IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i10p4271-d361784.html
   My bibliography  Save this article

Technical Efficiencies and Yield Variability Are Comparable Across Organic and Conventional Farms

Author

Listed:
  • Amritbir Riar

    (Department of International Cooperation, Research Institute of Organic Agriculture (FiBL), Ackerstrasse 113, CH-5070 Frick, Switzerland)

  • Lokendra S. Mandloi

    (bioRe Research, bioRe Association India, Kasrawad 451228, India)

  • Ramadas Sendhil

    (ICAR-Indian Institute of Wheat and Barley Research (IIWBR), P.O. Box-158, Agrasain Marg, Karnal 132001, India)

  • Randhir S. Poswal

    (Division of Agricultural Extension, Indian Council of Agricultural Research (Ministry of Agriculture and Farmers Welfare), Krishi Anusandhan Bhawan-1, Pusa, New-Delhi 110012, India)

  • Monika M. Messmer

    (Department of Crop Sciences, Research Institute of Organic Agriculture (FiBL), Ackerstrasse 113, CH-5070 Frick, Switzerland)

  • Gurbir S. Bhullar

    (Department of International Cooperation, Research Institute of Organic Agriculture (FiBL), Ackerstrasse 113, CH-5070 Frick, Switzerland)

Abstract

Cotton is essentially a smallholder crop across tropical countries. Being a major cash crop, it plays a decisive role in the livelihoods of cotton-producing farmers. Both conventional and organic production systems offer alternative yet interesting propositions to cotton farmers. This study was conducted in Nimar valley, a prominent cotton-producing region of central India, with the aim of categorically evaluating the contribution of management and fixed factors to productivity on conventional and organic cotton farms. A study framework was developed considering the fixed factors, which cannot be altered within reasonable limits of time, capacity and resources, e.g., landholding or years of age and/or practice; and management factors, which can be altered/influenced within a reasonable time by training, practice and implementation. Using this framework, a structured survey of conventional and organic farms operating under comparable circumstances was conducted. Landholding and soil types were significant contributors/predictors of yield on organic farms. In contrast, landholding was not the main factor related to yields on conventional farms, which produced the highest yields when led by farmers with more than five years of formal education and living in a joint family. Nitrogen application, the source of irrigation (related to timely and adequate supply), crop rotation and variables related to adequate plant population (seed source, germination rate and plant thinning) were the main management factors limiting cotton yields among conventional and organic farms. Both organic and conventional farms in the Nimar valley exhibited a similar pattern of variation in cotton yields and technical efficiency. This study highlights the enormous scope for improving cotton productivity in the region by improving technical efficiency, strengthening extension services and making appropriate policy interventions.

Suggested Citation

  • Amritbir Riar & Lokendra S. Mandloi & Ramadas Sendhil & Randhir S. Poswal & Monika M. Messmer & Gurbir S. Bhullar, 2020. "Technical Efficiencies and Yield Variability Are Comparable Across Organic and Conventional Farms," Sustainability, MDPI, vol. 12(10), pages 1-12, May.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:10:p:4271-:d:361784
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/10/4271/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/10/4271/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Coventry, D.R. & Poswal, R.S. & Yadav, Ashok & Riar, Amritbir Singh & Zhou, Yi & Kumar, Anuj & Chand, Ramesh & Chhokar, R.S. & Sharma, R.K. & Yadav, V.K. & Gupta, R.K. & Mehta, Anil & Cummins, J.A., 2015. "A comparison of farming practices and performance for wheat production in Haryana, India," Agricultural Systems, Elsevier, vol. 137(C), pages 139-153.
    2. 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.
    3. Seraina Vonzun & Monika M. Messmer & Thomas Boller & Yogendra Shrivas & Shreekant S. Patil & Amritbir Riar, 2019. "Extent of Bollworm and Sucking Pest Damage on Modern and Traditional Cotton Species and Potential for Breeding in Organic Cotton," Sustainability, MDPI, vol. 11(22), pages 1-12, November.
    4. Harun Cicek & Gurbir S. Bhullar & Lokendra S. Mandloi & Christian Andres & Amritbir S. Riar, 2020. "Partial Acidulation of Rock Phosphate for Increased Productivity in Organic and Smallholder Farming," Sustainability, MDPI, vol. 12(2), pages 1-13, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shradha S. Aherkar & Surendra B. Deshmukh & Nitin. M. Konde & Aadinath N. Paslawar & Tanay Joshi & Monika M. Messmer & Amritbir Riar, 2023. "Studies on Morphophysiological and Biochemical Parameters for Sucking Pest Tolerance in Organic Cotton," Agriculture, MDPI, vol. 13(7), pages 1-18, July.

    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. Kapoor, Rajni & Das, Nimai, 2021. "A Pragmatic Study for Enhancing Agricultural Efficiency Through Labor Freedom," Asian Journal of Agriculture and Rural Development, Asian Economic and Social Society (AESS), vol. 11(04), January.
    2. Khezrimotlagh, Dariush & Kaffash, Sepideh & Zhu, Joe, 2022. "U.S. airline mergers’ performance and productivity change," Journal of Air Transport Management, Elsevier, vol. 102(C).
    3. Christian Growitsch & Tooraj Jamasb & Christine Müller & Matthias Wissner, 2016. "Social Cost Efficient Service Quality: Integrating Customer Valuation in Incentive Regulation—Evidence from the Case of Norway," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, chapter 0, pages 71-91, Springer.
    4. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499, March.
    5. Alperovych, Yan & Hübner, Georges & Lobet, Fabrice, 2015. "How does governmental versus private venture capital backing affect a firm's efficiency? Evidence from Belgium," Journal of Business Venturing, Elsevier, vol. 30(4), pages 508-525.
    6. Wang, Zhao-Hua & Zeng, Hua-Lin & Wei, Yi-Ming & Zhang, Yi-Xiang, 2012. "Regional total factor energy efficiency: An empirical analysis of industrial sector in China," Applied Energy, Elsevier, vol. 97(C), pages 115-123.
    7. repec:lan:wpaper:1115 is not listed on IDEAS
    8. Azarnoosh Kafi & Behrouz Daneshian & Mohsen Rostamy-Malkhalifeh, 2021. "Forecasting the confidence interval of efficiency in fuzzy DEA," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 41-59.
    9. Ruiqing Yuan & Xiangyang Xu & Yanli Wang & Jiayi Lu & Ying Long, 2024. "Evaluating Carbon-Emission Efficiency in China’s Construction Industry: An SBM-Model Analysis of Interprovincial Building Heating," Sustainability, MDPI, vol. 16(6), pages 1-16, March.
    10. Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & de Pinho Matos, Giordano Bruno Braz, 2015. "Statistical evaluation of Data Envelopment Analysis versus COLS Cobb–Douglas benchmarking models for the 2011 Brazilian tariff revision," Socio-Economic Planning Sciences, Elsevier, vol. 49(C), pages 47-60.
    11. Kristiaan Kerstens & Ignace Van de Woestyne, 2018. "Enumeration algorithms for FDH directional distance functions under different returns to scale assumptions," Annals of Operations Research, Springer, vol. 271(2), pages 1067-1078, December.
    12. Bo Li & Muhammad Mohiuddin & Qian Liu, 2019. "Determinants and Differences of Township Hospital Efficiency among Chinese Provinces," IJERPH, MDPI, vol. 16(9), pages 1-16, May.
    13. Ahmad, Usman, 2011. "Financial Reforms and Banking Efficiency: Case of Pakistan," MPRA Paper 34220, University Library of Munich, Germany.
    14. Nijkamp, P. & Stough, R. & Sahin, M., 2009. "Impact of social and human capital on business performance of migrant entrepreneurs - a comparative dutch-us study," Serie Research Memoranda 0017, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    15. Bowlin, W. F., 1995. "A characterization of the financial condition of the United States' aerospace-defense industrial base," Omega, Elsevier, vol. 23(5), pages 539-555, October.
    16. Zhang, Chonghui & Bai, Chen & Su, Weihua & Balezentis, Tomas, 2024. "The centralised data envelopment analysis model integrated with cost information and utility theory for power price setting under carbon peak strategy at the firm-level," Energy, Elsevier, vol. 292(C).
    17. Mika Kortelainen & Timo Kuosmanen, 2007. "Eco-efficiency analysis of consumer durables using absolute shadow prices," Journal of Productivity Analysis, Springer, vol. 28(1), pages 57-69, October.
    18. Ashrafi, Ali & Seow, Hsin-Vonn & Lee, Lai Soon & Lee, Chew Ging, 2013. "The efficiency of the hotel industry in Singapore," Tourism Management, Elsevier, vol. 37(C), pages 31-34.
    19. Büschken, Joachim, 2009. "When does data envelopment analysis outperform a naïve efficiency measurement model?," European Journal of Operational Research, Elsevier, vol. 192(2), pages 647-657, January.
    20. repec:lan:wpaper:4471 is not listed on IDEAS
    21. Muhammad Jam e Kausar Ali Asghar & Abdul Zahid Khan & Hafiz Ghufran Ali Khan, 2019. "Economies of Scale and Efficiency of Mutual Funds in Pakistan," Global Regional Review, Humanity Only, vol. 4(1), pages 96-103, March.
    22. António Afonso & Ana Patricia Montes & José M. Domínguez, 2024. "Measuring Tax Burden Efficiency in OECD Countries: An International Comparison," CESifo Working Paper Series 11333, CESifo.

    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:jsusta:v:12:y:2020:i:10:p:4271-:d:361784. 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.