{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T16:59:14Z","timestamp":1771865954786,"version":"3.50.1"},"reference-count":44,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T00:00:00Z","timestamp":1606435200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["GS"],"published-print":{"date-parts":[[2022,1,3]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>A core challenge of assessing regional agricultural drought vulnerability (RADV) is to reveal what vulnerability factors, under which kinds of synergistic combinations and at what strengths, will lead to higher vulnerability: namely, the influence patterns of RADV.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>A two-phased grey rough combined model is proposed to identify influence patterns of RADV from a new perspective of learning and mining historical cases. The grey entropy weight clustering with double base points is proposed to assess degrees of RADV. The simplest decision rules that reflect the complex synergistic relationships between RADV and its influencing factors are extracted using the rough set approach.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The results exemplified by China's Henan Province in the years 2008\u20132016 show higher degrees of RADV in the north and west regions of the province, in comparison with the south and east. In the patterns with higher RADV, the higher proportion of agricultural population appears in all decision rules as a core feature. A smaller quantity of water resources per unit of cultivated land area and a lower adaptive capacity, involving levels of irrigation technology and economic development, present a significant synergistic influence relationship that distinguishes the features of higher vulnerability from those of the lower.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The proposed grey rough combined model not only evaluates temporal dynamics and spatial differences of RADV but also extracts the decision rules between RADV and its influencing factors. The identified influence patterns inspire managerial implications for preventing and reducing agricultural drought through its historical evolution and formation mechanism.<\/jats:p><\/jats:sec>","DOI":"10.1108\/gs-07-2020-0090","type":"journal-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T22:59:49Z","timestamp":1606431589000},"page":"230-251","source":"Crossref","is-referenced-by-count":5,"title":["Identifying influence patterns of regional agricultural drought vulnerability using a two-phased grey rough combined model"],"prefix":"10.1108","volume":"12","author":[{"given":"Huifang","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1539-663X","authenticated-orcid":false,"given":"Liping","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaoguo","family":"Dang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxin","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"issue":"4","key":"key2022010314075522400_ref001","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s10113-008-0058-4","article-title":"Application of fuzzy models to assess susceptibility to droughts from a socio-economic perspective","volume":"8","year":"2008","journal-title":"Regional Environmental Change"},{"issue":"4","key":"key2022010314075522400_ref002","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/S0195-9255(02)00012-4","article-title":"Developing economic vulnerability indices of environmental disasters in small island regions","volume":"22","year":"2002","journal-title":"Environmental Impact Assessment Review"},{"key":"key2022010314075522400_ref003","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.scitotenv.2018.07.023","article-title":"Multi-dimensional assessment of drought vulnerability in Africa: 1960\u20132100","volume":"644","year":"2018","journal-title":"The Science of the Total Environment"},{"key":"key2022010314075522400_ref004","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agwat.2018.05.013","article-title":"Interactive effects of plastic film mulching with supplemental irrigation on winter wheat photosynthesis, chlorophyll fluorescence and yield under simulated precipitation conditions","volume":"207","year":"2018","journal-title":"Agricultural Water Management"},{"key":"key2022010314075522400_ref005","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.gloplacha.2018.01.011","article-title":"Drought vulnerability assessment of maize in Sub-Saharan Africa: insights from physical and social perspectives","volume":"162","year":"2018","journal-title":"Global and Planetary Change"},{"key":"key2022010314075522400_ref006","unstructured":"Bogardi, J. and Birkmann, J. 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