E. Hashempour; M.B. Farhangi; N. Ghorbanzadeh; M. Fazeli Sangani
Abstract
Introduction: Due to the increasing development of edible oil processing industries, large amounts of wastewater and solid wastes (SW) are inevitable in these industries. Organic wastes can be used as soil conditioners in agriculture due to the high content of organic matter and nutrient loads. Phosphorus ...
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Introduction: Due to the increasing development of edible oil processing industries, large amounts of wastewater and solid wastes (SW) are inevitable in these industries. Organic wastes can be used as soil conditioners in agriculture due to the high content of organic matter and nutrient loads. Phosphorus solubilizing bacteria including Bacillus spp., Pseudomonads and Rhizobium spp. can release phosphorus from insoluble organic and mineral sources in soil. Most soils in the semi-arid regions, including southern parts of Guilan province, have low organic matter content and do not support plant cultivation due to the low fertility and instability of soils. Hence, industrial wastes can be applied as a suitable and low-cost source of organic materials and nutrients in these soils. As phosphorus is one of the most important essential nutrients in plant nutrition which is also present in oil refinery soild wastes and P solubilizing bacteria can release phosphorus from the organic phase of the wastes and make it available in the soil solution, this study aimed to investigate the available phosphorus (Pava) content of soil after simultaneous addition of olive refinery-solid wastes and P solubilizing Bacillus spp.
Materials and Methods: the solid waste obtained from Ganje Rudbar oil refinery plant (located in Rudbar, Guilan province) and a soil sample was collected from a surface layer (0-30 cm) of a pasture, located in Lowshan area (Guilan province). A native strain of Bacillus sp. was isolated from the sampled soil based on its P-solubilizing ability in Sperber medium. An indicator strain, Bacillus persicus was also included in the experiments. P-solubilizing ability of the indicator strain was also evaluated in Sperber medium. The experiment was conducted in a completely randomized design based on factorial arrangement and three replications. Factors included three levels of solid waste (0, 2 and 4%), three levels of inoculated bacteria (no bacteria, native Bacillus sp. and Bacillus persicus) and eleven sampling times (0, 2, 7, 14, 28, 42, 56, 86, 116, 146, and 176 days). Different levels of solid waste were added to the soil, inoculated with bacteria (106 cell/g), and incubated at laboratory condition (~25 ºC) for six months. The moisture content of the soil mixtures fixed around 0.7 FC and kept constant during the incubation period. Sampling was done at desired times. The pH, organic carbon (OC), soil Basal Respiration (BR), available phosphorus concentration (Pava), and phosphatase enzyme activity were measured in soil samples. Data analysis and means comparison were done by Duncans’ test using SAS software package.
Results and Discussion: The studied soil was loam in texture, and had slightly alkaline pH, moderate Pava, and low OC content. The studied solid waste contained considerable OC and total P load. The effect of solid waste (SW), bacteria, sampling time and their interactions were significant on most of the measured characteristics (p < 0.05). SW application decreased soil pH and mixtures inoculated with native Bacillus sp. had lower pH values compared to those inoculated with Bacillus persicus, probably due to the greater effect of Bacillus spp. on SW decomposition compared with B. persicus. The highest average BR was attained in mixtures contained 4% SW which was 1.24 and 1.73 times greater than that in mixtures contained 2 and 0% SW, respectively. While the effect of SW on soil BR was obvious, bacteria inoculation had different impact on soil organic material decomposition and the lowest BR was measured in soil (0% SW) inoculated with Bacillus persicus. OC content of mixtures increased with SW application. The highest OC level (3.21 g 100g-1) was obtained in uninoculated mixture contained 4% SW, which was significantly greater than OC levels in mixtures inoculated with bacteria (p < 0.05). The lowest OC level (3.21 g 100g-1) was observed in uninoculated soil (0% SW). SW application significantly increased Pava. The greatest Pava concentration (142.77 mg Kg-1) was attained in uninoculated mixture contained 4% SW which was not significantly different from Pava concentration in 4% SW-mixture inoculated with native Bacillus sp. (P > 0.05). In control treatments (0% SW), Bacillus persicus was efficient in P release from soil native organic carbon and/or phosphate minerals. However, among the soils contained 2% SW, those inoculated with native Bacillus sp. had the highest Pava concentration. The average Pava concentration in the 4% SW-mixtures was 136.33 mg Kg-1 which was 3.5 times greater than that in control treatment (0% SW). Although soil Pava was related to phosphatase enzyme, this enzyme activity was not affected by treatments. In the P-releasing trend, it was found that 4% SW-mixtures had the highest Pava concentration after 6 months of incubation, and bacteria inoculation made the P-release trend to be flatter compared to control.
Conclusion: The application of oil refinery plant-solid waste improved the basal respiration of the studied soil and increased available phosphorus concentration. The comparison of applied solid waste levels showed that the inoculation of soil with Bacillus bacteria had a positive effect on available phosphorus concentration only at 2% solid waste level.
A. Lakzian; M. Fazeli Sangani; Alireza Astaraei; A. Fotovat
Abstract
This study was conducted to evaluate using terrain attributes derived from digital elevation model (DEM) as ancillary data to predict soil organic carbon (SOC) by implementing different statistical and geostatistical techniques. A linear regression model (LR), Artificial Neural Network model (ANN), ordinary ...
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This study was conducted to evaluate using terrain attributes derived from digital elevation model (DEM) as ancillary data to predict soil organic carbon (SOC) by implementing different statistical and geostatistical techniques. A linear regression model (LR), Artificial Neural Network model (ANN), ordinary kriging (OK), ordinary co-kriging (OCK), regression kriging (RK) and kriging with an external drift (KED) were performed to predict spatial distribution of SOC in an area of 2400 km2 in mashhad, iran. The SOC was measured for 200 soil samples of the study area and their corresponding Terrain attributes value was extracted from derived from 10-m resolution DEM. correlation between measured SOC and individual terrain attributes was determined, the number of 160 data were used for model development and 40 as validation data set. Resulting maps of different interpolation methods were compared to evaluate map quality using MAE and R2 criteria calculated from plotting measured versus estimated data. The results showed that there is a significant but not strong correlation between SOC and terrain attributes. The comparison of estimation techniques showed that the KED technique with wetness index as ancillary data has the best performance (MAE=0.18 %, R2=0.67) of all, but no significant difference with RK. There were modest differences between maps created with geostaistical technique but sensible difference with LR and ANN ones. The results of this study propose that although there is a significant correlation between SOC and terrain attributes therefore It can be use for enhancing the quality of map, but it is not able to express the spatial variability of SOC as it is necessary for detailed soil map. Because there is other factors controlling SOC spatial distribution
D. Namdar Khojasteh; M. Shorafa; M. Fazeli Sangani
Abstract
Abstract
Time domain reflectometry (TDR) is a widely used method for measuring the dielectric constant (Ka) and bulk electrical conductivity (σa) in soils. The TDR-measured σa and Ka can be used to calculate the soil solution electrical conductivity, (σp). A theoretical model describing a linear ...
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Abstract
Time domain reflectometry (TDR) is a widely used method for measuring the dielectric constant (Ka) and bulk electrical conductivity (σa) in soils. The TDR-measured σa and Ka can be used to calculate the soil solution electrical conductivity, (σp). A theoretical model describing a linear relationship between bulk electrical conductivity, σa, and dielectric constant, Ka, in moist soil was already presented. By using this linear relationship, the pore water electrical conductivity, σp, can be estimated in a wide range of soil types without soil-specific calibration. The objective of this study was to evaluate the linear model presented previously for TDR. The previous study was on light texture soils but in this study we used clay, clay loam, loam, silty clay and silty clay loam textures. The results showed that the linear model performed well for light texture soils but not for heavy textures. Such poor result for heavy texture is mainly due to this fact that dielectric constant pore water was lower than 80 which was proposed as default by model. This study showed that for heavy texture soils dielectric constant of pore water is smaller than light textured soils.
Keywords: Time-domain Reflectometry, Electrical conductivity, Dielectric constant
