Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops

Abstract

Summary Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high‐throughput techniques based on machine vision, robotics, and computing (plant phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning, and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focuses on how field‐based plant phenomics can enable next‐generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis, and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from ‘Green Revolution’ traits such as harvest index become exhausted. Application of LiDAR, thermal imaging, leaf and canopy spectral reflectance, Chl fluorescence, and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high‐throughput phenotyping could enable the next generation of crop research and breeding is presented.

Co-Evolution of Sink and Source in the Recent Breeding History of Winter Wheat in Germany

“…Aerial imaging is proposed as a promising strategy to estimate the canopy photosynthesis and thereby light utilization on a spatial and temporal scale by multispectral-sensing. The advancement of these techniques will, together with genomics, facilitate a more efficient selection of source parameters and thereby accelerate progress in the final yield (Furbank et al, 2019).…”

“…Complementary crosses between genotypes with high sink capacity and those with high source capacity resulted in progeny with substantial yield improvement (Reynolds et al, 2017), suggesting the co-limitation of source and sink on yield. It has been suggested that the selection of crossing partners based on physiological traits is a promising strategy to achieve a higher crop productivity via breeding, which is not least facilitated by the increasingly automated phenotyping techniques Reynolds and Langridge, 2016;Reynolds et al, 2017;Furbank et al, 2019). A precise description of the sink and source characteristics of possible genetic resources for future winter wheat varieties is, together with the application of genomic tools, a purposive strategy to promote the genetic gain via strategic complementary crosses.…”

High-Throughput Phenotyping of Indirect Traits for Early-Stage Selection in Sugarcane Breeding

“…The strong influence of environment and measurement conditions on canopy temperature is evident from this and other studies. While high heritability of canopy temperature has been reported previously in other crops, they have relied largely upon cooled thermal cameras that can only be carried aboard manned aircraft [43]. Issues associated with lightweight uncooled thermal cameras, such as thermal drift and non-uniformities in images have been reported extensively along with statistical models for correction [44].…”

Uncovering candidate genes involved in photosynthetic capacity using unexplored genetic variation in Spring Wheat

“…The current study was conducted on the flag leaf and scaling leaf-level data to the canopy can be complicated as leaf age and leaf angle can play a crucial role when integrating leaf level photosynthetic traits at the canopy scale 67 . However, leaf level measurements have been previously associated with higher yields [68][69][70] and in the present study, traits measured at leaf level explained a significant proportion of the variation of BM_PM, HI, TGW, GM2 and RUE measured at different growth stages.…”