Land evaluation approaches comparing TOPSIS and SAW with parametric methods for rice cultivation.

Population growth has resulted in an increase in land exploitation on a large scale. Therefore, to increase crop yield and sustainable use of soil, it is necessary to exploit the land according to its potential. Due to land suitability assessment's multifactor nature, it needs a method for evaluating the factors simultaneously; in this case, multi-criteria decision models can be used. Therefore, this study aimed to compare the efficiency of the parametric method (square root) with multi-criteria decision-making approaches (technique for order of preference by similarity to ideal solution (TOPSIS) and simple additive weighting (SAW)) for evaluating land suitability in some rice cultivated areas in Khuzestan province, southwest Iran. A total of 28 rice farms were selected in the study area, and a pedon was dug, examined, and sampled in each. Several physicochemical land characteristics were used for the evaluation process, such as soil, climate, and topographical factors. According to the results, soil texture is the main limiting factor for rice farming in the study region, and organic carbon, salinity, and alkalinity ranked next. The range of land index for rice cultivation calculated by the square root method was from 19.3 to 70.9, from 49 to 95.3 by TOPSIS, and from 3.57 to 74.7 by SAW. The calculated explanatory coefficients between the actual yield and land indices for rice products estimated by the square root method, the TOPSIS approach, and the SAW method were 0.44, 0.63, and 0.60, respectively. This result confirms the high accuracy of TOPSIS method compared to SAW and square root methods. TOPSIS is therefore the ideal method for prioritizing options based on the simulation of the ideal answer because it is highly technical and robust in its decision-making approach. Furthermore, it uses the standardization method, equations, mathematical matrices, and suitable weights. Overall, it can be recommended as a suitable efficiency approach for land suitability evaluation.

Ecological potential assessment and land use area estimation of agricultural lands based on multi-time images of Sentinel-2 using ANP-WLC and GIS in Bastam, Iran.

The use of land-based ecological potential is a key management factor in achieving sustainable development and conserving soil and water resources. The purpose of this study is to use multi-time images of Sentinel-2 to determine the area of agricultural lands and evaluate their ecological potential in Bastam, Semnan Province, Iran. Therefore, in the first step, the most common agricultural lands (including apricot and grape orchards), field crops (including wheat and forage maize), and their phenological period were determined. Then, the information was classified using, namely support vector machine, maximum likelihood, and minimum distance. In the next step, soil, topography, and climate data were extracted according to expert opinions and the analysis of the basin's natural potential. The ecological evaluation models of the region were applied to homogeneous units, and after calculating the ecological potential index by the integrated ANP-WLC model, the ecological potential map was generated. The results of the land use classification showed that the support vector machine model and the minimum distance, respectively, had the best and worst performance with kappa coefficient of 0.82 and 0.61. The highest area of cultivated lands (3423 hectares) was estimated for wheat and the lowest (738 hectares) for forage maize. Moreover, the results of the ecological potential evaluation showed that 60% of apricot orchards, and 40.19% of the wheat fields, were in the class of good ecological potential. Overall, 2290 hectares were in the poor class, and 4030 hectares in the excellent class in terms of ecological potential.

Applying fuzzy inference system and analytic network process based on GIS to determine land suitability potential for agricultural.

Determining soil characteristics and land limitations through the land suitability assessment process is a very important step, due to its important role in sustainable management and reducing the effects of land degradation for the continuous production of agricultural crops. In this study, land suitability potential for wheat cultivation was determined by analytic network process (ANP)-fuzzy and traditional parametric methods including Storie and square root in lands with an area of about 4300 ha located in northwestern Iran. For this purpose, according to the land use maps, topography, slope, geology, and satellite image of the study area, the initial soil units were identified and soil was sampled by digging profiles in these units from the soil horizons of the profiles. According to the results, the ANP-fuzzy method had a more accurate evaluation in determining the land suitability potential, based on the determination coefficient (R2) value between wheat yield and land suitability index compared to parametric methods. The application of ANP-fuzzy method determined 2328.05 ha, 53.79% of land highly suitable (S1), 1577.69 ha, 36.45% moderately suitable (S2), 419.6 ha, 9.69% marginally suitable (S3), and 3.02 ha, 0.07% currently unsuitable (N1). The most important limiting characteristics were slope, soil depth, ESP, Gypsum, pH, and soil salinity. Therefore, due to optimal weighting of characteristics, determining suitable limits of critical points, and applying ideal fuzzy membership functions, the ANP-fuzzy method can be applied as a superior method for assessing land potential for sustainable agricultural crop production in other parts of the world.