Bottlenecks and opportunities in field-based high-throughput phenotyping for heat and drought stress. (20th January 2021)

• Record Type: Journal Article
• Authors: Hein, Nathan T; Ciampitti, Ignacio A; Jagadish, S V Krishna
• Editors: Cooper, Mark
• Is Part Of: Journal of experimental botany. Volume 72:Number 14(2021)
• Page Start: 5102
• Page End: 5116
• Publication Date: 2021-01-20
• Subjects: Drought stress -- field-based high-throughput phenotyping -- heat stress -- photosynthetic efficiency -- remote sensing -- time of day of flowering -- water-soluble carbohydrates -- yield estimation; Botany -- Periodicals; Botany, Experimental -- Periodicals; Plant physiology -- Periodicals; 580
• DOI: 10.1093/jxb/erab021
• Languages: English
• ISSNs: 0022-0957

Abstract

Abstract : This review focuses on developing high-throughput phenotyping approaches to quantify key physiological traits at high temporal frequency, involving diverse germplasm to incorporate greater heat and drought stress resilience in crops. Abstract: Flowering and grain-filling stages are highly sensitive to heat and drought stress exposure, leading to significant loss in crop yields. Therefore, phenotyping to enhance resilience to these abiotic stresses is critical for sustaining genetic gains in crop improvement programs. However, traditional methods for screening traits related to these stresses are slow, laborious, and often expensive. Remote sensing provides opportunities to introduce low-cost, less biased, high-throughput phenotyping methods to capture large genetic diversity to facilitate enhancement of stress resilience in crops. This review focuses on four key physiological traits and processes that are critical in understanding crop responses to drought and heat stress during reproductive and grain-filling periods. Specifically, these traits include: (i) time of day of flowering, to escape these stresses during flowering; (ii) optimizing photosynthetic efficiency; (iii) storage and translocation of water-soluble carbohydrates; and (iv) yield and yield components to provide in-season yield estimates. Moreover, we provide an overview of current advances in remote sensing in capturing these traits, and discuss the limitations with existing technology as well as future direction of research to develop high-throughput phenotyping approaches. In the future, phenotyping these complex traits will require sensor advancement, high-quality imagery combined with machine learning methods, and efforts in transdisciplinary science to foster integration across disciplines.