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

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  • Rimsha Habeeb
  • Ijaz Hussain
  • Nadhir Al-Ansari
  • Saad Sh. Sammen
  • Tahir Mehmood

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.

Suggested Citation

  • Rimsha Habeeb & Ijaz Hussain & Nadhir Al-Ansari & Saad Sh. Sammen & Tahir Mehmood, 2022. "A Proposed Comparative Algorithm for Regional Crop Yield Assessment: An Application of Characteristic Objects Method," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, March.
    • Handle: RePEc:hin:jnlmpe:8224953
    DOI: 10.1155/2022/8224953
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