Digital soil mapping of land suitability index of rain-fed wheat (Case study: Ghorveh, Kurdistan Province)

Introduction:

 Rapid population growth in developing countries implies that more food will be required to meet the demands of this population. Wheat as one of the most important grain crops in the world is a great source of food for human which is planted under a wide range of environments and its production influences on local food security. The production of wheat per unit area in Iran is low compared to developed countries in the world. One of the main causes for this low yield is that the suitable land for planting has not been recognized. Therefore, to overcome this problem, land suitability assessment is needed, which can help to increase crop yield. The first step in agricultural land use planning is land-suitability assessment which is often conducted to determine which type of land use is suitable for a particular location Digital mapping approach have been applied to link between soil observations and auxiliary variables to understand spatial and temporal variation in soil class and other soil properties. Little attempt has been made for using Digital mapping approach to digitally map land suitability classes Therefore, this paper applied land suitability assessment framework and digital soil mapping approach to map land suitability for rain-fed wheat in Kurdistan province.

Materials and Methods:

 The study area is located in Kurdistan Province, western Iran. It surrounds the city of Ghorveh and covers a region of 6500 ha. The climate is semi-arid whose features can be performed using a cold and rainy winter and a moderate and dry summer. The mean yearly rainfall is 369.8 mm and over 90% of the rain falls between November and March. The mean temperature (10.8℃) is relatively cool. Soil moisture and temperature regimes are Xeric and Mesic, respectively. The physiography units include piedmont, fan, hills, and mountain and slope varies from gentle to very steep. At first land unit component map was prepared by Mahler physiography method, then, 17 representative profiles in each land unit component were dug and described. 105 auger samples also were taken at three depths (0-20, 20-50 and 50-100 cm). Soil texture, acidity, organic carbon, CaCO3, gypsum, ESP, electrical conductivity and gravel were measured in all soil samples. Topography and climate data were also recorded. Numeric ratings of soil, topography and climate parameters based on land requirements of wheat were determined and land suitability index using parametric method were calculated. Then land suitability classes of wheat were determined. A set of auxiliary variables (i.e. land unit component, terrain attributes and remotely sensed data) to predict land suitability classes of rain-fed wheat. In order to generate land suitability class map, artificial neural network were applied to make relation between auxiliary variables and land suitability classes.

Results and Discussion:

 The results showed that the area has about 36.61% N2 class, 40.32% N1 class and 22.53% S3 class. The validation results of the model based on the statistical indices including root mean square error, mean error, and determination coefficient (6.56, 4.81, 0. 68, respectively), indicates that the artificial neural network model has suitable accuracy. Auxiliary data including MrVBF index, LS factor, MRRTF index, slope, Land unit component, VDCN and band 2 were the most important for prediction of wheat land suitability index in digital method. The major limitation of the study area to plant rain-fed wheat were rainfall in the flowering stage, sever slope, shallow soil depth, high pH and gravel. Therefore, to increase production and sustainable agricultural system it is suggested land improvement operations such as terracing, decreasing pH, supplementary irrigation and gathering gravel. The highest values of rain-fed land suitability index were observed in the units physiographic of river plain and plateau, while the lowest value were observed in the units physiography of mountain and hill which had high slope, shallow soil and high gravel. These results were confirmed by one-way ANOVA and Duncan tests.

Conclusion:

 Based on the results of statistics indices artificial neural network had suitable accuracy for predicting land suitability index of wheat. In general, the study area, because of limitation of sever slope, shallow soil, high pH, and gravel, has low land suitability index for rain-fed wheat. Hence, to improve land suitability of the study area and increasing its production, suitable land improvement operations is required.