Land Evaluation and Suitability
Moslem Zarrini Bahador; Javad Givi; Ruhollah Taghizadeh Mehrjerdi
Abstract
IntroductionWheat is one of the key cereals that provides a nutrition source to millions of people around the world. By conducting applied studies, the limitations of soil and climate that reduce the yield per unit area must be understood and solutions should be provided to address these limitations. ...
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IntroductionWheat is one of the key cereals that provides a nutrition source to millions of people around the world. By conducting applied studies, the limitations of soil and climate that reduce the yield per unit area must be understood and solutions should be provided to address these limitations. One of these strategies is a detailed study and spatial prediction of yield at points with different soil and climate characteristics. Models that predict crop yield can estimate the yield regarding climate, landscape, soil and management constraints. Considering the arid and semi-arid climate of Iran, the shortage of yield per unit area and the growing population, the country needs new research and strategies to increase yield per unit area. For this purpose, the first step is to examine the spatial variations of the yield. In the present study, the factors affecting the rainfed wheat yield in order of importance and efficiency of different methods of estimating spatial variations were investigated and the predicted yield of this crop was mapped digitally. Materials and MethodsThe study area, with an area of 6700 hectares is located in Badr watershed, around Ghorveh city, Kurdistan province, west of Iran. The mean annual air temperature is 12.1oC and the average annual precipitation is 345.8 mm. The soils of the area were classified in the orders of Entisols, Inceptisols and Mollisols and in 32 soil families, according to the last version of Keys to Soil Taxonomy. Based on hypercube technique, 125 observation points were selected, soil profiles were dug and described at these points and soil samples were collected from horizons of the profiles. Some physical and chemical characteristics of the soils were determined according to the standard laboratory methods. Rainfed wheat yield was measured at each side of one soil profile in a 1m×1m quadrangle. In the present study, in addition to geomorphological data, different types of auxiliary variables such as some of the primary and secondary derivatives of digital elevation model (DEM) and Landsat satellite image data were used. To find out the affecting auxiliary topographic and plantcover data on rainfed wheat yield prediction in order of importance,ReliefAttributeEval algorithm of WEKA software was used. Artificial neural network, decision tree Analysis, discriminant analysis, and averaging k-nearest neighbors are the models that were used in this research for prediction of rainfed wheat yield. Results and Discussion Calcium carbonate, organic carbon and coarse fragments, respectively with variability coefficients of 174.4, 62.4 and 61.3%, had the highest variation and pH, CEC and sand, respectively with 3.6, 16.9 and 20.3% variability coefficients showed the least variability in the soils of the studied area. In addition to geomorphological data, the parameters that were taken from the digital elevation model include elevation, slope percentage, slope aspect, slope curvature, slope surface curvature, longitudinal curvature, slope relative position, wetness index, multiresolution valley bottom flatness index, multiresolution ridge top flatness index, valley depth, channel network base level, modified catchment area, catchment slope, catchment slope aspect and catchment height. The environmental parameters that were taken from the Landsat 8 satellite imagery, include the normalized differential vegetation and the soil-adjusted vegetation indices. The ReliefAttributeEval algorithm in Weka software, in order of decreasing importance, identified geomorphology, relative slope position, longitudinal curvature, multi-resolution ridge top flatness index, slope, normalized differential vegetation index and soil-adjusted vegetation index as the most important factors affecting rainfed wheat production in the studied area. The amount of rainfed wheat yield was predicted by the models of artificial neural network, decision tree analysis, discriminant analysis, and averaging k-nearest neighbors. The error criteria for this prediction and a significant correlation between measured and estimated values of the rainfed wheat yield, indicate a higher accuracy for the averaging k-nearest neighbors model, compared to other models. The spatial distribution of the rainfed wheat yield, predicted by the averaging k-nearest neighbors model, was mapped. In the Badr watershed, the yields are continuously reduced towards the mountains. In this landscape, as the slope increases, depth and water storage capacity of the soil decrease mainly in the presence of Entisols. These soils are seen in the eastern, southern and western parts of the watershed. At lower elevations, the soils are deeper and are mainly Inceptisols. Rainfed wheat yield increases in the piedmont landscape, including hill, glacie and alluvial fan. Conclusion In order of decreasing importance, geomorphology, relative slope position, longitudinal curvature, multi-resolution ridge top flatness index, slope, normalized differential vegetation index and soil-adjusted vegetation index are the most important factors affecting rainfed wheat production in the studied area. The averaging k-nearest neighbors model has a higher accuracy for rainfed wheat yield prediction, compared to other models. In the Badr watershed, the rainfed wheat yield is continuously reduced towards the mountains in the eastern, southern and western parts, where mainly Entisols are present. The yield increases in the Inceptisols, located on the piedmont landscape.
Land Evaluation and Suitability
Javad Givi; Hojat Dialami; Mehdi Naderi Khorasgani
Abstract
Background and objective: In the assessment of land suitability, the land-production capacity is identified and the type of use is determined in proportion to that capacity. In this regard, the FAO approach has been used by many scholars in different parts of the world and Iran in land suitability assessment ...
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Background and objective: In the assessment of land suitability, the land-production capacity is identified and the type of use is determined in proportion to that capacity. In this regard, the FAO approach has been used by many scholars in different parts of the world and Iran in land suitability assessment studies. In this approach, the most commonly used method is the parametric method. The FAO approach uses Boolean logic to assess land suitability. This logic has been criticized by a number of land evaluation researchers. Because it does not take into account the continuous nature of the soil variations along the earth's surface and the uncertainty in the measurements. To overcome these shortcomings, the fuzzy analytical hierarchy process (FAHP) was presented to determine the land suitability classes. Land suitability should be determined based on a fuzzy analytical hierarchy process, in which, unlike the FAO method, unequal importance for different land characteristics and continuity of soil variations are considered. This research was carried out with the aim of qualitative land suitability evaluation in Dashtestan area, Bushehr province for Kabkab date palm (Phoenix dactylifera L. cv Kabkab) plantation, using two methods of FAO parametric (second root formula) and fuzzy analytical hierarchy process (FAHP) and comparing these two methods. Materials and methods: The study area is located in Dashtestan region, Bushehr province, Iran; between latitudes 29º 12΄ and 29º 31΄ N and longitudes 51º 09΄ and 51º 59΄ E. Its surface area is 23000 ha. The mean annual rainfall in the area is 250 mm and its mean annual temperature is 27 °C. The soil temperature and moisture regimes are hyperthermic and ustic, respectively. The physiographic unit which is river alluvial plain is very gently sloping. 80 % of the Kabkab date palm plantation is present in the study area. In order to achieve the objectives of this research, 50 date palm groves, each with an area of at least 0.5 ha and a palm of Kababab cultivar, aged 20 to 25 years, with the same management level and having different soil, were selected as observation points. Then a soil profile was dug randomely in each date palm grove, with dimensions of 1.5 (length), 1 (width) and 1.5 (depth) meters and described, using soil profile description guide. Soils were sampled from different horizons of the profiles and the required physical and chemical analyses were carried out on the samples, according to the standard laboratory methods. The drilling site was chosen to have a date palm tree in each of the four corners of the profile. The yield of the four trees located in four corners of each profile was measured and their average yield was considered as the final yield for the corresponding profile. Meteorological data was collected for a 10 year period from the nearest synoptic station (Borazjan station, Borazjan, Bushehr). Land indices were calculated, using soil and climatic data and parametric (second root formula) and fuzzy AHP methods. Weighted average of the climatic and the soil data was used and finally a land index was calculated for each soil profile. In the fuzzy AHP method, relative weight of each of the studied criteria was determined by analytical hierarchy analysis with establishment of pair wise matrix. Degree of membership for each soil and climatic criteria was also determined through membership functions and finally, land suitability classes were determined. At the end, accuracy of the methods was also compared. Landscape characteristics such as slope, drainage and soil depth were not considered in the land evaluation, because these characteristics did not show any limitation for the date production in the study area. Results: The results of qualitative land suitability evaluation based on fuzzy AHP method showed that 96.6 and 3.4 percent of the studied area are classified as S2 and S3, respectively. This is in the case that based on parametric (second root formula) method, 82 and 18 percent of the studied area are marginally suitable and non-suitable, respectively. According to these results, higher land suitability classes were obtained, based on fuzzy AHP than through parametric method. Correlation between the calculated land index and the measured yield, determined for the fuzzy AHP method was higher than the one obtained for the parametric method. This proves that the fuzzy AHP is a more appropriate method for land suitability assessment for Kabkab date palm plantation in the studied area than the parametric method (second root formula). Conclusion: According to the results of this research, the fuzzy AHP is a more appropriate method for qualitative land suitability evaluation than the parametric method (second root formula) for Kabkab date palm plantation in the studied area in Bushehr province.
