eprintid: 4041
rev_number: 11
eprint_status: archive
userid: 6
dir: disk0/00/00/40/41
datestamp: 2011-11-17 09:25:27
lastmod: 2013-07-02 07:56:54
status_changed: 2011-11-17 09:25:27
type: article
metadata_visibility: show
contact_email: Library-ICRISAT@CGIAR.ORG
item_issues_count: 0
creators_name: Ayoubi, S
creators_name: Sahrawat, K L
icrisatcreators_name: Sahrawat, K L
affiliation: Isfahan University of Technology(Isfahan)
affiliation: ICRISAT(Patancheru)
country: Iran
country: India
title: Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran
ispublished: pub
subjects: s2.11
full_text_status: restricted
keywords: artificial neural network; barley yield; multivariate regression; soil properties
abstract: 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.
date: 2011
date_type: published
publication: Archives of Agronomy and Soil Science
volume: 57
number: 5
publisher: Taylor & Francis
pagerange: 549-565
refereed: TRUE
issn: 0365-0340
official_url: http://dx.doi.org/10.1080/03650341003631400
related_url_url: http://scholar.google.co.in/scholar?as_q=Comparing+multivariate+regression+and+artificial+neural+network+to+predict+barley+production+from+soil+characteristics+in+northern+Iran&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=title&as_sauthors=&as
related_url_type: author
citation:   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     
document_url: http://oar.icrisat.org/4041/1/ArcOfAgronAndSoilSci57_5_549-565_2011.pdf