Literature DB >> 33738743

Assessment of rice and wheat production efficiency based on data envelopment analysis.

Muhammad Shoaib Aslam1, Pan Huan Xue2, Shahid Bashir3, Yazeed Alfakhri4, Mohammad Nurunnabi4,5, Van Chien Nguyen6.   

Abstract

Global warming, energy consumption (EC), and food safety have caused an increase of focus regarding agricultural crop productivity with a principal focus on CEs from crop farming. This study analyzes Pakistan, India, and China's rice and wheat production rating through the CCR and SBM DEA framework. The recorded rice (0.60) and wheat (1.00) production, through the CCR approach, can be considered the highest productivity. The rating productivity of the parallel DMUs for the CCR (or BCC) framework average degree of technical productivity of SBM model of wheat and rice production, which does not adhere to the degree of 100% amongst all countries. Keeping the area's efficiency in mind, the average technical productivity rating recorded through CCR is 0.87, and SBM is 0.86 and is significantly lower than the ideal rating in the original DEA. By decreasing tomato output through farmers' productive operations, energy can be conserved by 21.4% compared to its current level by enhancing the utilization of essential resources, chemical fertilizers, farmyard manure, and water bear comparatively greater trading weights. It is eminent to decrease energy usage and carbon discharge in rice production. Similarly, the high yield and adequate rice plantation methods should be encouraged in the given region.
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Entities:  

Keywords:  Agricultural production efficiency; Emerging economies; Environmental-economic reforms; Rice and wheat efficiency; Slack-based DEA

Year:  2021        PMID: 33738743     DOI: 10.1007/s11356-021-12892-z

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  1 in total

1.  The Use of Big Data Combined with Artificial Intelligence Neural Network Technology in Urban Spatial Evaluation System.

Authors:  Lei Wang; Yujie Liang; Gaizhen Shang; Zhiyong Song; Peng Gao
Journal:  Comput Intell Neurosci       Date:  2022-06-10
  1 in total

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