نوع مقاله : مقاله پژوهشی
نویسندگان
1 علوم و مهندسی خاک، دانشکدگان کشاورزی و منابع طبیعی دانشگاه تهران،کرج، البرز
2 گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشکدگان کشاورزی و منابع طبیعی دانشگاه تهران، کرج، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Given the critical role of land suitability assessment in the optimal management of natural resources and sustainable development, selecting an accurate and reliable assessment method is essential. Multi-criteria decision-making (MCDM) approaches provide a robust framework for evaluating alternatives based on criteria of varying importance., In this study, the MARCOS MCDM method was employed to assess land suitability for irrigated corn, and its performance was compared with that of the traditional parametric method. The MARCOS method operates on the basis of a decision matrix, where alternatives (in this case, soil profiles) are evaluated against a set of criteria (land characteristics). Criterion weights were determined using the best-worst method (BWM), which determined electrical conductivity as the most influential factor, while climate was deemed the least important. Results indicated that the square root method achieved acceptable accuracy in land suitability assessment, with a correlation coefficient of 0.72 between crop yield and the land index. In contrast, the MARCOS method, with a lower correlation coefficient of 0.27, failed to deliver sufficient accuracy in classifying land suitability and was unable to correctly identify fully suitable land classes. While the square root method is a well-established technique in land suitability evaluation and produced favorable results in this study, the MARCOS method—despite incorporating ideal and anti-ideal solutions and applying weighted criteria—tended to generalize the area into just two broad classes: unsuitable and low suitability. This limitation highlights the need for careful consideration when selecting MCDM methods for land evaluation tasks.
کلیدواژهها [English]
In today's world, food scarcity is one of the main challenges humanity faces, and it intensifies daily with the increasing global population. This issue necessitates the effective and optimal use of existing food resources. One of the critical factors in ensuring food security is the proper management of agricultural lands Therefore, land use must be conducted intelligently and sustainably to meet the growing population's needs. The suitability of lands for specific crops and agricultural methods plays a key role in increasing productivity. If lands are utilized correctly and in accordance with soil and climate characteristics, production levels can be increased while preventing environmental degradation. Thus, paying attention to land suitability and its proper management is not only essential for ensuring a sufficient food supply but also for maintaining the ecological balance of the land. Multi-criteria decision-making methods, by creating appropriate structures, help facilitate the decision-making process. The Marcos method is a multi-criteria decision-making approach. Since this method has not been previously used in land suitability assessments, this research examines and compares the Marcos method with the parametric method, one of the common methods for land suitability assessment.
The study area is located within the Abyek lands in Qazvin Province. In this study, physical and chemical data from 275 soil profiles, along with data on the slope and climate of the area, were utilized. Characteristics such as the percentage of lime, gypsum, organic carbon, gravel, slope, and climate were used as criteria.
In the square root method, the degree of limitation was determined by comparing the criteria related to each profile with the requirements of the corn product. Finally, the land suitability class was obtained using the land index.
In the Makos method, eight land characteristics were used as criteria, and soil profiles were used as options to form the decision matrix.
Since this method is not capable of weighting criteria, the criteria were weighted using the best-worst method.
After determining the land suitability class, digital maps were prepared using the random forest model, and the land suitability class for the area was obtained using the mentioned methods.
The results of this article showed that the square root method, with a correlation coefficient of 0.72, has performed land suitability assessment with acceptable accuracy. In contrast, the Marcos method, with an accuracy of 0.27, has not been able to perform land suitability assessment satisfactorily.
Based on the square root method, out of 57,132 hectares of land in the region, 26.67% of the land falls into the very suitable class, 42.10% into the relatively suitable class, 13.98% into the low suitability class, and 17.22% into the unsuitable class.
The Marcos method was unable to separate the class of very suitable land, placing 0.32% of the land in the relatively suitable class, 74.71% in the low suitability class, and 93.27% in the unsuitable class.
The parametric method is a standard method for evaluating land suitability. The present study compared the square root method with the multi-criteria decision-making method of Marcos. The results showed that the Marcos method did not provide acceptable results. The reason for this could be that the Marcos method is oriented towards the ideal state. Based on land characteristics and expert opinions, the most important criteria of this study were salinity and alkalinity. In the Marcos method, due to its orientation towards the ideal state, areas with high salinity and alkalinity were classified as unsuitable, and other areas were determined to have low suitability.
Conceptualization, Fereydoon Sarmadian; methodology, Fereydoon Sarmadian; software, Fereydoon Sarmadian, Elahe Karbakhsh-Ravari; validation, Fereydoon Sarmadian, Elahe Karbakhsh-Ravari; formal analysis, Fereydoon Sarmadian, Elahe Karbakhsh-Ravari investigation, Elahe Karbakhsh-Ravari; resources, Elahe Karbakhsh-Ravari; data curation, Fereydoon Sarmadian, Elahe Karbakhsh-Ravari; writing—original draft preparation, Elahe Karbakhsh-Ravari; writing—review and editing, Elahe Karbakhsh-Ravari; visualization, Elahe Karbakhsh-Ravari; supervision, Fereydoon Sarmadian; project administration, Fereydoon Sarmadian; funding acquisition, Fereydoon Sarmadian. All authors have read and agreed to the published version of the manuscript.”All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.
Data available on request from the authors.
The authors would like to thank all participants of the present study.
The authors avoided data fabrication, falsification, plagiarism, and misconduct.
The author declares no conflict of interest.
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