Location: Soil and Water Management ResearchTitle: A simple quantitative model to predict leaf area index in sorghum
|NARAYANAN, SRUTHI - Kansas State University|
|AIKEN, ROBERT - Kansas State University|
|PRASAD, P.V. VARA - Kansas State University|
|PAUL, GEORGE - Texas A&M Agrilife|
|YU, JIANMING - Iowa State University|
Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/2/2013
Publication Date: 1/5/2014
Citation: Narayanan, S., Aiken, R.M., Prasad, P., Xin, Z., Paul, G., Yu, J. 2014. A simple quantitative model to predict leaf area index in sorghum. Agronomy Journal. 106(1):219-226.
Technical Abstract: Leaf area index (LAI) is a widely used physiological parameter to quantify the vegetative canopy structure of crops. Over the years, several models to estimate LAI have been developed with various degrees of complexity and inherent shortcomings. The LAI simulation models proposed so far for sorghum [Sorghum bicolor (L.) Moench] either lack details of the leaf area dynamics of expanding leaves or demand exhaustive measurements. The objective of this study was to develop a simple quantitative model to predict the LAI of sorghum by introducing a new method for simulation of the leaf area of expanding leaves. The proposed model relates LAI to thermal time. It calculates LAI from an algorithm considering the total number of mature leaves, the area of mature leaves, the area of expanding leaves, and plant density. The performance of the model was tested using LAI data collected using a nondestructive method under field conditions. The slope of the regression of modeled LAI on observed LAI varied for photoperiod-sensitive and -insensitive genotypes in 2010. The coefficients of determination (R squared) between modeled and observed LAI were 0.96 in 2009 and 0.99 (photoperiod insensitive) and 0.95 (photoperiod sensitive) in 2010. The inclusion of expanding leaves in the model improved its accuracy. The model provides an accurate estimate of LAI at any given day of the vegetative growing season based only on thermal time and making use of default coefficients demonstrated in this research.