Soil Genesis and Classification
samaneh Tajik; shamsollah ayoubi; mohmmad mehdi darvisihi; hossein khademi
Abstract
Introduction Soil snails constitute an important part of the forest ecosystem and play an essential role in litter decomposition and soil calcium concentration. Snails are known as bioindicators because of narrow distribution, short lifetime, and high sensitivity (22, 24). The abundance and distribution ...
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Introduction Soil snails constitute an important part of the forest ecosystem and play an essential role in litter decomposition and soil calcium concentration. Snails are known as bioindicators because of narrow distribution, short lifetime, and high sensitivity (22, 24). The abundance and distribution of soil snails are dependent on different environmental conditions, such as precipitation, pH, soil calcium, and plant cover. Also, soil properties are mainly related to topographic parameters. Because ecosystem components have complex relationships, we need powerful models to find effective factors and spatial variations of the soil fauna (23). Linear Regression and random forest are popular and applicable models in soil science. Up to the present, no study has investigated the effect of soil parameters on snail abundance using linear regression and random forest. This study was performed to investigate the effect of soil properties and topographic parameters on the abundance of soil snails and their distribution in a part of forest area located in Bahramnia forest, an experimental site in Golestan Province, in the north of Iran. Materials and Methods This study was conducted in Shast Kalate (Bahramnia) forest, an experimental forest of Gorgan University of Agricultural Sciences and Natural Resources, located at the eastern Caspian region, north of Iran (36° 43′ 27″ N latitudes, 54°24′ 57″ E longitudes). 153 soil samples were collected from 0-10 cm; then soil snails were gathered and classified into the Gastropoda taxonomic class group. Soil properties, such as Soil particle size distribution (clay, silt, and sand), soil pH, electrical conductivity (EC), calcium carbonate equivalent (CCE), soil organic carbon (OC), total nitrogen (TN), and Soil microbial respiration (Resp), were measured via laboratory analysis. Also, digital elevation model and satellite images were used to determine the topographic parameters, such as Elevation, slope, slope aspect (Aspect), land surface temperature (land temp) wetness index (WI) and normalized difference vegetation index (NDVI). We used linear regression and nonlinear random forest models for investigating linear and nonlinear relationships between soil properties, topographic parameters, and the abundance of soil snails. Likewise, sensitive analysis was done to find the importance of the input parameters. Results and Discussion The PCA analysis showed that first and second components explain 38 and 21 percent of the variation. In the first component, EC, OC, TN, pH, and silt were the most variable, and in the second component CCE, Clay, OC, sand, and EC were the most important parameters. In both components, topographic parameters had no effect. The PCA graph showed that CCE, sand, and pH had the most correlation with snail abundance and EC, Resp, OC, and TN affected their abundance. The validation results of regression and random forest models showed that random forests have more accuracy (0.49) and low error (1.82). In addition, the sensitive analysis showed that CCE, pH, EC, OC, aspects, elevation, and land temp are the most important parameters on snail abundance. Different studies reported that pH and CCE are effective parameters on snail abundance (20, 17). Also, Ondina., et al. (27) reported that EC has an important effect on soil snail abundance. We hypothesize that topographic parameters affect soil snail nonlinearly and by affecting soil properties. Aspect is one of the topographic parameters that, via an effect on land temperature, land cover, and pH (8), has an important role in soil snails. In this way, elevation, by affecting pH, wetness, land temperature, OC, and TN, affects soil snail abundance (13). Land temperature is the other topographic parameter that is affected by aspect and elevation and had a significant effect on snail abundance by affecting OC and wetness (17). Conclusion Based on the results, nonlinear random forest model had more accuracy than linear regression in predicting snail abundance. Results showed that calcium carbonate equivalent, pH, EC, and organic carbon were the most effective soil priorities on snail abundance. There was no linear relation between soil properties and soil snails, but in the nonlinear model, we found their role. Aspect, elevation, and land temperature were the most effective parameters on snail abundance that probably affected soil properties, such as calcium carbonate and soil moisture.
Behrooz pourmohamadali; M.H. Salehi; S.J. Hosseinifard; H. Shirani; I. Esfandiarpour Borujeni
Abstract
Introduction In recent decades, due to a significant increase of pistachio cultivation and uncontrolled exploitation of groundwater resources as well as reducing rainfall precipitation, groundwater level has dropped and the quantity and the quality of water has also been reduced. Therefore, agricultural ...
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Introduction In recent decades, due to a significant increase of pistachio cultivation and uncontrolled exploitation of groundwater resources as well as reducing rainfall precipitation, groundwater level has dropped and the quantity and the quality of water has also been reduced. Therefore, agricultural producers, researchers and policy makers need to pay more attention to appropriate land management as an important strategy to achieve greater yield per unit area and optimal use of soil and water resources. Crop yield prediction regarding its temporal and spatial variations has an important role in developing proper management programs. However, few studies have been carried out in relation to pistachio yield prediction using an acceptable range of features on regional scale. In the present study, pistachio yield modeling was performed by multivariate linear regression and artificial neural networkbased on soil, water and management features. Materials and Methods 129 orchard plots in different areas of Rafsanjan and Anar were identified and selected. The study area is located between 54° 56′ and 56° 41′ E, 29° 54′ and 31° 13′ N. Soil sampling, was performed from the areas under pistachio canopy and three soil depths of 0 to 40, 40 to 80 and 80 to 120 cm in each plot, fully expanded sub-terminal leaflets were randomly collected from non-fruiting branches, during the late July through August. Irrigation water of all orchards was also sampled. Moreover, for each orchard, a questionnaire was prepared to collect management and yield data. Soil quality indicators including particle size distribution, pH in saturated soil paste, electrical conductivity of saturated extract, soluble sodium, soluble calcium, soluble magnesium, available phosphorus and available potassium were determined for soil samples. The concentrations of phosphorus, potassium, iron, zinc, copper, manganese, calcium and magnesium in leaf samples and electrical conductivity in water samples, were also calculated. Finally, a dependent variable (pistachio yield) and 50 independent variables including soil, water and plant characteristics were used for modeling. For this purpose, stepwise multiple linear regression and artificial neural network technique were applied. Then, the study area was divided into 4 parts with the highest pistachio orchards densities and regression models were run for each part, separately. The ability of models to yield prediction was evaluated using the root mean square error (RMSE), relative root mean square error (% RMSE), adjusted coefficient of determination (adj - R2) and Durbin - Watson statistic (D – W). Results and Discussion The average of yield in the study area is about 1,700 kilograms per hectare. Results indicated that multiple linear regression could explain only 26 percent of the pistachio yield variation, but its accuracy increased when data became more homogeneous via dividing the study area into four parts. The model adjusted-R2 for Noogh, Anar, eastern suburbs and western suburbs orchards rose to about 92.4, 81.5, 95 and 53.6 percent, respectively. In all regression models except the model of western suburbs, at least one of the characteristics associated with irrigation water was significant. Artificial neural network with 9 neurons in a hidden layer, Tangent - sigmoid activation function and Levenberg - Marquardt training function, has a 98.3 percent accuracy in predicting pistachio yield in the study area (% RMSE = 13.8). Conclusion Multivariate linear regression model did not accurately predict the pistachio yield for the whole of study area whereas increasing data homogeneity and decreasing sources of variations, reduced complexity of relationships between features which resulted in increasing of the efficiency of linear regression to modeling these relationships. These models were highly sensitive to irrigation water features. Therefore, special attention should be paid to modern irrigation techniques and proper management approaches in order to enhance water efficiency. Overall, artificial neural network had greater accuracy compared to multivariate linear regression for pistachio yield modeling. This indicates the existence of non-linear and complex relationships between pistachio yield and the factors affecting yield and also the necessity of using modern and robust data mining tools for crop yield estimating. It seems that artificial intelligence techniques can be used as an efficient tool for developing proper management programs.
