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Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran

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Relation http://oar.icrisat.org/4041/
http://dx.doi.org/10.1080/03650341003631400
 
Title Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran
 
Creator Ayoubi, S
Sahrawat, K L
 
Subject Soil Science
 
Description In this study artificial neural network (ANN) models were designed to predict the
biomass and grain yield of barley from soil properties; and the performance of
ANN models was compared with earlier tested statistical models based on
multivariate regression. Barley yield data and surface soil samples (0–30 cm
depth) were collected from 1 m2 plots at 112 selected points in the arid region
of northern Iran. ANN yield models gave higher coefficient of determination and
lower root mean square error compared to the multivariate regression, indicating
that ANN is a more powerful tool than multivariate regression. Sensitivity
analysis showed that soil electrical conductivity, sodium absorption ratio, pH,
total nitrogen, available phosphorus, and organic matter consistently influenced
barley biomass and grain yield. A comparison of the two methods to identify the
most important factors indicated that while in the ANN analysis, soil organic
matter (SOM) was included among the most important factors; SOM was
excluded from the most important factors in the multivariate analysis. This
significant discrepancy between the two methods was apparently a consequence
of the non-linear relationships of SOM with other soil properties. Overall, our
results indicated that the ANN models could explain 93 and 89% of the total
variability in barley biomass and grain yield, respectively. The performance of the
ANN models as compared to multivariate regression has better chance for
predicting yield, especially when complex non-linear relationships exist among
the factors. We suggest that for further potential improvement in predicting
the barley yield, factors other than the soil properties considered such as soil
micronutrient status and soil and crop management practices followed during the
growing season, need to be included in the models.
 
Publisher Taylor & Francis
 
Date 2011
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
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Identifier http://oar.icrisat.org/4041/1/ArcOfAgronAndSoilSci57_5_549-565_2011.pdf
Ayoubi, S and Sahrawat, K L (2011) Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran. Archives of Agronomy and Soil Science, 57 (5). pp. 549-565. ISSN 0365-0340