Bagheri Bodaghabadi M.,Islamic Azad University at Najafabad |
MartInez-Casasnovas J.,University of Lleida |
Salehi M.H.,Shahrekord University |
Mohammadi J.,Shahrekord University |
And 3 more authors.
Pedosphere | Year: 2015
Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks (ANNs) were developed to map soil units using digital elevation model (DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base (WRB) classification criteria than the Soil Taxonomy (ST) system, but more soil classes could be predicted when using ST (7 soils in the case of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error (interpolation error) and validation error (extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology as soil-forming factors, should be used as ANN input data. © 2015 Soil Science Society of China.
Bagheri Bodaghabadi M.,Shahrekord University |
Salehi M.H.,Shahrekord University |
Martinez-Casasnovas J.A.,University of Lleida |
Mohammadi J.,Shahrekord University |
And 2 more authors.
Catena | Year: 2011
Topography has an important influence on the distribution of soils and their properties, especially in hilly lands, and related data are easily available, measurable and recognizable from digital elevation models (DEMs). To our knowledge, little attention has previously been paid to the effect of DEM attributes on the distribution of soils, using ordination methods. The objective of this study was to analyze relationships between topographical properties derived from DEM and soil distribution and to discuss their applicability in Digital Soil Mapping (DSM). The study was carried out in the Borujen area of central Zagros, Iran. A total of 13 plots (each one of 6.75. ha) were set up to calculate the percentages of the dominant soil series. Fifteen DEM attributes, including slope, aspect, curvature, maximum and minimum curvature, planform curvature, profile curvature, tangent curvature, wetness index, power index, sediment index, area solar radiation, direct radiation, diffuse radiation and direct duration were also computed. Canonical Correspondence Analysis (CCA) was used to summarize the data set and to evaluate the expected relationships. The results obtained show that there was a relatively strong correspondence between soils' series distribution and topographical properties. The DEM attributes that best related to the first axis were maximum curvature, slope and sediment index, all of which significantly positive correlated, and wetness index, direct duration and minimum curvature, all of which were negatively related. The second axis showed a negative trend with wetness index, direct duration and aspect, and a positive trend with sediment index and slope. These gradients were closely related to the first three canonical axes and explained 71.8% of the total variance of the soil series. The residual variance (28.2% of the total variance) was related to other soil forming factors, like parent material and vegetation cover, which were not investigated in this study. Considering that DEMs are still the most important source of environmental information, understanding the role of topographical factors in a region should help us to identify soils and their properties better and enable us to apply these derivates as input data in DSM. © 2011 Elsevier B.V.
Alamdari P.,University of Tabriz |
Jafarzadeh A.A.,University of Tabriz |
Oustan S.,University of Tabriz |
Toomanian N.,Agriculture and Natural Resource Research Center
Journal of Food, Agriculture and Environment | Year: 2010
In present study various forms of iron were investigated in three physiographic units of hill, piedmont and river alluvial plain in one transect of Dashte-Tabriz to assess the effects of pedogenic processes. Seven pedons were selected, sampled and analyzed for soil physicochemical properties and different forms of iron (Fe); Fed, citrate-bicarbonate-dithionate extractable Fe; Fe o, acid oxalate-extractable and Fe p, Na-pyrophosphate-extractable Fe. According to obtained results, there is some variation of iron oxides concentration which might be related to the rate of weathering, moisture content and pedogenic accumulations. A wide relative variation in mean value of Fe d(5.13 - 9.35 g kg -1), Fe (2.62 - 4.52 g kg -1) and Fe (0.18 -1.72 g kg -1) was observed among physiographic units. Piedmont plain with aridic moisture regime had lowest amount of Fe d and highest Fe o/Fe d ratio. The Fe d values are higher than Fe o values in all soils, indicating that a considerable fraction is present in crystalline form. Fe o.accumulates in B horizons and it is higher in mollisols than in other soil orders but Fe o has reducing pattern in profiles and it is high in profiles with limited in drainage. Based on linear coefficient of correlation, Fe has significant positive correlation with OC.
Karami M.,University Putra Malaysia |
Karami M.,Agriculture and Natural Resource Research Center |
Alimon A.R.,University Putra Malaysia |
Goh Y.M.,University Putra Malaysia
Small Ruminant Research | Year: 2011
This study was carried out to determine the effects of dietary antioxidant supplementations of vitamin E, Andrographis paniculata Nees and Curcuma longa L. on lipid and color stability of chevon. Four dietary treatments of eight goats each were randomly assigned to basal diet 70% concentrate and 30% oil palm fronds (CN), CN. +. 400. mg/kg vitamin E (VE), 0.5% turmeric (TU) or 0.5% Andrographis paniculata (AP). After 14 weeks of feeding, the goats were slaughtered and goat meat was sampled, then vacuum- packaged and conditioned for three post mortem aging periods (0, 7 and 14 days) in a chiller (4°C). Meat tenderness was improved (P<0.05) at 14 days aging in biceps femoris (BF) muscle. All antioxidant supplements improved (P<0.05) color of the meat. The supplementation of dietary antioxidants had significantly (P<0.05) improved the L (lightness), a (redness), b (yellowness) as well as the chroma and hue angle values. Post mortem aging periods significantly (P<0.05) influenced on redness, yellowness, chroma and hue angle. Similarly, the thiobarbituric acid reactive substances (TBARS) value of the chevon was shown to be affected by the dietary supplementation of antioxidants. It is concluded that TU and AP are potential dietary antioxidant supplements like VE, for the purpose of color stability and preventing of lipid oxidation, particularly in post mortem aging periods of the biceps femoris muscle. © 2011 Elsevier B.V.
Parvizi Y.,Agriculture and Natural Resource Research Center |
Gorji M.,University of Tehran |
Mahdian M.H.,Agriculture Research and Education Organization |
Omid M.,University of Tehran
World Academy of Science, Engineering and Technology | Year: 2010
Soil organic carbon (SOC) plays a key role in soil fertility, hydrology, contaminants control and acts as a sink or source of terrestrial carbon content that can affect the concentration of atmospheric CO2. SOC supports the sustainability and quality of ecosystems, especially in semi-arid region. This study was conducted to determine relative importance of 13 different exploratory climatic, soil and geometric factors on the SOC contents in one of the semiarid watershed zones in Iran. Two methods canonical discriminate analysis (CDA) and feed-forward back propagation neural networks were used to predict SOC. Stepwise regression and sensitivity analysis were performed to identify relative importance of exploratory variables. Results from sensitivity analysis showed that 7-2-1 neural networks and 5 inputs in CDA models output have highest predictive ability that explains %70 and %65 of SOC variability. Since neural network models outperformed CDA model, it should be preferred for estimating SOC.