vahid alah jahandideh mahjan abadi; alidad karami; sayed roholla mousavi; H. Asadi Rahmani
Abstract
Introduction:Soil quality as an important part from soil resource sustainability, consistently isinfluenced by human activities.Today, the presence of accurate information about variability of soil quality properties is considered more than ever to apply this information in economic modeling, environmental ...
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Introduction:Soil quality as an important part from soil resource sustainability, consistently isinfluenced by human activities.Today, the presence of accurate information about variability of soil quality properties is considered more than ever to apply this information in economic modeling, environmental predictions, accurate farming and natural resources management. Soil quality is defined as: “capacity of the soil to function, within the ecosystem and land-use boundaries, to sustain biological productivity, maintain environmental quality, and promote plant and animal health”; therefore, it is one of the most important factors in developing sustainable land management and sustaining the global biosphere. The definition of soil quality encompasses physical, chemical and biological characteristics, and it is related to fertility and soil health. Many indicators can be used to describe soil quality, but it is important to take into account sensitivity, required time, and related properties, than can be explained. Properties related to organic matter content, such as microbial respiration, microbial biomass carbon (MBC) and enzymatic activity (urease and phosphatases) can be used as soil quality indicators. They provide early information about mineralization processes, nutrient availability and fertility, as well as effects resulting from changes in land use or agricultural practices (e.g. tillage or application of different types of organic matter). In this context, biological properties have been used as soil quality indicators, because of their relationship with organic matter content, terrestrial arthropofauna, lichen, microbial community (biomass or functional groups), metabolic products as ergosterol or glomalin and soil activities as microbial respiration and enzyme production. This study was carried out for evaluation the spatial variability of biological soil quality indicators in wheat farms of Pasargad plain.
Materials and Methods: After reviewing the initial map of Pasargad, a total of 60 samples were provided using a systematic grid square sampling pattern with 500×500 m over the 1200 ha area of Pasargad at surface soil depth (0-30 cm). The characteristics of soil including organic carbon, pH, EC, microbial respiration, microbial biomass carbon , soil alkaline phosphatase and urease enzymes activity, ratio of microbial biomass carbon to organic carbon (MBC/OC) andmicrobial metabolic quotient(qCO2) were measured and calculated. Results were analysed with SPSS, Excel, GS+, and ArcGIS sotwares. Summary statistics were calculated for the 60 samples including mean, maximum and minimum, coefficient of variation (CV), kurtosis and skewness. In addition, Pearson correlation coefficients were calculated for untransformed data. For evaluation of different interpolation methods of soil characteristics in Pasargad plain root mean square error (RMSE), mean bias error (MBE) and mean absolute error (MAE) were used. We also constructed maps of the spatial distributions for each individual variable using best interpolators including kriging, inverse distance weighting (IDW) and cokriging methods.
Results and Discussion; The results showed that in the most cases the studied properties had too much variation. Based on the coefficient of variation, pHand qCO2had the lowest and highest variations, respectively. There was significant linear correlation between most of soil properties. From lognormal transformation was used for normalization of EC and qCO2. Best model for single semivariogram of organic carbon, microbial respiration, urease enzyme activity, microbial biomass carbon, qCO2 and MBC/OC in the soil was spherical model, for pH in the soilwas exponential model and for EC and phosphatase enzyme activity was gaussian model. Also, the best interpolator for pH, EC, organic carbon, microbial biomass carbon, urease activity, qCO2and MBC/OC was kriging, for alkaline phosphatase activity was inverse distance weight, and for microbial respiration was cokriging method. Amount of pH increased from north to south of Pasargad plain, but amounts of EC and organic carbon were inverse of pH.The higher amounts of microbial respiration and urease activity were observed at the south and east, respectively. The amount of phosphatase activity in the soil of Pasargad plain was scattered, and wide area in the plain had the activity between 215-275 µg PNP/g.hr. The higher amount of MBC and MBC/OCand lower amount of qCO2were observed at the west.
Conclusions: The biological soil properties were sensitive and rapid indicators of effects of soil management. Generally, according to the spatial variabilitymap, the areas in the region are critical situations in terms of biological indicators of soil. So the management techniques that are applied by farmers in these areas have to be changed. The results of this study used in the improvement of regional planning for sustainable management of soil.
alidad karami; R. Zara; vahid alah jahandideh mahjan abadi
Abstract
Introduction: Fractal geometry concepts have been widely applied as a useful tool to describe complex natural phenomena, in particular,for a better understanding of soil physical systems. However, limited information is available on the fractal characteristics of soil properties or soil aggregation. ...
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Introduction: Fractal geometry concepts have been widely applied as a useful tool to describe complex natural phenomena, in particular,for a better understanding of soil physical systems. However, limited information is available on the fractal characteristics of soil properties or soil aggregation. A soil aggregate is made of closely packed sand, silt, clayand organic particles building upsoil structure. Soil aggregation is a soil quality index integrating the chemical, physical, andbiological processes involved in the genesis of soil structure. Soil structure and its stability are important issuesfor many agronomic and environmental processes. Thus, quantitative description of soil structure is very important. Soil forming factors in different soils (various orders) and forms affect the soil structureformation. Characterizing aggregate size distribution for different soil orders using fractal theory is necessary for evaluating the impact of soil forming factors on soil structure and quantifying the relationship between fractal dimension and other important soil properties. Therefore, the aims of this research were quantifying the structure of different soil orders using fractal geometry, mean weight diameter of aggregates (MWD)and geometric mean diameter of aggregates (GMD). In addition, MWD and GMD indices and fractal parameters of soil aggregate size distribution were compared toevaluate soil structure and determinethe relationship between fractal parameters with MWD, GMDand other soil properties.
Materials and Methods: Fractal models which simulate soil structure are also used to better understand soil behaviors. Aggregate size distribution is determined by sieving a fixed amount of soil mass under mechanical stress and is commonly synthesized by the MWD, GMDand fractal dimensions such as the fragmentation fractal dimensions. Therefore, aggregate size distribution and its stability variation were evaluated using some fractal models and MWD and GMD (empirically indices).In the current study, the original data were obtained from analysis of diagnostic horizons of seven important soil orderslocated in Fars Province in the Southern Iran. Soil samples were collected from diagnostic horizons of seven soil orders includingEntisols, Vertisols, Aridisols, Mollisols, Alfisols, Histosols and Inceptisols. The measured physico-chemical properties of soil were aggregate size distribution, soil particle size percentage (sand, silt, and clay), saturation percentage (SP), organic carbon (OC), pH, calcium carbonate equivalent (TNV), gypsum content, soil electrical conductivity (EC) and soil bulk density (BD). The MWD and GMD indices, the fractal dimensions and fractal parameters of aggregates were then calculated. Relationships between soil properties with MWD, GMD and the fractal dimension were also determined.
Results and Discussion: The results showed that there was a significant correlation between fractal dimension of Riue and Sposito and Taylor and Wheatcraft models and soil aggregate stability indices (MWD and GMD indices of aggregates) with the other soil characteristics. This correlation between fractal parameters with organic matter, bulk density, clay and sand percentage was stronger than other soil properties. There was a significant and negative correlation (p< 0.01) between fractal dimension of Riue and Sposito and Taylor and Wheatcraft models with mean weight diameter of aggregates and geometric mean diameter of aggregates. Inverse correlation between fractal dimension and aggregate stability indices illustrateed thatlower fractal dimensionswere calculated for the soils with more stable aggregates which have the highest mean weight diameter of aggregates and geometric mean diameter of aggregates. Subsequently, the fractal dimension of aggregates could reflect the aggregate stability factors. The values of coefficient of determination (R2) and mean error (ME), root mean square error (RMSE), residual some of squares (RSS), mean square of non-fitted (Sr2) and Akaike) AIC (statistical criteria indicated that Taylor and Wheatcraft model had the better performance. Although largerfractal dimensions were estimated by Riue and Sposito modelwhich can be explained by the great model sensitivity, this model overall performed well.
Conclusion: The results indicated that fractal theory can be used to characterize soil structure at different soil orders and fractal dimensions of soil aggregate seems to be more effective in this regard, except forHistosols. Fractal dimension can be estimated using some easily available soil properties. Fractal theory can be applied to characterize and quantify soil structure in different soil orders of Fars Province.
