Exploring natural genetic diversity in a bread wheat multi-founder population: Dual imaging of photosynthesis and stomatal kinetics.

A better understanding of crop phenotype under dynamic environmental conditions will help inform the development of new cultivars with superior adaptation to constantly changing field conditions. Recent research has shown that optimising photosynthetic and stomatal conductance traits holds promise for improved crop performance. However, standard phenotyping tools such as gas-exchange systems are limited by their throughput. In this work, a novel approach based on a bespoke gas-exchange chamber allowing combined measurement of the quantum yield of photosystem II (PSII) with an estimation of stomatal conductance via thermal imaging, was used to phenotype a range of bread wheat (Triticum aestivum L.) genotypes, that were a sub-set of a multi-founder experimental population. Datasets were further supplemented by measurement of photosynthetic capacity and stomatal density. First, we showed that measurement of stomatal traits using our dual imaging system compared to standard IRGA methods showed good agreement between the two methods (R2=0.86) for the rapidity of stomatal opening (Ki), with the dual-imager method resulting in less intra-genotype variation. Using the dual-imaging methods, and traditional approaches we found broad and significant variation in key traits, including photosynthetic CO2 uptake at saturating light and ambient CO2 concentration (Asat), photosynthetic CO2 uptake at saturating light and elevated CO2 concentration (Amax), the maximum velocity of Rubisco for carboxylation (Vcmax), time for stomatal opening (Ki), and leaf evaporative cooling. Anatomical analysis revealed significant variation in flag leaf adaxial stomatal density. Associations between traits highlighted significant relationships between leaf evaporative cooling, leaf stomatal conductance under low (gsmin) and high (gsmax) light intensity, and the operating efficiency of PSII (Fq'/Fm'), highlighting the importance of stomatal conductance and stomatal rapidity in maintaining optimal leaf temperature for photosynthesis in wheat. Additionally, gsmin and gsmax were positively associated, indicating that potential combination of preferable traits (i.e. inherently high gsmax, low Ki and maintained leaf evaporative cooling) are present in wheat. This work highlights for the first time the effectiveness of thermal imaging in screening dynamic stomatal conductance in a large panel of wheat genotypes. The wide phenotypic variation observed suggested the presence of exploitable genetic variability in bread wheat for dynamic stomatal conductance traits and photosynthetic capacity for targeted optimisation within future breeding programs.

Phenomic and genomic prediction of yield on multiple locations in winter wheat.

Genomic selection has recently become an established part of breeding strategies in cereals. However, a limitation of linear genomic prediction models for complex traits such as yield is that these are unable to accommodate Genotype by Environment effects, which are commonly observed over trials on multiple locations. In this study, we investigated how this environmental variation can be captured by the collection of a large number of phenomic markers using high-throughput field phenotyping and whether it can increase GS prediction accuracy. For this purpose, 44 winter wheat (Triticum aestivum L.) elite populations, comprising 2,994 lines, were grown on two sites over 2 years, to approximate the size of trials in a practical breeding programme. At various growth stages, remote sensing data from multi- and hyperspectral cameras, as well as traditional ground-based visual crop assessment scores, were collected with approximately 100 different data variables collected per plot. The predictive power for grain yield was tested for the various data types, with or without genome-wide marker data sets. Models using phenomic traits alone had a greater predictive value (R2 = 0.39-0.47) than genomic data (approximately R2 = 0.1). The average improvement in predictive power by combining trait and marker data was 6%-12% over the best phenomic-only model, and performed best when data from one full location was used to predict the yield on an entire second location. The results suggest that genetic gain in breeding programmes can be increased by utilisation of large numbers of phenotypic variables using remote sensing in field trials, although at what stage of the breeding cycle phenomic selection could be most profitably applied remains to be answered.

Longer epidermal cells underlie a quantitative source of variation in wheat flag leaf size.

The wheat flag leaf is the main contributor of photosynthetic assimilates to developing grains. Understanding how canopy architecture strategies affect source strength and yield will aid improved crop design. We used an eight-founder population to investigate the genetic architecture of flag leaf area, length, width and angle in European wheat. For the strongest genetic locus identified, we subsequently created a near-isogenic line (NIL) pair for more detailed investigation across seven test environments. Genetic control of traits investigated was highly polygenic, with colocalisation of replicated quantitative trait loci (QTL) for one or more traits identifying 24 loci. For QTL QFll.niab-5A.1 (FLL5A), development of a NIL pair found the FLL5A+ allele commonly conferred a c. 7% increase in flag and second leaf length and a more erect leaf angle, resulting in higher flag and/or second leaf area. Increased FLL5A-mediated flag leaf length was associated with: (1) longer pavement cells and (2) larger stomata at lower density, with a trend for decreased maximum stomatal conductance (Gsmax ) per unit leaf area. For FLL5A, cell size rather than number predominantly determined leaf length. The observed trade-offs between leaf size and stomatal morphology highlight the need for future studies to consider these traits at the whole-leaf level.

The Other Mechanisms by Which the Rht Genes Improve the Harvest Index of Wheat.

Uncovering the mechanism that underlies the relationship between crop height and grain yield would potentially inform the strategies for improving wheat with optimal height. The aim of the research reported here was to identify the attributes able to produce wheat yield increases in Rht genotypes without further straw-shortening. Attention was given to examination in a controlled environment the question of the mechanistic foundation that determined the relationship between wheat height and yield in lines (Rht-B1b, Rht-D1b, Rht-B1c, Rht-D1c) compared to wild types in Mercia background. In addition to height reduction, this research revealed three other mechanisms by which the Rht genes may also improve the Harvest Index (HI) of wheat: (i) low Specific Leaf Area (SLA), (ii) increased Mean Residence Time (MRT) of Nitrogen (N), and (iii) increased grain number on spike.

AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice.

Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging. Here, we present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials. We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping. We found that the AirMeasurer-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.

Grain carbon isotope composition is a marker for allocation and harvest index in wheat.

The natural 13 C abundance (δ13 C) in plant leaves has been used for decades with great success in agronomy to monitor water-use efficiency and select modern cultivars adapted to dry conditions. However, in wheat, it is also important to find genotypes with high carbon allocation to spikes and grains, and thus with a high harvest index (HI) and/or low carbon losses via respiration. Finding isotope-based markers of carbon partitioning to grains would be extremely useful since isotope analyses are inexpensive and can be performed routinely at high throughput. Here, we took the advantage of a set of field trials made of more than 600 plots with several wheat cultivars and measured agronomic parameters as well as δ13 C values in leaves and grains. We find a linear relationship between the apparent isotope discrimination between leaves and grain (denoted as Δδcorr ), and the respiration use efficiency-to-HI ratio. It means that overall, efficient carbon allocation to grains is associated with a small isotopic difference between leaves and grains. This effect is explained by postphotosynthetic isotope fractionations, and we show that this can be modelled by equations describing the carbon isotope composition in grains along the wheat growth cycle. Our results show that 13 C natural abundance in grains could be useful to find genotypes with better carbon allocation properties and assist current wheat breeding technologies.

A toolkit to rapidly modify root systems through single plant selection.

BACKGROUND: The incorporation of root traits into elite germplasm is typically a slow process. Thus, innovative approaches are required to accelerate research and pre-breeding programs targeting root traits to improve yield stability in different environments and soil types. Marker-assisted selection (MAS) can help to speed up the process by selecting key genes or quantitative trait loci (QTL) associated with root traits. However, this approach is limited due to the complex genetic control of root traits and the limited number of well-characterised large effect QTL. Coupling MAS with phenotyping could increase the reliability of selection. Here we present a useful framework to rapidly modify root traits in elite germplasm. In this wheat exemplar, a single plant selection (SPS) approach combined three main elements: phenotypic selection (in this case for seminal root angle); MAS using KASP markers (targeting a root biomass QTL); and speed breeding to accelerate each cycle. RESULTS: To develop a SPS approach that integrates non-destructive screening for seminal root angle and root biomass, two initial experiments were conducted. Firstly, we demonstrated that transplanting wheat seedlings from clear pots (for seminal root angle assessment) into sand pots (for root biomass assessment) did not impact the ability to differentiate genotypes with high and low root biomass. Secondly, we demonstrated that visual scores for root biomass were correlated with root dry weight (r = 0.72), indicating that single plants could be evaluated for root biomass in a non-destructive manner. To highlight the potential of the approach, we applied SPS in a backcrossing program which integrated MAS and speed breeding for the purpose of rapidly modifying the root system of elite bread wheat line Borlaug100. Bi-directional selection for root angle in segregating generations successfully shifted the mean root angle by 30° in the subsequent generation (P ≤ 0.05). Within 18 months, BC2F4:F5 introgression lines were developed that displayed a full range of root configurations, while retaining similar above-ground traits to the recurrent parent. Notably, the seminal root angle displayed by introgression lines varied more than 30° compared to the recurrent parent, resulting in lines with both narrow and wide root angles, and high and low root biomass phenotypes. CONCLUSION: The SPS approach enables researchers and plant breeders to rapidly manipulate root traits of future crop varieties, which could help improve productivity in the face of increasing environmental fluctuations. The newly developed elite wheat lines with modified root traits provide valuable materials to study the value of different root systems to support yield in different environments and soil types.

Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat.

Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability, and the ability to analyze big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack Light Detection and Ranging (LiDAR) device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis. The use of LiDAR can acquire millions of 3D points to represent spatial features of crops, and CropQuant-3D can extract meaningful traits from large, complex point clouds. In a case study examining the response of wheat varieties to three different levels of nitrogen fertilization in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in the canopy, with strong correlations with manual measurements. Hence, we demonstrate that this system could consistently perform 3D trait analysis at a larger scale and more quickly than heretofore possible and addresses challenges in mobility, throughput, and scalability. To ensure our work could reach non-expert users, we developed an open-source graphical user interface for CropQuant-3D. We, therefore, believe that the combined system is easy-to-use and could be used as a reliable research tool in multi-location phenotyping for both crop research and breeding. Furthermore, together with the fast maturity of LiDAR technologies, the system has the potential for further development in accuracy and affordability, contributing to the resolution of the phenotyping bottleneck and exploiting available genomic resources more effectively.

Breeding crops for climate resilience.

In enhancing the resilience of our crops to the impacts of climate change, selection objectives need to address increased variability in the production environment. This encompasses the effects of more variable rainfall and temperatures than currently experienced, including extreme weather events, and changes in pest and pathogens distribution with the increased likelihood of major pest and disease outbreaks as well as occurrence of novel pathogens. Farmers manage the inevitable risks associated with cropping by planting varieties that deliver high yields and good quality under optimal conditions but minimise losses when the seasons are bad. Breeders and agronomists work to support farmers in specific target environments, but increased climate variability has meant that they need to broaden the adaptability of varieties grown and increase the yield stability to help minimise climate-induced risks and build resilience.

Wheat root systems as a breeding target for climate resilience.

In the coming decades, larger genetic gains in yield will be necessary to meet projected demand, and this must be achieved despite the destabilizing impacts of climate change on crop production. The root systems of crops capture the water and nutrients needed to support crop growth, and improved root systems tailored to the challenges of specific agricultural environments could improve climate resiliency. Each component of root initiation, growth and development is controlled genetically and responds to the environment, which translates to a complex quantitative system to navigate for the breeder, but also a world of opportunity given the right tools. In this review, we argue that it is important to know more about the 'hidden half' of crop plants and hypothesize that crop improvement could be further enhanced using approaches that directly target selection for root system architecture. To explore these issues, we focus predominantly on bread wheat (Triticum aestivum L.), a staple crop that plays a major role in underpinning global food security. We review the tools available for root phenotyping under controlled and field conditions and the use of these platforms alongside modern genetics and genomics resources to dissect the genetic architecture controlling the wheat root system. To contextualize these advances for applied wheat breeding, we explore questions surrounding which root system architectures should be selected for, which agricultural environments and genetic trait configurations of breeding populations are these best suited to, and how might direct selection for these root ideotypes be implemented in practice.

Genotypic, Developmental and Environmental Effects on the Rapidity of gs in Wheat: Impacts on Carbon Gain and Water-Use Efficiency.

Stomata are the primary gatekeepers for CO2 uptake for photosynthesis and water loss via transpiration and therefore play a central role in crop performance. Although stomatal conductance (gs ) and assimilation rate (A) are often highly correlated, studies have demonstrated an uncoupling between A and gs that can result in sub-optimal physiological processes in dynamic light environments. Wheat (Triticum aestivum L.) is exposed to changes in irradiance due to leaf self-shading, moving clouds and shifting sun angle to which both A and gs respond. However, stomatal responses are generally an order of magnitude slower than photosynthetic responses, leading to non-synchronized A and gs responses that impact CO2 uptake and water use efficiency ( iWUE). Here we phenotyped a panel of eight wheat cultivars (estimated to capture 80% of the single nucleotide polymorphism variation in North-West European bread wheat) for differences in the speed of stomatal responses (to changes in light intensity) and photosynthetic performance at different stages of development. The impact of water stress and elevated [CO2] on stomatal kinetics was also examined in a selected cultivar. Significant genotypic variation was reported for the time constant for stomatal opening (Ki, P = 0.038) and the time to reach 95% steady state A (P = 0.045). Slow gs opening responses limited A by ∼10% and slow closure reduced iWUE, with these impacts found to be greatest in cultivars Soissons, Alchemy and Xi19. A decrease in stomatal rapidity (and thus an increase in the limitation of photosynthesis) (P < 0.001) was found during the post-anthesis stage compared to the early booting stage. Reduced water availability triggered stomatal closure and asymmetric stomatal opening and closing responses, while elevated atmospheric [CO2] conditions reduced the time for stomatal opening during a low to high light transition, thus suggesting a major environmental effect on dynamic stomatal kinetics. We discuss these findings in terms of exploiting various traits to develop ideotypes for specific environments, and suggest that intraspecific variation in the rapidity of stomatal responses could provide a potential unexploited breeding target to optimize the physiological responses of wheat to dynamic field conditions.

Review: New sensors and data-driven approaches-A path to next generation phenomics.

At the 4th International Plant Phenotyping Symposium meeting of the International Plant Phenotyping Network (IPPN) in 2016 at CIMMYT in Mexico, a workshop was convened to consider ways forward with sensors for phenotyping. The increasing number of field applications provides new challenges and requires specialised solutions. There are many traits vital to plant growth and development that demand phenotyping approaches that are still at early stages of development or elude current capabilities. Further, there is growing interest in low-cost sensor solutions, and mobile platforms that can be transported to the experiments, rather than the experiment coming to the platform. Various types of sensors are required to address diverse needs with respect to targets, precision and ease of operation and readout. Converting data into knowledge, and ensuring that those data (and the appropriate metadata) are stored in such a way that they will be sensible and available to others now and for future analysis is also vital. Here we are proposing mechanisms for "next generation phenomics" based on our learning in the past decade, current practice and discussions at the IPPN Symposium, to encourage further thinking and collaboration by plant scientists, physicists and engineering experts.

The effects of training population design on genomic prediction accuracy in wheat.

Genomic selection offers several routes for increasing the genetic gain or efficiency of plant breeding programmes. In various species of livestock, there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeder's equation. Accurate predictions of genomic breeding value are central to this, and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable predictions with higher accuracy. To quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops, we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F2:4 bi- and tri-parental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25 K segregating SNP markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Genomic prediction accuracies of yield BLUEs were 0.125-0.127 using two different cross-validation approaches and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasise the importance of the training panel design in relation to the genetic material to which the resulting prediction model is to be applied.

GplusE: beyond genomic selection.

GplusE is a strategy for genomic selection in which the accuracy of assessment in the reference population for a primary trait such as yield is increased by the incorporation of data from high- throughput field phenotyping platforms. This increase in precision comes from both exploiting genetic relationships between traits and reducing the effect of environmental influences upon them. We describe a collaborative project among researchers and breeders to develop a large reference population of elite UK wheat lines. This will be used to test the method, to study the design of the reference population, and to test genotyping strategies and imputation methods. Finally, it will provide data to pump-prime the application of genomic selection to UK winter wheat breeding.

Understanding the impact of crop and food production on the water environment--using sugar as a model.

The availability of fresh water and the quality of aquatic ecosystems are important global concerns, and agriculture plays a major role. Consumers and manufacturers are increasingly sensitive to sustainability issues related to processed food products and drinks. The present study examines the production of sugar from the growing cycle through to processing to the factory gate, and identifies the potential impacts on water scarcity and quality and the ways in which the impact of water use can be minimised. We have reviewed the production phases and processing steps, and how calculations of water use can be complicated, or in some cases how assessments can be relatively straightforward. Finally, we outline several ways that growers and sugar processors are improving the efficiency of water use and reducing environmental impact, and where further advances can be made. This provides a template for the assessment of other crops.

Genotypic differences in deep water extraction associated with drought tolerance in wheat.

The ability of roots to extract soil moisture is critical for maintaining yields during drought. However, the extent of genotypic variation for rooting depth and drought tolerance in Northern European wheat (Triticum aestivum L.) germplasm is not known. The objectives of this study were to measure genotypic differences in root activity, test relationships between water use and yield, examine trade-offs between yield potential and investment of biomass in deep roots, and identify genotypes that contrast in deep root activity. A diverse set of 21 wheat genotypes was evaluated under irrigated and managed drought conditions in the field. Root activity was inferred from patterns of water extraction from the soil profile. Genotypes were equally capable of exploiting soil moisture in the upper layers, but there were significant genotypic differences in rates of water uptake after anthesis in deeper soil layers. For example, across the three years of the study, the variety Xi19 showed consistently deeper root activity than the variety Spark; Xi19 also showed greater drought tolerance than Spark. There were positive correlations between water extraction from depth and droughted yields and drought tolerance, but correlations between deep water use and yield potential were not significant or only weakly negative. With appropriate screening tools, selection for genotypes that can better mine deep soil water should improve yield stability in variable rainfall environments.

Genetic and management approaches to boost UK wheat yields by ameliorating water deficits.

Faced with the challenge of increasing global food production, there is the need to exploit all approaches to increasing crop yields. A major obstacle to boosting yields of wheat (an important staple in many parts of the world) is the availability and efficient use of water, since there is increasing stress on water resources used for agriculture globally, and also in parts of the UK. Improved soil and crop management and the development of new genotypes may increase wheat yields when water is limiting. Technical and scientific issues concerning management options such as irrigation and the use of growth-promoting rhizobacteria are explored, since these may allow the more efficient use of irrigation. Fundamental understanding of how crops sense and respond to multiple abiotic stresses can help improve the effective use of irrigation water. Experiments are needed to test the hypothesis that modifying wheat root system architecture (by increasing root proliferation deep in the soil profile) will allow greater soil water extraction thereby benefiting productivity and yield stability. Furthermore, better knowledge of plant and soil interactions and how below-ground and above-ground processes communicate within the plant can help identify traits and ultimately genes (or alleles) that will define genotypes that yield better under dry conditions. Developing new genotypes will take time and, therefore, these challenges need to be addressed now.

Possible changes to arable crop yields by 2050.

By 2050, the world population is likely to be 9.1 billion, the CO(2) concentration 550 ppm, the ozone concentration 60 ppb and the climate warmer by ca 2 degrees C. In these conditions, what contribution can increased crop yield make to feeding the world? CO(2) enrichment is likely to increase yields of most crops by approximately 13 per cent but leave yields of C4 crops unchanged. It will tend to reduce water consumption by all crops, but this effect will be approximately cancelled out by the effect of the increased temperature on evaporation rates. In many places increased temperature will provide opportunities to manipulate agronomy to improve crop performance. Ozone concentration increases will decrease yields by 5 per cent or more. Plant breeders will probably be able to increase yields considerably in the CO(2)-enriched environment of the future, and most weeds and airborne pests and diseases should remain controllable, so long as policy changes do not remove too many types of crop-protection chemicals. However, soil-borne pathogens are likely to be an increasing problem when warmer weather will increase their multiplication rates; control is likely to need a transgenic approach to breeding for resistance. There is a large gap between achievable yields and those delivered by farmers, even in the most efficient agricultural systems. A gap is inevitable, but there are large differences between farmers, even between those who have used the same resources. If this gap is closed and accompanied by improvements in potential yields then there is a good prospect that crop production will increase by approximately 50 per cent or more by 2050 without extra land. However, the demands for land to produce bio-energy have not been factored into these calculations.

A preliminary study on genotypic differences in transcript abundance of drought-responsive genes in sugar beet.

In this study, four sugar beet genotypes of differing responses to drought were selected from a field experiment conducted under well-watered and water-limited conditions in 2004. In addition, two candidate genes: 2-cysteine peroxiredoxin (2-cys prx) and Nucleoside Diphosphate Kinase (NDPK), thought to be associated with drought tolerance, were chosen from a previous proteomics study in sugar beet. An expression analysis of the two drought-regulated genes using semi-quantitative reverse transcription Polymerase Chain Reaction (RT-PCR) indicated that there were genotypic differences in the transcript abundance of the candidate genes with the differences in the expression level of 2-cys prx being likely associated with the drought responses of the genotypes in a two-year field study. However, the expression analysis of the genes has to be investigated at different stages of the stress period on more genotypes.