Plot size matters: interference from intergenotypic competition in plant phenotyping studies

Does late water deficit induce root growth or senescence in wheat?

In crops like wheat, terminal drought is one of the principal stress factors limiting productivity in rain-fed systems. However, little is known about root development after heading, when water uptake can be critical to wheat crops. The impact of water-stress on root growth was investigated in two wheat cultivars, Scout and Mace, under well-watered and post-anthesis water stress in three experiments. Plants were grown outside in 1.5-m long pots at a density similar to local recommended farming practice. Differences in root development were observed between genotypes, especially for water stress conditions under which Scout developed and maintained a larger root system than Mace. While under well-watered conditions both genotypes had shallow roots that appeared to senesce after heading, a moderate water stress stimulated shallow-root growth in Scout but accelerated senescence in Mace. For deep roots, post-heading biomass growth was observed for both genotypes in well-watered conditions, while under moderate water stress, only Scout maintained net growth as Mace deep roots senesced. Water stress of severe intensity affected both genotypes similarly, with root senescence at all depths. Senescence was also observed above ground. Under well-watered conditions, Scout retained leaf greenness (i.e. stay-green phenotype) for slightly longer than Mace. The difference between genotypes accentuated under moderate water stress, with rapid post-anthesis leaf senescence in Mace while Scout leaf greenness was affected little if at all by the stress. As an overall result, grain biomass per plant ('yield') was similar in the two genotypes under well-watered conditions, but more affected by a moderate stress in Mace than Scout. The findings from this study will assist improvement in modelling root systems of crop models, development of relevant phenotyping methods and selection of cultivars with better adaptation to drought.

Grain yield trade-offs in spike-branching wheat can be mitigated by elite alleles affecting sink capacity and post-anthesis source activity

Introducing variations in inflorescence architecture, such as the 'Miracle-Wheat' (Triticum turgidum convar. compositum (L.f.) Filat.) with a branching spike, has relevance for enhancing wheat grain yield. However, in the spike-branching genotypes, the increase in spikelet number is generally not translated into grain yield advantage because of reduced grains per spikelet and grain weight. Here, we investigated if such trade-offs might be a function of source-sink strength by using 385 recombinant inbred lines developed by intercrossing the spike-branching landrace TRI 984 and CIRNO C2008, an elite durum (T. durum L.) cultivar; they were genotyped using the 25K array. Various plant and spike architectural traits, including flag leaf, peduncle, and spike senescence rate, were phenotyped under field conditions for 2 consecutive years. On chromosome 5AL, we found a new modifier QTL for spike branching, branched headt3 (bht-A3), which was epistatic to the previously known bht-A1 locus. Besides, bht-A3 was associated with more grains per spikelet and a delay in flag leaf senescence rate. Importantly, favourable alleles, viz. bht-A3 and grain protein content (gpc-B1) that delayed senescence, are required to improve grain number and grain weight in the spike-branching genotypes. In summary, achieving a balanced source-sink relationship might minimize grain yield trade-offs in Miracle-Wheat.

Field phenotyping for African crops: overview and perspectives

Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.

Exploring the trade-off between individual fitness and community performance of wheat crops using simulated canopy shade

The genetic heritage of wheat (Triticum spp.) crops has been shaped by millions of years of predomestication natural selection, often driven by competition among individuals. However, genetic improvements in yield potential are thought to involve selection towards reduced competitiveness, thus enhancing adaptation to the crop environment. We investigated potential trade-offs between individual plant fitness and community performance using a population of introgression lines carrying chromosome segments of wild emmer (nondomesticated) in the background of an elite durum cultivar. We focused on light as a primary factor affecting plant-plant interactions and assessed morphological and biomass phenotypes of single plants grown in mixtures under sunlight and a simulated canopy shade, and the relevance of these phenotypes for the monoculture community in the field. We found that responses to canopy shade resemble responses to high density and contribute to both the individual and the community. Stepwise regressions suggested that grain number per spike and its persistence under shade are essential attributes of productive communities, advocating their use as a breeding target during early-generation selection. Overall, multiple phenotypes attained under shade could better explain community performance. Our novel, applicable, high-throughput set-up provides new prospects for studying and selecting single-plant phenotypes in a canopy-like environment.

Crop germplasm: Current challenges, physiological-molecular perspective, and advance strategies towards development of climate-resilient crops

Germplasm is a long-term resource management mission and investment for civilization. An estimated ∼7.4 million accessions are held in 1750 plant germplasm centres around the world; yet, only 2% of these assets have been utilized as plant genetic resources (PGRs). According to recent studies, the current food yield trajectory will be insufficient to feed the world's population in 2050. Additionally, possible negative effects in terms of crop failure because of climate change are already being experienced across the world. Therefore, it is necessary to reconciliation of research advancement and innovation of practices for further exploration of the potential of crop germplasm especially for the complex traits associated with yield such as water- and nitrogen use efficiency. In this review, we tried to address current challenges, research gaps, physiological and molecular aspects of two broad spectrum complex traits such as water- and nitrogen-use efficiency, and advanced integrated strategies that could provide a platform for combined stress management for climate-smart crop development. Additionally, recent development in technologies that are directly related to germplasm characterization was highlighted for further molecular utilization towards the development of elite varieties.

Soybean resilience to drought is supported by partial recovery of photosynthetic traits

Climate change affects precipitation dynamics and the variability of drought frequency, intensity, timing, and duration. This represents a high risk in spring-sown grain legumes such as soybean. Yet, under European conditions, no evidence supports the potential recovery and resilience of drought-tolerant soybean cultivars after episodic drought, at different growth stages. A field experiment was conducted using a representative drought-tolerant cultivar of soybean (cv. Acardia), in 2020 and 2021, on sandy soils in Germany, applying four water regimes (irrigated, rainfed, early-drought, and late-drought stress). Drought stress was simulated by covering the plots during the event of rain with 6 × 6 m rainout shelters, at the vegetative (V-stage) and flowering (Fl-stage) stages. Drought response was quantified on plant height, chlorophyll fluorescence ratio (ChlF ratio), chlorophyll content (Chlc), and leaf surface temperature (LST), at different intervals after simulating drought until pod filling. Grain yield and yield components were quantified at the end of the growing season. Compared to rainfed conditions, a drought at V-stage and Fl-stage reduced significantly plant height, ChlF ratio, and Chlc by 20%, 11%, and 7%, respectively, but increased LST by 21% during the recovery phase. There was no recovery from drought except for Chlc after V-stage in 2021, that significantly recovered by 40% at the end of the growing season, signifying a partial recovery of the photochemical apparatus. Especially, there was no recovery observed in LST, implying the inability of soybean to restore LST within the physiological functional range (Graphical abstract). Under rainfed conditions, the grain yield reached 2.9 t ha^-1 in 2020 and 5.2 t ha^-1 in 2021. However, the episodic drought reduced the yield at V-stage and Fl-stage, by 63% and 25% in 2020, and 21% and 36% in 2021, respectively. To conclude, the timing of drought was less relevant for soybean resilience; however, pre- and post-drought soil moisture, drought intensity, and drought duration were likely more important. A drought-tolerant soybean cultivar may partially be drought-resilient due to the recovery of photosynthetic traits, but not the leaf thermal traits. Overall, these findings will accelerate future efforts by plant breeders, aimed at improving soybean drought resilience.

Crop resilience via inter-plant spacing brings to the fore the productive ideotype

Natural selection favors the competitive ideotype, enabling native plants to survive in the face of intense competition. The productive ideotype is the goal of artificial selection to achieve high crop yields via the efficient use of resources in a self-competition regime. When breeding is established under inter-genotypic competition, the competitive ideotype dominates and may fictitiously become selectable. The productive ideotype becomes selectable at the nil-competition regime, where widely spaced individuals prevent plant-to-plant interference for any input. Principal reasons bring to the fore the productive ideotype that combines low competitiveness and improved plant yield efficiency. Crop spacing via the productive ideotype is mandated to alleviate the varying optimum density and ensure efficient use of resources inter-seasonally, cope with intra-field variation and optimize resource use, compensate for missing plants and promote stability, counteract unpredictable stresses and offer a buffer against environmental diversity, and adopt low-input agriculture to conserve natural resources and the environment. For breeding toward the productive ideotype, nil-competition is the due condition to overcome the confounding effects of competition, maximize phenotypic differentiation and facilitate selection from an early segregating generation, optimize heritability due to moderated environmental variance and experimental designs that sample spatial heterogeneity, apply high selection pressure focusing exclusively on the targeted genotype, and avoid the risk of bias selection or loss of desired genotypes due to proximity to empty hills. The view of a modern crop variety composed of genotype(s) belonging to the productive ideotype is a viable option to reach crop resilience serving sustainability in enormously fluctuating agroecosystems.

Genetic basis and adaptive implications of temperature-dependent and temperature-independent effects of drought on chickpea reproductive phenology

Water deficit often hastens flowering of pulses partially because droughted plants are hotter. Separating temperature-independent and temperature-dependent effects of drought is important to understand, model, and manipulate phenology. We define a new trait, drought effect on phenology (DEP), as the difference in flowering time between irrigated and rainfed crops, and use FST genome scanning to probe for genomic regions under selection for this trait in chickpea (Cicer arietinum). Owing to the negligible variation in daylength in our dataset, variation in phenology with sowing date was attributed to temperature and water; hence, genomic regions overlapping for early- and late-sown crops would associate with temperature-independent effects and non-overlapping genomic regions would associate with temperature-dependent effects. Thermal-time to flowering was shortened with increasing water stress, as quantified with carbon isotope composition. Genomic regions on chromosomes 4-8 were under selection for DEP. An overlapping region for early and late sowing on chromosome 8 revealed a temperature-independent effect with four candidate genes: BAM1, BAM2, HSL2, and ANT. The non-overlapping regions included six candidate genes: EMF1, EMF2, BRC1/TCP18, BZR1, NPGR1, and ERF1. Modelling showed that DEP reduces the likelihood of drought and heat stress at the expense of increased likelihood of cold stress. Accounting for DEP would improve genetic and phenotypic models of phenology.

Scaling up high-throughput phenotyping for abiotic stress selection in the field

High-throughput phenotyping (HTP) is in its infancy for deployment in large-scale breeding programmes. With the ability to measure correlated traits associated with physiological ideotypes, in-field phenotyping methods are available for screening of abiotic stress responses. As cropping environments become more hostile and unpredictable due to the effects of climate change, the need to characterise variability across spatial and temporal scales will become increasingly important. The sensor technologies that have enabled HTP from macroscopic through to satellite sensors may also be utilised here to complement spatial characterisation using envirotyping, which can improve estimations of genotypic performance across environments by better accounting for variation at the plot, trial and inter-trial levels. Climate change is leading to increased variation at all physical and temporal scales in the cropping environment. Maintaining yield stability under circumstances with greater levels of abiotic stress while capitalising upon yield potential in good years, requires approaches to plant breeding that target the physiological limitations to crop performance in specific environments. This requires dynamic modelling of conditions within target populations of environments, GxExM predictions, clustering of environments so breeding trajectories can be defined, and the development of screens that enable selection for genetic gain to occur. High-throughput phenotyping (HTP), combined with related technologies used for envirotyping, can help to address these challenges. Non-destructive analysis of the morphological, biochemical and physiological qualities of plant canopies using HTP has great potential to complement whole-genome selection, which is becoming increasingly common in breeding programmes. A range of novel analytic techniques, such as machine learning and deep learning, combined with a widening range of sensors, allow rapid assessment of large breeding populations that are repeatable and objective. Secondary traits underlying radiation use efficiency and water use efficiency can be screened with HTP for selection at the early stages of a breeding programme. HTP and envirotyping technologies can also characterise spatial variability at trial and within-plot levels, which can be used to correct for spatial variations that confound measurements of genotypic values. This review explores HTP for abiotic stress selection through a physiological trait lens and additionally investigates the use of envirotyping and EC to characterise spatial variability at all physical scales in METs.

The river shapes the genetic diversity of common reed in the Yellow River Delta via hydrochory dispersal and habitat selection

Understanding the driving mechanisms of local genetic diversity is a fundamental challenge under the global environmental changes. Rivers provide an excellent study system to demonstrate the effects of hydrochory dispersal and habitat selection on genetic diversity of riparian flora. In this study, we focused on the genetic variation of common reed (Phragmites australis) in the Yellow River Delta, China. Firstly, samples were collected in the Yellow River Delta, its neighboring wetland and its upstream plain. The genetic variation of P. australis was investigated using two chloroplast DNA fragments and eleven nuclear microsatellites. The findings showed that the genetic variation of P. australis in the Yellow River Delta belonged to two distinct lineages (haplotype O and haplotype P), which were similar to the upstream, and to the neighboring populations, respectively. Moreover, the genetic results suggested the potential dispersal of haplotype O from upstream to downstream. Secondly, we surveyed the plant functional traits of common reed from the Yellow River Delta in the field and in the common garden. The results showed significant differences between riverine and non-riverine populations in plant functional traits (e.g. specific leaf area and leaf length), haplotype composition and genetic clustering, which implied natural selection by habitat conditions. Lastly, we re-analyzed the plant performance data from a salt manipulation experiment with different haplotypes, and the results supported that salinity is a significant selective stressor on P. australis lineages in the Yellow River Delta. Our study highlights the significance of hydrochory dispersal and habitat selection in the river effects on genetic diversity of riparian flora, and provides important information for biodiversity conservation and wetland management in the Yellow River Delta.

Resolving a QTL complex for height, heading, and grain yield on chromosome 3A in bread wheat

Crop height (Ht), heading date (Hd), and grain yield (GY) are inter-related in wheat. Independent manipulation of each is important for adaptation and performance. Validated quantitative trait loci (QTLs) for all three co-locate on chromosome 3A in the Avalon×Cadenza population, with increased Ht, Hd, and GY contributed by Cadenza. We asked if these are linked or pleiotropic effects using recombinant lines, and showed that Ht and Hd effects are independent. The Chinese Spring equivalent to the newly defined Ht interval contained a gene cluster involved in cell wall growth and displaying high levels of differential transcript expression. The Hd locus is larger and rearranged compared with the reference genome, but FT2 (Flowering Locus T2) is of particular interest. The Hd effect acted independently of photoperiod and vernalization, but did exhibit seasonal genotype×environment interaction. Recombinants were phenotyped for GY in replicated field experiments. GY was most associated with Cadenza alleles for later Hd, supporting physiological studies using the same lines proposing that 'late' alleles at this locus increase spike fertility and grain number (GN). The work has uncoupled height from heading and yield, and shown that one of very few validated GY QTLs in wheat is probably mediated by phenological variation.

Field Phenomics: Will It Enable Crop Improvement?

Field phenomics has been identified as a promising enabling technology to assist plant breeders with the development of improved cultivars for farmers. Yet, despite much investment, there are few examples demonstrating the application of phenomics within a plant breeding program. We review recent progress in field phenomics and highlight the importance of targeting breeders' needs, rather than perceived technology needs, through developing and enhancing partnerships between phenomics researchers and plant breeders.

Breeding Beyond Monoculture: Putting the "Intercrop" Into Crops

Intercropping is both a well-established and yet novel agricultural practice, depending on one's perspective. Such perspectives are principally governed by geographic location and whether monocultural practices predominate. Given the negative environmental effects of monoculture agriculture (loss of biodiversity, reliance on non-renewable inputs, soil degradation, etc.), there has been a renewed interest in cropping systems that can reduce the impact of modern agriculture while maintaining (or even increasing) yields. Intercropping is one of the most promising practices in this regard, yet faces a multitude of challenges if it is to compete with and ultimately replace the prevailing monocultural norm. These challenges include the necessity for more complex agricultural designs in space and time, bespoke machinery, and adapted crop cultivars. Plant breeding for monocultures has focused on maximizing yield in single-species stands, leading to highly productive yet specialized genotypes. However, indications suggest that these genotypes are not the best adapted to intercropping systems. Re-designing breeding programs to accommodate inter-specific interactions and compatibilities, with potentially multiple different intercropping partners, is certainly challenging, but recent technological advances offer novel solutions. We identify a number of such technology-driven directions, either ideotype-driven (i.e., "trait-based" breeding) or quantitative genetics-driven (i.e., "product-based" breeding). For ideotype breeding, plant growth modeling can help predict plant traits that affect both inter- and intraspecific interactions and their influence on crop performance. Quantitative breeding approaches, on the other hand, estimate breeding values of component crops without necessarily understanding the underlying mechanisms. We argue that a combined approach, for example, integrating plant growth modeling with genomic-assisted selection and indirect genetic effects, may offer the best chance to bridge the gap between current monoculture breeding programs and the more integrated and diverse breeding programs of the future.

SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data

The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP.

Early vigour in wheat: Could it lead to more severe terminal drought stress under elevated atmospheric [CO2 ] and semi-arid conditions?

Early vigour in wheat is a trait that has received attention for its benefits reducing evaporation from the soil surface early in the season. However, with the growth enhancement common to crops grown under elevated atmospheric CO₂ concentrations (e[CO₂ ]), there is a risk that too much early growth might deplete soil water and lead to more severe terminal drought stress in environments where production relies on stored soil water content. If this is the case, the incorporation of such a trait in wheat breeding programmes might have unintended negative consequences in the future, especially in dry years. We used selected data from cultivars with proven expression of high and low early vigour from the Australian Grains Free Air CO₂ Enrichment (AGFACE) facility, and complemented this analysis with simulation results from two crop growth models which differ in the modelling of leaf area development and crop water use. Grain yield responses to e[CO₂ ] were lower in the high early vigour group compared to the low early vigour group, and although these differences were not significant, they were corroborated by simulation model results. However, the simulated lower response with high early vigour lines was not caused by an earlier or greater depletion of soil water under e[CO₂ ] and the mechanisms responsible appear to be related to an earlier saturation of the radiation intercepted. Whether this is the case in the field needs to be further investigated. In addition, there was some evidence that the timing of the drought stress during crop growth influenced the effect of e[CO₂ ] regardless of the early vigour trait. There is a need for FACE investigations of the value of traits for drought adaptation to be conducted under more severe drought conditions and variable timing of drought stress, a risky but necessary endeavour.

Magnesium Deficiency Reduced the Yield and Seed Germination in Wax Gourd by Affecting the Carbohydrate Translocation

Magnesium (Mg) is a particular mineral nutrient greatly affecting the size and activity of sink organs. Wax gourd crop with its fruits having fresh weight up to 20-50 kg per single fruit serves as an excellent experimental plant species for better understanding the role of varied Mg nutrition in sink strength and yield formation. This study aimed to investigate the effects of Mg deficiency on fruit yield and seed vigor in wax gourd grown under field conditions. Plants were grown under field conditions until maturity with increasing soil Mg applications. At the beginning of fruit formation, leaves were used to analyze concentrations of sucrose, starch and Mg as well as phloem export of sucrose. At maturity, fruit yield was determined and the seeds collected were used in germination studies and starch analysis. Low Mg supply resulted in a significant impairment in fruit fresh yield, which was closely associated with higher accumulation of starch and sucrose in source leaves and lower amount of sucrose in phloem exudate. Seeds obtained from Mg deficiency plants exhibited lower amount of starch and substantial reduction in both germination capacity and seedling establishment when compared to the seeds from the Mg adequate plants. Our study revealed that magnesium deficiency significantly diminished fruit yield of field-grown wax gourd, most probably by limiting the carbohydrate transport from source organs to developing fruit. Ensuring sufficient Mg supply to plant species with high sink size such as wax gourd, during the reproductive growth stage, is a critical factor for achieving higher fruit yield formation and also better vigor of next-generation seeds.

Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

Enhancing plant breeding to ensure global food security requires new technologies. For wheat phenotyping, only limited seeds and resources are available in early selection cycles. This forces breeders to use small plots with single or multiple row plots in order to include the maximum number of genotypes/lines for their assessment. High-throughput phenotyping through remote sensing may meet the requirements for the phenotyping of thousands of genotypes grown in small plots in early selection cycles. Therefore, the aim of this study was to compare the performance of an unmanned aerial vehicle (UAV) for assessing the grain yield of wheat genotypes in different row numbers per plot in the early selection cycles with ground-based spectral sensing. A field experiment consisting of 32 wheat genotypes with four plot designs (1, 2, 3, and 12 rows per plot) was conducted. Near infrared (NIR)-based spectral indices showed significant correlations (p < 0.01) with the grain yield at flowering to grain filling, regardless of row numbers, indicating the potential of spectral indices as indirect selection traits for the wheat grain yield. Compared with terrestrial sensing, aerial-based sensing from UAV showed consistently higher levels of association with the grain yield, indicating that an increased precision may be obtained and is expected to increase the efficiency of high-throughput phenotyping in large-scale plant breeding programs. Our results suggest that high-throughput sensing from UAV may become a convenient and efficient tool for breeders to promote a more efficient selection of improved genotypes in early selection cycles. Such new information may support the calibration of genomic information by providing additional information on other complex traits, which can be ascertained by spectral sensing.

All roads lead to growth: imaging-based and biochemical methods to measure plant growth

Plant growth is a highly complex biological process that involves innumerable interconnected biochemical and signalling pathways. Many different techniques have been developed to measure growth, unravel the various processes that contribute to plant growth, and understand how a complex interaction between genotype and environment determines the growth phenotype. Despite this complexity, the term 'growth' is often simplified by researchers; depending on the method used for quantification, growth is viewed as an increase in plant or organ size, a change in cell architecture, or an increase in structural biomass. In this review, we summarise the cellular and molecular mechanisms underlying plant growth, highlight state-of-the-art imaging and non-imaging-based techniques to quantitatively measure growth, including a discussion of their advantages and drawbacks, and suggest a terminology for growth rates depending on the type of technique used.

Genomic prediction of yield and root development in wheat under changing water availability

BACKGROUND

Deeper roots help plants take up available resources in deep soil ensuring better growth and higher yields under conditions of drought. A large-scale semi-field root phenotyping facility was developed to allow a water availability gradient and detect potential interaction of genotype by water availability gradient. Genotyped winter wheat lines were grown as rows in four beds of this facility, where indirect genetic effects from neighbors could be important to trait variation. The objective was to explore the possibility of genomic prediction for grain-related traits and deep root traits collected via images taken in a minirhizotron tube under each row of winter wheat measured.

RESULTS

The analysis comprised four grain-related traits: grain yield, thousand-kernel weight, protein concentration, and total nitrogen content measured on each half row that were harvested separately. Two root traits, total root length between 1.2 and 2 m depth and root length in four intervals on each tube were also analyzed. Two sets of models with or without the effects of neighbors from both sides of each row were applied. No interaction between genotypes and changing water availability were detected for any trait. Estimated genomic heritabilities ranged from 0.263 to 0.680 for grain-related traits and from 0.030 to 0.055 for root traits. The coefficients of genetic variation were similar for grain-related and root traits. The prediction accuracy of breeding values ranged from 0.440 to 0.598 for grain-related traits and from 0.264 to 0.334 for root traits. Including neighbor effects in the model generally increased the estimated genomic heritabilities and accuracy of predicted breeding values for grain yield and nitrogen content.

CONCLUSIONS

Similar relative amounts of additive genetic variance were found for both yield traits and root traits but no interaction between genotypes and water availability were detected. It is possible to obtain accurate genomic prediction of breeding values for grain-related traits and reasonably accurate predicted breeding values for deep root traits using records from the semi-field facility. Including neighbor effects increased the estimated additive genetic variance of grain-related traits and accuracy of predicting breeding values. High prediction accuracy can be obtained although heritability is low.

Ground-Based LiDAR Improves Phenotypic Repeatability of Above-Ground Biomass and Crop Growth Rate in Wheat

Highly repeatable, nondestructive, and high-throughput measures of above-ground biomass (AGB) and crop growth rate (CGR) are important for wheat improvement programs. This study evaluates the repeatability of destructive AGB and CGR measurements in comparison to two previously described methods for the estimation of AGB from LiDAR: 3D voxel index (3DVI) and 3D profile index (3DPI). Across three field experiments, contrasting in available water supply and comprising up to 98 wheat genotypes varying for canopy architecture, several concurrent measurements of LiDAR and AGB were made from jointing to anthesis. Phenotypic correlations at discrete events between AGB and the LiDAR-derived biomass indices were significant, ranging from 0.31 (P < 0.05) to 0.86 (P < 0.0001), providing confidence in the LiDAR indices as effective surrogates for AGB. The repeatability of the LiDAR biomass indices at discrete events was at least similar to and often higher than AGB, particularly under water limitation. The correlations between calculated CGR for AGB and the LiDAR indices were moderate to high and varied between experiments. However, across all experiments, the repeatabilities of the CGR derived from the LiDAR indices were appreciably greater than those for AGB, except for the 3DPI in the water-limited environment. In our experiments, the repeatability of either LiDAR index was consistently higher than that of AGB, both at discrete time points and when CGR was calculated. These findings provide promising support for the reliable use of ground-based LiDAR, as a surrogate measure of AGB and CGR, for screening germplasm in research and wheat breeding.

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

One of the major limitations for sugarcane genetic improvement is the low heritability of yield in the early stages of breeding, mainly due to confounding inter-plot competition effects. In this study, we investigate an indirect selection index (Si), developed based on traits correlated to yield (indirect traits) that were measured using an unmanned aerial vehicle (UAV), to improve clonal assessment in early stages of sugarcane breeding. A single-row early-stage clonal assessment trial, involving 2134 progenies derived from 245 crosses, and a multi-row experiment representative of pure-stand conditions, with an unrelated population of 40 genotypes, were used in this study. Both experiments were screened at several stages using visual, multispectral, and thermal sensors mounted on a UAV for indirect traits, including canopy cover, canopy height, canopy temperature, and normalised difference vegetation index (NDVI). To construct the indirect selection index, phenotypic and genotypic variance-covariances were estimated in the single-row and multi-row experiment, respectively. Clonal selection from the indirect selection index was compared to single-row yield-based selection. Ground observations of stalk number and plant height at six months after planting made from a subset of 75 clones within the single-row experiment were highly correlated to canopy cover (rg = 0.72) and canopy height (rg = 0.69), respectively. The indirect traits had high heritability and strong genetic correlation with cane yield in both the single-row and multi-row experiments. Only 45% of the clones were common between the indirect selection index and single-row yield based selection, and the expected efficiency of correlated response to selection for pure-stand yield based on indirect traits (44%–73%) was higher than that based on single-row yield (45%). These results highlight the potential of high-throughput phenotyping of indirect traits combined in an indirect selection index for improving early-stage clonal selections in sugarcane breeding.

Genotypic performance of Australian durum under single and combined water-deficit and heat stress during reproduction

In Mediterranean environments, water deficiency and heat during reproduction severely limit cereal crop production. Our research investigated the effects of single and combined pre-anthesis water-deficit stress and post-anthesis heat stress in ten Australian durum genotypes, providing a systematic evaluation of stress response at the molecular, physiological, grain quality and yield level. We studied leaf physiological traits at different reproductive stages, evaluated the grain yield and quality, and the associations among them. We profiled the expression dynamics of two durum microRNAs and their protein-coding targets (auxin response factors and heat shock proteins) involved in stress adaptation. Chlorophyll content, stomatal conductance and leaf relative water content were mostly reduced under stress, however, subject to the time-point and genotype. The influence of stress on grain traits (e.g., protein content) also varied considerably among the genotypes. Significant positive correlations between the physiological traits and the yield components could be used to develop screening strategies for stress improvement in breeding. Different expression patterns of stress-responsive microRNAs and their targets in the most stress-tolerant and most stress-sensitive genotype provided some insight into the complex defense molecular networks in durum. Overall, genotypic performance observed indicates that different stress-coping strategies are deployed by varieties under various stresses.

Effects of Vegetation Strips, Fertilizer Levels and Varietal Resistance on the Integrated Management of Arthropod Biodiversity in a Tropical Rice Ecosystem

Integrated biodiversity management aims to conserve the beneficial species components of production ecosystems and reduce the impacts of pests. In 2011 and 2013, experiments were conducted at Los Baños, Laguna, Philippines, to compare arthropod communities in rice plots and on levees with and without vegetation strips. Vegetation strips included spontaneous weeds, sesame and okra (2011), or mung bean (2013). The plots were treated with one of three nitrogen levels and in one experiment were planted with planthopper-resistant (IR62) and susceptible (IR64) rice varieties. Parasitoids and predators of lepidopteran pests and of the ricebug, Leptocorisa oratorius, were more abundant in high-nitrogen rice plots where their prey/hosts also had highest densities. Planthoppers and leafhoppers were more abundant in low-nitrogen plots. Weedy and sesame/okra bunds provided habitat for a range of natural enemies including spiders, parasitoids and predatory bugs, but did not have higher pest numbers than cleared bunds. Higher abundances of the predator Cythorhinus lividipennis and higher parasitism of planthopper (Nilaparvata lugens) eggs by Anagrus sp. were associated with sesame/okra bunds in late season rice plots. Mung bean also provided habitat for key predators and parasitoids that spilled over to adjacent rice; however, mung bean was also associated with higher numbers of lepidopteran and grain-sucking pests in the adjacent rice, albeit without increased damage to the rice. For ricebug in particular, damage was probably reduced by higher parasitoid:pest ratios adjacent to the vegetation strips. Varietal resistance and mung bean strips had an additive effect in reducing abundance of the planthopper Sogatella furcifera and the leafhopper Nephotettix virescens. Reduced numbers of these latter pests close to vegetation strips were often compensated for by other plant-sucking bugs, thereby increasing the intensity of potentially stabilizing interspecific interactions such as competition. We highlight the benefits of diversifying rice landscapes and the need to optimize vegetation strips, e.g., by including lepidopteran trap-plants, for intensive rice production systems.

Wheat drought tolerance in the field is predicted by amino acid responses to glasshouse-imposed drought

Water limits crop productivity, so selecting for a minimal yield gap in drier environments is critical to mitigate against climate change and land-use pressure. We investigated the responses of relative water content (RWC), stomatal conductance, chlorophyll content, and metabolites in flag leaves of commercial wheat (Triticum aestivum L.) cultivars to three drought treatments in the glasshouse and in field environments. We observed strong genetic associations between glasshouse-based RWC, metabolites, and yield gap-based drought tolerance (YDT; the ratio of yield in water-limited versus well-watered conditions) across 18 field environments spanning sites and seasons. Critically, RWC response to glasshouse drought was strongly associated with both YDT (r2=0.85, P<8E-6) and RWC under field drought (r2=0.77, P<0.05). Moreover, multiple regression analyses revealed that 98% of genetic YDT variance was explained by drought responses of four metabolites: serine, asparagine, methionine, and lysine (R2=0.98; P<0.01). Fitted coefficients suggested that, for given levels of serine and asparagine, stronger methionine and lysine accumulation was associated with higher YDT. Collectively, our results demonstrate that high-throughput, targeted metabolic phenotyping of glasshouse-grown plants may be an effective tool for selection of wheat cultivars with high field-derived YDT.

Evaluation of the Phenotypic Repeatability of Canopy Temperature in Wheat Using Continuous-Terrestrial and Airborne Measurements

Infrared canopy temperature (CT) is a well-established surrogate measure of stomatal conductance. There is ample evidence showing that genotypic variation in stomatal conductance is associated with grain yield in wheat. Our goal was to determine when CT repeatability is greatest (throughout the season and within the day) to guide CT deployment for research and wheat breeding. CT was measured continuously with ArduCrop wireless infrared thermometers from post-tillering to physiological maturity, and with airborne thermography on cloudless days from manned helicopter at multiple times before and after flowering. Our experiments in wheat, across two years contrasting for water availability, showed that repeatability for CT was greatest later in the season, during grain-filling, and usually in the afternoon. This was supported by the observation that repeatability for ArduCrop, and more so for airborne CT, was significantly associated (P < 0.0001) with calculated clear-sky solar radiation and to a lesser degree, vapor pressure deficit. Adding vapor pressure deficit to a model comprising either clear-sky solar radiation or its determinants, day-of-year and hour-of-day, made little to no improvement to the coefficient of determination. Phenotypic correlations for airborne CT afternoon sampling events were consistently high between events in the same year, more so for the year when soil water was plentiful (r = 0.7 to 0.9) than the year where soil water was limiting (r = 0.4 to 0.9). Phenotypic correlations for afternoon airborne CT were moderate between years contrasting in soil water availability (r = 0.1 to 0.5) and notably greater on two separate days following irrigation or rain in the drier year, ranging from r = 0.39 to 0.53 (P < 0.0001) for the midday events. For ArduCrop CT the pattern of phenotypic correlations, within a given year, was similar for both years: phenotypic correlations were higher during the grain-filling months of October and November and for hours-of-day from 11 onwards. The lowest correlations comprised events from hours-of-day 8 and 9 across all months. The capacity for the airborne method to instantaneously sample CT on hundreds of plots is more suited to large field experiments than the static ArduCrop sensors which measure CT continuously on a single experimental plot at any given time. Our findings provide promising support for the reliable deployment of CT phenotyping for research and wheat breeding, whereby the high repeatability and high phenotypic correlations between afternoon sampling events during grain-filling could enable reliable screening of germplasm from only one or two sampling events.

Use of F2 Bulks in Training Sets for Genomic Prediction of Combining Ability and Hybrid Performance

Developing training sets for genomic prediction in hybrid crops requires producing hybrid seed for a large number of entries. In autogamous crop species (e.g., wheat, rice, rapeseed, cotton) this requires elaborate hybridization systems to prevent self-pollination and presents a significant impediment to the implementation of hybrid breeding in general and genomic selection in particular. An alternative to F1 hybrids are bulks of F2 seed from selfed F1 plants (F1:2). Seed production for F1:2 bulks requires no hybridization system because the number of F1 plants needed for producing enough F1:2 seed for multi-environment testing can be generated by hand-pollination. This study evaluated the suitability of F1:2 bulks for use in training sets for genomic prediction of F1 level general combining ability and hybrid performance, under different degrees of divergence between heterotic groups and modes of gene action, using quantitative genetic theory and simulation of a genomic prediction experiment. The simulation, backed by theory, showed that F1:2 training sets are expected to have a lower prediction accuracy relative to F1 training sets, particularly when heterotic groups have strongly diverged. The accuracy penalty, however, was only modest and mostly because of a lower heritability, rather than because of a difference in F1 and F1:2 genetic values. It is concluded that resorting to F1:2 bulks is, in theory at least, a promising approach to remove the significant complication of a hybridization system from the breeding process.

Review: High-throughput phenotyping to enhance the use of crop genetic resources

Improved genetic, genomic and statistical technologies have increased the capacity to enrich breeding populations for key alleles underpinning adaptation and continued genetic gain. In turn, directed genomic selection together with increased heritability will reduce genetic variance to narrow the genetic base in many crop breeding programs. Diverse genetic resources (GR), including wild and weedy relatives, landraces and reconstituted synthetics, have potential to contribute novel alleles for key traits. Targeted trait identification may also identify genetic diversity in addressing new challenges including the need for modified root architecture, greater nutrient-use efficiency, and adaptation to warmer air and soil temperatures forecast with climate change. Yet while core collections and other GR sources have historically been invaluable for major gene control of disease and subsoil constraints, the mining of genetically (and phenotypically) complex traits in GR remains a significant challenge owing to reduced fertility, limited seed quantities and poor adaptation through linkage drag with undesirable alleles. High-throughput field phenomics (HTFP) offers the opportunity to capture phenotypically complex variation underpinning adaptation in traditional phenotypic selection or statistics-based breeding programs. Targeted HTFP will permit the reliable phenotyping of greater numbers of GR-derived breeding lines using smaller plot sizes and at earlier stages of population development to reduce the duration of breeding cycles and the loss of potentially important alleles with linkage drag. Two key opportunities are highlighted for use of HTFP in selection among GR-derived wheat breeding lines for greater biomass and stomatal conductance through canopy temperature.

Novel Alleles for Combined Drought and Heat Stress Tolerance in Wheat

Drought and heat waves commonly co-occur in many wheat-growing regions causing significant crop losses. The identification of stress associated quantitative trait loci, particularly those for yield, is problematic due to their association with plant phenology and the high genetic × environment interaction. Here we studied a panel of 315 diverse, spring type accessions of bread wheat (Triticum aestivum) in pots in a semi-controlled environment under combined drought and heat stress over 2 years. Importantly, we treated individual plants according to their flowering time. We found 134 out of the 145 identified loci for grain weight that were not associated with either plant phenology or plant height. The majority of loci uncovered here were novel, with favorable alleles widespread in Asian and African landraces providing opportunities for their incorporation into modern varieties through breeding. Using residual heterozygosity in lines from a nested association mapping population, we were able to rapidly develop near-isogenic lines for important target loci. One target locus on chromosome 6A contributed to higher grain weight, harvest index, thousand kernel weight, and grain number under drought and heat stress in field conditions consistent with allelic effects demonstrated in the genome-wide association study.

A low-cost method to rapidly and accurately screen for transpiration efficiency in wheat

BACKGROUND

Wheat (Triticum aestivum L.) productivity is commonly limited by the availability of water. Increasing transpiration efficiency (biomass produced per unit of water used, TE) can potentially lead to increased grain yield in water-limited environments ('more crop per drop'). Currently, the ability to screen large populations for TE is limited by slow, low-throughput and/or expensive screening procedures. Here, we propose a low-cost, low-technology, rapid, and scalable method to screen for TE. The method uses a Pot-in-Bucket system that allows continuous watering of the pots and frequent monitoring of water use. To investigate the robustness of the method across environments, and to determine the shortest trial duration required to get accurate and repeatable TE estimates in wheat, plants from 11 genotypes varying in phenology were sown at three dates and grown for different durations in a polyhouse with partial environmental control.

RESULTS

The method revealed significant genotypic variations in TE among the 11 studied wheat genotypes. Genotype rankings for TE were consistent when plants were harvested the same day, at the flag-leaf stage or later. For these harvests, genotype rankings were consistent across experiments despite changes in environmental conditions, such as evaporative demand.

CONCLUSIONS

These results indicate that (1) the Pot-In-Bucket system is suitable to screen TE for breeding purposes in populations with varying phenology, (2) multiple short trials can be carried out within a season to allow increased throughput of genotypes for TE screening, and (3) root biomass measurement is not required to screen for TE, as whole-plant TE and shoot-only TE are highly correlated, at least in wheat. The method is particularly relevant in developing countries where low-cost and relatively high labour input may be most applicable.

Integrating modelling and phenotyping approaches to identify and screen complex traits: transpiration efficiency in cereals

Following advances in genetics, genomics, and phenotyping, trait selection in breeding is limited by our ability to understand interactions within the plant and with the environment, and to identify traits of most relevance to the target population of environments. We propose an integrated approach that combines insights from crop modelling, physiology, genetics, and breeding to characterize traits valuable for yield gain in the target population of environments, develop relevant high-throughput phenotyping platforms, and identify genetic controls and their value in production environments. This paper uses transpiration efficiency (biomass produced per unit of water used) as an example of a complex trait of interest to illustrate how the approach can guide modelling, phenotyping, and selection in a breeding programme. We believe that this approach, by integrating insights from diverse disciplines, can increase the resource use efficiency of breeding programmes for improving yield gains in target populations of environments.

Improving Genomic Prediction in Cassava Field Experiments by Accounting for Interplot Competition

Plants competing for available resources is an unavoidable phenomenon in a field. We conducted studies in cassava (Manihot esculenta Crantz) in order to understand the pattern of this competition. Taking into account the competitive ability of genotypes while selecting parents for breeding advancement or commercialization can be very useful. We assumed that competition could occur at two levels: (i) the genotypic level, which we call interclonal, and (ii) the plot level irrespective of the type of genotype, which we call interplot competition or competition error. Modification in incidence matrices was applied in order to relate neighboring genotype/plot to the performance of a target genotype/plot with respect to its competitive ability. This was added into a genomic selection (GS) model to simultaneously predict the direct and competitive ability of a genotype. Predictability of the models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error (pRMSE) compared to that of the base model having no competitive component. Results from our real data studies indicated that <10% increase in accuracy was achieved with GS-interclonal competition model, but this value reached up to 25% with a GS-competition error model. We also found that the competitive influence of a cassava clone is not just limited to the adjacent neighbors but spreads beyond them. Through simulations, we found that a 26% increase of accuracy in estimating trait genotypic effect can be achieved even in the presence of high competitive variance.

Resistance and tolerance to the brown planthopper, Nilaparvata lugens (Stål), in rice infested at different growth stages across a gradient of nitrogen applications

High resource availability can reduce anti-herbivore resistance (a plant's ability to defend against herbivores and reduce damage) in rice, Oryza sativa L, but may also increase tolerance (a plant's ability to withstand damage by, for example, compensatory growth). Through a series of greenhouse, screenhouse and field experiments, this study examines fitness (survival and development × reproduction) of the brown planthopper, Nilaparvata lugens (Stål), on resistant (IR62) and susceptible (IR22) rice varieties and age-related rice tolerance to planthopper damage under varying resource (nitrogenous fertilizer) availability. Planthoppers reared on IR62 in the greenhouse had lower fitness than planthoppers on IR22. IR62 became increasingly resistant as plants aged. IR22 was generally more tolerant of planthopper damage, and tolerance increased in IR22, but declined in IR62, as the plants aged. Rice plants infested at pre-tillering stages (3-4 leaf stage) in the screenhouse had greater losses to root, shoot and grain yield per unit weight of planthopper than plants infested at tillering stages, particularly in IR22. These trends were mainly due to the impact of planthoppers during pre-tillering stages and the length of exposure to the planthoppers. High nitrogen compromised IR62 resistance, particularly in tillering plants in the greenhouse study; however, high nitrogen did not increase planthopper biomass-density on IR62 in greenhouse or field cages. Tolerance to damage in IR62 at mid-tillering stages declined under increasing levels of nitrogen, but nitrogen increased tolerance during late-tillering stages. Planthopper damage to IR22 in field cages was severe and hopperburn (plant death) occurred in 83% of IR22 plants under high nitrogen (60-150 kg N ha-1). In contrast, despite planthopper infestations, damage to IR62 was low in field-grown plants and productivity (tillers, roots, shoots and grain) increased in IR62 under increasing nitrogen. Our results indicate that, whereas nitrogenous fertilizer increases planthopper fitness on susceptible and resistant varieties, the net effects of high nitrogen on IR62 include decreased planthopper biomass-density (apparent in all experiments) and higher tolerance to damage during later growth stages (observed in the greenhouse, and during one of two seasons in field cages).

Differences in Small Plot and On-Farm Trials for Yield Response to Foliar Fungicide in Soybean

Soybean yield response variability to foliar fungicide applications was evaluated in on-farm replicated strip trials (OFTs) and small-plot trials (SPTs) from 2008 through 2015 in Iowa. A total of 230 OFTs and 49 SPTs were compared for yield response to pyraclostrobin, pyraclostrobin + fluxapyroxad, or trifloxystrobin + prothioconazole fungicides. OFTs (18 to 55 m wide and 200 to 800 m long strips) were harvested with farmers' combines equipped with yield monitors and GPS, while SPTs (3.0 to 4.6 m wide and 10.7 to 15.3 m long plots) were harvested by small research plot combines. Variance component and power analyses were conducted with a subset of data consisting of 12 OFTs and SPTs, each with pyraclostrobin and evaluated in 2008 and 2009. While average yield responses were similar, the residual random yield variation was smaller in OFTs than SPTs. Power analysis showed that SPTs need more replications than OFTs to detect the same overall treatment differences. To detect a yield response of 134 kg/ha, it would require at least three treatment replications with 12 locations in OFTs and seven replications with 12 locations in SPTs. Researchers need to acknowledge the differences in statistical power of detecting yield responses to foliar fungicide on soybean in different types of field experiments, especially with smaller plot sizes in situations with less foliar disease.

Methodology for High-Throughput Field Phenotyping of Canopy Temperature Using Airborne Thermography

Lower canopy temperature (CT), resulting from increased stomatal conductance, has been associated with increased yield in wheat. Historically, CT has been measured with hand-held infrared thermometers. Using the hand-held CT method on large field trials is problematic, mostly because measurements are confounded by temporal weather changes during the time required to measure all plots. The hand-held CT method is laborious and yet the resulting heritability low, thereby reducing confidence in selection in large scale breeding endeavors. We have developed a reliable and scalable crop phenotyping method for assessing CT in large field experiments. The method involves airborne thermography from a manned helicopter using a radiometrically-calibrated thermal camera. Thermal image data is acquired from large experiments in the order of seconds, thereby enabling simultaneous measurement of CT on potentially 1000s of plots. Effects of temporal weather variation when phenotyping large experiments using hand-held infrared thermometers are therefore reduced. The method is designed for cost-effective and large-scale use by the non-technical user and includes custom-developed software for data processing to obtain CT data on a single-plot basis for analysis. Broad-sense heritability was routinely >0.50, and as high as 0.79, for airborne thermography CT measured near anthesis on a wheat experiment comprising 768 plots of size 2 × 6 m. Image analysis based on the frequency distribution of temperature pixels to remove the possible influence of background soil did not improve broad-sense heritability. Total image acquisition and processing time was ca. 25 min and required only one person (excluding the helicopter pilot). The results indicate the potential to phenotype CT on large populations in genetics studies or for selection within a plant breeding program.

High-Throughput Phenotyping of Wheat and Barley Plants Grown in Single or Few Rows in Small Plots Using Active and Passive Spectral Proximal Sensing

In the early stages of plant breeding, breeders evaluate a large number of varieties. Due to limited availability of seeds and space, plot sizes may range from one to four rows. Spectral proximal sensors can be used in place of labour-intensive methods to estimate specific plant traits. The aim of this study was to test the performance of active and passive sensing to assess single and multiple rows in a breeding nursery. A field trial with single cultivars of winter barley and winter wheat with four plot designs (single-row, wide double-row, three rows, and four rows) was conducted. A GreenSeeker RT100 and a passive bi-directional spectrometer were used to assess biomass fresh and dry weight, as well as aboveground nitrogen content and uptake. Generally, spectral passive sensing and active sensing performed comparably in both crops. Spectral passive sensing was enhanced by the availability of optimized ratio vegetation indices, as well as by an optimized field of view and by reduced distance dependence. Further improvements of both sensors in detecting the performance of plants in single rows can likely be obtained by optimization of sensor positioning or orientation. The results suggest that even in early selection cycles, enhanced high-throughput phenotyping might be able to assess plant performance within plots comprising single or multiple rows. This method has significant potential for advanced breeding.

Robust phenotyping strategies for evaluation of stem non-structural carbohydrates (NSC) in rice

Rice plants (Oryza sativa) accumulate excess photoassimilates in the form of non-structural carbohydrates (NSCs) in their stems prior to heading that can later be mobilized to supplement photosynthate production during grain-filling. Despite longstanding interest in stem NSC for rice improvement, the dynamics of NSC accumulation, remobilization, and re-accumulation that have genetic potential for optimization have not been systematically investigated. Here we conducted three pilot experiments to lay the groundwork for large-scale diversity studies on rice stem NSC. We assessed the relationship of stem NSC components with 21 agronomic traits in large-scale, tropical yield trials using 33 breeder-nominated lines, established an appropriate experimental design for future genetic studies using a Bayesian framework to sample sub-datasets from highly replicated greenhouse data using 36 genetically diverse genotypes, and used 434 phenotypically divergent rice stem samples to develop two partial least-squares (PLS) models using near-infrared (NIR) spectra for accurate, rapid prediction of rice stem starch, sucrose, and total non-structural carbohydrates. We find evidence that stem reserves are most critical for short-duration varieties and suggest that pre-heading stem NSC is worthy of further experimentation for breeding early maturing rice.

Pot size matters revisited: does container size affect the response to elevated CO2 and our ability to detect genotypic variability in this response in wheat?

Many studies have investigated the effect of elevated CO2 (eCO2) in wheat, although few have evaluated the potential of genotypic variability in the response. Such studies are the next logical step in wheat climate change adaptation research, and they will require the evaluation of large numbers of genotypes. For practical reasons the preliminary studies are most likely to be conducted in controlled environments. There have been concerns that the root restriction related to container-grown plants can influence (1) the response to eCO2, (2) the detection of genotypic variability for various traits of interest, and (3) the ability to find the genotypes most responsive to eCO2. In the present study we evaluated two sizes of container - 1.4L pots and 7.5L columns - side-by side in a glasshouse environment and found that for 14 of 23 traits observed environment effects (ambient CO2, eCO2 or eCO2 and high temperature) were not consistent between plants grown in pots and in columns. More importantly, of the 21 traits showing genotypic variability, only 8 showed consistent genotype differences and rankings across both container types. Statistical analyses conducted separately for plants grown in pots or in columns showed different cultivars as being the most responsive to elevated CO2 and would thus, have led to different conclusions. This study is intended as a message of caution to controlled environment experimenters: using small containers can artificially create conditions that could either hide or overly express genotypic variability in some traits in response to eCO2 compared with what might be expected in larger containers.

Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.

A tillering inhibition gene influences root-shoot carbon partitioning and pattern of water use to improve wheat productivity in rainfed environments

Genetic modification of shoot and root morphology has potential to improve water and nutrient uptake of wheat crops in rainfed environments. Near-isogenic lines (NILs) varying for a tillering inhibition (tin) gene and representing multiple genetic backgrounds were phenotyped in contrasting, controlled environments for shoot and root growth. Leaf area, shoot and root biomass were similar until tillering, whereupon reduced tillering in tin-containing NILs produced reductions of up to 60% in total leaf area and biomass, and increases in total root length of up to 120% and root biomass to 145%. Together, the root-to-shoot ratio increased two-fold with the tin gene. The influence of tin on shoot and root growth was greatest in the cv. Banks genetic background, particularly in the biculm-selected NIL, and was typically strongest in cooler environments. A separate de-tillering study confirmed greater root-to-shoot ratios with regular tiller removal in non-tin-containing genotypes. In validating these observations in a rainfed field study, the tin allele had a negligible effect on seedling growth but was associated with significantly (P<0.05) reduced tiller number (-37%), leaf area index (-26%), and spike number (-35%) to reduce plant biomass (-19%) at anthesis. Root biomass, root-to-shoot ratio at early stem elongation, and root depth at maturity were all increased in tin-containing NILs. Soil water use was slowed in tin-containing NILs, resulting in greater water availability, greater stomatal conductance, cooler canopy temperatures, and maintenance of green leaf area during grain-filling. Together these effects contributed to increases in harvest index and grain yield. In both the controlled and field environments, the tin gene was commonly associated with increased root length and biomass, but the significant influence of genetic background and environment suggests careful assessment of tin-containing progeny in selection for genotypic increases in root growth.

Soil coring at multiple field environments can directly quantify variation in deep root traits to select wheat genotypes for breeding

We aim to incorporate deep root traits into future wheat varieties to increase access to stored soil water during grain development, which is twice as valuable for yield as water captured at younger stages. Most root phenotyping efforts have been indirect studies in the laboratory, at young plant stages, or using indirect shoot measures. Here, soil coring to 2 m depth was used across three field environments to directly phenotype deep root traits on grain development (depth, descent rate, density, length, and distribution). Shoot phenotypes at coring included canopy temperature depression, chlorophyll reflectance, and green leaf scoring, with developmental stage, biomass, and yield. Current varieties, and genotypes with breeding histories and plant architectures expected to promote deep roots, were used to maximize identification of variation due to genetics. Variation was observed for deep root traits (e.g. 111.4-178.5cm (60%) for depth; 0.09-0.22cm/°C day (144%) for descent rate) using soil coring in the field environments. There was significant variation for root traits between sites, and variation in the relative performance of genotypes between sites. However, genotypes were identified that performed consistently well or poorly at both sites. Furthermore, high-performing genotypes were statistically superior in root traits than low-performing genotypes or commercial varieties. There was a weak but significant negative correlation between green leaf score (-0.5), CTD (0.45), and rooting depth and a positive correlation for chlorophyll reflectance (0.32). Shoot phenotypes did not predict other root traits. This study suggests that field coring can directly identify variation in deep root traits to speed up selection of genotypes for breeding programmes.

Integration of phenotyping and genetic platforms for a better understanding of wheat performance under drought

Identifying markers for physiological traits of proven value in breeding, especially ones that are consistent across environments with different patterns of stress, strengthens the toolkit to increase confidence in the value and delivery from physiological breeding. To identify markers relevant to drought adaptation, this review will highlight the importance of development and implementation of robust and repeatable phenotyping that is relevant to the different target drought types, and practical examples of managed environment facilities in Australia and Mexico are given. These facilities can be used as models to: (i) improve reliability and consistency of environments and genetic responses to the environment at a global scale; (ii) improve the capacity to deliver quantitative trait loci (QTLs) as user-friendly markers for enriching populations; and (iii) illustrate the use of populations with a narrow range of variation for phenology allowing the identification of QTLs for drought-adaptive traits. However, the importance of further optimizing phenology and plant height at a global scale is highlighted. Finally, the impact of physiological trait-based crossing is demonstrated and supports the need for urgent development of robust genetic markers.