Identifying seed cotton yield and abiotic stress response in cotton (Gossypium hirsutum L.) grown in the Arizona low desert

Authors: Thompson, Alison; Thorp, Kelly; Herritt, Matthew

Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/11/2025
Publication Date: 4/10/2025
Citation: Thompson, A.L., Thorp, K.R., Herritt, M.T. 2025. Identifying seed cotton yield and abiotic stress response in cotton (Gossypium hirsutum L.) grown in the Arizona low desert. Crop Science. 65(2). Article 70058. https://doi.org/10.1002/csc2.70058.
DOI: https://doi.org/10.1002/csc2.70058

Interpretive Summary: Two different growing seasons at Maricopa Arizona provided an opportunity to examine the effects of record-breaking heat stress on estimates of nitrogen and water use efficiency and influential physiological measurements compared to a ‘less’ stressful year. The data showed that leaf chlorophyll, chlorophyll fluorescence, and nitrogen content can indicate degrees of resource use efficiency and adaptation in cotton and are beneficial for agronomists and breeders. The data also indicates that genetic gain for nitrogen, water, and possibly radiation use efficiency could plateau if thermotolerant characteristics are not incorporated.

Technical Abstract: Cotton (Gossypium sp.) is an important natural fiber crop that supports a multi-billion-dollar textile industry worldwide. In the last decade, U.S. based consumers have pushed for more environmentally friendly and sustainable cotton-based products. Understanding cotton resource use efficiency can improve cotton sustainability by informing better resource management and improve breeding strategies to develop climate-resilient cotton while increasing global production for a growing population. Six upland cotton varieties were grown in the Arizona low-desert at the Maricopa Agricultural Center and used to estimate agronomic nitrogen and crop water use efficiencies. Cotton responses to abiotic stressors, including high heat and low soil water content, were estimated from leaf chlorophyll and nitrogen content, and chlorophyll fluorescence throughout the growing season. A random forest classifier machine learning algorithm and principal component analysis were used to identify important relationships and temporal patterns between the measured traits. The models showed that measurements taken just before or during peak flower were most informative for estimating resource use efficiencies. The data showed that leaf chlorophyll and nitrogen content and chlorophyll fluorescence can indicate degrees of resource use efficiency in cotton and the measurements are beneficial for agronomists and breeders. The results from this study agree with previous reports that low and stable leaf chlorophyll content and increased FV/FM during heat stress are indicators of thermotolerance in cotton and are associated with improved nitrogen and water use efficiency under high heat and low soil water conditions. These findings suggest that genetic gain for nitrogen, water, and possibly radiation use efficiency could plateau if thermotolerant characteristics are not incorporated into cotton cultivars.