmers in the study area included the following: equipment, social, institutional availability and physical accessibility indices, education, access to agricultural extension services, age of farmers, assistance of household members in farming, membership in farmers’ organizations, and marital status of the farmers. Informed policies aimed at improving the income of smallholder farmers might consider the results of the explanatory variables included in this study.
Se aplicó un modelo de frontera estocástica a los datos de corte transversal para examinar si la disponibilidad y accesibilidad de la infraestructura agrícola para los agricultores emergentes incrementa su ingreso agrícola a través del aumento de la eficiencia. Utilizando un enfoque de muestreo estratificado, el estudio agrupó a los agricultores en dos; los que tenían la infraestructura agrícola y los que no la tenían. A través de una encuesta, los datos recolectados de una muestra de 150 pequeños agricultores en el área de estudio fueron analizados
utilizando el modelo de frontera. Las variables explicativas que fueron estadísticamente significativas y que influyeron en el ingreso agrícola de los agricultores emergentes en el área de estudio incluyen: equipo, índices de disponibilidad social,
institucional y de accesibilidad física, educación, acceso a servicios de extensión agrícola, edad de los agricultores, asistencia de los miembros del hogar en agricultura, membrecía de organizaciones de agricultores y estado civil de los agricultores. Las políticas informadas dirigidas a mejorar los ingresos de los pequeños agricultores pueden considerar los resultados de las variables explicativas incluidas en el estudio.
References
Abdul-Hanan, A. and A. Abdul-Rahaman. 2017. Technical efficiency of maize farmers in Ghana: a stochastic frontier approach. Int. J. Sci. 29(2), 110-118.
Adepoju, A.A. and K.K. Salman. 2013. Increasing agricultural productivity through rural infrastructure: evidence from Oyo and Osun States, Nigeria. Int. J. Appl. Agric. Apic. Res. 9(1-2), 1-10.
Akankwasa, K., G. Ortmann, and E. Wale. 2015. Early-stage adoption of improved banana “Matooke” hybrids in Uganda: a count data analysis based on farmers’ perceptions. Int. J. Innov. Technol. Manag. 13(1), 1-26. Doi: 10.1142/S0219877016500012
Andersen, P. and S. Shimokawa, 2007. Rural infrastructure and agricultural development. pp. 175-203. In: Bourguignon, F. and B. Pleskovic (eds.). Proceedings of the annual world bank conference on development economics. 2006, May 29-30, Tokyo, Japan. New York, The World Bank.
Anderson, K. and W.A. Masters. 2007. Distortions to agricultural incentives in Africa. Agricultural distortions working paper 56. The World Bank, Washington. Doi: 10.1596/978-0-8213-7652-2
Antwi, M.A. and O.I. Oladele. 2013. Impact of the land redistribution for agricultural development (LRAD) projects on livelihoods of beneficiaries in Ngaka Modiri Molema District, South Africa. J. Hum. Ecol. 42(3), 273-281. Doi: 10.1080/09709274.2013.11906601
Barro, R.J. 2001. Human capital and growth. Am. Econ. Rev. 91(2), 2-17. Doi: 10.1257/aer.91.2.12
Battese, G.E. and T.J. Coelli. 1995. A model for technical inefficiency effect in stochastic frontier production for panel data. Empir. Econ. 20(2), 325-332. Doi: 10.1007/BF01205442
Binam, J., J. Tonyè, N. Wandji, G. Nyambi, and M. Akoa. 2004. Factors affecting the technical efficiency among smallholder farmers in the slash and burn agriculture zone of Cameroon. Food Policy 29(5), 531-545. Doi: 10.1016/j.foodpol.2004.07.013
Birner, R., K. Davis, J. Pender, E. Nkonya, P. Anandajayasekeram, J. Ekboir, A. Mbabu, D.J. Spielman, D. Horna, S. Benin, and M. Cohen. 2009. From best practice to best fit: a framework for designing and analyzing pluralistic agricultural advisory services worldwide. J. Agr. Educ. Ext. 15(4), 341-355. Doi: 10.1080/13892240903309595
Bom, P.R.D. and J.E. Ligthart. 2014. Public infrastructure investment, output dynamics, and balanced budget fiscal rules. J. Econ. Dyn. Control 40, 334-354. Doi: 10.1016/j.jedc.2014.01.018
Byerlee, D., A. de Janvry, and E. Sadoulet. 2009. Agriculture for development: toward a new paradigm. Annu. Rev. Resour. Econ. 1, 15-31. Doi: 10.1146/annurev.resource.050708.144239
Cloete, P. 2010. Economic growth and development through agriculture: the case of the North West Province of South Africa. PhD dissertation, University of the Free State, Free Sate, South Africa. URL: http://scholar.ufs.ac.za:8080/xmlui/handle/11660/722 (accessed 24 July 2016).
DAFF. 2017. Economic review of the South African agriculture. URL: http://www.daff.gov.za/Daffweb3/Portals/0/Statistics%20and%20Economic%20Analysis/Economic%20Analysis/Economic%20Review%202016.pdf (accessed 2 May 2017).
DBSA. 1997. Operational information. Development information business unit, DBSA, Midrand, South Africa.
Eke, I.C. and J.A.L. Effiong. 2016. The effects of capital accumulation on crop production output in Nigeria. Int. J. Agric. Earth. Sci. 2(3), 62-81. URL: https://iiardpub.org/get/IJAES/VOL.%202%20NO.%203%202016/THE%20EFFECTS.pdf (accessed 14 September 2018).
Fakayode, B.S., O.A. Omotesho, A.B. Tsoho, and P.D. Ajayi. 2008. An economic survey of rural Infrastructures and agricultural productivity profiles in Nigeria. Eur. J. Soc. Sc. 7(2), 158-171.
Ferreira, T. 2015. Does education enhance productivity in smallholder agriculture? Causal evidence from Malawi. Stellenbosch working paper series no. WP05/2018. URL: https://resep.sun.ac.za/does-education-enhance-productivity-in-smallholderagriculture-causal-evidence-from-malawi/ (accessed 11 June 2019).
Frost, L., L.S. Jenkins, and B. Emmink. 2017. Improving access to health care in a rural regional hospital in South Africa: why do patients miss their appointments? Afr. J. Prim. Health Care Fam. Med. 9(1). Doi: 10.4102/phcfm.v9i1.1255
Ghosal, S. 2014. Soft or hard: infrastructure matters in rural economic empowerment. J. Infrastruct. Dev. 5(2), 137-149. Doi: 10.1177/0974930614521318
Harding, W.R., C.G.M. Archibald, and J.C. Taylor. 2005. The relevance of diatoms for water quality assessment in South Africa: a position paper. Water SA 31(1), 41-46. Doi: 10.4314/wsa.v31i1.5119
Headey, D., D.S.P. Rao, and M. Alauddin. 2005. Explaining agricultural productivity levels and growth: an international perspective. University of Queensland, School of economics, St. Lucia, Australia.
Idiong, I.C. 2007. Estimation of farm level technical efficiency in small-scale swamp rice production in Cross River state Nigeria: a Stochastic Frontier approach. World J. Agric. Res. 3(5), 653-658.
Jayne, T.S., D. Mather, and E. Mghenyi. 2010. Principal challenges confronting smallholder agriculture in Sub-Saharan Africa. World Dev. 38(10), 1384-1398. Doi: 10.1016/j.worlddev.2010.06.002
Jouanjean, M.A. 2013. Targeting infrastructure development to foster agricultural trade and market integration in developing countries: an analytical review. Overseas Development Institute, London. URL: https://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/8557.pdf (accessed 13 June 2018).
Khapayi, M. and P. Celliers. 2016. Factors limiting and preventing emerging farmers to progress to commercial agricultural farming in the King William’s Town area of the Eastern Cape Province, South Africa. S. Afr. J. Agric. Ext. 44(1), 25-41. Doi: 10.17159/2413-3221/2016/v44n1a374
Kokkinou, A. 2010. A note on theory of productive efficiency and Stochastic Frontier models. Eur. Res. Stud. 13(4),109-118. Doi: 10.35808/ersj/302
Kumar, V., K.G. Wankhede, and H.C. Gena. 2015. Role of cooperatives in improving livelihood of farmers on sustainable basis. Am. Educ. Res. J. 3(10), 1258-1266. Doi: 10.12691/education-3-10-8
Llanto, G.M. 2012. The impact of infrastructure on agricultural productivity. Discussion paper series No. 2012-12. Philippine Institute for Development Studies. Makati, Philippines. URL: https://ideas.repec.org/p/phd/dpaper/dp_2012-12.html (accessed 21 July 2018).
Mango, N., C. Makate, B. Hanyani-Mlambo, S. Siziba, and M. Lundy. 2015. A Stochastic Frontier analysis of technical efficiency in smallholder maize production in Zimbabwe: the post-fasttrack land reform outlook. Cogent Econ. Finance 3(1), 117-189. Doi: 10.1080/23322039.2015.1117189
Manjunath, S. and E. Kannan. 2017. Effects of rural infrastructure on agricultural development: a district level analysis in Karnataka, India. J. Infrastruct. Dev. 9(2), 113-126. Doi: 10.1177/0974930617732258
Masunda, S. and A.R. Chiweshe. 2015. A stochastic frontier analysis on farm level technical efficiency in Zimbabwe: a case of Marirangwe smallholder dairy farmers. J. Dev. Agric. Econ. 7(6), 237-243. Doi: 10.5897/JDAE2014.0630
Montshwe, D.B. 2006. Factors affecting participation in mainstream cattle markets by smallholder cattle farmers in South Africa. MSc Thesis, University of Free State, Free State, South Africa.
Munyanyi, W. 2013. Agricultural infrastructure development imperative for sustainable food production: a Zimbabwean perspective. Russ. J. Agric. Soc. Econ. Sci. 12(2), 13-21. Doi: 10.18551/rjoas.2013-12.02
Nadeem, N., K. Mushtaq, and M.I. Javed. 2011. Impact of social and physical infrastructure on agricultural productivity in Punjab, Pakistan - A production function approach. Pak. J. Life Soc. Sci. 9(2), 153-158.
Oduro-Ofori, E., A.P. Aboagye, and N.A.E. Acquaye. 2014. Effects of education on the agricultural productivity of farmers in the Offinso Municipality. Int. J. Dev. Res. 4(9), 1951-1960. URL: https://www.journalijdr.com/sites/default/files/issue-pdf/1839.pdf (accessed 06 June 2020).
Rosegrant, M.W. and P. Hazell. 2000. Transforming the rural Asian economy: the unfinished revolution. Oxford University Press, Hong Kong.
Saiyut, P., I. Bunyasiri, P. Sirisupluxana, and I. Mahathanaseth. 2018. The impact of age structure on technical efficiency in Thai agriculture. Kasetsart J. Soc. Sci. 39(3), 1-7. Doi: 10.1016/j.kjss.2017.12.015
Satish, P. 2006. Rural infrastructure and growth. Proceedings of the 66th Annual Conference of the Indian Society of Agricultural Economics. 2006, November 8-10. Umiam (Barapani), Meghalaya, India.
Statistics South Africa. 2012. Statistical release P0301.4, Census 2011. Embargoed until: 30 October 2012 10:00. Statistics South Africa, Pretoria.
Stilwell, T. and M.N. Makhura. 2004. Rural infrastructure development. Paper presented during DBSA Knowledge Week. 2004, November 1-5. Midrand, South Africa.
Tripathi, A. and A.R. Prasad. 2009. Agricultural development in India since independence: a study on progress, performance, and determinants. Journal of Emerging Knowledge on Emerging Markets 1(1), 63-91. Doi: 10.7885/1946-651X.1007
Vyas, S. and L. Kumaranayake. 2006. Constructing socio-economic status indices: how to use principal components analysis. Health Policy Plann. 21(6), 459-468. Doi: 10.1093/heapol/czl029
Willemse, J. 2000. Vyfjaarplan nodig vir landbouhulp. Landbou Weekblad 17 March 2000.
How to Cite
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
CrossRef Cited-by
1. Joko Sutrisno, Eddy Tri Haryanto, Puji Harsono, Bambang Pujiasmanto, Reismaya Wanamertan Nugroho. (2023). The improvement of horticultural villages sustainability in Central Java Province, Indonesia. Open Agriculture, 8(1) https://doi.org/10.1515/opag-2022-0222.
2. Adderly Mamani-Flores, David Eleazar Barra-Quispe, Tania Laura Barra-Quispe. (2022). Gestión de ecosistemas de agricultura orgánica en tiempos de Covid-19. Revista Venezolana de Gerencia, 27(97), p.144. https://doi.org/10.52080/rvgluz.27.97.10.
3. Gordon Gilja, Neven Kuspilić, Martina Lacko, Davor Romić. (2023). Reconstruction of Recharge and Discharge Pattern in the Polder Drainage Canal Network. Hydrology, 10(3), p.60. https://doi.org/10.3390/hydrology10030060.
4. Alexandra Pliakoura, Grigorios N. Beligiannis, Athanasia Mavrommati, Achilleas Kontogeorgos. (2024). Strengths and weaknesses for the young farmers to abide in the Greek countryside: a triangulation approach. Journal of Agribusiness in Developing and Emerging Economies, 14(4), p.733. https://doi.org/10.1108/JADEE-10-2022-0235.
5. Batizi Serote, Salmina Mokgehle, Grany Senyolo, Christian du Plooy, Samkelisiwe Hlophe-Ginindza, Sylvester Mpandeli, Luxon Nhamo, Hintsa Araya. (2023). Exploring the Barriers to the Adoption of Climate-Smart Irrigation Technologies for Sustainable Crop Productivity by Smallholder Farmers: Evidence from South Africa. Agriculture, 13(2), p.246. https://doi.org/10.3390/agriculture13020246.
Dimensions
PlumX
Article abstract page views
Downloads
License
Copyright (c) 2020 Agronomía Colombiana
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
© Centro Editorial de la Facultad de Ciencias Agrarias, Universidad Nacional de Colombia
Reproduction and quotation of material appearing in the journal is authorized provided the following are explicitly indicated: journal name, author(s) name, year, volume, issue and pages of the source. The ideas and observations recorded by the authors are their own and do not necessarily represent the views and policies of the Universidad Nacional de Colombia. Mention of products or commercial firms in the journal does not constitute a recommendation or endorsement on the part of the Universidad Nacional de Colombia; furthermore, the use of such products should comply with the product label recommendations.
The Creative Commons license used by Agronomia Colombiana journal is: Attribution - NonCommercial - ShareAlike (by-nc-sa)
Agronomia Colombiana by Centro Editorial of Facultad de Ciencias Agrarias, Universidad Nacional de Colombia is licensed under a Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional License.
Creado a partir de la obra en http://revistas.unal.edu.co/index.php/agrocol/.
