Submitted to: National Cotton Council Beltwide Cotton Conference
Publication Type: Abstract only
Publication Acceptance Date: 1/6/2012
Publication Date: 1/6/2012
Citation: Mahan, J.R., Payton, P.R., Bange, M. 2012. Application of an Australian model to predict fiber characteristics of cotton grown in Texas [abstract]. National Cotton Council Beltwide Cotton Conference. p.107. Interpretive Summary:
Technical Abstract: Temperature varies continuously and is a pervasive influence on virtually all aspects of cotton growth and development. Variation in cotton yield and quality can be broadly correlated with seasonal temperature patterns. Within-season temperature variation correlates with yield and quality variation on bolls on a plant. Season-to-season temperature variation correlates with yield and quality variation among years for a geographic location. Region-to-region temperature variation correlates with differences in yield and quality that are generally associated with growing regions. Heat units provide a convenient method for monitoring and quantifying effects of temperature on cotton growth and development and have been used to optimize management in production settings. Bange et al. (2010) used heat unit accumulation as part of an approach for predicting micronaire from daily temperature during fibre thickening. This approach was evaluated using archival data from genotype evaluation experiments for three sites across Texas. The sites tested were Lubbock, College Station, and Corpus Christi for a period of 10 years from 2000 to 2010. With no modification to the prediction tool were able to account for a significant proportion of the variation across the three sites and years (r2 = 0.35). Bange et al reported r2= 0.34 for a similar analysis of Australian cotton. The predictive tool does not account for the impact of water stress on micronaire that might be of significance in Texas. Improvements in the predictive capability for the USA may result from using a USA day degree crop development approach, and making some adjustments for crop size (ie. no of nodes in a crop contributing to yield quality of crops) for particular regions. This simple model might provide a management tool for predicting and managing environmental effects and fiber development.