{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T13:10:02Z","timestamp":1771679402664,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T00:00:00Z","timestamp":1697587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UCSC 2023"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>Agricultural product prices are subject to various uncertainties, including unpredictable weather conditions, pest infestations, and market fluctuations, which can significantly impact agricultural yields and productivity. Accurately assessing and understanding price is crucial for farmers, policymakers, and stakeholders in the agricultural sector to make informed decisions and implement appropriate risk management strategies. This study used the ordered weighted average (OWA) operator and its extensions as mathematical aggregation techniques incorporating ordered weights to capture and evaluate the factors influencing price variation. By generating different vectors related to different inputs to the traditional formulation, it is possible to aggregate information to calculate and provide a new view of the outcomes. The results of this research can help enhance risk management practices in agriculture and support decision-making processes to mitigate the adverse effects of price.<\/jats:p>","DOI":"10.3390\/axioms12100985","type":"journal-article","created":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T10:36:56Z","timestamp":1697625416000},"page":"985","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Using the Ordered Weighted Average Operator to Gauge Variation in Agriculture Commodities in India"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6993-5889","authenticated-orcid":false,"given":"Sandeep","family":"Wankhade","sequence":"first","affiliation":[{"name":"Department of Mathematics, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7949-9147","authenticated-orcid":false,"given":"Manoj","family":"Sahni","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India"}]},{"given":"Cristhian","family":"Mellado-Cid","sequence":"additional","affiliation":[{"name":"Faculty of Economics and Administrative Sciences, Universidad Cat\u00f3lica de la Sant\u00edsima Concepci\u00f3n, Concepci\u00f3n 4070129, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0087-2226","authenticated-orcid":false,"given":"Ernesto","family":"Leon-Castro","sequence":"additional","affiliation":[{"name":"Faculty of Economics and Administrative Sciences, Universidad Cat\u00f3lica de la Sant\u00edsima Concepci\u00f3n, Concepci\u00f3n 4070129, Chile"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.inffus.2020.01.006","article-title":"Large-Scale Decision-Making: Characterization, Taxonomy, Challenges and Future Directions from an Artificial Intelligence and Applications Perspective","volume":"59","author":"Ding","year":"2020","journal-title":"Inf. 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