Comparing the Efficiency of TOPSIS, AHP and Square Root Methods in Cultivation Priority Determination for Wheat, Barley and Maize under Sprinkler Irrigation in Dasht-e-Moghan

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytical Hierarchy Process (AHP) methods are important research fields for soil sustainable management and suitable usage that have been developed in the recent decade. In the present research work, these methods along with Square Root method have been used for determination of cultivation priority of wheat, barley and maize in 12000 ha of Dasht-e-Moghan with 167 soil profiles. After soil sampling and analysis, the data of soil texture, percentage of particles larger than sand, soil depth, lime, gypsum, available water capacity, pH, OC, available P and K, CEC, ESP, slope, flooding, drainage and region climate in different land units were produced by weighted coefficients. Besides soil classification, finally eight properties of soil depth, lime, gypsum, pH, EC, ESP, slope and climate were selected for calculation of cultivation priority determination. Principal component analysis and Euclidean distance methods applied for selection of the effective properties on crops yield for application in priority determination methods and the criteria weights determined via pair-wise comparison matrix method. In all three methods cultivation priorities in land units were allocated to barley, wheat and maize respectively except in units 13 and 16, because of pH limitation. Multivariate variance analysis was used for comparison of the methods difference and the results revealed significant difference of all the three methods at 0.05 probability level. Allocated score mean values were calculated as 0.390, 0.381, 0.229 in TOPSIS, 0.380, 0.353, 0.267 in Square Root and 0.341, 0.337, 0.322 in AHP for barely, wheat and maize respectively and mean comparing of crops scores with LSD test showed the TOPSIS better accuracy than the others, which was related to twin comparing nature of alternative (crop) from positive and negative ideal, data standardization with Euclidean norm method, using mathematical equations and matrixes as well as suitable weights.