IDEAS home Printed from https://ideas.repec.org/a/ags/agreko/345071.html
   My bibliography  Save this article

Impact of mulching technology adoption on output and net return to yam farmers in Osun State, Nigeria

Author

Listed:
  • Akinola, A.A.
  • Sofoluwe, N.A.

Abstract

Soil erosion and nutrient depletion present a threat to the food security and sustainability of agricultural production in sub-Saharan Africa (SSA). However, limited rigorous empirical work on the economics of soil conservation exists. This study examines the factors affecting the adoption of mulching technology and its attendant impact on yam output supply and net returns among sampled yam farmers in Osun State, Nigeria. Probit model and propensity score matching were used to analyse the factors influencing the adoption of mulching technology and its impact on yam output and net returns among yam farmers respectively. The study shows that seed quantity and access to credit are the most significant factors influencing the adoption of mulching technology. Yam farmers in the study area who adopted mulching technology were found to experience a higher output supply than non-adopters, which resulted in a positive and significant effect on their output and net return. Hence, policies targeted at increasing yam output through increasing soil fertility need to include mulching technology as a potentially viable option.

Suggested Citation

  • Akinola, A.A. & Sofoluwe, N.A., 2012. "Impact of mulching technology adoption on output and net return to yam farmers in Osun State, Nigeria," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 51(2), June.
  • Handle: RePEc:ags:agreko:345071
    DOI: 10.22004/ag.econ.345071
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/345071/files/Impact%20of%20mulching%20technology%20adoption%20on%20output%20and%20net%20return%20to%20yam%20farmers%20in%20Osun%20State%20%20Nigeria.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.345071?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. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    2. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    3. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
    4. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    5. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
    6. Alberto Abadie & David Drukker & Jane Leber Herr & Guido W. Imbens, 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 290-311, September.
    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. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    2. Tommaso Nannicini, 2007. "Simulation-based sensitivity analysis for matching estimators," Stata Journal, StataCorp LP, vol. 7(3), pages 334-350, September.
    3. Dettmann, Eva & Becker, Claudia & Schmeißer, Christian, 2010. "Is there a Superior Distance Function for Matching in Small Samples?," IWH Discussion Papers 3/2010, Halle Institute for Economic Research (IWH).
    4. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    5. Alberto Abadie & Guido W. Imbens, 2008. "On the Failure of the Bootstrap for Matching Estimators," Econometrica, Econometric Society, vol. 76(6), pages 1537-1557, November.
    6. Mishra, Ashok K. & Kumar, Anjani & Joshi, Pramod K. & D'souza, Alwin, 2016. "Impact of contracts in high yielding varieties seed production on profits and yield: The case of Nepal," Food Policy, Elsevier, vol. 62(C), pages 110-121.
    7. Stephen L. Morgan & David J. Harding, 2006. "Matching Estimators of Causal Effects," Sociological Methods & Research, , vol. 35(1), pages 3-60, August.
    8. Sascha O. Becker & Marco Caliendo, 2007. "Sensitivity analysis for average treatment effects," Stata Journal, StataCorp LP, vol. 7(1), pages 71-83, February.
    9. Tymon Słoczyński, 2015. "The Oaxaca–Blinder Unexplained Component as a Treatment Effects Estimator," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(4), pages 588-604, August.
    10. Gustavo Canavire-Bacarreza & Luis Castro Peñarrieta & Darwin Ugarte Ontiveros, 2021. "Outliers in Semi-Parametric Estimation of Treatment Effects," Econometrics, MDPI, vol. 9(2), pages 1-32, April.
    11. Miet Maertens & Liesbeth Colen & Johan F. M. Swinnen, 2011. "Globalisation and poverty in Senegal: a worst case scenario?," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 38(1), pages 31-54, March.
    12. David McKenzie & John Gibson & Steven Stillman, 2010. "How Important Is Selection? Experimental vs. Non-Experimental Measures of the Income Gains from Migration," Journal of the European Economic Association, MIT Press, vol. 8(4), pages 913-945, June.
    13. Martey, Edward & Kuwornu, John K.M. & Adjebeng-Danquah, Joseph, 2019. "Estimating the effect of mineral fertilizer use on Land productivity and income: Evidence from Ghana," Land Use Policy, Elsevier, vol. 85(C), pages 463-475.
    14. Roberto Gabriele & Marco Zamarian & Enrico Zaninotto, 2006. "Assessing the economic impact of public industrial policies: an empirical investigation on subsidies," ROCK Working Papers 039, Department of Computer and Management Sciences, University of Trento, Italy, revised 12 Jun 2008.
    15. McKenzie, David & Gibson, John & Stillman, Steven, 2006. "How important is selection ? Experimental versus non-experimental measures of the income gains from migration," Policy Research Working Paper Series 3906, The World Bank.
    16. Edgar Silva Quintero & José Alberto Molina & J. Ignacio Gimenez-Nadal, 2016. "How Forced Displacements Caused by a Violent Conflict Affect Wages in Colombia," Working Papers id:10876, eSocialSciences.
    17. Deschamps-Laporte, Jean-Philippe, 2013. "The impact of extension services on farming households in Western Kenya: A propensity score approach," Working Papers 2013:5, Örebro University, School of Business, revised 10 Jun 2013.
    18. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    19. Eliasson, Kent, 2006. "The Role of Ability in Estimating the Returns to College Choice: New Swedish Evidence," Umeå Economic Studies 691, Umeå University, Department of Economics.
    20. Martin Huber & Eva-Maria Oe{ss}, 2024. "A joint test of unconfoundedness and common trends," Papers 2404.16961, arXiv.org, revised Jun 2024.

    More about this item

    Keywords

    Crop Production/Industries;

    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:agreko:345071. 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/aeasaea.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.