Distribution of Articles published per year
(2010 - 2018)
(2010 - 2018)
Total number of journals
Article 0 Reads 0 Citations Changes in Germination and Seedling Growth of Different Cultivars of Cumin to Drought Stress Published: 01 January 2018
Cercetari Agronomice in Moldova, doi: 10.2478/cerce-2018-0008
Cumin (Cuminum cyminum L.) is one the most appropriate choice for investing in dry and semi dry areas. In order to analyse influence of drought stress on germination and seedling growth of two masses of cumin, an experiment was conducted in seed technology laboratory of Faculty of Agriculture of Islamic Azad University of Isfahan, in 2016. In this experiment, polyethylene glycol (PEG 6000) at six levels (0, -0.144, -0.18, -0.216 and -0.288 MP) and NaCl at six levels (0, 4, 5, 6, 7, and 8 ds/m) and distilled water as control were applied to investigate the influence of dryness and salinity stresses on seed germination and seedling growth of two cultivars of cumin plant masses gathered from Mashhad-e-Ardahal and Kerman, then fulfilled in two separate factorial trials, on the basis of randomized design with four replications. Cultivar had significant influence on germination percentage, germination uniformity, radicle length, plumule length, fresh radicle weight, dry radicle weight, fresh and dry plumule weight. Drought stress impact on all treatments, except germination uniformity, fresh radicle weight and dry radicle weight was meaningful, but, just radicle length, plumule length, fresh plumule weight and dry plumule weight significantly affected by interaction between cultivar and drought stress. The rate of germination, germination percentage, as well as seedling growth and establishment were considerably lowered with the rise of stress levels using PEG. Control treatment had obtained the highest germination percentage, mean time of germination, radicle and plumule length, fresh plumule weight and seed stamina index. Taking all traits into account, this experiment found that Mashhad-e-Ardahal was most tolerant hybrid to water stress conditions.
Article 0 Reads 0 Citations Assessment of ET-HS Model for Estimating Crop Water Demand and Its Effects on Yield and Yield Components of Barley and W... Published: 01 January 2017
Cercetari Agronomice in Moldova, doi: 10.1515/cerce-2017-0034
In order to estimate the water requirement of barley and wheat by using of ET-HS model, a research was conducted at Research Farm of Islamic Azad University, Isfahan (Khorasgan) Branch, Iran. ET-HS model is used to determine irrigation water quantity and irrigation schedule for different crop. The study was based on randomized complete block design (RCBD) with three replications and six treatments. The irrigation treatments included irrigation to supply 50, 75, 100, 125 and 150% of crop water demand on the basis of ET-HS model during growing season and control treatment (conventional irrigation), which was irrigation on the basis of 70 mm evaporation from Class A evaporation pan during growing season. In barley experiment, the highest values for number of fertile tiller, maximum LAI, total dry matter in maximum LAI stage, number of grain per spike, a thousand seed weight (35.56 g), grain yield (7877.9 kg/ha), biological yield (17689.7 kg/ha) and harvest index (44.45%) was obtained for irrigation according to 100% of crop water demand on the basis of ET-HS model. In wheat experiment, the highest number of fertile spike, number of grain per spike, 1000 grain weight, grain yield, biological yield was obtained for irrigation treatment on the basis of 100% ET-HS model; moreover, the maximum harvest index was related to control treatment, followed by irrigation on the basis of 100% of ET-HS model. Conclusively, the appropriate irrigation treatment was 100% of crop water demand on the basis of ET-HS model during the growth season for both crops.
Article 0 Reads 0 Citations In vitrobacterial decontamination ofKelussia odoratissimaseed during dormancy breaking Published: 01 January 2014
Research on Crops, doi: 10.5958/j.2348-7542.15.1.034
Kelussia odoratissima (Apiaceae) is an endangered medicinal plant indigenous to Iran. Seeds of this plant have a long-term dormancy. This study aimed at obtaining in vitro method to reduce microbial contamination, overcome seed dormancy and to disclose the type of classification system of seed dormancy. After using completely randomized design and Duncan's Multiple Range Test, the significance of between individual group's means was assessed. Results revealed that only one gram negative bacterium strain, Klebsiella sp., was isolated from the contaminated culture. Applying 500 mg/l copper sulphate for 6 min during seed sterilization, the contamination was eradicated. During cold stratification, to estimate the effect of exogenous application of cytikinin hormones, N6-furfurylaninopurine (Kin) and 6-benzylaminopurine (BAP) alone or in combination with each other, pre-soaking treatment with tioureae as a nitrogen compound and seed osmopriming with polyethylene glycole (PEG 6000), alone or in combination with BAP, the highest final seed germination (92.5%) was obtained on medium containing 1 mg/l BAP combined with 1 mg/l Kin. The lowest mean germination time (45.81 days) was also obtained on medium containing 0.25 mg/l BAP. Since using tiourea was simple and economical, with 80% final seed germination (using 0.2% thiourea) this can be an excellent way to promote seed germination of this plant. Considering the efficacy of treatments and growing of zygotic embryo without cold temperature exposure, seed dormancy mechanism identified as non-deep physiological dormancy and particularly chemical dormancy classification.
PROCEEDINGS-ARTICLE 0 Reads 2 Citations Evaluation of Genetic Algorithms for tuning SVM parameters in multi-class problems Published: 01 November 2010
2010 11th International Symposium on Computational Intelligence and Informatics (CINTI), doi: 10.1109/cinti.2010.5672224
Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization to solve this kind of problem. The proposed method is compared with grid algorithm, a traditional method for parameter setting, by conducting some experiments using different benchmark data sets. The results observed show better performance of hybrid GA-SVM method by improving classification accuracy.