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Research Paper | April 1, 2014

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Estimate non-linear regression models for use in growth analysis of rice (Oryza sativa L.)

Ebrahim Azarpour, Maral Moraditochaee, Hamid Reza Bozorgi

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J. Bio. Env. Sci.4(4), 276-286, April 2014

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Abstract

Growth indices are useful for interpreting plant reaction to the environmental factors. Growth analysis is a valuable method in the quantitative analysis of crop growth, development and crop production. There are many regression models to describe the sigmoid growth patterns. By considering that, the parameters of nonlinear regression models have physiological meanings, they are preferable relation to linear regression models. The aim of this study was to collect and evaluate the high visibility non-linear regression models in the growth analysis studies of rice plant. An experiment was conducted using 3 rice cultivars (Hashemi, Ali kazemi and Khazar) in 4 nitrogen fertilizer management conditions (N1, control (no N fertilizer); N2, 30 kg N/ha; N3, 60 kg N/ha; N4, 90 kg N/ha) in randomized complete block design with 3 replications in a paddy light soil at Guilan province (Rice Research Institute, Iran, Rasht, central of Guilan and Rudsar, East of Guilan), during 2009 year. In this research all models were fitted to leaf area index (LAI), Total of dry weight (TDW) and leaf dry weight (LDW). Results indicated that all of the used models at this study described well the variation pattern of leaf area index (LAI), Total of dry weight (TDW) and leaf dry weight (LDW) by time (day after planting). And these models can be used in the growth analysis studies.

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Estimate non-linear regression models for use in growth analysis of rice (Oryza sativa L.)

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