Showing 12 results for Ramezani
R. Ramezani, A. Karbassi,
Volume 6, Issue 2 (summer 2002)
Abstract
In this research, sunflower oil that was extracted and refined at Shiraz Narges Oil Company was packed in four different containers, namely, clear PET (polyethylene terephtalate), yellow PET, yellow HDPE (high density polyethylene), and metal can. Samples were kept at ambient temperature in the shelf exposed to normal light for a period of 1 year. Peroxide values were determined at 45-day intervals and TBA and anisidine values were measured at 0, 6 and 12-month periods. In order to determine the effect of artificial light, some samples in PET and HDPE containers were kept in a wooden box equiped with four (20 w) fluorescent lamps and the peroxide values of the samples were determined. Light transmittance properties of the packaging materials were measured using a spectrophotometer over a wavelength range of 350 nm to 800 nm.
The data indicated that the greatest variations in peroxide, TBA and anisidine values were observed in samples in HDPE containers (significantly different at 5% level) kept under normal light and ambient temperature for a period of 1 year. It was also shown that the shelf life of sunflower oil in HDPE container was less than 6 months while for the other packaging materials it was more than one year. Samples exposed to artificial light indicated that the highest peroxide values belonged to samples in clear PET while those in yellow PET proved to have the lowest. Finally, PET container proved to be the most suitable container for sunflower oil followed by metal can. Yellow PET with the lowest transmittance percentage (350-800 nm) and peroxide value (when exposed to 20 w fluorescent lamp) could be substituted for clear PET. HDPE container proved to be unsatisfactory for sunflower oil due to high oxidation rate.
A. M. Amini, M. Ramezani,
Volume 10, Issue 1 (spring 2006)
Abstract
The general goal of this research was to study the effective factors on the success of the poultry-farm cooperatives in Isfahan province. Based on multi-stage cluster sampling and Chocran formulas, nine cooperatives and 173 members were selected from 15 cooperatives with 1768 members. After completion of questionnaires, the AHP method was employed for scaling. Statistical tests (factor analysis and alpha coefficient) indicated that this research has a high construction validity and reliability. The research results indicated that cooperative functions toward fulfillment of members' demands were weak. The results of the path model indicated that effective factors on the success of cooperatives (from highest to the lowest) are: knowledge of cooperative principles by the members, extra-organizational factors, education quality, managers' skills, participation in cooperative affairs, and members' education level. Also, the success of cooperatives much relies on inter-organizational factors. The amount of members' share from education, participation in cooperative's affairs, the managers' specialty and skills, and knowledge of cooperatives' principles are very weak. Research results indicated that there is a direct and statistically significant correlation between these independent variables and the success of cooperatives in Isfahan province.
A. Ramezanian, M. Rahemi,
Volume 10, Issue 4 (winter 2007)
Abstract
To evaluate the effects of chemical and hand fruit thinning on pistachio flower bud retention, experiments were conducted during 1382 and 1383. In the first year, ethephon treatments at the concentrations of 100 and 200 mg L-1, urea at 2.5% and 5%, naphthaleneacetic acid (NAA) 125 and 250 mg L-1 and naphthaleneacetamide (NAD) 250 and 500 mg L-1 were sprayed on two branches of nine uniform trees with four replications. During the second year, hand fruit thinning treatments were used in addition to the previous treatments. Chemical treatments reduced flower bud abscission among them ethephon treatments were the most effective on flower bud retention. As compared with other treatments, hand thinning treatments had also positive effect on flower bud retention. Fruit thinning also increased kernel weight, reduced the number of nuts per ounce and decreased blank fruits. Fruit thinning had no significant effect on the yield of branches during ‘on’ year.
F. Mahmoodi, R. Jafari, H. R. Karimzadeh, N. Ramezani,
Volume 19, Issue 71 (spring 2015)
Abstract
This study aimed to evaluate the performance of TM satellite data acquired in June 2009 to map soil salinity in southeast of Isfahan province. Ground salinity data (EC) was collected within 9 pixels, covering an area of approximately 8100 m2 using stratified random sampling technique at 53 sample sites. Spectral indices including TM bands, BI, SI1, SI2 and SI3, PC1, PC2, PC3 and also multiple linear regression modeling and maximum likelihood classification techniques were applied to the geometrically corrected image. Results of regression analysis showed that the TM band 4 had the strongest relationship with EC data (R2=0.48) and also the relationship of the modeling image using TM 3, TM 4, TM5 and PC3 was significant at the 99% confidence level. The accuracy assessment of the stratified TM4 and modeling image into five classes including 0-4, 4-20, 20-60, 60-100 and EC>100 ds/m indicated that there was more than 86% agreement with the field measurements of EC data. Therefore, it can be concluded that the discretely classified salinity maps have higher accuracy than regression methods for identifying broad areas of saline soils, and can be used as appropriate tools to manage and combat soil salinization.
N. Moshtagh, R. Jafari, S. Soltani , N. Ramezani,
Volume 19, Issue 73 (fall 2015)
Abstract
Spatial estimation of evapotranspiration (ET) rates is essential for agriculture and water resources management. This study aimed to estimate ET v an ET estimation algorithm called Surface Energy Balance Algorithms for Land (SEBAL) and also by using TM June 2009 satellite data in Damaneh region of Isfahan province. To calculate the ET, all the energy balance components and related parameters including net radiation, surface albedo, incoming and emitting shortwave and longwave radiation, surface emissivity, soil heat flux, sensible heat flux, NDVI vegetation index, Leaf Area Index(LAI), and surface temperature were extracted from the geometrically and radiometrically corrected TM images. Results showed that ET rate was about 7.2 mm day-1 in agricultural areas, which was almost equal to 6.99 mm day-1 extracted from the FAO Penman-Monteith method in the synoptic weather station of Daran. Results here indicate that the extraction of ET rate which is almost equal to plant water requirements from remote sensing data can be used in selecting appropriate plants for agriculture and rehabilitation purposes in extensive arid and semi-arid regions of Isfahan province where severe droughts and water shortage are major problems.
S. M. Seyedian, M. Karami Moghadam, Y. Ramezani,
Volume 21, Issue 4 (Winter 2018)
Abstract
The study of flow patterns in front of intake has been attracted the attention of researchers during the past decades to explore the mechanism of flow and sediment entry to the intake. In this study, the separation and stream tube dimensions were investigated in water intakes installed to rectangular and trapezoidal main channel. These researches were carried out with experimental and fluent models. The results of experimental and fluent models have a good conformity. It was found that, in trapezoidal main channel, the stream tube width decreases near the bed and increases near the surface and separation dimensions reduced and led to reduction of sediment entry and increase of efficiency
K. Vafaei, O. Bazrafshan, H. Ramezanietedali,
Volume 24, Issue 2 (Summer 2020)
Abstract
Estimating the ecological water footprint and the virtual water trade in different agricultural crops in arid and semi-arid regions can help better manage the limited water resources.This research calculated temporal and spatial ecological water footprint of rain-fed and irrigated almond production in national and provincial scale using during 2008 to 2014. The results show that annual average water footprint in rainfed almond is 9.2 m3/kg, which the share of green and grey water is 72% and 28%, respectively which Ilam and Kohgiloyeh & Boyerahmad have a largest share in green water footprint with 91% and 90%, respectively. In adition to, in irrigated almond, the annual average water footprint is 11.4 m3/kg, which the share of green, blue and grey water is 0.19%, 71% and 10%, respectively. Sistan & Balouchestan, Khuzestana and Hormozgan have the highest share in blue water footprint. The total volume of water footprint of rain-fed and irrigated almond production is 1923 and 8242 MCM, respectively. Also, results show that about 92 percent of the total volume virtual water (equivalent to 9343 MCM per year) in almond production, has been exported to other countries through the virtual water trade.
M. Ahmadi, H. Ramezani Etedali, A. Kaviai, A.r. Tavakkoli,
Volume 27, Issue 1 (Spring 2023)
Abstract
Studying the effects of drought in mountainous areas is facing problems due to the inappropriate distribution of stations, the lack of long-term data, and areas lacking statistics. Therefore, the main objective of this research was to investigate the drought indices of Kurdistan province using TRMM satellite data and ECMWF dataset, as well as to evaluate their accuracy against the data of land stations in Kurdistan province. First, ECMWF precipitation data for the 2000-2020 period and TRMM precipitation data for the 2000-2019 period were obtained and evaluated using RMSE, MBE, and correlation coefficient statistics. Spearman's correlation coefficient showed a significant relationship between the TRMM satellite precipitation data and the ECMWF dataset with ground stations at the 5% level, and the value of this coefficient was between 0.95-0.85. According to the results, it can be acknowledged that the TRMM satellite rainfall and ECMWF dataset in the monthly time scale had proper accuracy at the Kurdistan province level. Therefore, these two sources were used to examine the drought indices. SPI, SPEI, and ZSI drought indices were calculated in different monthly periods (1-48), PNI in different monthly, seasonal, and annual periods in Kurdistan province (Saqqez, Qorveh, Bijar, Sanandaj stations). Spearman's correlation coefficient indicated a significant relationship at the 5% level between the SPI, ZSI, PNI, and SPEI index of the ECMWF dataset with ground stations. The results of the SPI index showed that the lowest RMSE value for the TRMM satellite at the Saqqez station and the three months was equal to 0.45, and for the ECMWF dataset at the Sanandaj station and the 24 months was equal to 0.35.
S. Koohi, B. Bahmanabadi, Z. Partovi, F. Safari, M. Khajevand Sas, H. Ramezani Etedali, B. Ghiasi,
Volume 27, Issue 4 (Winter 2023)
Abstract
Water supply remains a significant challenge in arid and semi-arid regions, and in addressing this concern, unconventional water sources have gained prominence. Notably, the extraction of water from air humidity, classified as an unconventional water source has seen increased adoption. Diverse techniques have been developed to achieve this goal, with the utilization of mesh networks being particularly prevalent. Consequently, this study assesses the evaluation of the performance of the ERA5 dataset in the simulation of atmospheric variables that influence the ability to assess water harvesting from air humidity (including temperature, wind speed, and water vapor pressure). Also, the possibility of water harvesting from air humidity was investigated in Qazvin Province. The outcomes demonstrated the benefit of incorporating adjustment coefficients in estimating temperature and wind speed using the ERA5 dataset. Based on these findings, the northwestern and southern regions of the province (Kuhin and Takestan) exhibit notable potential during spring and summer for water harvesting from the atmosphere. The peak water harvesting for these stations in the summer is estimated at 10.2 and 9.7 l/day.m2, respectively. Using the ERA5 reanalysis dataset, the annual average potential for water harvesting in the stations was evaluated at 7.9 and 4.6 l/day.m2, respectively. Notably, the minimum water harvesting capacity during the summer season recorded in Qazvin is equal to 3.39 l/day.m2, which can be planned for use in irrigation requirements of green spaces, fields, or gardens.
F. Safari, H. Ramezani Etedali, A. Kaviani, L. Khosravi,
Volume 28, Issue 4 (Winter 2024)
Abstract
Climatic factors play an important role in the growth and development of plants and affect agriculture. The tolerance threshold of plants for each of these factors is limited. Any change in these factors can directly and indirectly have significant effects on agricultural production. Meanwhile, temperature stress is one of the most important damaging phenomena that causes many problems for production and yield. In this research, the time of occurrence of temperature stress with a statistical period of 44 years (1980-2023) and the relationship between air temperature with yield and biomass were investigated. According to meteorological data, June, July, and August were known as the hottest months of the year. On the other hand, the most heat waves were observed in July and August in the years 1997, 2014, and 2018, which led to a decrease in the quality of the product or the loss of the plant. According to the model evaluation results, the accuracy of the model in simulating days to flowering and days to maturity was confirmed using R2 (0.8 and 0.51) and NRMSE (15.36 and 7.12). Also, the model was simulated for the studied fields with deviation percentages of 1.92, 5.65, 4.94, 1.58, 0.96, and 1.49%, respectively. It showed that the model had a satisfactory performance and could be used for maize production planning. Next, the relationship between temperature, yield, and biomass was investigated, and there was a negative and significant relationship between temperature, yield, and biomass at the 99% confidence level.
H. Ramezani Etedali, S. Koohi,
Volume 29, Issue 1 (Spring 2025)
Abstract
Agriculture, as a crucial economic and social sector in Iran, has always been significantly influenced by weather conditions, water availability, and farm management practices. Enhancing productivity and optimizing resource management in crop production are essential to achieving sustainable agricultural development and ensuring food security. This research aimed to investigate how much wheat, barley, and corn production, separately from irrigated and rainfed crops, will be affected by the severity of climatic drought (based on the CMIP6) in Iran. This research was carried out using the amount of wheat, barley, and corn production in all the provinces, which was provided by the Agricultural Jihad Organization during the years 1371 to 1402. Climate data was obtained from the NEX-GDDP database, and the De Martonne aridity index was calculated to investigate changes in aridity under climate scenarios. The results indicated that during the baseline period, the production of rainfed wheat, barley, and corn under semi-arid to very arid climatic conditions was approximately 2,076, 434, and 15 thousand tons per year, respectively. With the intensification of arid conditions across the country, these production levels are projected to increase to 3,333, 693, and 16 thousand tons under the SSP2 scenario and further rise to 3,558, 842, and 16 thousand tons under the SSP5 scenario. Additionally, the production of irrigated wheat, barley, and corn in semi-arid to very arid climatic conditions during the baseline period stands at approximately 6,240, 1,683, and 5,842 thousand tons, respectively. Under the SSP2 climate scenario, the production is expected to reach about 7,126, 1,757, and 6,253 thousand tons, while in the SSP5 scenario, the estimated production is approximately 7,348, 1,780, and 6,324 thousand tons. The findings revealed notable spatial differences in crop production across the country, highlighting that the climatic conditions, particularly in the central, southern, southeastern, and southwestern regions, are becoming increasingly arid. It is crucial to implement smart planning and policies, adopt advanced technologies, and improve the management of water and soil resources to mitigate the adverse impacts of these changes and better adapt to evolving conditions. Addressing these challenges and implementing effective measures are essential steps toward achieving sustainability in the agriculture and natural resources sectors.
H. Ramezani Etedali, M. Ahmadi,
Volume 29, Issue 2 (Summer 2025)
Abstract
change, accurately predicting wheat production is essential for developing precision agriculture. Remote sensing enables the indirect prediction of crop production before harvest. This research investigates the application of the random forest method and support vector regression for simulating wheat production across ten selected farms in Qazvin Plain from 2019 to 2020, employing NDVI, MSAVI, and EVI vegetation indices. Sentinel 2 satellite data was utilized for the vegetation indices. Production data for the ten wheat fields was obtained from the Agricultural Jihad Organization of Qazvin Province. Evaluation of support vector regression and random forest to assess both the observed and simulated wheat production data was conducted using R2, MBE, RMSE, and MAE statistics. To explore the simulation of wheat production using vegetation indices, seven methods were defined: methods 1 to 3 examine each index separately; methods 4 to 6 focus on binary combinations of the indices; and method 7 considers the combined effects of all three indices. The support vector regression model provided good estimates of wheat production in all methods, except methods one and four, in the test phase, with a coefficient of determination of more than 0.98 and a low RMSE. The random forest model showed significant results in all methods except methods two and six during the test phase, achieving a 95% probability (P-value=0.00) with a coefficient of determination greater than 0.8. Overall, this research highlights the importance and potential of machine learning techniques for timely crop production prediction as a strong foundation for regional food security.
