Showing 7 results for Bayat
H. Bayat, A.a. Mahbobi, M.a. Hajabbasi, M.r. Mosaddeghi,
Volume 11, Issue 42 (winter 2008)
Abstract
Tillage is one of the important managing factors that can destroy or improve soil structure. Soil structure is affected by the machines and shape of the wheels. Field experiments were conducted at Hamadan Agricultural Research Station on a coarse loamy mixed mesic Calcixerolic Xerocrepts soil to measure and evaluate the effects of tillage and wheel-induced compaction on selected soil physical properties. Treatments included tillage methods (Moldboard Plow and Chisel Plow, (MP, CP)) performed using three customary tractors in Iran [John Deer (J), Romany (R) and Massey Ferguson ( MF) ]. Traffic zone and non traffic zone were other treatments. A split-plot design with three replications was used in a completely randomized arrangement of treatments. Soil samples were taken at the end of wheat growth season in traffic and non- traffic zone and from four layers and compared for bulk density (BD), cone index (CI), and mean weight diameter (MWD). The influence of both tillage methods on BD in most soil depths was not significant, meanwhile, BD was higher in the deeper layers. Wheel traffic did not affect BD significantly, but its effect decreased by increasing the depth. Commonly, conservation tillage increased structural stability as evaluated by MWD. Cone index illustrated the same trend as for BD, with some variation because of it higher sensitivity, so it was significantly was increased in CP rather than in MP for the traffic zone. Such a difference was not observed in non-traffic zone. The CI was also significantly increased in traffic zone compared with non-traffic zone. J significantly increased CI in two first layer in comparing with MF, but there was not significant difference between J and R. The MWD was increased by chisel plow in non-traffic zone and this increment was significant in fourth soil layer (22.5- 30 cm). Wheel traffic caused the increase of MWD in the second layer and significant difference was not observed in other layers. Overall, R caused less destruction in soil structure and tillage methods changed some of soil physical properties.
M. Bayat, B. Rabiei, M. Rabiee, A. Moumeni,
Volume 12, Issue 45 (fall 2008)
Abstract
To study relationship between grain yield and important agronomic traits of rapeseed in paddy fields as second culture, fourteen varieties of spring rapeseed were grown in a randomized complete block design of experiment with three replications at Rice Research Institute of Iran, Rasht, during 2005-2006. Analysis of variance showed that there were significant differences between varieties for most of traits. Broad sense heritability ranged from 0.29 for pod length to 0.99 for days to maturity. Phenotypic and genotypic coefficients of variation for days to maturity and the number of pods in secondary branches were the lowest and highest, respectively. Moreover, genetic advance with 5% of selection intensity varied from 3.68% (0.25 cm) for pod length in main branch to 31.48% (915.58 Kg.ha-1) for grain yield. Results from genotypic correlation coefficients demonstrated that there were positive significant correlations between grain yield and the number of secondary branches, the number of pod in main and secondary branches, pod length in secondary branches, pod diameter in main and secondary branches, 1000-grain weight and oil percentage, and negative significant correlations between grain yield and days to 90% of flowering and days to maturity. Path analysis on genotypic correlations for grain yield as a dependent variable and the other traits as independent variables showed that the 1000-grain weight and the number of pods in secondary branches had the highest direct effects and days to 90% of flowering had low and negative direct effect on grain yield. Therefore, indirect selection for increasing 1000-grain weight and the number of pods in secondary branches are recommended for improving grain yield in rapeseed as second culture in paddy fields.
K. Bayat, S. M. Mirlatifi,
Volume 16, Issue 61 (fall 2012)
Abstract
Global solar radiation (Rs( on a horizontal surface in the estimation of evapotranspiration of plants and hydrology studies is an important factor. Average daily global solar radiation on a horizontal surface was estimated by artificial neural networks (ANNs) and five empirical models including FAO (No.56), Hargreaves-Samani, Mahmood-Hubard, Bahel and Annandale. The weather data was selected from Karaj, Shiraz, and Ramsar weather stations, which have arid, semi arid and very humid climates (based on De Martonne classification). Daily solar radiation was measured at the three sites selected. The ANN, with actual duration of sunshine and maximum possible duration of sunshine as input parameters, generated daily solar radiation estimates with highest level of accuracy among all models tested. Rs estimates by ANNs with only temperature indices as input and by Hargreaves-Samani, Annandale and Mahmood-Hubard, which are all temperature oriented models, had lower accuracy at all three sites. In contrast, ANNs with actual duration of sunshine and maximum possible sunshine hours as inputs in Karaj, Shiraz and Ramsar station with root mean square error (RMSE) of 2.08, 1.85 and 2.05 Mj m-2 day-1 respectively were the best models. After ANNs, FAO-56 model which is based on sunshine hours produced results closer to the measured values. Rs estimates by ANNs with only temperature indices as input and by Hargreaves-Samani, Annandale and Mahmood-Hubard which are all temperature oriented models, had lower accuracy at all the three sites. These models are not appropriate for estimating daily global solar radiation.
A. R. Vaezi, Z. Bayat, M. Foroumadi,
Volume 22, Issue 2 (Summer 2018)
Abstract
Soil erosion by surface runoff introduced as surface erosion is one of the main mechanisms of land degradation in the hill slopes. Slope characteristics including aspect and gradient can control the differences of soil properties along the hillslope. This study was conducted to investigate the effect of slope aspect and gradient on variations of some soil properties in the short slopes. Five hills including both north and south aspects with different gradients (9-10%,
13-16%, 17-22%, 29-31% and 33-37%) were considered in a semi-arid region with 30 ha in area, in the west of Zanjan, northwest of Iran. The hills were weakly covered with pasture vegetation covers. Soil samples were collected along the slopes from two depths (0-5 cm and 5-15 cm) in four positions with 2 m distance along each slope with two replications. A total of 160 soil samples were analyzed for particle size distribution (sand, silt and clay), gravel and bulk density. Surface erosion was determined based on the variation of grain size distribution and bulk density. Differences of the grain size distribution and surface erosion between the two slope aspects and among the slope gradients were analyzed using the Tukey test. No significant difference was found between slope aspects in surface soil erosion. Nevertheless, surface soil erosion was affected by slope gradient in each slope aspect (R2= 0.78, p< 0.05). Surface erosion in the north slopes was more dependent on the slope gradient, as compared to the corresponding south slopes. In the south slopes, surface erosion was affected by the movement of silt particles from soil surface, while in the north slopes, it was significantly affected by the loss of clay particles.
S. Rahmati, A. R. Vaezi, H. Bayat,
Volume 23, Issue 1 (Spring 2019)
Abstract
Saturated hydraulic conductivity (Ks) is one of the most important soil physical characteristics that plays a major role in the soil hydrological behaviour. It is mainly affected by the soil structure characteristics. Aggregate size distribution is a measure of soil structure formation that can affect Ks. In this study, variations of Ks were investigated in various aggregate size distributions in an agricultural soil sample. Toward this aim, eight different aggregate size distributions with the same mean weight diameter (MWD= 4.9 mm) were provided using different percentages of aggregate fractions consisting of (< 2, 2-4, 4-8 and 8-11mm). The Ks values along with other physicochemical properties were determined in different aggregate size distributions. Based on the results, significant differences were found among the aggregate size distributions in Ks, particle size distribution, porosity, aggregate stability, electrical conductivity (EC), organic matter and calcium carbonate. The aggregate size distributions with a higher percentage of coarse aggregates (4-8 and 8-11 mm) also showed higher Ks as well as clay percentage. A positive correlation was also observed between Ks and clay, aggregate stability and EC, whereas sand showed a negative correlation with Ks. No significant correlations were found between Ks and silt, porosity and organic matter. Further, multiple linear regression analysis showed that clay and aggregate stability were the two soil properties controlling Ks in the aggregate size distributions (R2=0.80, p<0.01). Aggregate stability was recognized as the most important indicator for evaluating the Ks variations in various aggregate size distributions.
E. Masoumi, R. Ajalloeian, A.a. Nourbakhsh, M. Bayat,
Volume 26, Issue 3 (Fall 2022)
Abstract
Since clay is widely used in most construction projects, the issue of improving clay soils has considerable importance. This study aimed to optimize the variables affecting the properties of geopolymer and improve their mechanical properties using Isfahan blast furnace slag. Taguchi's statistical design method was used to model three process variables (blast furnace slag, water, and alkali sodium hydroxide agent) with four different values in the mixing design. Geopolymer was used to optimize the uniaxial compressive strength. Sixteen geopolymer compositions determined by mini-tab software were prepared and their uniaxial compressive strength was measured. The obtained results were modeled by analysis of variance, and then the interactions of the three variables on the uniaxial compressive strength of geopolymer were investigated using two and 3D diagrams. Then, the variables were optimized and the proposed values for the optimal sample were examined at temperatures of 25, 50, and 70°C and at times of 3, 7, 14, and 28 days of operation. A comparison of the results predicted by the models and the results of the experiments confirmed the validity of the models. Also, the scanning electron microscopy (SEM) images showed that the porosity will reduce from 7 to 28 days. It indicated that the use of the geopolymerization method has a significant role in stabilizing weak clay soils with low plasticity. The effect of fibers and geopolymer to reinforce was also investigated and for better evaluation, it was compared with soil stabilization with Portland cement. The results showed that in the most optimal geopolymer composition, the bearing resistance of clay has increased by more than 3400%. Meanwhile, fibers along with geopolymer with optimal percentage and length (0.1% by weight of geopolymer composition and length of 12 mm) were able to increase the uniaxial compressive strength of clay by nearly 4000%, which shows the excellent effect of using cellular fibers parameter whit the geopolymer in this research.
V. Habibi Arbatani, M. Akbari, Z. Moghaddam, A.m. Bayat,
Volume 26, Issue 4 (Winiter 2023)
Abstract
In recent years, indirect methods such as remote sensing and data mining have been used to estimate soil salinity. In this research, the electrical conductivity of 94 soil samples from 0 to 100 cm was measured using the Hypercube technique in the Saveh plain. 23 types of input data were used in the form of topographic and spectral categories. Land area parameters such as the Topographic Wetness Index (TWI), Terrain Classification Index (TCI), Stream Power Index (STP), Digital Elevation Model (DEM), and Length of Slope (LS) were considered as topographic inputs using Arc-GIS and SAGA software. Also, salinity spatial and vegetation indices were extracted from Landsat 8 images and were considered spectral inputs. The GMDH neural network was used to model salinity with a ratio of 70% for training and 30% for validation. The results showed that the soil salinity values were between 0.1 and 18 with mean and standard deviation of 5 and 4.7 dS/m, respectively. Also, the results of modeling indicated that the statistical parameters R2, MBE, and NRMSE in the training step were 0.80, 0.06, and 42.1%, respectively. The same values in the validation step were 0.79, 0.13, and 48.7%, respectively. Therefore, the application of spectral, topographic, and GMDH neural network indices for modeling soil salinity is effective.
