Irrigation
N. Salamati; A. Danaie; V. Yaaghoobi
Abstract
Introduction Drought stress is the most important environmental factor limiting growth and development of plants worldwide. Growth reduction due to drought stress has been reported more than other environmental stresses. So far, many studies have been conducted on the relationship and correlation ...
Read More
Introduction Drought stress is the most important environmental factor limiting growth and development of plants worldwide. Growth reduction due to drought stress has been reported more than other environmental stresses. So far, many studies have been conducted on the relationship and correlation between important agronomic traits in rapeseed, which have introduced 1000-grain weight, number of seeds per pod and number of pods per plant as the most important traits with high correlation in yield. The results showed that the application of drought stress had an effect on the yield components of sesame and the cultivars that were more sensitive to drought stress had a greater decrease in their yield. The aims of this study were to investigate (1) the effect of consumed water volume as the independent variable on other variables of the study, and (2) the effect of total independent variables (yield components and other independent factors) on yield and water productivity (dependent variables). Finally, the most important independent variables affecting water productivity and the most sensitive variables to the amount of consumed water were determined.Materials and MethodsIn order to achieve aforementioned objectives of this study, an experiment was conducted during two growing season of 2011-2011 and 2010-2011 in Behbahan Agricultural Research Station. The experiment was conducted as randomized complete block design with 4 replications. The applied amount of water in drip irrigation was composed of four levels of 50, 75, 100 and 125% water requirement in main plots and two canola varieties Hyola 401 and RGS003 in sub plots were placed.Results and Discussion The results of the analysis of variance of the regression model showed that the higher absolute value of beta coefficients and t-statistic of each independent variable caused that variable to be introduced as the most sensitive independent variable affecting the dependent variable. Therefore, the independent variable of water volume, with beta coefficient of 0.860 and t-statistic of 13.246 had the greatest effect on plant height variable. In terms of yield, the studied variables (the number of pods per plant, the number of seeds per pod, and 1000-seed weight, consumed water volume, flowering period, growth period and plant height) showed 74.1% of variation (R2 = 0.741) of dependent variable (Yield of canola). The consumed water volume with the highest absolute value of beta coefficient of 0.563 and t-statistic with 2.967 had the most significant effect on yield at the level of 1%. Among the dependent variables, the consumed water volume with the highest absolute value of beta -1.013 and t-statistic at -12.415 had the most significant effect on water productivity at the level of 1%. consumed of water volume with the highest absolute value of beta coefficient of 0.563 and t-statistic with 2.967 had the most significant effect on performance at the level of 1%. The results of Pearson correlation coefficient showed that the highest correlation between the number of pods per plant and seed per pod with both plant height were calculated to be 0.763 and 0.849, respectively, indicating that increasing plant height was effective in increasing the number of pods per plant and seed per pod.ConclusionThe results of analysis of variance of regression model showed the effect on volume of consumed water as an dependent variable through other variables (number of pods per plant, number of seeds per pod, yield, water productivity, 1000-seed weight, flowering period, growth period and plant height). Results showed a significant effect of all variables at the level of 1%, except for the variable of flowering period which had a significant effect but just at 5%. The volume of consumed water by r= 66.2% on grain yield variation in the pods, had the most significant effect on yield components. Therefore, seed number in the pods received the most negative effect from reducing water consumption due to drought stress. With increasing the growth period of canola, water productivity showed a significant decrease at 1%. The results of Pearson correlation coefficient showed that grain water productivity had a negative and significant correlation at the level of 1% with all variables. The highest correlation between water productivity (r = -0.939) was calculated with volume of consumed water, which indicates the importance of reducing water consumption in increasing canola water productivity.
Irrigation
A,. Uossef gomrokchi; J. Baghani; F. Abbasi
Abstract
Introduction: One of the modeling methods researchers have considered in various sciences in recent years is artificial neural network modeling. In addition to the artificial neural network and regression models, today, the capabilities of data mining methods have been used to improve the output results ...
Read More
Introduction: One of the modeling methods researchers have considered in various sciences in recent years is artificial neural network modeling. In addition to the artificial neural network and regression models, today, the capabilities of data mining methods have been used to improve the output results of prediction models and field information analysis. Tree models (decision trees) along with decision rules are one of the data mining methods. Tree models are a way of representing a set of rules that lead to a category or value. These models are made by sequentially separating data into separate groups, and the goal in this process is to increase the distance between groups in each separation. Research shows that plant yield is a function of various plant, climatic, and water, and soil management conditions. Therefore, calculating the amount of plant yield and related indices follows complex nonlinear relationships that also have special difficulty in modeling. Considering that the response of irrigated wheat to different inputs in different climates by field method is time-consuming, costly, and in some cases impossible, so the introduction of an efficient model that can predict yield and analyze yield sensitivity to various parameters is a great help. It will be to solve this problem. This study aimed to develop and evaluate the capability of three models of the neural network, tree, and multivariate linear regression in predicting wheat yield based on parameters affecting its yield in major wheat production hubs in the country. Materials and Methods: The information used in this study includes the volume of water consumption and yield of irrigated wheat and the committees related to these two indicators in irrigated wheat fields under the management of farmers (241 farms) in the provinces of Khuzestan, Fars, Golestan, Hamadan, Kermanshah, Khorasan Razavi, Ardabil, East Azerbaijan, West Azerbaijan, Semnan, south of Kerman and Qazvin, which were harvested in a field study in the 2016-17 growing season. According to the Ministry of Jihad for Agriculture statistics, these provinces have the highest area under irrigated wheat cultivation in the country and cover about 70% of the area under cultivation and production of this crop in the country. One of the most widely used monitored neural networks is the Perceptron multilayer network with error replication algorithm, which is suitable for a wide range of applications such as pattern recognition, interpolation, prediction, and process modeling. In the present study, in order to develop the neural network, the capabilities of R software with Neuralnet package have been used. After the normalization step, the data were randomized. This step aims to have a set of inputs and outputs in which the input-output categories do not have a special system. After the randomization of the data, the amount of information that should be used in the network training process is determined. This part of the data was considered for training (70%) and another part for network test (30%). Perceptron neural network activator functions in the implementation of network training and testing. The hyperbolic tangent activity function has been used to limit the range of output data from each neuron and the pattern-to-pattern training process. In the present study and the neural network modeling capability, the tree model method has been used to predict wheat yield. Tree modeling is one of the most powerful and common tools for classification and forecasting. The tree model, unlike the neural network model, produces the law. One of the advantages of the decision tree over the neural network is that it is resistant to input data noise. The tree model divides the data into different sections based on binary divisions. Each data partition can be re-subdivided into another binary, and a model fitted to each subdivision. In this research, the capabilities of WEKA software have been used to run a tree model. It is worth noting that after grouping, the prediction model is applied to the grouped data. Results and Discussion: In this study, the efficiency of three models of the artificial neural network, multivariate linear regression, and tree model to predict the performance of irrigated wheat in major production areas in the country was evaluated based on field information recorded in 241 farms. The results showed that the coefficient of explanation of the model in predicting the yield of wheat production in the model of artificial neural network and a multivariate linear regression model was 0.672 and 0.577, respectively, which was applied by grouping the data by tree method. The coefficient of explanation has been increased to 0.762. The output results of the tree model showed that the major wheat production areas in Iran in terms of water consumption could be divided into four independent groups. Finally, it can be concluded that the tree model, considering the purposeful grouping in the input data, can be used as a powerful tool in estimating irrigated wheat yield in major wheat production areas in Iran. Conclusion: In this study, the need to use data mining methods in analyzing field information and organizing large databases and the usefulness of data mining methods, especially the decision tree in estimating wheat crop yield, were investigated and compared with other forecasting methods. The general results of the research show that purposeful separation of input data into forecasting models can increase the output accuracy of forecasting models. However, it is not possible to provide a general approach to selecting or not selecting a forecasting model in different regions. In some studies, neural networks have shown a high ability to predict the performance of different products, but it is important to note that if there is sufficient data and correct understanding of the factors affecting the dependent variable, the accuracy of the models can be applied by data mining methods. It also improved the neural network. In a general approach, considering the accuracy of estimating the predicted models under study, these techniques can be used to estimate other late-finding characteristics of plants and soil.
