Sh. Ayobi, M. H. Alizadeh,
Volume 10, Issue 2 (7-2006)
Abstract
Conventional soil survey methods for soils within the watersheds in Iran require a significant budget with many soil surveyors and much time. Additionally, no accurate and reliable information exists on the spatial variability of superface soil parameters in order to predict the soil loss by different models (RUSLE, PISAC, EUPOSEM, MORGAN). Also information on planning and management activities is lacking. These limitations call for methods of estimating soil properties using minimum sampling derived from important terrain parameters. This study was performed to develop soil-landscape models in three geological units (E2Sc, Ku, Plc), in a part of Mehr- watershed, Sabzevar. Six soil variables selected for this study were topsoil clay, gravel, sand, organic matter content, field capacity and bulk density measured at 316 sites on a regular 100m grid. Topographic attributes were calculated by a digital elevation model with 100m spacing. Finally, multiple linear regression analyses relating soil to topographic attributes were performed and then models were validated by additional sample points (78 of 316). The developed regression models showed significant relationships between surface soil properties and topographic attributes such as elevation, slope, aspect, wetness index, stream power index and sediment transport index. The mean errors and root mean square errors in the validation of the models were low and acceptable. The regression equations could explain only 26 to 72 % of the variability measured in the soil attributes in the watershed scale with 100m spacing.
M Noruzi, A Jalalian, Sh Ayoubi, H Khademi,
Volume 12, Issue 46 (1-2009)
Abstract
Crop yield, soil properties and erosion are strongly affected by terrain parameters. Therefore, knowledge about the effects of terrain parameters on strategic crops such as wheat production will help us with sustainable management of landscape. This study was conducted in 900ha, of Ardal district, Charmahal and Bakhtiari Province to develop regression models on wheat yield components vs. terrain parameters. Wheat yield and its components were measured in 100 points. Points were distributed randomly in stratified geomorphic surfaces. Yield components were measured by harvesting of 1 m2 plots. Terrain parameters were calculated by a 3×3 m spacing from digital elevation model. The result of descriptive statistics showed that all variables followed a normal distribution. The highest and lowest coefficient of variance (CV) was related to grain yield (0.36) and thousand seeds weight (0.13), respectively. Multiple regression models were established between yield components and terrain parameters attributes. The predictive models were validated using validation data set (20% of all data). The regression analysis revealed that wetness index and curvature were the most important attributes which explained about 45-78% of total yield components variability within the study area. The overall results indicated that topographic attributes may control a significant variability of rain-fed wheat yield. The result of validation analysis confirmed the above-stated conclusion with low RMSE and ME measures.
