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Predictive model for grain cracking in terms of rice plant and pancile morphology derived from multivariate analysis.

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Title Predictive model for grain cracking in terms of rice plant and pancile morphology derived from multivariate analysis.
 
Creator Raju, S.N.
Nagin, Chand.
Bhasyam, M. K.
Srinivas, T.
 
Subject 01 Rice
05 Processing and Engineering
 
Description Rice plant and panicle morphology in relation to grain cracking were
studied in 17 varieties at four stages of maturity. The data were subjected to
principal components analysis in order to identify structure within the data and
reduce dimensionality. The first four factors collectively accounted for 72% of the
trace. The first factor was dominated by yield and biomass related attributes, and
the second with percentage cracked grains and grain moisture-III stage. The projection
of varieties onto the planes defined by the first, second, third and fourth
factors reflected scattering of varieties over all the quadrants indicating desirable
wide variations in the varieties studied, and the results could be generalised. The
discussions mainly centred on grain cracking which was primarily influenced by
plant height, panicle length, grains/panicle, sequence of grain maturity and the
microenvironments—particularly towards the last stages of maturity. Predictive
models in terms of the above attributes explained more than 96% of cracking.
The role of agronomic practices in controlling grain cracking is discussed. It is
indicated that dwarf plants influenced by microenvironment need different
panicle morphology and physiological sequence to minimise cracking as compared
to tall plants which generally escape the influence of microenvironment.
 
Date 1995
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
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Identifier http://ir.cftri.com/4916/1/Journal_of_the_Science_of_Food_and_Agriculture_1995_68_141-152.pdf
Raju, S.N. and Nagin, Chand. and Bhasyam, M. K. and Srinivas, T. (1995) Predictive model for grain cracking in terms of rice plant and pancile morphology derived from multivariate analysis. Journal of the Science of Food and Agriculture, 68 (2). pp. 141-152.