Combining field performance with controlled environment plant imaging to identify the genetic control of growth and transpiration underlying yield response to water-deficit stress in wheat

Genotype × environment × management analysis to define allometric rules between leaves and stems in wheat

Allometric rules provide insights into structure-function relationships across species and scales and are commonly used in ecology. The fields of agronomy, plant phenotyping, and modeling also need simplifications such as those provided by allometric rules to reconcile data at different temporal and spatial levels (organs/canopy). This study explores the variations in relationships for wheat in terms of the distribution of crop green area between leaves and stems, and the allocation of above-ground biomass between leaves and stems during the vegetative period, using a large dataset covering different years, countries, genotypes, and management practices. The results showed that the relationship between leaf and stem area was linear, genotype-specific, and sensitive to radiation. The relationship between leaf and stem biomass depended on genotype and nitrogen fertilization. The mass per area, associating area and biomass for both leaf and stem, varied strongly by developmental stage and was significantly affected by environment and genotype. These allometric rules were evaluated and shown to have satisfactory performance, and their potential use is discussed with regard to current phenotyping techniques and plant/crop models. Our results enable the definition of models and minimum datasets required for characterizing diversity panels and making predictions in various genotype × environment × management contexts.

Survival mechanisms of chickpea (Cicer arietinum) under saline conditions

Salinity is a significant abiotic stress that is steadily increasing in intensity globally. Salinity is caused by various factors such as use of poor-quality water for irrigation, poor drainage systems, and increasing spate of drought that concentrates salt solutions in the soil; salinity is responsible for substantial agricultural losses worldwide. Chickpea (Cicer arietinum) is one of the crops most sensitive to salinity stress. Salinity restricts chickpea growth and production by interfering with various physiological and metabolic processes, downregulating genes linked to growth, and upregulating genes encoding intermediates of the tolerance and avoidance mechanisms. Salinity, which also leads to osmotic stress, disturbs the ionic equilibrium of plants. Survival under salinity stress is a primary concern for the plant. Therefore, plants adopt tolerance strategies such as the SOS pathway, antioxidative defense mechanisms, and several other biochemical mechanisms. Simultaneously, affected plants exhibit mechanisms like ion compartmentalization and salt exclusion. In this review, we highlight the impact of salinity in chickpea, strategies employed by the plant to tolerate and avoid salinity, and agricultural strategies for dealing with salinity. With the increasing spate of salinity spurred by natural events and anthropogenic agricultural activities, it is pertinent to explore and exploit the underpinning mechanisms for salinity tolerance to develop mitigation and adaptation strategies in globally important food crops such as chickpea.

Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean

Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs.

Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches

Introduction

Phenomics has emerged as important tool to bridge the genotype-phenotype gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its component traits over a different growth phase of plant will immensely help dissect genetic basis of biomass production. Based on RGB images, models have been developed to predict biomass recently. However, it is very challenging to find a model performing stable across experiments. In this study, we recorded RGB and NIR images of wheat germplasm and Recombinant Inbred Lines (RILs) of Raj3765xHD2329, and examined the use of multimodal images from RGB, NIR sensors and machine learning models to predict biomass and leaf area non-invasively.

Results

The image-based traits (i-Traits) containing geometric features, RGB based indices, RGB colour classes and NIR features were categorized into architectural traits and physiological traits. Total 77 i-Traits were selected for prediction of biomass and leaf area consisting of 35 architectural and 42 physiological traits. We have shown that different biomass related traits such as fresh weight, dry weight and shoot area can be predicted accurately from RGB and NIR images using 16 machine learning models. We applied the models on two consecutive years of experiments and found that measurement accuracies were similar suggesting the generalized nature of models. Results showed that all biomass-related traits could be estimated with about 90% accuracy but the performance of model BLASSO was relatively stable and high in all the traits and experiments. The R² of BLASSO for fresh weight prediction was 0.96 (both year experiments), for dry weight prediction was 0.90 (Experiment 1) and 0.93 (Experiment 2) and for shoot area prediction 0.96 (Experiment 1) and 0.93 (Experiment 2). Also, the RMSRE of BLASSO for fresh weight prediction was 0.53 (Experiment 1) and 0.24 (Experiment 2), for dry weight prediction was 0.85 (Experiment 1) and 0.25 (Experiment 2) and for shoot area prediction 0.59 (Experiment 1) and 0.53 (Experiment 2).

Discussion

Based on the quantification power analysis of i-Traits, the determinants of biomass accumulation were found which contains both architectural and physiological traits. The best predictor i-Trait for fresh weight and dry weight prediction was Area_SV and for shoot area prediction was projected shoot area. These results will be helpful for identification and genetic basis dissection of major determinants of biomass accumulation and also non-invasive high throughput estimation of plant growth during different phenological stages can identify hitherto uncovered genes for biomass production and its deployment in crop improvement for breaking the yield plateau.

The battle of crops against drought: Genetic dissection and improvement

With ongoing global climate change, water scarcity-induced drought stress remains a major threat to agricultural productivity. Plants undergo a series of physiological and morphological changes to cope with drought stress, including stomatal closure to reduce transpiration and changes in root architecture to optimize water uptake. Combined phenotypic and multi-omics studies have recently identified a number of drought-related genetic resources in different crop species. The functional dissection of these genes using molecular techniques has enriched our understanding of drought responses in crops and has provided genetic targets for enhancing resistance to drought. Here, we review recent advances in the cloning and functional analysis of drought resistance genes and the development of technologies to mitigate the threat of drought to crop production.

Capturing crop adaptation to abiotic stress using image-based technologies

Farmers and breeders aim to improve crop responses to abiotic stresses and secure yield under adverse environmental conditions. To achieve this goal and select the most resilient genotypes, plant breeders and researchers rely on phenotyping to quantify crop responses to abiotic stress. Recent advances in imaging technologies allow researchers to collect physiological data non-destructively and throughout time, making it possible to dissect complex plant responses into quantifiable traits. The use of image-based technologies enables the quantification of crop responses to stress in both controlled environmental conditions and field trials. This paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency, while discussing their advantages and drawbacks. We present a detailed review of traits involved in abiotic tolerance, which have been quantified by a range of imaging sensors under high-throughput phenotyping facilities or using unmanned aerial vehicles in the field. We also provide an up-to-date compilation of spectral tolerance indices and discuss the progress and challenges in machine learning, including supervised and unsupervised models as well as deep learning.

Drought Tolerant Near Isogenic Lines of Pusa 44 Pyramided With qDTY2.1 and qDTY3.1, Show Accelerated Recovery Response in a High Throughput Phenomics Based Phenotyping

Reproductive stage drought stress (RSDS) is a major challenge in rice production worldwide. Cultivar development with drought tolerance has been slow due to the lack of precise high throughput phenotyping tools to quantify drought stress-induced effects. Most of the available techniques are based on destructive sampling and do not assess the progress of the plant's response to drought. In this study, we have used state-of-the-art image-based phenotyping in a phenomics platform that offers a controlled environment, non-invasive phenotyping, high accuracy, speed, and continuity. In rice, several quantitative trait loci (QTLs) which govern grain yield under drought determine RSDS tolerance. Among these, qDTY2.1 and qDTY3.1 were used for marker-assisted breeding. A set of 35 near-isogenic lines (NILs), introgressed with these QTLs in the popular variety, Pusa 44 were used to assess the efficiency of image-based phenotyping for RSDS tolerance. NILs offered the most reliable contrast since they differed from Pusa 44 only for the QTLs. Four traits, namely, the projected shoot area (PSA), water use (WU), transpiration rate (TR), and red-green-blue (RGB) and near-infrared (NIR) values were used. Differential temporal responses could be seen under drought, but not under unstressed conditions. NILs showed significant level of RSDS tolerance as compared to Pusa 44. Among the traits, PSA showed strong association with yield (80%) as well as with two drought tolerances indices, stress susceptibility index (SSI) and tolerance index (TOL), establishing its ability in identifying the best drought tolerant NILs. The results revealed that the introgression of QTLs helped minimize the mean WU per unit of biomass per day, suggesting the potential role of these QTLs in improving WU-efficiency (WUE). We identified 11 NILs based on phenomics traits as well as performance under imposed drought in the field. The study emphasizes the use of phenomics traits as selection criteria for RSDS tolerance at an early stage, and is the first report of using phenomics parameters in RSDS selection in rice.

New approaches to improve crop tolerance to biotic and abiotic stresses

During the last years, a great effort has been dedicated at the development and employment of diverse approaches for achieving more stress-tolerant and climate-flexible crops and sustainable yield increases to meet the food and energy demands of the future. The ongoing climate change is in fact leading to more frequent extreme events with a negative impact on food production, such as increased temperatures, drought, and soil salinization as well as invasive arthropod pests and diseases. In this review, diverse "green strategies" (e.g., chemical priming, root-associated microorganisms), and advanced technologies (e.g., genome editing, high-throughput phenotyping) are described on the basis of the most recent research evidence. Particularly, attention has been focused on the potential use in a context of sustainable and climate-smart agriculture (the so called "next agriculture generation") to improve plant tolerance and resilience to abiotic and biotic stresses. In addition, the gap between the results obtained in controlled experiments and those from application of these technologies in real field conditions (lab to field step) is also discussed.

Using high-throughput multiple optical phenotyping to decipher the genetic architecture of maize drought tolerance

BACKGROUND

Drought threatens the food supply of the world population. Dissecting the dynamic responses of plants to drought will be beneficial for breeding drought-tolerant crops, as the genetic controls of these responses remain largely unknown.

RESULTS

Here we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days, and collected multiple optical images, including color camera scanning, hyperspectral imaging, and X-ray computed tomography images. We develop high-throughput analysis pipelines to extract image-based traits (i-traits). Of these i-traits, 10,080 were effective and heritable indicators of maize external and internal drought responses. An i-trait-based genome-wide association study reveals 4322 significant locus-trait associations, representing 1529 quantitative trait loci (QTLs) and 2318 candidate genes, many that co-localize with previously reported maize drought responsive QTLs. Expression QTL (eQTL) analysis uncovers many local and distant regulatory variants that control the expression of the candidate genes. We use genetic mutation analysis to validate two new genes, ZmcPGM2 and ZmFAB1A, which regulate i-traits and drought tolerance. Moreover, the value of the candidate genes as drought-tolerant genetic markers is revealed by genome selection analysis, and 15 i-traits are identified as potential markers for maize drought tolerance breeding.

CONCLUSION

Our study demonstrates that combining high-throughput multiple optical phenotyping and GWAS is a novel and effective approach to dissect the genetic architecture of complex traits and clone drought-tolerance associated genes.

Comparative transcriptome profiling of a resistant vs susceptible bread wheat (Triticum aestivum L.) cultivar in response to water deficit and cold stress

Bread wheat (Triticum aestivum L.) is one of the most important agricultural plants wearing abiotic stresses, such as water deficit and cold, that cause its productivity reduction. Since resistance to abiotic factors is a multigenic trait, therefore modern genome-wide approaches can help to involve various genetic material in breeding. One technique is full transcriptome analysis that reveals groups of stress response genes serving marker-assisted selection markers. Comparing transcriptome profiles of the same genetic material under several stresses is essential and makes the whole picture. Here, we addressed this by studying the transcriptomic response to water deficit and cold stress for two evolutionarily distant bread wheat varieties: stress-resistant cv. Saratovskaya 29 (S29) and stress-sensitive cv. Yanetzkis Probat (YP). For the first time, transcriptomes for these cultivars grown under abiotic stress conditions were obtained using Illumina based MACE technology. We identified groups of genes involved in response to cold and water deficiency stresses, including responses to each stress factor and both factors simultaneously that may be candidates for resistance genes. We discovered a core group of genes that have a similar pattern of stress-induced expression changes. The particular expression pattern was revealed not only for the studied varieties but also for the published transcriptomic data on cv. Jing 411 and cv. Fielder. Comparative transcriptome profiling of cv. S29 and cv. YP in response to water deficit and cold stress confirmed the hypothesis that stress-induced expression change is unequal within a homeologous gene group. As a rule, at least one changed significantly while the others had a relatively lower expression. Also, we found several SNPs distributed throughout the genomes of cv. S29 and cv. YP and distinguished the studied varieties from each other and the reference cv. Chinese Spring. Our results provide new data for genomics-assisted breeding of stress-tolerant wheat cultivars.

Novel Salinity Tolerance Loci in Chickpea Identified in Glasshouse and Field Environments

A better understanding of the genetics of salinity tolerance in chickpea would enable breeding of salt tolerant varieties, offering potential to expand chickpea production to marginal, salinity-affected areas. A Recombinant Inbred Line population was developed using accelerated-Single Seed Descent of progeny from a cross between two chickpea varieties, Rupali (salt-sensitive) and Genesis836 (salt-tolerant). The population was screened for salinity tolerance using high-throughput image-based phenotyping in the glasshouse, in hydroponics, and across 2 years of field trials at Merredin, Western Australia. A genetic map was constructed from 628 unique in-silico DArT and SNP markers, spanning 963.5 cM. Markers linked to two flowering loci identified on linkage groups CaLG03 and CaLG05 were used as cofactors during genetic analysis to remove the confounding effects of flowering on salinity response. Forty-two QTL were linked to growth rate, yield, and yield component traits under both control and saline conditions, and leaf tissue ion accumulation under salt stress. Residuals from regressions fitting best linear unbiased predictions from saline conditions onto best linear unbiased predictions from control conditions provided a measure of salinity tolerance per se, independent of yield potential. Six QTL on CaLG04, CaLG05, and CaLG06 were associated with tolerance per se. In total, 21 QTL mapped to two distinct regions on CaLG04. The first distinct region controlled the number of filled pods, leaf necrosis, seed number, and seed yield specifically under salinity, and co-located with four QTL linked to salt tolerance per se. The second distinct region controlled 100-seed weight and growth-related traits, independent of salinity treatment. Positional cloning of the salinity tolerance-specific loci on CaLG04, CaLG05, and CaLG06 will improve our understanding of the key determinants of salinity tolerance in chickpea.

A Dual Strategy of Breeding for Drought Tolerance and Introducing Drought-Tolerant, Underutilized Crops into Production Systems to Enhance Their Resilience to Water Deficiency

Water scarcity is the primary constraint on crop productivity in arid and semiarid tropical areas suffering from climate alterations; in accordance, agricultural systems have to be optimized. Several concepts and strategies should be considered to improve crop yield and quality, particularly in vulnerable regions where such environmental changes cause a risk of food insecurity. In this work, we review two strategies aiming to increase drought stress tolerance: (i) the use of natural genes that have evolved over time and are preserved in crop wild relatives and landraces for drought tolerance breeding using conventional and molecular methods and (ii) exploiting the reservoir of neglected and underutilized species to identify those that are known to be more drought-tolerant than conventional staple crops while possessing other desired agronomic and nutritive characteristics, as well as introducing them into existing cropping systems to make them more resilient to water deficiency conditions. In the past, the existence of drought tolerance genes in crop wild relatives and landraces was either unknown or difficult to exploit using traditional breeding techniques to secure potential long-term solutions. Today, with the advances in genomics and phenomics, there are a number of new tools available that facilitate the discovery of drought resistance genes in crop wild relatives and landraces and their relatively easy transfer into advanced breeding lines, thus accelerating breeding progress and creating resilient varieties that can withstand prolonged drought periods. Among those tools are marker-assisted selection (MAS), genomic selection (GS), and targeted gene editing (clustered regularly interspaced short palindromic repeat (CRISPR) technology). The integration of these two major strategies, the advances in conventional and molecular breeding for the drought tolerance of conventional staple crops, and the introduction of drought-tolerant neglected and underutilized species into existing production systems has the potential to enhance the resilience of agricultural production under conditions of water scarcity.

Dissecting new genetic components of salinity tolerance in two-row spring barley at the vegetative and reproductive stages

Soil salinity imposes an agricultural and economic burden that may be alleviated by identifying the components of salinity tolerance in barley, a major crop and the most salt tolerant cereal. To improve our understanding of these components, we evaluated a diversity panel of 377 two-row spring barley cultivars during both the vegetative, in a controlled environment, and the reproductive stages, in the field. In the controlled environment, a high-throughput phenotyping platform was used to assess the growth-related traits under both control and saline conditions. In the field, the agronomic traits were measured from plots irrigated with either fresh or saline water. Association mapping for the different components of salinity tolerance enabled us to detect previously known associations, such as HvHKT1;5. Using an "interaction model", which took into account the interaction between treatment (control and salt) and genetic markers, we identified several loci associated with yield components related to salinity tolerance. We also observed that the two developmental stages did not share genetic regions associated with the components of salinity tolerance, suggesting that different mechanisms play distinct roles throughout the barley life cycle. Our association analysis revealed that genetically defined regions containing known flowering genes (Vrn-H3, Vrn-H1, and HvNAM-1) were responsive to salt stress. We identified a salt-responsive locus (7H, 128.35 cM) that was associated with grain number per ear, and suggest a gene encoding a vacuolar H+-translocating pyrophosphatase, HVP1, as a candidate. We also found a new QTL on chromosome 3H (139.22 cM), which was significant for ear number per plant, and a locus on chromosome 2H (141.87 cM), previously identified using a nested association mapping population, which associated with a yield component and interacted with salinity stress. Our study is the first to evaluate a barley diversity panel for salinity stress under both controlled and field conditions, allowing us to identify contributions from new components of salinity tolerance which could be used for marker-assisted selection when breeding for marginal and saline regions.

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.

Variable water cycles have a greater impact on wheat growth and soil nitrogen response than constant watering

Current climate change models project that water availability will become more erratic in the future. With soil nitrogen (N) supply coupled to water availability, it is important to understand the combined effects of variable water and N supply on food crop plants (above- and below-ground). Here we present a study that precisely controls soil moisture and compares stable soil moisture contents with a controlled wetting-drying cycle. Our aim was to identify how changes in soil moisture and N concentration affect shoot-root biomass, N acquisition in wheat, and soil N cycling. Using a novel gravimetric platform allowing fine-scale control of soil moisture dynamics, a 3 × 3 factorial experiment was conducted on wheat plants subjected to three rates of N application (0, 25 and 75 mg N/kg soil) and three soil moisture regimes (two uniform treatments: 23.5 and 13% gravimetric moisture content (herein referred to as Well-watered and Reduced water, respectively), and a Variable treatment which cycled between the two). Plant biomass, soil N and microbial biomass carbon were measured at three developmental stages: tillering (Harvest 1), flowering (Harvest 2), and early grain milk development (Harvest 3). Reduced water supply encouraged root growth when combined with medium and high N. Plant growth was more responsive to N than the water treatments imposed, with a 15-fold increase in biomass between the high and no added N treatment plants. Both uniform soil water treatments resulted in similar plant biomass, while the Variable water treatment resulted in less biomass overall, suggesting wheat prefers consistency whether at a Well-watered or Reduced water level. Plants did not respond well to variable soil moisture, highlighting the need to understand plant adaptation and biomass allocation with resource limitation. This is particularly relevant to developing irrigation practices, but also in the design of water availability experiments.

Drought Stress Tolerance in Wheat and Barley: Advances in Physiology, Breeding and Genetics Research

Climate change is a major threat to most of the agricultural crops grown in tropical and sub-tropical areas globally. Drought stress is one of the consequences of climate change that has a negative impact on crop growth and yield. In the past, many simulation models were proposed to predict climate change and drought occurrences, and it is extremely important to improve essential crops to meet the challenges of drought stress which limits crop productivity and production. Wheat and barley are among the most common and widely used crops due to their economic and social values. Many parts of the world depend on these two crops for food and feed, and both crops are vulnerable to drought stress. Improving drought stress tolerance is a very challenging task for wheat and barley researchers and more research is needed to better understand this stress. The progress made in understanding drought tolerance is due to advances in three main research areas: physiology, breeding, and genetic research. The physiology research focused on the physiological and biochemical metabolic pathways that plants use when exposed to drought stress. New wheat and barley genotypes having a high degree of drought tolerance are produced through breeding by making crosses from promising drought-tolerant genotypes and selecting among their progeny. Also, identifying genes contributing to drought tolerance is very important. Previous studies showed that drought tolerance is a polygenic trait and genetic constitution will help to dissect the gene network(s) controlling drought tolerance. This review explores the recent advances in these three research areas to improve drought tolerance in wheat and barley.

Drought Stress Tolerance in Wheat and Barley: Advances in Physiology, Breeding and Genetics Research

Climate change is a major threat to most of the agricultural crops grown in tropical and sub-tropical areas globally. Drought stress is one of the consequences of climate change that has a negative impact on crop growth and yield. In the past, many simulation models were proposed to predict climate change and drought occurrences, and it is extremely important to improve essential crops to meet the challenges of drought stress which limits crop productivity and production. Wheat and barley are among the most common and widely used crops due to their economic and social values. Many parts of the world depend on these two crops for food and feed, and both crops are vulnerable to drought stress. Improving drought stress tolerance is a very challenging task for wheat and barley researchers and more research is needed to better understand this stress. The progress made in understanding drought tolerance is due to advances in three main research areas: physiology, breeding, and genetic research. The physiology research focused on the physiological and biochemical metabolic pathways that plants use when exposed to drought stress. New wheat and barley genotypes having a high degree of drought tolerance are produced through breeding by making crosses from promising drought-tolerant genotypes and selecting among their progeny. Also, identifying genes contributing to drought tolerance is very important. Previous studies showed that drought tolerance is a polygenic trait and genetic constitution will help to dissect the gene network(s) controlling drought tolerance. This review explores the recent advances in these three research areas to improve drought tolerance in wheat and barley.

High-throughput 3D modelling to dissect the genetic control of leaf elongation in barley (Hordeum vulgare)

To optimize shoot growth and structure of cereals, we need to understand the genetic components controlling initiation and elongation. While measuring total shoot growth at high throughput using 2D imaging has progressed, recovering the 3D shoot structure of small grain cereals at a large scale is still challenging. Here, we present a method for measuring defined individual leaves of cereals, such as wheat and barley, using few images. Plant shoot modelling over time was used to measure the initiation and elongation of leaves in a bi-parental barley mapping population under low and high soil salinity. We detected quantitative trait loci (QTL) related to shoot growth per se, using both simple 2D total shoot measurements and our approach of measuring individual leaves. In addition, we detected QTL specific to leaf elongation and not to total shoot size. Of particular importance was the detection of a QTL on chromosome 3H specific to the early responses of leaf elongation to salt stress, a locus that could not be detected without the computer vision tools developed in this study.

Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.

The use of thermal time in plant studies has a sound theoretical basis provided that confounding effects are avoided

The use of thermal time is essential in plant studies and crop growth modeling because correcting time for temperature allows working in fluctuating conditions as if temperature was constant. However, thermal time is often seen as a loose concept because of a multitude of thermal functions and case-specific parameter values. Our hypothesis is that these different formalisms and parameterization could emerge from common principles and a common response of plant development to temperature, but with several counfounding factors which are not taken into account. We first show that these calculations of thermal time are based on sound common principles and mathematical formalisms. We test, via a modelling exercise of nine case studies using maize plants grown in three field sites, how a given "ground truth" response of plant development rate to temperature can be affected if an experimenter either considers or ignores confounding factors. We also show that apparent differences in temperature responses between phenological stages of the growth cycle, between day and night, or between plant genotypes may be due to the confounding effects of evaporative demand, the range of temperatures, and the time interval at which measurements are taken. On the basis of our findings, we propose that the critical point in the use of a given formalism of thermal time calculation is to ensure that the chosen model is compatible with the temporal definition, temperature range, and environmental scenario in the considered dataset.

Vertical patterns of photosynthesis and related leaf traits in two contrasting agricultural crops

To include within-canopy leaf acclimation responses to light and other resource gradients in photosynthesis modelling, it is imperative to understand the variation of leaf structural, biochemical and physiological traits from canopy top to bottom. In the present study, leaf photosynthetic traits for top and bottom canopy leaves, canopy structure and light profiles, were measured over one growing season for two contrasting crop types, winter barley (Hordeum vulgare L.) and rape seed (Brassica napus L.). With the exception of quantum yield, other traits such as maximum photosynthetic capacity (Amax), dark respiration, leaf nitrogen and chlorophyll contents, and leaf mass per area, showed consistently higher (P<0.05) values for top leaves throughout the growing season and for both crop types. Even though Amax was higher for top leaves, the bottom half of the canopy intercepted more light and thus contributed the most to total canopy photosynthesis up until senescence set in. Incorporating this knowledge into a simple top/bottom-leaf upscaling scheme, separating top and bottom leaves, resulted in a better match between estimated and measured total canopy photosynthesis, compared with a one-leaf upscaling scheme. Moreover, aggregating to daily and weekly temporal resolutions progressively increased the linearity of the leaf photosynthetic responses to light for top leaves.

Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat

Improving traits in wheat has historically been challenging due to its large and polyploid genome, limited genetic diversity and in-field phenotyping constraints. However, within recent years many of these barriers have been lowered. The availability of a chromosome-level assembly of the wheat genome now facilitates a step-change in wheat genetics and provides a common platform for resources, including variation data, gene expression data and genetic markers. The development of sequenced mutant populations and gene-editing techniques now enables the rapid assessment of gene function in wheat directly. The ability to alter gene function in a targeted manner will unmask the effects of homoeolog redundancy and allow the hidden potential of this polyploid genome to be discovered. New techniques to identify and exploit the genetic diversity within wheat wild relatives now enable wheat breeders to take advantage of these additional sources of variation to address challenges facing food production. Finally, advances in phenomics have unlocked rapid screening of populations for many traits of interest both in greenhouses and in the field. Looking forwards, integrating diverse data types, including genomic, epigenetic and phenomics data, will take advantage of big data approaches including machine learning to understand trait biology in wheat in unprecedented detail.

Components of Water Use Efficiency Have Unique Genetic Signatures in the Model C4 Grass Setaria

Plant growth and water use are interrelated processes influenced by genetically controlled morphological and biochemical characteristics. Improving plant water use efficiency (WUE) to sustain growth in different environments is an important breeding objective that can improve crop yields and enhance agricultural sustainability. However, genetic improvement of WUE using traditional methods has proven difficult due to the low throughput and environmental heterogeneity of field settings. To overcome these limitations, this study utilizes a high-throughput phenotyping platform to quantify plant size and water use of an interspecific Setaria italica × Setaria viridis recombinant inbred line population at daily intervals in both well-watered and water-limited conditions. Our findings indicate that measurements of plant size and water use are correlated strongly in this system; therefore, a linear modeling approach was used to partition this relationship into predicted values of plant size given water use and deviations from this relationship at the genotype level. The resulting traits describing plant size, water use, and WUE all were heritable and responsive to soil water availability, allowing for a genetic dissection of the components of plant WUE under different watering treatments. Linkage mapping identified major loci underlying two different pleiotropic components of WUE. This study indicates that alleles controlling WUE derived from both wild and domesticated accessions can be utilized to predictably modulate trait values given a specified precipitation regime in the model C4 genus Setaria.

The physiological and genetic basis of combined drought and heat tolerance in wheat

Drought and heat stress cause losses in wheat productivity in major growing regions worldwide, and both the occurrence and the severity of these events are likely to increase with global climate change. Water deficits and high temperatures frequently occur simultaneously at sensitive growth stages, reducing wheat yields by reducing grain number or weight. Although genetic variation and underlying quantitative trait loci for either individual stress are known, the combination of the two stresses has rarely been studied. Complex and often antagonistic physiology means that genetic loci underlying tolerance to the combined stress are likely to differ from those for drought or heat stress tolerance alone. Here, we review what is known of the physiological traits and genetic control of drought and heat tolerance in wheat and discuss potential physiological traits to study for combined tolerance. We further place this knowledge in the context of breeding for new, more tolerant varieties and discuss opportunities and constraints. We conclude that a fine control of water relations across the growing cycle will be beneficial for combined tolerance and might be achieved through fine management of spatial and temporal gas exchange.

High-fidelity detection of crop biomass quantitative trait loci from low-cost imaging in the field

Field-based, rapid, and nondestructive techniques for assessing plant productivity are needed to accelerate the discovery of genotype-to-phenotype relationships in next-generation biomass grass crops. The use of hemispherical imaging and light attenuation modeling was evaluated against destructive harvest measures with respect to their ability to accurately capture phenotypic and genotypic relationships in a field-grown grass crop. Plant area index (PAI) estimated from below-canopy hemispherical images, as well as a suite of thirteen traits assessed by manual destructive harvests, were measured in a Setaria recombinant inbred line mapping population segregating for aboveground productivity and architecture. A significant correlation was observed between PAI and biomass production across the population at maturity (r 2 = .60), as well as for select diverse genotypes sampled repeatedly over the growing season (r 2 = .79). Twenty-seven quantitative trait loci (QTL) were detected for manually collected traits associated with biomass production. Of these, twenty-one were found in four clusters of colocalized QTL. Analysis of image-based estimates of PAI successfully identified all four QTL hot spots for biomass production. QTL for PAI had greater overlap with those detected for traits associated with biomass production than with those for plant architecture and biomass partitioning. Hemispherical imaging is an affordable and scalable method, which demonstrates how high-throughput phenotyping can identify QTL related to biomass production of field trials in place of destructive harvests that are labor, time, and material intensive.

Variation in shoot tolerance mechanisms not related to ion toxicity in barley

Soil salinity can severely reduce crop growth and yield. Many studies have investigated salinity tolerance mechanisms in cereals using phenotypes that are relatively easy to measure. The majority of these studies measured the accumulation of shoot Na+ and the effect this has on plant growth. However, plant growth is reduced immediately after exposure to NaCl before Na+ accumulates to toxic concentrations in the shoot. In this study, nondestructive and destructive measurements are used to evaluate the responses of 24 predominately Australian barley (Hordeum vulgare L.) lines at 0, 150 and 250mM NaCl. Considerable variation for shoot tolerance mechanisms not related to ion toxicity (shoot ion-independent tolerance) was found, with some lines being able to maintain substantial growth rates under salt stress, whereas others stopped growing. Hordeum vulgare spp. spontaneum accessions and barley landraces predominantly had the best shoot ion independent tolerance, although two commercial cultivars, Fathom and Skiff, also had high tolerance. The tolerance of cv. Fathom may be caused by a recent introgression from H. vulgare L. spp. spontaneum. This study shows that the most salt-tolerant barley lines are those that contain both shoot ion-independent tolerance and the ability to exclude Na+ from the shoot (and thus maintain high K+:Na+ ratios).

Genetic architecture and temporal patterns of biomass accumulation in spring barley revealed by image analysis

BACKGROUND

Genetic mapping of phenotypic traits generally focuses on a single time point, but biomass accumulates continuously during plant development. Resolution of the temporal dynamics that affect biomass recently became feasible using non-destructive imaging.

RESULTS

With the aim to identify key genetic factors for vegetative biomass formation from the seedling stage to flowering, we explored growth over time in a diverse collection of two-rowed spring barley accessions. High heritabilities facilitated the temporal analysis of trait relationships and identification of quantitative trait loci (QTL). Biomass QTL tended to persist only a short period during early growth. More persistent QTL were detected around the booting stage. We identified seven major biomass QTL, which together explain 55% of the genetic variance at the seedling stage, and 43% at the booting stage. Three biomass QTL co-located with genes or QTL involved in phenology. The most important locus for biomass was independent from phenology and is located on chromosome 7HL at 141 cM. This locus explained ~20% of the genetic variance, was significant over a long period of time and co-located with HvDIM, a gene involved in brassinosteroid synthesis.

CONCLUSIONS

Biomass is a dynamic trait and is therefore orchestrated by different QTL during early and late growth stages. Marker-assisted selection for high biomass at booting stage is most effective by also including favorable alleles from seedling biomass QTL. Selection for dynamic QTL may enhance genetic gain for complex traits such as biomass or, in the future, even grain yield.

Quantifying Wheat Sensitivities to Environmental Constraints to Dissect Genotype × Environment Interactions in the Field

Yield is subject to strong genotype-by-environment (G × E) interactions in the field, especially under abiotic constraints such as soil water deficit (drought [D]) and high temperature (heat [H]). Since environmental conditions show strong fluctuations during the whole crop cycle, geneticists usually do not consider environmental measures as quantitative variables but rather as factors in multienvironment analyses. Based on 11 experiments in a field platform with contrasting temperature and soil water deficit, we determined the periods of sensitivity to drought and heat constraints in wheat (Triticum aestivum) and determined the average sensitivities for major yield components. G × E interactions were separated into their underlying components, constitutive genotypic effect (G), G × D, G × H, and G × H × D, and were analyzed for two genotypes, highlighting contrasting responses to heat and drought constraints. We then tested the constitutive and responsive behaviors of two strong quantitative trait loci (QTLs) associated previously with yield components. This analysis confirmed the constitutive effect of the chromosome 1B QTL and explained the G × E interaction of the chromosome 3B QTL by a benefit of one allele when temperature rises. In addition to the method itself, which can be applied to other data sets and populations, this study will support the cloning of a major yield QTL on chromosome 3B that is highly dependent on environmental conditions and for which the climatic interaction is now quantified.

Translating knowledge about abiotic stress tolerance to breeding programmes

Plant breeding and improvements in agronomic practice are making a consistent contribution to increasing global crop production year upon year. However, the rate of yield improvement currently lags behind the targets set to produce enough food to meet the demands of the predicted global population in 2050. Furthermore, crops that are exposed to harmful abiotic environmental factors (abiotic stresses, e.g. water limitation, salinity, extreme temperature) are prone to reduced yields. Here, we briefly describe the processes undertaken in conventional breeding programmes, which are usually designed to improve yields in near-optimal conditions rather than specifically breeding for improved crop yield stability under stressed conditions. While there is extensive fundamental research activity that examines mechanisms of plant stress tolerance, there are few examples that apply this research to improving commercial crop yields. There are notable exceptions, and we highlight some of these to demonstrate the magnitude of yield gains that could be made by translating agronomic, phenological and genetic solutions focused on improving or mitigating the effect of abiotic stress in the field; in particular, we focus on improvements in crop water-use efficiency and salinity tolerance. We speculate upon the reasons for the disconnect between research and research translation. We conclude that to realise untapped rapid gains towards food security targets new funding structures need to be embraced. Such funding needs to serve both the core and collaborative activities of the fundamental, pre-breeding and breeding research communities in order to expedite the translation of innovative research into the fields of primary producers.

Rooting for cassava: insights into photosynthesis and associated physiology as a route to improve yield potential

Contents 50 I. 50 II. 52 III. 54 IV. 55 V. 57 VI. 57 VII. 59 60 References 61 SUMMARY: As a consequence of an increase in world population, food demand is expected to grow by up to 110% in the next 30-35 yr. The population of sub-Saharan Africa is projected to increase by > 120%. In this region, cassava (Manihot esculenta) is the second most important source of calories and contributes c. 30% of the daily calorie requirements per person. Despite its importance, the average yield of cassava in Africa has not increased significantly since 1961. An evaluation of modern cultivars of cassava showed that the interception efficiency (ɛ_i ) of photosynthetically active radiation (PAR) and the efficiency of conversion of that intercepted PAR (ɛ_c ) are major opportunities for genetic improvement of the yield potential. This review examines what is known of the physiological processes underlying productivity in cassava and seeks to provide some strategies and directions toward yield improvement through genetic alterations to physiology to increase ɛ_i and ɛ_c . Possible physiological limitations, as well as environmental constraints, are discussed.

Salinity tolerance loci revealed in rice using high-throughput non-invasive phenotyping

High-throughput phenotyping produces multiple measurements over time, which require new methods of analyses that are flexible in their quantification of plant growth and transpiration, yet are computationally economic. Here we develop such analyses and apply this to a rice population genotyped with a 700k SNP high-density array. Two rice diversity panels, indica and aus, containing a total of 553 genotypes, are phenotyped in waterlogged conditions. Using cubic smoothing splines to estimate plant growth and transpiration, we identify four time intervals that characterize the early responses of rice to salinity. Relative growth rate, transpiration rate and transpiration use efficiency (TUE) are analysed using a new association model that takes into account the interaction between treatment (control and salt) and genetic marker. This model allows the identification of previously undetected loci affecting TUE on chromosome 11, providing insights into the early responses of rice to salinity, in particular into the effects of salinity on plant growth and transpiration.

Image-based plant phenotyping can be used to collect data with high temporal and spatial resolution. Here, the authors develop a computationally efficient method using smoothing splines and a new marker-by-trait association model to identify loci in a diverse rice population associated with early response to salinity.

The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system

Crop phenotyping is a major bottleneck in current plant research. Field-based high-throughput phenotyping platforms are an important prerequisite to advance crop breeding. We developed a cable-suspended field phenotyping platform covering an area of ~1ha. The system operates from 2 to 5m above the canopy, enabling a high image resolution. It can carry payloads of up to 12kg and can be operated under adverse weather conditions. This ensures regular measurements throughout the growing period even during cold, windy and moist conditions. Multiple sensors capture the reflectance spectrum, temperature, height or architecture of the canopy. Monitoring from early development to maturity at high temporal resolution allows the determination of dynamic traits and their correlation to environmental conditions throughout the entire season. We demonstrate the capabilities of the system with respect to monitoring canopy cover, canopy height and traits related to thermal and multi-spectral imaging by selected examples from winter wheat, maize and soybean. The system is discussed in the context of other, recently established field phenotyping approaches; such as ground-operating or aerial vehicles, which impose traffic on the field or require a higher distance to the canopy.