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Energy productivity and efficiency of maize accounting for the choice of growing season and environmental factors: An empirical analysis from Bangladesh

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  • Rahman, Sanzidur
  • Rahman, Md. Sayedur

Abstract

The paper evaluates sustainability of maize cultivation in Bangladesh in terms of energy use while taking into account factors affecting choice of the growing season and farmers' production environment using a sample selection framework applied to stochastic frontier models. Results reveal that the probability of growing winter maize is influenced positively by gross return, irrigation, subsistence pressure, soil suitability and temperature variability whereas extension contact influences choice negatively. Significant differences exist between winter and summer maize regarding yield, specific energy, net energy balance, energy use efficiency and technical energy efficiency although both systems are highly sustainable and efficient. The energy output from winter maize is 199,585 MJ/ha which is 53.9% higher than the summer maize output of 129,701 MJ/ha. Also, energy input use of winter maize is 110.6% higher than the summer maize. Energy inputs from mechanical power, seeds, fertilizers and organic manures significantly increase energy productivity of winter maize whereas only mechanical power influences summer maize productivity. However, temperature variation and rainfall significantly reduce energy productivity of summer maize. Policy implications include investments in soil conservation and irrigation, development of weather resistant varieties and raising maize price will boost maize cultivation in Bangladesh, a highly sustainable production technology.

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  • Rahman, Sanzidur & Rahman, Md. Sayedur, 2013. "Energy productivity and efficiency of maize accounting for the choice of growing season and environmental factors: An empirical analysis from Bangladesh," Energy, Elsevier, vol. 49(C), pages 329-336.
  • Handle: RePEc:eee:energy:v:49:y:2013:i:c:p:329-336
    DOI: 10.1016/j.energy.2012.10.042
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    2. Sanzidur Rahman & Basanta Kumar Barmon, 2018. "Total Factor Energy Productivity and Efficiency Changes of the Gher (Prawn-Carp-Rice) Farming System in Bangladesh: A Stochastic Input Distance Function Approach," Energies, MDPI, Open Access Journal, vol. 11(12), pages 1-17, December.
    3. Dong Tian & Min Zhang & Xuejian Wei & Jing Wang & Weisong Mu & Jianying Feng, 2018. "GIS-Based Energy Consumption and Spatial Variation of Protected Grape Cultivation in China," Sustainability, MDPI, Open Access Journal, vol. 10(9), pages 1-21, September.
    4. Zhihai Yang & Dong Wang & Tianyi Du & Anlu Zhang & Yixiao Zhou, 2018. "Total-Factor Energy Efficiency in China’s Agricultural Sector: Trends, Disparities and Potentials," Energies, MDPI, Open Access Journal, vol. 11(4), pages 1-16, April.
    5. Menegaki, Angeliki N., 2013. "Accounting for unobserved management in renewable energy & growth," Energy, Elsevier, vol. 63(C), pages 345-355.
    6. Rahman, Sanzidur & Kazal, Mohammad Mizanul Haque, 2015. "Whether crop diversification is energy efficient: An empirical analysis from Bangladesh," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 745-754.
    7. Rahman, Sanzidur & Hasan, M. Kamrul, 2014. "Energy productivity and efficiency of wheat farming in Bangladesh," Energy, Elsevier, vol. 66(C), pages 107-114.

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    More about this item

    Keywords

    Energy productivity and efficiency; Season selection decision; Stochastic production frontier; Maize; Bangladesh;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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