GIS and multi-criteria decision-making analysis assessment of land suitability for rapeseed farming in calcareous soils of semi-arid regions

Date published

2019-04-19

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Elsevier

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Article

ISSN

1470-160X

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Citation

Ostovari Y, Honarbakhsh A, Sangoony H, et al., (2019) GIS and multi-criteria decision-making analysis assessment of land suitability for rapeseed farming in calcareous soils of semi-arid regions. Ecological Indicators, Volume 103, August 2019, pp. 479-487

Abstract

To reverse the negative environmental properties effect on fertile lands for agriculture, land suitability evaluation is the first step in the designing the most sustainable land use and management systems. The aim of the present study was to develop and evaluate a land suitability model for rapeseed farming using topography factors, soil data and remote sensing data in calcareous soils of semi-arid regions northwestern Iran. For this purpose, stratified random sampling was used to select a set of 92 soil samples of agricultural land use from 0 to 30 cm depth. For land suitability assessment, the opinions of 19 local experts were used to make a decision for the weight of topography, soil data and remote sensing data factors by an analytic hierarchy process (AHP) from multi-criteria analysis. The input data including climate, topography, soil and remote sensing data were included that are related to rapeseed production. The results indicate the highest specific weight belongs to the soil texture (0.341), calcium carbonate equivalent (0.171) and elevation (0.114), respectively. Land suitability evaluation based on the United Nations Food and Agriculture Organization classification system indicated that 0.81% (420.8 ha) of the studied area was for high suitable (S1), 42.33% (21940.2 ha) was for moderately suitable (S2) and 11.78% (6104 ha) was for marginally suitable (S3) class. The 39.72% (20586.4) and 0.95% (492.1 ha) of studied area were located as currently not-suitable and permanently not-suitable for rapeseed productions, respectively.

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Github

Keywords

Environment factors, Modeling, Topography, Ecological, Remote sensing data

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Attribution-NonCommercial-NoDerivatives 4.0 International

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