Wheat (Triticum aestivum L.) production is increasingly challenged by simultaneous drought and heatwaves. We assessed the effect of both stresses combined on whole plant water use and carbohydrate partitioning in eight bread wheat genotypes that showed contrasting tolerance. Plant water use was monitored throughout growth, and water-soluble carbohydrates (WSC) and starch were measured following a 3day heat treatment during drought. Final grain yield was increasingly associated with aboveground biomass and total water use with increasing stress intensity. Combined drought and heat stress immediately reduced daily water use in some genotypes and altered transpiration response to vapor pressure deficit during grain filling, compared to drought only. In grains, glucose and fructose concentrations measured 12 days after anthesis explained 43 and 40% of variation in final grain weight in the main spike, respectively. Starch concentrations in grains offset the reduction in WSC following drought or combined drought and heat stress in some genotypes, while in other genotypes both stresses altered the balance between WSC and starch concentrations. WSC were predominantly allocated to the spike in modern Australian varieties (28-50% of total WSC in the main stem), whereas the stem contained most WSC in older genotypes (67-87%). Drought and combined drought and heat stress increased WSC partitioning to the spike in older genotypes but not in the modern varieties. Ability to maintain transpiration, especially following combined drought and heat stress, appears essential for maintaining wheat productivity.
Background Stomata are tiny pores on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious and not high throughput. This impedes research on stomatal biology and hinders efforts to develop resilient crops with optimised stomatal patterning. We have developed a rapid non-destructive method to phenotype stomatal traits in three crop species: wheat, rice and tomato. Results The method consists of two steps. The first is the non-destructive capture of images of the leaf surface from plants in their growing environment using a handheld microscope; a process that only takes a few seconds compared to minutes for other methods. The second is to analyse stomatal features using a machine learning model that automatically detects, counts and measures stomatal number, size and aperture. The accuracy of the machine learning model in detecting stomata ranged from 88 to 99%, depending on the species, with a high correlation between measures of number, size and aperture using the machine learning models and by measuring them manually. The rapid method was applied to quickly identify contrasting stomatal phenotypes. Conclusions We developed a method that combines rapid non-destructive imaging of leaf surfaces with automated image analysis. The method provides accurate data on stomatal features while significantly reducing time for data acquisition and analysis. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.
Appropriate timing of seed germination is crucial for the survival and propagation of plants, and for crop yield, especially in environments prone to salinity or drought. However, the exact mechanisms by which seeds perceive changes in soil conditions and integrate them to trigger germination remain elusive, especially once the seeds are non-dormant. In this study, we determined that the Arabidopsis ERECTA (ER), ERECTA-LIKE1 (ERL1), and ERECTA-LIKE2 (ERL2) leucine-rich-repeat receptor-like kinases regulate seed germination and its sensitivity to changes in salt and osmotic stress levels. Loss of ER alone, or in combination with ERL1 and/or ERL2, slows down the initiation of germination and its progression to completion, or arrests it altogether under saline conditions, until better conditions return. This function is maternally controlled via the tissues surrounding the embryo, with a primary role being played by the properties of the seed coat and its mucilage. These relate to both seed-coat expansion and subsequent differentiation and to salinity-dependent interactions between the mucilage, subtending seed coat layers and seed interior in the germinating seed. Salt-hypersensitive er105, er105 erl1.2, er105 erl2.1 and triple-mutant seeds also exhibit increased sensitivity to exogenous ABA during germination, and under salinity show an enhanced up-regulation of the germination repressors and inducers of dormancy ABA-insensitive-3, ABA-insensitive-5, DELLA-encoding RGL2, and Delay-Of-Germination-1. These findings reveal a novel role of the ERECTA receptor-kinases in the sensing of conditions at the seed surface and the integration of developmental, dormancy and stress signalling pathways in seeds. They also open novel avenues for the genetic improvement of plant adaptation to changing drought and salinity patterns.
Background: Stomata are tiny pores located on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and any variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious, which impedes research on stomatal physiology and hinders efforts to develop resilient crops with optimised stomatal patterning. We developed a rapid non-destructive method to phenotype stomatal traits in four species: wheat, rice, tomato, and Arabidopsis. Results: The method consists of two steps. The first step is to capture images of a leaf surface directly and non-destructively using a handheld microscope, which only takes a few seconds compared to minutes using other methods. This rapid method also provides higher quality images for automated data analysis. The second step is to analyse stomatal features using a machine-learning model that automatically detects, counts stomata and measures size. The accuracy of the machine-learning model in detecting stomata ranged from 89% to 96%, depending on the species. Conclusions: We developed a method that combines rapid non-destructive imaging of leaf surfaces with automated image analysis. The method provides accurate data on stomatal features while significantly reducing time for data acquisition. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.
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