High-throughput phenotyping of physiological traits for wheat resilience to high temperature and drought stress

Interannual and local fluctuations in wheat crop yield are mostly explained by abiotic constraints. Heatwaves and drought, which are among the top stressors, commonly co-occur, and their frequency is increasing with global climate change. High-throughput methods were optimized to phenotype wheat plants under controlled water deficit and high temperature, with the aim to identify phenotypic traits conferring adaptative stress responses. Wheat plants of 10 genotypes were grown in a fully automated plant facility under 25/18 degrees C day/night for 30 d, and then the temperature was increased for 7 d (38/31 degrees C day/night) while maintaining half of the plants well irrigated and half at 30% field capacity. Thermal and multispectral images and pot weights were registered twice daily. At the end of the experiment, key metabolites and enzyme activities from carbohydrate and antioxidant metabolism were quantified. Regression machine learning models were successfully established to predict plant biomass using image-extracted parameters. Evapotranspiration traits expressed significant genotype-environment interactions (GxE) when acclimatization to stress was continuously monitored. Consequently, transpiration efficiency was essential to maintain the balance between water-saving strategies and biomass production in wheat under water deficit and high temperature. Stress tolerance included changes in carbohydrate metabolism, particularly in the sucrolytic and glycolytic pathways, and in antioxidant metabolism. The observed genetic differences in sensitivity to high temperature and water deficit can be exploited in breeding programmes to improve wheat resilience to climate change. High-throughput phenotyping highlighted the importance of canopy architecture to fine-tune transpiration. The interplay between sucrolytic and glycolytic pathways is essential to tolerate drought at high temperature in wheat.

Automated summary

Interannual and local fluctuations in wheat crop yield are primarily influenced by abiotic constraints such as heatwaves and drought. This study aimed to identify phenotypic traits that contribute to adaptive stress responses in wheat under water deficit and high temperature conditions. High-throughput methods were used to phenotype 10 wheat genotypes, and regression machine learning models successfully predicted plant biomass based on image-extracted parameters. The study highlights the importance of transpiration efficiency and the interplay between carbohydrate metabolism and antioxidant metabolism in wheat's stress tolerance. The observed genetic differences can be utilized in breeding programs to enhance wheat resilience to climate change.