Evaluating the efficiency of future crop pattern modelling using the CLUE-S approach in an agricultural plain

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dc.authorid0000-0002-7788-3839
dc.authorid0000-0002-0642-5566
dc.authorid0000-0002-0666-7784
dc.authorid0000-0002-6781-2658
dc.contributor.authorAkin, Anil
dc.contributor.authorErdogan, Nurdan
dc.contributor.authorBerberoglu, Sueha
dc.contributor.authorcilek, Ahmet
dc.contributor.authorErdogan, Akif
dc.contributor.authorDonmez, Cenk
dc.contributor.authorSatir, Onur
dc.date.accessioned2026-02-12T21:05:10Z
dc.date.available2026-02-12T21:05:10Z
dc.date.issued2022
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractLand Use Land Cover (LULC) change detection is an essential source of information for understanding the magnitude of environmental change to implement future development strategies. Sophisticated techniques (i.e. modelling) have been applied in the last decades worldwide for accurate LULC classification and future pro-jections. However, using these techniques in heterogeneous agricultural regions to extract crop-related infor-mation is still challenging. This study aimed to evaluate the efficiency and applicability of crop pattern prediction for the year 2050 with the CLUE-S model in an agricultural plain. The model was calibrated and validated based on the LULC changes to model future changes of the crop pattern by 2050. Twelve driving factors were utilised to quantify the relationship of LULC classes. The statistical relationship among the factors was examined with a Binomial Logistic Regression approach. Additionally, the magnitude of change in agricultural crop patterns between 2015 and 2050 was calculated according to local/regional policies and incorporated to the model as scenario layer. Future model results indicated that the cotton would increase by % 45 whereas maize would decrease by % 10 compared to 2015. The model performance was evaluated using the ground truth from the field observations considering the agricultural policies through the ROC (Receiver Operating Characteristic) indicators. The mean ROC value for the agricultural crop patterns was calculated as 0.71, while ROC values for other LULC classes were over 0.90. Overall a 0.79 ROC value was achieved as the model accuracy.
dc.description.sponsorshipTUBITAK (The Scientific and Technological Research Council of Turkey); Developing Spatial Information Technologies based Decision Support Systems for Water Management in Seyhan River Basin [115Y063]
dc.description.sponsorshipThe authors would like to thank TUBITAK (The Scientific and Technological Research Council of Turkey), Turkey for financial support of the Developing Spatial Information Technologies based Decision Support Systems for Water Management in Seyhan River Basin; ID: 115Y063 project.
dc.identifier.doi10.1016/j.ecoinf.2022.101806
dc.identifier.issn1574-9541
dc.identifier.issn1878-0512
dc.identifier.scopus2-s2.0-85137852297
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.ecoinf.2022.101806
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6833
dc.identifier.volume71
dc.identifier.wosWOS:000864021800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofEcological Informatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260212
dc.subjectCrop-pattern modelling
dc.subjectLULC change
dc.subjectCLUE-s model
dc.titleEvaluating the efficiency of future crop pattern modelling using the CLUE-S approach in an agricultural plain
dc.typeArticle

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