<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran"^^ . "In this study artificial neural network (ANN) models were designed to predict the\r\nbiomass and grain yield of barley from soil properties; and the performance of\r\nANN models was compared with earlier tested statistical models based on\r\nmultivariate regression. Barley yield data and surface soil samples (0–30 cm\r\ndepth) were collected from 1 m2 plots at 112 selected points in the arid region\r\nof northern Iran. ANN yield models gave higher coefficient of determination and\r\nlower root mean square error compared to the multivariate regression, indicating\r\nthat ANN is a more powerful tool than multivariate regression. Sensitivity\r\nanalysis showed that soil electrical conductivity, sodium absorption ratio, pH,\r\ntotal nitrogen, available phosphorus, and organic matter consistently influenced\r\nbarley biomass and grain yield. A comparison of the two methods to identify the\r\nmost important factors indicated that while in the ANN analysis, soil organic\r\nmatter (SOM) was included among the most important factors; SOM was\r\nexcluded from the most important factors in the multivariate analysis. This\r\nsignificant discrepancy between the two methods was apparently a consequence\r\nof the non-linear relationships of SOM with other soil properties. Overall, our\r\nresults indicated that the ANN models could explain 93 and 89% of the total\r\nvariability in barley biomass and grain yield, respectively. The performance of the\r\nANN models as compared to multivariate regression has better chance for\r\npredicting yield, especially when complex non-linear relationships exist among\r\nthe factors. We suggest that for further potential improvement in predicting\r\nthe barley yield, factors other than the soil properties considered such as soil\r\nmicronutrient status and soil and crop management practices followed during the\r\ngrowing season, need to be included in the models."^^ . "2011" . . "57" . "5" . . "Taylor & Francis"^^ . . . "Archives of Agronomy and Soil Science"^^ . . . "03650340" . . . . . . . . . . "K L"^^ . "Sahrawat"^^ . "K L Sahrawat"^^ . . "S"^^ . "Ayoubi"^^ . "S Ayoubi"^^ . . . . . . "Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran (PDF)"^^ . . . . . "Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran (Other)"^^ . . . . . . "Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran (Other)"^^ . . . . . . "Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran (Other)"^^ . . . . . . "Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran (Other)"^^ . . . . . . "Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran (Other)"^^ . . . . . "HTML Summary of #4041 \n\nComparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran\n\n" . "text/html" . . . "Soil Science"@en . .