Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/112818
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorRaza, D-
dc.creatorShu, H-
dc.creatorEhsan, M-
dc.creatorFan, H-
dc.creatorAbdelrahman, K-
dc.creatorAslam, H-
dc.creatorQuddoos, A-
dc.creatorAslam, RW-
dc.creatorNazeer, M-
dc.creatorFnais, MS-
dc.creatorSardar, A-
dc.date.accessioned2025-05-09T00:55:08Z-
dc.date.available2025-05-09T00:55:08Z-
dc.identifier.urihttp://hdl.handle.net/10397/112818-
dc.language.isoenen_US
dc.publisherCogent OAen_US
dc.rights© 2025 the Author(s). Published by Informa UK Limited, trading as Taylor & Francis Groupen_US
dc.rightsthis is an open Access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), which permits unre-stricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.en_US
dc.rightsThe following publication Raza, D., Shu, H., Ehsan, M., Fan, H., Abdelrahman, K., Aslam, H., … Sardar, A. (2025). Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability. Cogent Food & Agriculture, 11(1) is available at https://doi.org/10.1080/23311932.2024.2448597.en_US
dc.subject2SFCaen_US
dc.subjectAgricultureen_US
dc.subjectMachine learningen_US
dc.subjectSustainable development goalen_US
dc.subjectWheat demanden_US
dc.titleEvaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainabilityen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume11-
dc.identifier.issue1-
dc.identifier.doi10.1080/23311932.2024.2448597-
dcterms.abstractAccurate insights into the spatial distribution of cultivated areas, land use for effective agricultural management, and improvement of food security planning, especially in developing countries. Therefore, this study examined the impact of land changes and population growth on agricultural land and wheat crop productivity. First, by incorporating more than three decades of satellite data (1990–2022) and different Landsat missions with machine learning algorithms, high-confidence classes were defined for different land features, including cropland. Second, the wheat grown area was identified using the cropland extraction based wheat acreage assessment method (CLE-WAAM). Third, population dynamics were examined by applying an exponential growth model to forecast population growth and predict food demand. These findings necessitate the integrated methodological development for wheat demand and supply mechanisms using the two-step floating catchment area (2SFCA) approach for a more thorough analysis of socioeconomic developments. The results revealed that the cropland area was transformed into non-cropland, with a percentage of 8.01. A 79% rise in the population occured between 1990 and 2022, with a projected increase of 112% by 2030. Specifically, the wheat cultivation area decreased by 28%, despite stagnant parameters observed since 2000. The proposed method contributes efficiently to the United Nations’ sustainable development goal (02: Zero Hunger) using satellite, geospatial, and statistical data integration.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationCogent food & agriculture, 2025, v. 11, no. 1, 2448597-
dcterms.isPartOfCogent food & agriculture-
dcterms.issued2025-
dc.identifier.scopus2-s2.0-85214942753-
dc.identifier.eissn2331-1932-
dc.identifier.artn2448597-
dc.description.validate202505 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThe Major program of the national natural Science Foundation of China (no. 42394065); the Special Fund of the State laboratory of information engineering in Surveying, Mapping and remote Sensing, Wuhan University; the researchers Supporting project number (RSP2025R351), King Saud University, Riyadh, Saudi Arabiaen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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