Welcome to International Network for Natural Sciences | INNSpub

Paper Details

Research Paper | April 1, 2014

| Download 2

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

Ebrahim Azarpour, Maral Moraditochaee, Hamid Reza Bozorgi

Key Words:

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


JBES 2014 [Generate Certificate]


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.


Copyright © 2014
By Authors and International Network for
Natural Sciences (INNSPUB)
This article is published under the terms of the Creative
Commons Attribution Liscense 4.0

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

Adelson Paulo, A. 2003. Analysis of variance of primary data on plant growth analysis. Pesquisa Agropecuária Brasileira 38 (1), 1-10.

Boschetti M, Bocchi S, Stroppiana D, Brivio PA. 2006. Estimation of parameters describing morpho-physiological features of Mediterranean rice varieties for modeling purposes. Italian Journal of Agro meteorology 3, 40-49.

Causton DR, Venus JC. 1981. The biometry of plant growth. London: Edward Arnold.

Hunt R. 1990. Basic growth analysis. London:Unwin Hyman.

Chag T, Bardenas EA, Del Rosario AC. 1965. The morphology and varietal characteristics of the rice plant. Los Baños, Laguna, The Philippines. 40 pp.

Counce PA, Keisling TC, Mitchell AJ. 2000. A Uniform, Objective, and Adaptive System for Expressing Rice Development. Crop Science 40, 436-443.

Evans GC. 1972. The quantitative analysis of plant growth. Oxford: Blackwell Scientific Publications.

Gardner F, Pearce R, Mitchell RL. 1985. Physiology of crop plants. Iowa state university Press. Ames. USA.

Garnier E, Farrar JF, Poorter H, Dale JE. 1999. Variation in leaf structure: an ecophysiological perspective. New Phytologist (special issue) 143, 1-221.

GoudriaanJ, van Laar HH. 1994. Modelling potential crop growth processes. Dordrecht: Kluwer Academic Publishers.

Khush G. 2003. Productivity improvements in rice. Nutrition Reviews 61(6), 114-116.

Read JM, Birch CPD, Milne JA. 2002. Heath Mod: a model of the impact of seasonal grazing by sheep on upland heaths dominated by Calluna vulgaris (heather). Biological Conservation 105(3), 279-292.

Thiyagarajan TM, Gujja B. 2013. Transforming Rice Production with SRI (System of Rice Intensification) Knowledge and Practice. NATIONAL CONSORTIUM ON SRI (NCS). 206 pp.

Yin X, Goudriaan J, Lantinga EA, Vos J, Spiertz HJ. 2003. A Flexible Sigmoid Function of Determinate Growth. Annals of Botany 9(3), 361-371.


Style Switcher

Select Layout
Chose Color
Chose Pattren
Chose Background