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Evolution of Overall Cotton Production and Its Determinants: Implications for Developing Countries Using Pakistan Case

Author

Listed:
  • Muhammad Umer Arshad

    (Pakistan Research Center, Inner Mongolia Honder College of Arts and Sciences, Hohhot 010010, China)

  • Yuanfeng Zhao

    (College of Economics and Management, Inner Mongolia Agriculture University, Hohhot 010010, China)

  • Omer Hanif

    (School of Business and Tourism, Southern Cross University, East Lismore, NSW 2480, Australia)

  • Faiza Fatima

    (Department of Botany, University of Agriculture, Faisalabad 38000, Punjab, Pakistan)

Abstract

Managing the declining yield of non-food crops has opened new strategic challenges amidst global uncertainties. The COVID-19 scenario has increased awareness of natural lifestyle and eco-friendly products, largely dependent on non-food crop material. This strategic shift requires moving beyond traditional farm practices to improve agricultural production efficiency, and developing countries in particular have shown a consistent loss in their self-sufficiency of industrial crops despite being major exporters of non-food crop materials. However, existing studies analyze production efficiencies of non-food crops from general or theoretical aspects often by virtual estimates from breaking down the multiple factors of crop productivity. This study examined multiple factors of crop production to identify “which crop inputs have been inefficiently used overtime” by tracking efficiency changes and various input issues in overall cotton production from practical aspects, i.e., scaling non-constant returns of those multiple factors would allow for the violation of various situations. Accordingly, a stochastic frontier approach was employed to measure the production frontier and efficiency relationship using time-series data of Pakistan’s cotton production from 1971–2018—a specific non-food crop perspective from a top-ranked cotton-producing country that has recently been shifted towards being a non-exporter of cotton due to low yield. The coefficient of area, seed, and labor indicates the positive relationship with cotton production, while fertilizer, irrigation, electricity, and machinery are statistically negative. This implies that policymakers need priority-based strategies for the judicial use of synthetic fertilizers, irrigation, a subsidy policy, and technology adoption, which could significantly improve the efficiencies of cotton productivity from the same land resources. Being adaptable to other developing economies, the analysis would strategically facilitate designing and developing affordable technology-driven solutions and their customized extensions towards sustainable non-food crop production practices and Agri-Resources efficiencies.

Suggested Citation

  • Muhammad Umer Arshad & Yuanfeng Zhao & Omer Hanif & Faiza Fatima, 2022. "Evolution of Overall Cotton Production and Its Determinants: Implications for Developing Countries Using Pakistan Case," Sustainability, MDPI, vol. 14(2), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:2:p:840-:d:723072
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    References listed on IDEAS

    as
    1. Malik Shahzad Shabbir & Nusrat Yaqoob, 2019. "The impact of technological advancement on total factor productivity of cotton: a comparative analysis between Pakistan and India," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-16, December.
    2. Ozgul Calicioglu & Alessandro Flammini & Stefania Bracco & Lorenzo Bellù & Ralph Sims, 2019. "The Future Challenges of Food and Agriculture: An Integrated Analysis of Trends and Solutions," Sustainability, MDPI, vol. 11(1), pages 1-21, January.
    3. Jianxu Liu & Changrui Dong & Shutong Liu & Sanzidur Rahman & Songsak Sriboonchitta, 2020. "Sources of Total-Factor Productivity and Efficiency Changes in China’s Agriculture," Agriculture, MDPI, vol. 10(7), pages 1-18, July.
    4. Dan Pan & Jiaqing Yang & Guzhen Zhou & Fanbin Kong, 2020. "The influence of COVID-19 on agricultural economy and emergency mitigation measures in China: A text mining analysis," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-20, October.
    5. Oliver T. Coomes & Bradford L. Barham & Graham K. MacDonald & Navin Ramankutty & Jean-Paul Chavas, 2019. "Leveraging total factor productivity growth for sustainable and resilient farming," Nature Sustainability, Nature, vol. 2(1), pages 22-28, January.
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    Cited by:

    1. Liting Gao & Qianhui Gao & Marcin Lorenc, 2022. "Comparison of Total Factor Productivity of Rice in China and Japan," Sustainability, MDPI, vol. 14(12), pages 1-16, June.

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