Sahar Binesh; Gholam abbas Akbari; Elias Soltani; Fatmeh Amini
Abstract
In order to examine the germination response of basil medicinal plant’s seeds (Ocimum basilicum L.) to temperature and determination of cardinal temperatures for germination percentage and rate, a compound decomposition experiment was performed through a fully random design with four reptile and ...
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In order to examine the germination response of basil medicinal plant’s seeds (Ocimum basilicum L.) to temperature and determination of cardinal temperatures for germination percentage and rate, a compound decomposition experiment was performed through a fully random design with four reptile and six thermal levels (8, 15, 20, 25, 30, and 35 degree centigrade) in seed technology laboratory of Abou-Reyhan campus in Univerity of Tehran. In this study, 22 Basil masses were evaluated including “Tehran”, Green Shahr-e-Rey”, “Green Birjand”, “Purple Birjand”, “Green Shiraz”, “Green Zabol”, “Zahedan”, “Green Zahedan”, “Kermanshah”, “Green Pishva”, “Purple Pishva”, “Green Malayer”, “Khash”, “Local green Tonekabon”, “Green Isfahan II”, “Purple Isfahan II”, “Green Isfahan III”, “Green Isfahan IV”, “Green Mash’had”, “American green Napolta”, “Italian Genovese”, and “Switzerland” . Based on the results of variance analysis, temperature impact, genotype, and their interaction on germination percent and germination rate was significant at the 5% level. Optimal range of temperature for germination percent and germination rate was obtained as 19.10-27.78 and 20.32-29.89 degrees centigrade, respectively. In most masses, the highest germination rate was observed at 25 degrees centigrade. Among all evaluated masses in current research, Isfahan III was appropriated the highest germination rate in all temperatures. The results of experiment showed that the response of germination percentage and germination rate to temperature was well described through Beta function and segmented function, respectively, and cardinal temperatures can be determined for Basil using these two models.
Najebullah Ebrahimi; Benjamin Torabi; Afshin Soltani; Ebrahim Zenali
Abstract
To analyze the growth, it is necessary to access to accurate and well-arranged data obtained from measuring leaf area and dry matter accumulation. The purpose of this study was to evaluate different nonlinear regression models to study the trend of changes in leaf area index and dry matter production ...
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To analyze the growth, it is necessary to access to accurate and well-arranged data obtained from measuring leaf area and dry matter accumulation. The purpose of this study was to evaluate different nonlinear regression models to study the trend of changes in leaf area index and dry matter production and to estimate the parameters related to the growth analysis. The experiment was conducted on faba bean "cv. Barkat" in a split-plot experiment based on randomized complete block design with three planting dates and four densities in four replications. In this study, the beta and logistic models were fitted to the leaf surface data and the beta, Gompertz and logistic models to dry matter production. AICc benchmark showed that the beta model was fitted to the leaf surface data the better than the logistic model. LAImax in different densities varied between 2.3 to 5.3, tm between 131.9 and 144.2, and te between 158.7 and 163.5 days after planting. AICc benchmark showed that the beta model was fitted to the dry matter accumulation data the better than the Gompertz and logistic models. Wmax in different densities varied between 725.1 and 1484.3 g/m2, tm between 138.3 and 146.4 and te between 162.60 and 179.0 days after planting. Grain yield varied from 231 to 2744 g/m2, and with increasing density in each planting date, grain yield showed the increased trend. The results showed that yield changes were directly affected by maximum leaf area index, maximum dry matter accumulation and crop growth rate.
Javad Taie; Ebrahim Amiri; Ahmad Aien; Naser Boroumand; Mehrangiz Jokar
Abstract
Crop Simulation Models are advanced tools to estimate crop yield and optimizing of crop management practices. This study was conducted in order to evaluate DSSAT model under autumn cropping system condition in Jiroft, Iran, 2012-2014. The field experiment which repeated in two sequential years and three ...
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Crop Simulation Models are advanced tools to estimate crop yield and optimizing of crop management practices. This study was conducted in order to evaluate DSSAT model under autumn cropping system condition in Jiroft, Iran, 2012-2014. The field experiment which repeated in two sequential years and three replications were performed as randomized complete block design in split plots. The main factor was planting date (17th, 23th, 29th September and 5th October) and its subfactor was potato cultivars (Sante, Satina and Boren). Data of first and second years of field experiment was applied respectively for calibration and evaluation of model. Data-base requirement for model was created from 1. climatic data includes: temperature, radiation, relative humidity, wind and precipitation; 2. crop data attained from field experiment, 3. soil data. Results of statistical evaluation of model showed the good fitness of simulated and actual yield performance. Simulated tuber yields were similar to their observed value with RMSE of 19% (1210 kg/ha) and high correlation between observed and simulated tuber yield (R2>0.9). Simulated biomass had 2673.5 kg/ha difference to its observed value that showed low estimation precision (RMSE>30%). This model couldn’t predict tuber initiation stage correctly (RMSE>30%). Therefore, it was concluded that DSSAT model predicted potato yield correctly but due to weak precision in estimation of phenological stage, had low assurance for use in autumn cropping system of potato under autumn cropping system in Jiroft region.
Hojjat Salehzadeh; Manouchehr Gholi pour; Hamid Abbasdokht; Mehdi Baradaran
Abstract
Nitrogen (N) affects adversely the tobacco yield quantity and quality as it increases yield, Chlorine and nicotine contents, but decrease potassium content. This experiment was aimed at optimization of (the balance between) N concentration in leaf, stem and root to increase both yield quantity and quality ...
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Nitrogen (N) affects adversely the tobacco yield quantity and quality as it increases yield, Chlorine and nicotine contents, but decrease potassium content. This experiment was aimed at optimization of (the balance between) N concentration in leaf, stem and root to increase both yield quantity and quality (high potassium, low Chlorine and medium nicotine contents) using artificial neural network. Two field experiments based on complete block design with three replications were conducted in Tirtash and Urmia tobacco research centers. Treatments were factorial arrangement of two N sources (urea and nitrate ammonium) and four application patterns (basal, 2/3 basal and 1/3 after initiation of rapid growth (AIRG), 1/2 basal and 1/2 at AIRG, 1/3 basal and 2/3 at AIRG). The N concentration of leaf, stem and root (model inputs) was measured in 30, 50, 70, 85 and 100 days after transplanting. After harvesting, the quantity of cured leaf and its Cl, K and nicotine content (model outputs) were also determined. The results indicated that a model with one hidden layer and configuration of 15-15-4 is appropriate and there were no significant different between two N sources. The best pattern was use of nitrate ammonium in 2/3 basal and urea 1/3 basal. The average value of optimized N concentration was 3.06, 2.42 and 1.5 percent for leaf, stem and root, respectively. These optimized concentrations can lead to potential increase in quality and quantity of tobacco which should be taken into consideration by breeders and agronomists.