Farzaneh Bandehelahi; Isa Esfandiarpour-Boroujeni; Alireza Karimi; Mohammad Hady Farpoor; Zohreh Mosleh; Morteza Fattahi
Abstract
Introduction Landscape represents a large portion of land/terrain that is either formed by a repetition of similar or dissimilar relief/molding types or an association of dissimilar relief/molding types (e.g., valley, piedmont, mountain, etc.). It is usually affected by a set of natural (e.g., climate, ...
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Introduction Landscape represents a large portion of land/terrain that is either formed by a repetition of similar or dissimilar relief/molding types or an association of dissimilar relief/molding types (e.g., valley, piedmont, mountain, etc.). It is usually affected by a set of natural (e.g., climate, organisms, parent material, topography, time, erosion, sedimentation, etc.) and/or artificial (e.g., artifacts) factors. Soil is one of the most important components of landscape that is affected by various factors such as water and wind. Aeolian or alluvial sediments (from seasonal rivers) in arid areas cause the formation of different landforms and change the landscapes in these areas. Therefore, the study of geoforms in arid regions can lead to a better understanding of geomorphological processes and soil change in these areas. There are various methods, including soil micromorphology and clay mineralogy, to understand the alteration of landscapes and the soils change on them. The aim of this study was to investigate the physical and chemical properties, clay mineralogy and micromorphology of soils in various geomorphic units of Davaran Region, Rafsanjan.Materials and Methods Seven dominant geomorphic units (geoforms) of the region, including pediment, margin of fan and cultivated clay flat, alluvial fan, desert pavement, margin of pediment and sand sheet, active drainage, margin of fan and uncultivated clay flat were selected using Google Earth images and field studies. Nineteen pedons were excavated and described in the geomorphic units. After selecting a representative pedon in each of the geoforms, their genetic horizons were sampled. Besides, in order to conduct soil micromorphology studies, undisterbed and oriented samples were collected from selected horizons. After transferring the samples to the laboratory, their physical and chemical properties were measured using standard methods. In addition, clay mineralogy studies were performed by X-ray diffraction method and micromorphological studies were done using a petrographic microscope. Finally, soil classification was performed based on both Soil Taxonomy (2014) and WRB (2015) systems.Results and Discussion Results showed that gypsification and calcification are the dominant soil forming processes in the studied region, which have led to the formation of Gypsic and Calcic horizons. This has placed the soils in the Gypsids and Calcids suborders based on the Soil Taxonomy system and the Gypsisols and Calcisols reference soil groups according to WRB system. The representative pedon in the margin of fan and cultivated clay flat (pedon 2) geoform lacks a salic horizon based on the Soil Taxonomy; while it is in the Solonchak reference soil group of the WRB. Also, the presence of argillic horizon in the representative pedon of the margin of fan and uncultivated clay flat geoform (pedon 7) indicates presence of a more humid paleoclimate in the history of the region. The results of clay mineralogy showed that the predominant minerals in the region include chlorite, illite, kaolinite, and smectite. The illite, chlorite, and kaolinite are inherited from papent materials of the soils, and the smectite has a transformation origin (from palygorskite and illite). Addition of this mineral by aeolian or alluvial sediments could not also be neglected. The micromorphological results indicated that the soil pores were mainly chamber. The presence of carbonates and gypsum in the studied soils has caused that the b-fabric in the most horizons to be Calcitic Gypsic Crystallitic. Gypsum was observed in the form of vermicular, lenticular, interlocked gypsum plates and subhedral shapes. Other pedofeatures in the studied soils include calcite nodule and limestone.Conclusion The simultaneous presence of aeolian and alluvial sediments in the different geoforms of Davaran region has caused the formation of stratified soils. Existence of dry climate and lack of significant vegetation in the region from one hand, and the addition of different sedimentary layers at different times (which causes soil rejuvination) on the other hand, has caused that the soils of the region, in general, not to be highly developed. As a result, few differences were observed among soils in different geoforms. Comparing the results of two soil classification systems for the studied soils showed that in general there is a relatively good correlation between them. Totally, the role of climate and parent material in alteration of the studied soils is evident; so that the physical and chemical properties, clay mineralogy and micromorphology of soils in different geoforms have been affected.
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.
