IDEAS home Printed from https://ideas.repec.org/p/ags/aaae23/364802.html
   My bibliography  Save this paper

Quantifying the smallholder farmers’ choice of sustainable agricultural practices and productivity in the context of climate change- Evidence from maize based farming households in Nigeria

Author

Listed:
  • Kolapo, Adetomiwa
  • Lasisi, Abisoye Lukman

Abstract

In this study, we examine the impact of adoption of sustainable agricultural practices on productivity of maize farmers in Nigeria using multivariate probit model and endogeneity corrected frontier model. Our study is motivated by the extant literature showing the sensitivity of Nigeria’s agricultural sector to the impact of climate change and the subsequent effect on smallholder farmers. Hence, our contribution to the literature is in four folds. One, we adopt a novel estimation technique, endogeneity corrected stochastic frontier model, which controls for biasedness and inconsistency in the effect of adoption of sustainable practices while accounting for the endogeneity of both frontiers and the inefficiency variable. Two, we establish that variables such as age of the household head, gender, farming experience, farm size, years of formal education, membership of association, access to extension service, location and access to credit significantly impact the farmers’ choices and adoption of sustainable agricultural practices. Three, we find that quantity of farm size, seed, fertilizer, the interaction of labour with farm size, herbicides and fertilizer, and the interaction of farm size with seed, herbicides and fertilizers contain significant predictive content for the efficiency of maize production in Nigeria. Finally, we show that technical inefficiency is a function of age, gender, farming experience, membership in association, access to extension services, access to credit, mean annual rainfall and sustainable agricultural practices adoption index. We therefore conclude, that adoption of sustainable agricultural practices by the maize farmers will reduce the negative impact of climate change and increase the efficiency maize production in Nigeria. The findings of our study have important implications for government and investors in the agricultural sector.

Suggested Citation

  • Kolapo, Adetomiwa & Lasisi, Abisoye Lukman, 2023. "Quantifying the smallholder farmers’ choice of sustainable agricultural practices and productivity in the context of climate change- Evidence from maize based farming households in Nigeria," 2023 Seventh AAAE/60th AEASA Conference, September 18-21, 2023, Durban, South Africa 364802, African Association of Agricultural Economists (AAAE).
  • Handle: RePEc:ags:aaae23:364802
    DOI: 10.22004/ag.econ.364802
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/364802/files/17.%20Sustainable%20practices%20in%20Nigeria.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.364802?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    2. David R. Lee, 2005. "Agricultural Sustainability and Technology Adoption: Issues and Policies for Developing Countries," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(5), pages 1325-1334.
    3. Tegbaru, Amare & Menkir, Abebe & Nasser Baco, Mohamed & Idrisou, Latifou & Sissoko, Dioukou & Eyitayo, Ayinde O. & Abate, Tsedeke & Tahirou, Abdoulaye, 2020. "Addressing gendered varietal and trait preferences in West African maize," World Development Perspectives, Elsevier, vol. 20(C).
    4. T. O Ojo & L.J. S Baiyegunhi & A. O Salami, 2019. "Impact of Credit Demand on the Productivity of Rice Farmers in South West Nigeria," Journal of Economics and Behavioral Studies, AMH International, vol. 11(1), pages 166-180.
    5. Abdul-Rahaman, Awal & Abdulai, Awudu, 2018. "Do farmer groups impact on farm yield and efficiency of smallholder farmers? Evidence from rice farmers in northern Ghana," Food Policy, Elsevier, vol. 81(C), pages 95-105.
    6. Frikkie Mare & Yonas T. Bahta & Walter Van Niekerk, 2018. "The impact of drought on commercial livestock farmers in South Africa," Development in Practice, Taylor & Francis Journals, vol. 28(7), pages 884-898, October.
    7. Menale Kassie & Precious Zikhali & Kebede Manjur & Sue Edwards, 2009. "Adoption of sustainable agriculture practices: Evidence from a semi‐arid region of Ethiopia," Natural Resources Forum, Blackwell Publishing, vol. 33(3), pages 189-198, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohammed, Sadick & Abdulai, Awudu, 2021. "Extension Participation and Improved Technology Adoption: Impact on Efficiency and Welfare of Farmers in Ghana," 2021 Conference, August 17-31, 2021, Virtual 315362, International Association of Agricultural Economists.
    2. Lim, Krisha & Wichmann, Bruno & Luckert, Martin, 2021. "Adaptation, spatial effects, and targeting: Evidence from Africa and Asia," World Development, Elsevier, vol. 139(C).
    3. Mamiit, Rusyan Jill & Yanagida, John & Villanueva, Donald, 2020. "Farm locations and dwelling clusters: Do they make production and technical efficiency spatially contagious?," Food Policy, Elsevier, vol. 92(C).
    4. Pham, Huong-Giang & Chuah, Swee-Hoon & Feeny, Simon, 2021. "Factors affecting the adoption of sustainable agricultural practices: Findings from panel data for Vietnam," Ecological Economics, Elsevier, vol. 184(C).
    5. Jacob Asravor & Francis Tsiboe & Richard K. Asravor & Alexander N. Wiredu & Manfred Zeller, 2024. "Technology and managerial performance of farm operators by age in Ghana," Journal of Productivity Analysis, Springer, vol. 61(3), pages 279-303, June.
    6. Bravo-Ureta, Boris E. & Higgins, Daniel & Arslan, Aslihan, 2020. "Irrigation infrastructure and farm productivity in the Philippines: A stochastic Meta-Frontier analysis," World Development, Elsevier, vol. 135(C).
    7. Marcos A. Lastiri-Hernández & D. Álvarez-Bernal & R. Moncayo-Estrada & G. Cruz-Cárdenas & J. T. Silva García, 2021. "Adoption of phytodesalination as a sustainable agricultural practice for improving the productivity of saline soils," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 8798-8814, June.
    8. Kuhle Prudence Mnisi & Abdul Latif Alhassan, 2021. "Financial structure and cooperative efficiency: A pecking‐order evidence from sugarcane farmers in Eswatini," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 92(2), pages 261-281, June.
    9. Elias, Hailu, 2019. "Impact of Credit Constraints on Agricultural Productivity in the face of Climate Variability: Panel Data Evidence from Rural Ethiopia," Ethiopian Journal of Economics, Ethiopian Economics Association, vol. 28(02), October.
    10. Ayobami Adetoyinbo & Verena Otter, 2022. "Can producer groups improve technical efficiency among artisanal shrimpers in Nigeria? A study accounting for observed and unobserved selectivity," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-33, December.
    11. Adetomiwa Kolapo & Akeem Abiade Tijani & Seyi Olalekan Olawuyi & Adeyera James Kolapo & Temitope Oluwaseun Ojo & Nolwazi Zanele Khumalo & Khalid. Mohamed Elhindi & Hazem Kassem, 2025. "Psychological perspectives on smallholder farmers' choice of climate change adaptation strategies and productivity nexus in Southwest, Nigeria," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 71(4), pages 185-202.
    12. Satya Paul & Sriram Shankar, 2020. "Estimating efficiency effects in a panel data stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 53(2), pages 163-180, April.
    13. Barra, Cristian & Lagravinese, Raffaele & Zotti, Roberto, 2015. "Explaining (in)efficiency in higher education: a comparison of parametric and non-parametric analyses to rank universities," MPRA Paper 67119, University Library of Munich, Germany.
    14. Keshvari, Abolfazl & Kuosmanen, Timo, 2013. "Stochastic non-convex envelopment of data: Applying isotonic regression to frontier estimation," European Journal of Operational Research, Elsevier, vol. 231(2), pages 481-491.
    15. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    16. Christian von Hirschhausen & Astrid Cullmann & Andreas Kappeler, 2006. "Efficiency analysis of German electricity distribution utilities - non-parametric and parametric tests," Applied Economics, Taylor & Francis Journals, vol. 38(21), pages 2553-2566.
    17. Christine Amsler & Peter Schmidt & Wen-Jen Tsay, 2019. "Evaluating the CDF of the distribution of the stochastic frontier composed error," Journal of Productivity Analysis, Springer, vol. 52(1), pages 29-35, December.
    18. Sawosri, Arieska Wening & Mußhoff, Oliver, 2020. "Risk and time preferences of farmers in India and Indonesia," EFForTS Discussion Paper Series 32, University of Goettingen, Collaborative Research Centre 990 "EFForTS, Ecological and Socioeconomic Functions of Tropical Lowland Rainforest Transformation Systems (Sumatra, Indonesia)".
    19. Oleg Badunenko & Daniel J. Henderson, 2024. "Production analysis with asymmetric noise," Journal of Productivity Analysis, Springer, vol. 61(1), pages 1-18, February.
    20. Danuse Nerudova & Marian Dobranschi, 2019. "Alternative method to measure the VAT gap in the EU: Stochastic tax frontier model approach," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-38, January.

    More about this item

    Keywords

    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaae23:364802. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaaeaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.