The Application of Multi-Criteria Decision-Making Model in Land Suitability Assessment

IntroductionLand suitability analysis and land use mapping are one of the most practical applications of Geographic Information Systems in land resource management. Complexities in soil have briefly limited studies on how it functions (Karlen, 2008). There are many methods from different centers including food and agriculture organizations (FAO), to evaluate land suitability. These methods are based on the characteristics of the land and the needs of the plant. Soil quality indicators are a set of measurable soil characteristics that affect crop production or the environment and are sensitive to land use change, management or conservation operations. (Brejda, 2000; Aparico and Costa, 2007). As a result, there is a global need for environmental issues, improvement of soil quality assessment methods for sustainable agricultural development and recognition of the sustainability of soil management and land use systems. Until now, various methods have been used to collect data, measure and evaluate soil quality, and laboratory analysis is the most common method, which has the advantage of being easy to use and characterizing and the quantitative characteristics of the test on different soil quality indicators (and Wang, 1998 Gong). Criteria for soil quality indicators should be a set of physical, chemical, biological characteristics or a combination of them (Doran and Parkin, 1997).Materials and MethodsIn the present study, the qualitative assessment of land suitability was investigated using fuzzy and parametric hierarchical analysis process models for the irrigated wheat and alfalfa crops. Soil characteristics, climatic conditions, topography and accessibility were selected based on the Food and Agriculture Organization framework and expert opinions. The interpolation function was used to plot values to points in terms of quality/ terrain characteristics for the type of operation and the evaluation was performed based on parametric and fuzzy analytical hierarchy process models. The process of evaluation is based on the FAO qualitative land evaluation system (FAO 1976a, b, 1983, 1985), which compares climatic conditions and land qualities/characteristics including topography, erosion hazard, wetness, soil physical properties, soil fertility, and chemical properties, soil salinity and alkalinity with each specific crop requirements developed by Sys et al. (1991a, b, 1993). Based on morphological and physical/chemical properties of soil profiles some 10 land units were identified in the study area.Climate data related to different stages of wheat growth were taken from ten years of meteorological data of the region (2007-2017) and the climatic requirements of the crop were extracted from the Table developed by (Sys et al., 1993). An interpolation technique using the ArcGIS ver 10.3 helped in managing the spatial data and visualizing the land index results in both models for preparing the final land suitability evaluation maps. The FAHP method and (Chang, 1996) method, which is a very simple method for generalizing the hierarchical analysis process to the fuzzy space, was used in order to assign weight to the criteria through. This method is based on computational mean of the experts’ opinion and the time normalization method and the use of triangular fuzzy numbers. A pairwise comparison matrix has been made fuzzy based on the experts’ opinion and using the triangular fuzzy numb. After calculating the weights of the criteria in the present research through the FAHP method, the entire criteria maps were overlaid through the use of the GIS function and the suitability maps were prepared for the main criteria. The main suitability maps went through weight overlaying eventually and the final map of suitability for wheat and alfalfa cultivation was produced. Results and DiscussionThe results of this study showed that the FAHP was an efficient strategy to increase the accuracy of weight allocation to criteria that affect the analysis of ground fit. The inability of conventional decision-making methods to account for uncertainty paves the way for the use of fuzzy decision-making methods. One of the drawbacks of the AHP is its inability to account for the uncertainty of judgments in pairwise comparison matrices. This defect is compensated by the FAHP method. Instead of considering a specific number in a pairwise comparison, a range of values in the FAHP is used for uncertainty for decision makers. The present research method can be useful for prioritizing lands, improving exploitation, conserving resources, and creating sustainable management. The results of this study, considering the main criteria of cultivation in the study area and the opinion of domestic experts, can provide useful insights into choosing the appropriate cultivation pattern in the region. The use of different fuzzy AHP methods as well as comparing the results of different fuzzy AHP methods in future research is recommended

Water management for sustainable irrigated agriculture in the Zayandeh Rud Basin, Esfahan Province, Iran

Irrigation systemsCropping systemsIrrigated farmingRiver basinsTopographyGeomorphologyClimateHydrologyWater qualityGroundwaterSoil salinitySustainable agricultureIranEsfahan ProvinceZayandeh Rud BasinChadegan Reservoir

Irrigated area by NOAA-Landsat upscaling techniques

In Murray-Rust, H.; Droogers, P. (Eds.), Water for the future: Linking irrigation and water allocation in the Zayandeh Rud Basin, Iran. Colombo, Sri Lanka: IWM

TOP SOIL SALINITY PREDICTION IN SOUTH-WESTERN PART OF URMIA LAKE WITH GROUND WATER DATA

Drying of Urmia Lake in the north-west of Iran threatens all the agricultural lands around the Lake. Therefore, soil salinity appears to be the major threat to the agricultural lands in the area. The aim of the present study was to investigate the spatial variation of top soil salinity by taking into account of underground water quality data as secondary information. The research was performed on a grid of 500 m in an area of 5000 ha. Soil samples were gathered during the autumn of 2009 and were repeated in the spring of 2010. Electrical conductivity of soil samples was measured in a 1:2.5 soil to water suspension. Then covariance functions were build for each data set and soil salinity prediction were done on a grid of 100 m using kriging estimator with taking into account the mean variation. Afterwards sodium activity ratio derived from underground water quality database was used as covariate to develop cross-semivarograms in prediction of top soil salinity using cokriging method. Results demonstrated that soil salinity varied from values lower than 0.5 to more than 35 dSm-1 as a function of distance to the Lake. Cross-validating the results from salinity predictions using only kriging estimator to that of cokriging with sodium activity ratio data revealed that kriging offered better estimations with ME of 0.04 for autumn 2009 and -0.12 for spring 2010. Cokriging estimator had more smoother and diffused boundaries than that of kriging and resulted in more bias estimations (ME= -0.11 and -0.21 for first and second data sets). Although kriging method had better performance in top soil salinity prediction, but cokring method resulted in smoother boundaries and reduced the negative effects of mean variation in the area

Top soil salinity prediction in South-Western part of Urmia Lake with ground water data

Drying of Urmia Lake in the north-west of Iran threatens all the agricultural lands around the Lake. Therefore, soil salinity appears to be the major threat to the agricultural lands in the area. The aim of the present study was to investigate the spatial variation of top soil salinity by taking into account of underground water quality data as secondary information. The research was performed on a grid of 500 m in an area of 5000 ha. Soil samples were gathered during the autumn of 2009 and were repeated in the spring of 2010. Electrical conductivity of soil samples was measured in a 1:2.5 soil to water suspension. Then covariance functions were build for each data set and soil salinity prediction were done on a grid of 100 m using kriging estimator with taking into account the mean variation. Afterwards sodium activity ratio derived from underground water quality database was used as covariate to develop cross-semivarograms in prediction of top soil salinity using cokriging method. Results demonstrated that soil salinity varied from values lower than 0.5 to more than 35 dSm-1 as a function of distance to the Lake. Cross-validating the results from salinity predictions using only kriging estimator to that of cokriging with sodium activity ratio data revealed that kriging offered better estimations with ME of 0.04 for autumn 2009 and -0.12 for spring 2010. Cokriging estimator had more smoother and diffused boundaries than that of kriging and resulted in more bias estimations (ME= -0.11 and -0.21 for first and second data sets). Although kriging method had better performance in top soil salinity prediction, but cokring method resulted in smoother boundaries and reduced the negative effects of mean variation in the area

Spatial Prediction of Soil Salinity Using Kriging with Measurement Errors and Probabilistic Soft Data

In this study it is shown how kriging with measurement errors (KME) is useful as opposed to more conventional kriging methods. The goal of the study was to properly account for field measured soil electrical conductivity (EC) as soft data for the spatial prediction of soil salinity. Samplings were done in autumn 2009 (first dataset), spring and autumn 2010 (second and third datasets) around Uromieh Lake, northwest of Iran. The salinity was measured both in the field and laboratory for the first and second datasets. The first dataset was used for error measurements from which an error variance can be estimated. The measured errors were then used for characterizing probabilistic type soft data using the second dataset. The KME with only soft data (SKME), KME with both soft and hard data (HSKME) and ordinary kriging methods were compared. Validation criteria, mean error (ME) and mean squared error (MSE) were used for comparing the methods. Finally, the SKME method was applied as a way of improving the salinity prediction for the third dataset where only field measured soil salinity data were available. Comparing different kriging methods, Ordinary Kriging (OK) showed the best results among the comparing methods with ME and MSE equal to 0.12 and 0.55 respectively. SKME with ME equal to 0.13 was slightly different from OK and SKME with ME equal to 0.24 resulted in more bias predictions among others. KME method has shown to be useful for soil salinity monitoring and can effectively reduce sampling time