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- W2187126867 abstract "This study was conducted to estimate the yield and investigate the factors affecting irrigated wheat yield in different tillage methods in Iran using neural networks. Data were collected from 330 farms in 12 different regions of Isfahan, Fars and Khuzestan provinces in Iran by using face to face questionnaire method. Information collected from each field includes climate, soil texture, EC of soil, soil structure, crop rotation, crop residues management method, tillage method, depth of tillage, the amount of (nitrogen, phosphorus and potash) fertilizers consumed, method and efficiency of the amount of water used (as input variables of the neural network) and biomass yield (total seed yield and straw) as the output variable of neural network algorithm. Multilayer Perceptron (MLP) Neural Network of feed forward kind was used in this study. The results showed that the proposed model can predict the biomass yield with a determination coefficient of 90% for different tillage methods. Thus based on managerial inputs relating to soil and irrigation, the mentioned model was proposed as a good approximation of the yield. Sensitivity analysis was carried out using the method of variations of coefficient of determination. As the results showed, the inputs of the management of residues, climate, crop rotation and tillage technique had the highest sensitivity coefficient in the model of achieving the yield. Among the management factors (crop residues management, tillage method, irrigation method and crop rotation), the most effective factor was residue management and the next important factors were crop rotation and tillage method. In conservation systems, tillage method selection with crop residues management at the farm level and also selecting the appropriate crop rotation are aimed at achieving the best results. The least important parameters in the model of assessing the biomass yield were type of soil texture and the irrigation efficiency. Among the factors related to the soil, the soil structure was the most important factor in this model, the effect of which was much lower than the rest of factors." @default.
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- W2187126867 date "2014-01-01" @default.
- W2187126867 modified "2023-10-01" @default.
- W2187126867 title "A Model for the Estimation of Yield and Investigation on Factors Affecting Irrigated Wheat Production in Various Tillage Methods (Using Artificial Neural Networks)" @default.
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