IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i7p1032-d1424885.html
   My bibliography  Save this article

Data Envelopment Analysis (DEA) to Estimate Technical and Scale Efficiencies of Smallholder Pineapple Farmers in Ghana

Author

Listed:
  • Kwaku Boakye

    (Department of Economics, Applied Statistics and Int’l Business, College of Business, New Mexico State University, Las Cruces, NM 88001, USA
    Department of Agricultural Economics and Extension, School of Agriculture, College of Agriculture and Natural Science, University of Cape Coast, Cape Coast 00233, Ghana)

  • Yu-Feng Lee

    (Department of Economics, Applied Statistics and Int’l Business, College of Business, New Mexico State University, Las Cruces, NM 88001, USA)

  • Festus F. Annor

    (Department of Agricultural Economics and Extension, School of Agriculture, College of Agriculture and Natural Science, University of Cape Coast, Cape Coast 00233, Ghana)

  • Samuel K. N. Dadzie

    (Department of Agricultural Economics and Extension, School of Agriculture, College of Agriculture and Natural Science, University of Cape Coast, Cape Coast 00233, Ghana)

  • Iddrisu Salifu

    (Department of Applied Economics, School of Economics, University of Cape Coast, Cape Coast 00233, Ghana
    Centre for Coastal Management-Africa Centre of Excellence in Coastal Resilience, Department of Fisheries and Aquatic Sciences, University of Cape Coast, Cape Coast 00233, Ghana)

Abstract

This study focuses on evaluating the technical and scale efficiencies of smallholder pineapple farmers in Ghana’s Central Region. We surveyed 320 participants selected using random sampling and applied an input-oriented Data Envelopment Analysis (DEA) method to gauge their technical, pure, and scale efficiencies. Our findings indicate that the mean technical efficiency among these farmers is 0.505, with individual scores ranging from 0.079 to 1.000. Notably, 90.82% of the farmers are operating below maximum efficiency levels, suggesting a potential input reduction of up to 49.5% while maintaining current production levels. Relaxing the assumption of constant returns under Variable Returns to Scale (VRS) conditions reveals a notable improvement in technical efficiency, with 10.82% more farmers achieving optimal efficiency levels. Furthermore, our analysis highlights scale inefficiencies, with 67.26% of farmers operating below optimal scale levels. By increasing production by 22.8%, these scale-inefficient farmers could enhance their efficiency and productivity within existing technological frameworks. These findings underscore the importance of collaborative efforts among policymakers, practitioners, and stakeholders within the agricultural value chain to implement interventions such as improving access to technology and innovation for smallholder farmers and making necessary investments in farmer education and training programs to enhance both technical and scale efficiencies in Ghana’s pineapple sector. Such initiatives can drive sustainable growth, improve farmers’ livelihoods, and bolster the sector’s overall competitiveness.

Suggested Citation

  • Kwaku Boakye & Yu-Feng Lee & Festus F. Annor & Samuel K. N. Dadzie & Iddrisu Salifu, 2024. "Data Envelopment Analysis (DEA) to Estimate Technical and Scale Efficiencies of Smallholder Pineapple Farmers in Ghana," Agriculture, MDPI, vol. 14(7), pages 1-14, June.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:7:p:1032-:d:1424885
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/7/1032/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/14/7/1032/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), 2011. "Handbook on Data Envelopment Analysis," International Series in Operations Research and Management Science, Springer, number 978-1-4419-6151-8, December.
    2. Watto, Muhammad, 2013. "Measuring Groundwater Irrigation Efficiency in Pakistan: A DEA Approach Using the Sub-vector and Slack-based Models," 2013 Conference (57th), February 5-8, 2013, Sydney, Australia 152204, Australian Agricultural and Resource Economics Society.
    3. F. Hosseinzadeh Lotfi & G. R. Jahanshahloo & M. Khodabakhshi & M. Rostamy-Malkhlifeh & Z. Moghaddas & M. Vaez-Ghasemi, 2013. "A Review of Ranking Models in Data Envelopment Analysis," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-20, July.
    4. Fried, Harold O. & Lovell, C. A. Knox & Schmidt, Shelton S. (ed.), 2008. "The Measurement of Productive Efficiency and Productivity Growth," OUP Catalogue, Oxford University Press, number 9780195183528.
    5. Gao, Penghui & Secor, William & Escalante, Cesar L., 2021. "U.S. agricultural banks’ efficiency under COVID-19 Pandemic conditions: A two-stage DEA analysis," 2021 Annual Meeting, August 1-3, Austin, Texas 312923, Agricultural and Applied Economics Association.
    6. Watto, Muhammad Arif & Mugera, Amin William, 2013. "Measuring Groundwater Irrigation Efficiency in Pakistan: A DEA Approach Using the Sub-vector and Slack-based Models," Working Papers 144943, University of Western Australia, School of Agricultural and Resource Economics.
    7. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    8. Jin, Peizhen & Peng, Chong & Song, Malin, 2019. "Macroeconomic uncertainty, high-level innovation, and urban green development performance in China," China Economic Review, Elsevier, vol. 55(C), pages 1-18.
    9. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    10. Gong, Binlei, 2020. "Agricultural productivity convergence in China," China Economic Review, Elsevier, vol. 60(C).
    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. Rita Bastião & Nuno de Sousa Pereira, 2020. "Performance in the Delivery of Primary Health Care Services: A Longitudinal Analysis," CEF.UP Working Papers 2002, Universidade do Porto, Faculdade de Economia do Porto.
    2. Manuel Salas-Velasco, 2020. "Measuring and explaining the production efficiency of Spanish universities using a non-parametric approach and a bootstrapped-truncated regression," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 825-846, February.
    3. Barnabé Walheer, 2020. "Output, input, and undesirable output interconnections in data envelopment analysis: convexity and returns-to-scale," Annals of Operations Research, Springer, vol. 284(1), pages 447-467, January.
    4. Matthias Klumpp & Dominic Loske, 2021. "Sustainability and Resilience Revisited: Impact of Information Technology Disruptions on Empirical Retail Logistics Efficiency," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    5. Duk Hee Lee & Il Won Seo & Ho Chull Choe & Hee Dae Kim, 2012. "Collaboration network patterns and research performance: the case of Korean public research institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 925-942, June.
    6. Feng Li & Qingyuan Zhu & Jun Zhuang, 2018. "Analysis of fire protection efficiency in the United States: a two-stage DEA-based approach," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(1), pages 23-68, January.
    7. Margareta Gardijan & Zrinka Lukač, 2018. "Measuring the relative efficiency of the food and drink industry in the chosen EU countries using the data envelopment analysis with missing data," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(3), pages 695-713, September.
    8. Mehdiloozad, Mahmood & Zhu, Joe & Sahoo, Biresh K., 2018. "Identification of congestion in data envelopment analysis under the occurrence of multiple projections: A reliable method capable of dealing with negative data," European Journal of Operational Research, Elsevier, vol. 265(2), pages 644-654.
    9. A. Guerrini & G. Romano & L. Carosi & F. Mancuso, 2017. "Cost Savings in Wastewater Treatment Processes: the Role of Environmental and Operational Drivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(8), pages 2465-2478, June.
    10. Tao Ding & Ya Chen & Huaqing Wu & Yuqi Wei, 2018. "Centralized fixed cost and resource allocation considering technology heterogeneity: a DEA approach," Annals of Operations Research, Springer, vol. 268(1), pages 497-511, September.
    11. Mehdiloo, Mahmood & Podinovski, Victor V., 2019. "Selective strong and weak disposability in efficiency analysis," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1154-1169.
    12. Imanirad, Raha & Cook, Wade D. & Aviles-Sacoto, Sonia Valeria & Zhu, Joe, 2015. "Partial input to output impacts in DEA: The case of DMU-specific impacts," European Journal of Operational Research, Elsevier, vol. 244(3), pages 837-844.
    13. M. Lábaj & M. Luptáčik & E. Nežinský, 2014. "Data envelopment analysis for measuring economic growth in terms of welfare beyond GDP," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(3), pages 407-424, August.
    14. Isabel Narbón-Perpiñá & Maria Teresa Balaguer-Coll & Marko Petrović & Emili Tortosa-Ausina, 2020. "Which estimator to measure local governments’ cost efficiency? The case of Spanish municipalities," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 11(1), pages 51-82, March.
    15. Ricardo Ocaña-Riola & Carmen Pérez-Romero & Mª Isabel Ortega-Díaz & José Jesús Martín-Martín, 2021. "Multilevel Zero-One Inflated Beta Regression Model for the Analysis of the Relationship between Exogenous Health Variables and Technical Efficiency in the Spanish National Health System Hospitals," IJERPH, MDPI, vol. 18(19), pages 1-18, September.
    16. Necmi Avkiran & Alan McCrystal, 2014. "Intertemporal analysis of organizational productivity in residential aged care networks: scenario analyses for setting policy targets," Health Care Management Science, Springer, vol. 17(2), pages 113-125, June.
    17. Wen-Chi Yang & Wen-Min Lu, 2023. "Achieving Net Zero—An Illustration of Carbon Emissions Reduction with A New Meta-Inverse DEA Approach," IJERPH, MDPI, vol. 20(5), pages 1-20, February.
    18. Abolghasem, Sepideh & Gómez-Sarmiento, Juliana & Medaglia, Andrés L. & Sarmiento, Olga L. & González, Andrés D. & Díaz del Castillo, Adriana & Rozo-Casas, Juan F. & Jacoby, Enrique, 2018. "A DEA-centric decision support system for evaluating Ciclovía-Recreativa programs in the Americas," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 90-101.
    19. Andor, Mark A. & Parmeter, Christopher & Sommer, Stephan, 2019. "Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes," European Journal of Operational Research, Elsevier, vol. 274(1), pages 240-252.
    20. Amirteimoori, Alireza & Cezar, Asunur & Zadmirzaei, Majid & Susaeta, Andres, 2024. "Environmental performance evaluation in the forest sector: An extended stochastic data envelopment analysis approach," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).

    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:gam:jagris:v:14:y:2024:i:7:p:1032-:d:1424885. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.