A Proposed Comparative Algorithm for Regional Crop Yield Assessment: An Application of Characteristic Objects Method

Author:

Habeeb Rimsha1ORCID,Hussain Ijaz1ORCID,Al-Ansari Nadhir2ORCID,Sammen Saad Sh.3ORCID

Affiliation:

1. Department of Statistics, Quaid-I-Azam University, Islamabad, Pakistan

2. Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea 971 87, Sweden

3. Department of Civil Engineering, College of Engineering, University of Diyala, Diyala Governorate, Iraq

Abstract

The agriculture sector plays a vibrant role in the economic prosperity of advanced and developing countries. It is a crucial source of revenue for the majority of the population. Nevertheless, unfortunately, in Pakistan, the share of the agricultural sector in Gross Domestic Product (GDP) is gradually declining. Therefore, comprehensive strategies and actions need to be developed and implement to enhance the agricultural productivity of Pakistan. In this study, an attempt has been made to examine the crop yield revenue of Punjab, Pakistan, by ranking the districts according to their contribution to the agricultural GDP of Pakistan's economy. A Multi-Criteria Decision Making (MCDM) technique, namely, characteristic objects method (COMET), which is entirely free of the rank reversal paradox, is used for this purpose. However, to make a fair comparison, in this research, a comprehensive framework is proposed to normalize the crop yield revenue of Punjab under probabilistic nature. The proposed framework is applied to various districts of Punjab, Pakistan, from 1992 to 2019. It is concluded that Jhang, Faisalabad, and Rahim Yar Khan (RYK) are the highest-ranked districts, while Nankana Sahib, Rawalpindi, and Islamabad are the lowest-ranked districts of Punjab, Pakistan, according to their contribution to the agricultural GDP of Pakistan's economy. Outcomes associated with this research would be helpful to build precise and accurate budget allocation policies.

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enhancing Crop Yield through Weed Density Estimation and Management: A Comprehensive Review;2023 IEEE International Conference on ICT in Business Industry & Government (ICTBIG);2023-12-08

2. A Review on Crop Disease Classification and Prevention of Pest using Deep Transfer Ensemble Learning in Agriculture;2023 IEEE International Conference on ICT in Business Industry & Government (ICTBIG);2023-12-08

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