فهرست مطالب dan struve

 In order to simulate the yield and water productivity of cucumber plant (Cucumis sativus L.), an experiment was conducted in the form of a completely randomized block design with three irrigation levels of 100, 85 and 75% of the water requirement in two growing seasons during 2017 and 2018 and using perceptron neural networks (MLP) and support vector machine (SVM) methods were used and finally, to select the appropriate and optimal model, the indices of explanatory coefficient, mean squared error and normalized mean squared error were used. The amount of irrigation water, number of leaves on the plant, temperature, evaporation rate and relative humidity were selected as input data and 60%, 20% and 20% of the total data were allocated for training, validation and testing of the model, respectively. The results showed that the MLP neural network with the inputs of irrigation water and number of leaves was more accurate in simulating fruit yield and water productivity in cucumber plants with an explanation coefficient of 0.92 and 0.86, respectively. The results of the sensitivity analysis indicated that the irrigation water input parameters are the most important effective parameters on the water consumption efficiency model and cucumber fruit yield with sensitivity coefficients of 0.9 and 0.86, respectively.

In order to investigate the effect of irrigation deficit on cucumbers grown in greenhouse conditions, an experiment was conducted in a completely randomized block design with three irrigation levels of 100, 85 and 75% water requirement in two growing seasons during the years 2018 and 2019.Performance and quantitative and qualitative performance indicators were measured during the experiment. The results showed that in the first crop of treatments of 85 and 75 percent of water requirement, the amount of water consumption productivity decreased by 6.12 and 15.73 percent compared to the control treatment, respectively. Also in the second crop, the amount of water consumption in treatments of 85 and 75% of water requirement compared to the control treatment has decreased by 3.4 and 12.6%. Analysis of data related yield indices and plant growth indices showed that with decreasing water consumption, water productivity and performance are significantly reduced.

This study was conducted to investigate the effect of silica nanoparticles on the yield and water productivity of cucumber plant and to determine the most appropriate method of application of silica nanoparticles (leaf and root nutrition)In order to investigate the effects of silica nanoparticles on cucumber yield, an experiment was conducted in a factorial design based on completely randomized block design with 4 replications in two growing seasons during 2018and2019 in the greenhouse of Shahid Chamran University of Ahvaz, Iran. Treatments included silica nanoparticles with concentrations of 0,25,50,75 and 100 ppm and two methods of foliar application on leaves and root feeding.The results showed that the highest and lowest values of fruit weight in the first and second crops were in the treatment of 50mg/l nanoparticles by foliar application (first crop:weight10484.3and second crop:weight8039g) and 0mg/l nanoparticles were obtained by root solution (first culture:weight7700g and second culture: weight6280g). Also, the highest amount of water productivity was related to the treatment of nanoparticles with a concentration of 50ppm under foliar application (first culture:71.32and second culture:64.83).This study showed that the use of silica nanoparticles can have a positive effect on cucumber yield and caused a 32.11and21.2% increase in yield in the first and second crops, respectively, compared to the control treatment. The use of nanoparticles increases water productivity and increases cucumber plant yield, the use of silica nanoparticles with a concentration of 50 ppm as foliar application on the leaves can be a good option to increase yield in cucumber.