Linked Life Data

17915256

Source:http://linkedlifedata.com/resource/pubmed/id/17915256

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2007-11-12
pubmed:abstractText
Process-based crop simulation models require employment of new knowledge for continuous improvement. To simulate growth and development of different genotypes of a given crop, most models use empirical relationships or parameters defined as genetic coefficients to represent the various cultivar characteristics. Such a loose introduction of different cultivar characteristics can result in bias within a simulation, which could potentially integrate to a high simulation error at the end of the growing season when final yield at maturity is predicted. Recent advances in genetics and biomolecular analysis provide important opportunities for incorporating genetic information into process-based models to improve the accuracy of the simulation of growth and development and ultimately the final yield. This improvement is especially important for complex applications of models. For instance, the effect of the climate change on the crop growth processes in the context of natural climatic and soil variability and a large range of crop management options (e.g., N management) make it difficult to predict the potential impact of the climate change on the crop production. Quantification of the interaction of the environmental variables with the management factors requires fine tuning of the crop models to consider differences among different genotypes. In this paper we present this concept by reviewing the available knowledge of major genes and quantitative trait loci (QTLs) for important traits of rice for improvement of rice growth modelling and further requirements. It is our aim to review the assumption of the adequacy of the available knowledge of rice genes and QTL information to be introduced into the models. Although the rice genome sequence has been completed, the development of gene-based rice models still requires additional information than is currently unavailable. We conclude that a multidiscipline research project would be able to introduce this concept for practical applications.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0022-5193
pubmed:author
pubmed:issnType
Print
pubmed:day
7
pubmed:volume
249
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
593-605
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Gene-based modelling for rice: an opportunity to enhance the simulation of rice growth and development?
pubmed:affiliation
School of Agriculture, Ferdowsi University of Mashhad, P.O. Box 91775-1163, Mashhad, Iran. bannayan@uga.edu
pubmed:publicationType
Journal Article