Use of RGB Vegetation Indexes in Assessing Early Effects of Verticillium Wilt of Olive in Asymptomatic Plants in High and Low Fertility Scenarios

Verticillium Wilt of Olive, a disease caused by the hemibiotrophic vascular fungus Verticillium dahliae Kleb. presents one of the most important constraints to olive production in the world, with an especially notable impact in Mediterranean agriculture. This study evaluates the use of RGB vegetation indexes in assessing the effects of this disease during the biotrophic phase of host-pathogen interaction, in which symptoms of wilt are not yet evident. While no differences were detected by measuring stomatal conductance and chlorophyll fluorescence, results obtained from RGB indexes showed significant differences between control and inoculated plants for indexes Saturation, a*, b*, green Area (GA), normalized green-red difference index (NGRDI) and triangular greenness index (TGI), presenting a reduction in plant growth as well as in green and yellow color components as an effect of inoculation. These results were contrasted across two scenarios of mineral fertilization in soil and soil amended with two different olive mill waste composts, presenting a clear interaction between the host-pathogen relationship and plant nutrition and suggesting the effect of V. dahliae infection during the biotrophic phase was not related to plant water status.

Domestication and crop evolution of wheat and barley: Genes, genomics, and future directions

Wheat and barley are two of the founder crops of the agricultural revolution that took place 10,000 years ago in the Fertile Crescent and both crops remain among the world's most important crops. Domestication of these crops from their wild ancestors required the evolution of traits useful to humans, rather than survival in their natural environment. Of these traits, grain retention and threshability, yield improvement, changes to photoperiod sensitivity and nutritional value are most pronounced between wild and domesticated forms. Knowledge about the geographical origins of these crops and the genes responsible for domestication traits largely pre-dates the era of next-generation sequencing, although sequencing will lead to new insights. Molecular markers were initially used to calculate distance (relatedness), genetic diversity and to generate genetic maps which were useful in cloning major domestication genes. Both crops are characterized by large, complex genomes which were long thought to be beyond the scope of whole-genome sequencing. However, advances in sequencing technologies have improved the state of genomic resources for both wheat and barley. The availability of reference genomes for wheat and some of its progenitors, as well as for barley, sets the stage for answering unresolved questions in domestication genomics of wheat and barley.

Genetic dissection of agronomic and quality traits based on association mapping and genomic selection approaches in durum wheat grown in Southern Spain

Climatic conditions affect the growth, development and final crop production. As wheat is of paramount importance as a staple crop in the human diet, there is a growing need to study its abiotic stress adaptation through the performance of key breeding traits. New and complementary approaches, such as genome-wide association studies (GWAS) and genomic selection (GS), are used for the dissection of different agronomic traits. The present study focused on the dissection of agronomic and quality traits of interest (initial agronomic score, yield, gluten index, sedimentation index, specific weight, whole grain protein and yellow colour) assessed in a panel of 179 durum wheat lines (Triticum durum Desf.), grown under rainfed conditions in different Mediterranean environments in Southern Spain (Andalusia). The findings show a total of 37 marker-trait associations (MTAs) which affect phenotype expression for three quality traits (specific weight, gluten and sedimentation indexes). MTAs could be mapped on the A and B durum wheat subgenomes (on chromosomes 1A, 1B, 2A, 2B and 3A) through the recently available bread wheat reference assembly (IWGSC RefSeqv1). Two of the MTAs found for quality traits (gluten index and SDS) corresponded to the known Glu-B1 and Glu-A1 loci, for which candidate genes corresponding to high molecular weight glutenin subunits could be located. The GS prediction ability values obtained from the breeding materials analyzed showed promising results for traits as grain protein content, sedimentation and gluten indexes, which can be used in plant breeding programs.

The General Transcription Repressor TaDr1 Is Co-expressed With TaVrn1 and TaFT1 in Bread Wheat Under Drought

The general transcription repressor, TaDr1 gene, was identified during screening of a wheat SNP database using the Amplifluor-like SNP marker KATU-W62. Together with two genes described earlier, TaDr1A and TaDr1B, they represent a set of three homeologous genes in the wheat genome. Under drought, the total expression profiles of all three genes varied between different bread wheat cultivars. Plants of four high-yielding cultivars exposed to drought showed a 2.0-2.4-fold increase in TaDr1 expression compared to controls. Less strong, but significant 1.3-1.8-fold up-regulation of the TaDr1 transcript levels was observed in four low-yielding cultivars. TaVrn1 and TaFT1, which controls the transition to flowering, revealed similar profiles of expression as TaDr1. Expression levels of all three genes were in good correlation with grain yields of evaluated cultivars growing in the field under water-limited conditions. The results could indicate the involvement of all three genes in the same regulatory pathway, where the general transcription repressor TaDr1 may control expression of TaVrn1 and TaFT1 and, consequently, flowering time. The strength of these genes expression can lead to phenological changes that affect plant productivity and hence explain differences in the adaptation of the examined wheat cultivars to the dry environment of Northern and Central Kazakhstan. The Amplifluor-like SNP marker KATU-W62 used in this work can be applied to the identification of wheat cultivars differing in alleles at the TaDr1 locus and in screening hybrids.

The Plant-Transpiration Response to Vapor Pressure Deficit (VPD) in Durum Wheat Is Associated With Differential Yield Performance and Specific Expression of Genes Involved in Primary Metabolism and Water Transport

The regulation of plant transpiration was proposed as a key factor affecting transpiration efficiency and agronomical adaptation of wheat to water-limited Mediterranean environments. However, to date no studies have related this trait to crop performance in the field. In this study, the transpiration response to increasing vapor pressure deficit (VPD) of modern Spanish semi-dwarf durum wheat lines was evaluated under controlled conditions at vegetative stage, and the agronomical performance of the same set of lines was assessed at grain filling as well as grain yield at maturity, in Mediterranean environments ranging from water stressed to good agronomical conditions. A group of linear-transpiration response (LTR) lines exhibited better performance in grain yield and biomass compared to segmented-transpiration response (STR) lines, particularly in the wetter environments, whereas the reverse occurred only in the most stressed trial. LTR lines generally exhibited better water status (stomatal conductance) and larger green biomass (vegetation indices) during the reproductive stage than STR lines. In both groups, the responses to growing conditions were associated with the expression levels of dehydration-responsive transcription factors (DREB) leading to different performances of primary metabolism-related enzymes. Thus, the response of LTR lines under fair to good conditions was associated with higher transcription levels of genes involved in nitrogen (GS1 and GOGAT) and carbon (RCBL) metabolism, as well as water transport (TIP1.1). In conclusion, modern durum wheat lines differed in their response to water loss, the linear transpiration seemed to favor uptake and transport of water and nutrients, and photosynthetic metabolism led to higher grain yield except for very harsh drought conditions. The transpiration response to VPD may be a trait to further explore when selecting adaptation to specific water conditions.

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.

QTL and Candidate Gene Identification for Silique Length Based on High-Dense Genetic Map in Brassica napus L

Silique length (SL) is an important yield trait and positively correlates with seeds per silique and seed weight. In the present study, two double haploid (DH) populations, established from crosses Zhongshuang11 × R11 (ZR) and R1 × R2 (RR), containing 280 and 95 DH lines, respectively, were used to map quantitative trait loci (QTL) for SL. A high-dense genetic map from ZR population was constructed comprising 14,658 bins on 19 linkage groups, with map length of 2,198.85 cM and an average marker distance of 0.15 cM. Genetic linkage map from RR population was constructed by using 2,046 mapped markers anchored to 19 chromosomes with 2,217-cM map length and an average marker distance of 1.08 cM. Major QTL qSL_ZR_A09 and qSL_RR_A09b on A09 were identified from ZR and RR populations, respectively. Both QTL could be stably detected in four environments. QTL qSL_RR_A09b and qSL_ZR_A09 were located on 68.5-70.8 cM and 91.33-91.94 cM interval with R² values of 14.99-39.07% and 15.00-20.36% in RR and ZR populations, respectively. Based on the physical positions of single nucleotide polymorphism (SNP) markers flanking qSL_ZR_A09 and gene annotation in Arabidopsis, 26 genes were identified with SNP/Indel variation between parents and two genes (BnaA09g41180D and BnaA09g41380D) were selected as the candidate genes. Expression analysis further revealed BnaA09g41180D, encoding homologs of Arabidopsis fasciclin-like arabinogalactan proteins (FLA3), as the most promising candidate gene for qSL_ZR_A09. The QTL identification and candidate gene analysis will provide new insight into the genomic regions controlling SL in Brassica napus as well as candidate genes underlying the QTL.

Multiple Near-Isogenic Lines Targeting a QTL Hotspot of Drought Tolerance Showed Contrasting Performance Under Post-anthesis Water Stress

The complex quantitative nature of drought-related traits is a major constraint to breed tolerant wheat varieties. Pairs of near-isogenic lines (NILs) with a common genetic background but differing in a particular locus could turn quantitative traits into a Mendelian factor facilitating our understanding of genotype and phenotype interactions. In this study, we report our fast track development and evaluation of NILs from C306 × Dharwar Dry targeting a wheat 4BS QTL hotspot in C306, which confers drought tolerance following the heterogeneous inbreed family (HIF) analysis coupled with immature embryo culture-based fast generation technique. Molecular marker screening and phenotyping for grain yield and related traits under post-anthesis water stress (WS) confirmed four isoline pairs, viz., qDSI.4B.1-2, qDSI.4B.1-3, qDSI.4B.1-6, and qDSI.4B.1-8. There were significant contrasts of responses between the NILs with C306 QTL (+NILs) and the NILs without C306 QTL (-NILs). Among the four confirmed NIL pairs, mean grain yield per plant of the +NILs and -NILs showed significant differences ranging from 9.61 to 10.81 and 6.30 to 7.56 g, respectively, under WS condition, whereas a similar grain yield was recorded between the +NILs and -NILs under well-watered condition. Isolines of +NIL and -NIL pairs showed similar chlorophyll content (SPAD), assimilation rate (A), and transpiration rate (Tr) at the beginning of the stress. However, the +NILs showed significantly higher SPAD (12%), A (66%), stomatal conductance (75%), and Tr (97%) than the -NILs at the seventh day of stress. Quantitative RT-PCR analysis targeting the MYB transcription factor gene Triticum aestivum MYB 82 (TaMYB82), within this genomic region which was retrieved from the wheat reference genome TGACv1, also revealed differential expression in +NILs and -NILs under stress. These results confirmed that the NILs can be invaluable resources for fine mapping of this QTL, and also for cloning and functional characterization of the gene(s) responsible for drought tolerance in wheat.

In Vivo Phenotyping for the Early Detection of Drought Stress in Tomato

Drought stress imposes a major constraint over a crop yield and can be expected to grow in importance if the climate change predicted comes about. Improved methods are needed to facilitate crop management via the prompt detection of the onset of stress. Here, we report the use of an in vivo OECT (organic electrochemical transistor) sensor, termed as bioristor, in the context of the drought response of the tomato plant. The device was integrated within the plant's stem, thereby allowing for the continuous monitoring of the plant's physiological status throughout its life cycle. Bioristor was able to detect changes of ion concentration in the sap upon drought, in particular, those dissolved and transported through the transpiration stream, thus efficiently detecting the occurrence of drought stress immediately after the priming of the defence responses. The bioristor's acquired data were coupled with those obtained in a high-throughput phenotyping platform revealing the extreme complementarity of these methods to investigate the mechanisms triggered by the plant during the drought stress event.

Mediterranean Green Roof Simulation in Caldes de Montbui (Barcelona): Thermal and Hydrological Performance Test of Frankenia laevis L., Dymondia margaretae Compton and Iris lutescens Lam

Green roofs provide a number of environmental advantages like increasing urban biodiversity, reducing pollution, easing burdens on drainage systems, and lowering energy costs thanks to thermal insulation. Frankenia laevis, Dymondia margaretae and Iris lutescens were tested in a green roof installation. For all three species, we assessed two minimal irrigation treatments and one rain-fed treatment to resemble Mediterranean climate conditions analyzing the thermal and hydrological performance of all three species and their substrates through an evaluation of green cover, mortality, and biomass. The most influential factors registered for all three species are the relationship between air and water in the substrate and the interaction between green cover and substrate, respectively, for summer and winter seasons. In particular, D. margaretae preserved more water in its substrate than the other species both in summer and winter and after each rainfall event. F. laevis registered the highest level of variation in terms of substrate water content and of rainwater retention. I. lutescens achieved low hydrological performance, a limited amount of green cover, and slow growth. Our results suggest the absolute need of additional irrigation, managed in accordance with specific functional objectives, for all three species analyzed under Mediterranean conditions and different water regime.

Genetic dissection of the mechanism of flowering time based on an environmentally stable and specific QTL in Brassica napus

Flowering time is an important agronomic trait that is highly influenced by the environment. To elucidate the genetic mechanism of flowering time in rapeseed (Brassica napus L.), a genome-wide QTL analysis was performed in a doubled haploid population grown in winter, semi-winter and spring ecological conditions. Fifty-five consensus QTLs were identified after combining phenotype and genomic data, including 12 environment-stable QTLs and 43 environment-specific QTLs. Importantly, six major QTLs for flowering time were identified, of which two were considered environment-specific QTLs in spring ecological condition and four were considered environment-stable QTLs in winter and semi-winter ecological conditions. Through QTL comparison, 18 QTLs were colocalized with QTLs from six other published studies. Combining the candidate genes with their functional annotation, in 49 of 55 consensus QTLs, 151 candidate genes in B. napus corresponding to 95 homologous genes in Arabidopsis thaliana related to flowering were identified, including BnaC03g32910D (CO), BnaA02g12130D (FT) and BnaA03g13630D (FLC). Most of the candidate genes were involved in different flowering regulatory pathways. Based on re-sequencing and differences in sequence annotation between the two parents, we found that regions containing some candidate genes have numerous non-frameshift InDels and many non- synonymous mutations, which might directly lead to gene functional variation. Flowering time was negativly correlated with seed yield and thousand seed weight based on a QTL comparison of flowering time and seed yield traits, which has implications in breeding new early-maturing varieties of B. napus. Moreover, a putative flowering regulatory network was constructed, including the photoperiod, circadian clock, vernalization, autonomous and gibberellin pathways. Multiple copies of genes led to functional difference among the different copies of homologous genes, which also increased the complexity of the flowering regulatory networks. Taken together, the present results not only provide new insights into the genetic regulatory network underlying the control of flowering time but also improve our understanding of flowering time regulatory pathways in rapeseed.

Quantification of Extent, Density, and Status of Aquatic Reed Beds Using Point Clouds Derived from UAV–RGB Imagery

Quantification of reed coverage and vegetation status is fundamental for monitoring and developing lake conservation strategies. The applicability of Unmanned Aerial Vehicles (UAV) three-dimensional data (point clouds) for status evaluation was investigated. This study focused on mapping extent, density, and vegetation status of aquatic reed beds. Point clouds were calculated with Structure from Motion (SfM) algorithms in aerial imagery recorded with Rotary Wing (RW) and Fixed Wing (FW) UAV. Extent was quantified by measuring the surface between frontline and shoreline. Density classification was based on point geometry (height and height variance) in point clouds. Spectral information per point was used for calculating a vegetation index and was used as indicator for vegetation vitality. Status was achieved by combining data on density, vitality, and frontline shape outputs. Field observations in areas of interest (AOI) and optical imagery were used for reference and validation purposes. A root mean square error (RMSE) of 1.58 m to 3.62 m for cross sections from field measurements and classification was achieved for extent map. The overall accuracy (OA) acquired for density classification was 88.6% (Kappa = 0.8). The OA for status classification of 83.3% (Kappa = 0.7) was reached by comparison with field measurements complemented by secondary Red, Green, Blue (RGB) data visual assessments. The research shows that complex transitional zones (water–vegetation–land) can be assessed and support the suitability of the applied method providing new strategies for monitoring aquatic reed bed using low-cost UAV imagery.

Sources for the breeding of soft spring wheat in the conditions of Novosibirsk region

The sources were identified among collection samples characterized by highly pronounced economic and valuable features, which allows new geographically remote source material to be taken to the regional breeding practices. This research aims to assess the agronomic traits (duration of the growing period, lodging resistance and plant height, 1000-grain weight, grain weight and yield) in soft spring wheat varieties of different ecological and geographical origin. Estimation was carried out by a 9-point system of expression of the trait during the study, which allows identifying samples with the greatest expression of the trait in the years of study with respect to the average experience. 5439 samples have been studied over 28 years, with 1106 of them, over two years or more. The study was carried out according to the methods of VIR on plots of 2 m2. It was shown that the samples mainly had no correlation between the yield and the duration of the growing period, while the average dependence (г = 0.6) was revealed between the yield and the height of the plants. Varieties forming the intermediate (4.5-5 points) and above average (6-7) yield in a short growing period (69-85 days) were identified (Lutescens 675, Irkut-skaya 49, Simbirca, Hybrid F3 S-141, Hybrid F4, Hybrid F3 S-289 and Hybrid F4 S-2300 and Pamyati Vavenko-va). A high average score (8.6-9) at 1000 grains weight was shown for 16 varieties with variation from 37 g (N43 and IAO-9) to 56 g (Hofed 1). A high average score (8-9) in the evaluation of grain weight was shown for Pamyati Leont'eva, Ekada 70, Simbirtsit, Don Jose, Yong-Liang 4 and Long-Mai 11, which formed ears with an average weight from 0.96 to 2.30 g. A consistently high score (9) reflecting the yield was in the varieties Condestavel, PF 843025, Prilenskaya 19, Pamyati Leont'eva, Omskaya Krasa.

Haplotype Loci Under Selection in Canadian Durum Wheat Germplasm Over 60 Years of Breeding: Association With Grain Yield, Quality Traits, Protein Loss, and Plant Height

Durum wheat was introduced in the southern prairies of western Canada in the late nineteenth century. Breeding efforts have mainly focused on improving quality traits to meet the pasta industry demands. For this study, 192 durum wheat lines were genotyped using the Illumina 90K Infinium iSelect assay, and resulted in a total of 14,324 polymorphic SNPs. Genetic diversity changed over time, declining during the first 20 years of breeding in Canada, then increased in the late 1980s and early 1990s. We scanned the genome for signatures of selection, using the total variance Fst-based outlier detection method (Lositan), the hierarchical island model (Arlequin) and the Bayesian genome scan method (BayeScan). A total of 407 outliers were identified and clustered into 84 LD-based haplotype loci, spanning all 14 chromosomes of the durum wheat genome. The association analysis detected 54 haplotype loci, of which 39% contained markers with a complete reversal of allelic state. This tendency to fixation of favorable alleles corroborates the success of the Canadian durum wheat breeding programs over time. Twenty-one haplotype loci were associated with multiple traits. In particular, hap_4B_1 explained 20.6, 17.9 and 16.6% of the phenotypic variance of pigment loss, pasta b∗ and dough extensibility, respectively. The locus hap_2B_9 explained 15.9 and 17.8% of the variation of protein content and protein loss, respectively. All these pleiotropic haplotype loci offer breeders the unique opportunity for further improving multiple traits, facilitating marker-assisted selection in durum wheat, and could help in identifying genes as functional annotations of the wheat genome become available.

Omics Approaches for Engineering Wheat Production under Abiotic Stresses

Abiotic stresses greatly influenced wheat productivity executed by environmental factors such as drought, salt, water submergence and heavy metals. The effective management at the molecular level is mandatory for a thorough understanding of plant response to abiotic stress. Understanding the molecular mechanism of stress tolerance is complex and requires information at the omic level. In the areas of genomics, transcriptomics and proteomics enormous progress has been made in the omics field. The rising field of ionomics is also being utilized for examining abiotic stress resilience in wheat. Omic approaches produce a huge amount of data and sufficient developments in computational tools have been accomplished for efficient analysis. However, the integration of omic-scale information to address complex genetics and physiological questions is still a challenge. Though, the incorporation of omic-scale data to address complex genetic qualities and physiological inquiries is as yet a challenge. In this review, we have reported advances in omic tools in the perspective of conventional and present day approaches being utilized to dismember abiotic stress tolerance in wheat. Attention was given to methodologies, for example, quantitative trait loci (QTL), genome-wide association studies (GWAS) and genomic selection (GS). Comparative genomics and candidate genes methodologies are additionally talked about considering the identification of potential genomic loci, genes and biochemical pathways engaged with stress resilience in wheat. This review additionally gives an extensive list of accessible online omic assets for wheat and its effective use. We have additionally addressed the significance of genomics in the integrated approach and perceived high-throughput multi-dimensional phenotyping as a significant restricting component for the enhancement of abiotic stress resistance in wheat.

Measuring the dynamic photosynthome

Background

Photosynthesis underpins plant productivity and yet is notoriously sensitive to small changes in environmental conditions, meaning that quantitation in nature across different time scales is not straightforward. The 'dynamic' changes in photosynthesis (i.e. the kinetics of the various reactions of photosynthesis in response to environmental shifts) are now known to be important in driving crop yield.

Scope

It is known that photosynthesis does not respond in a timely manner, and even a small temporal 'mismatch' between a change in the environment and the appropriate response of photosynthesis toward optimality can result in a fall in productivity. Yet the most commonly measured parameters are still made at steady state or a temporary steady state (including those for crop breeding purposes), meaning that new photosynthetic traits remain undiscovered.

Conclusions

There is a great need to understand photosynthesis dynamics from a mechanistic and biological viewpoint especially when applied to the field of 'phenomics' which typically uses large genetically diverse populations of plants. Despite huge advances in measurement technology in recent years, it is still unclear whether we possess the capability of capturing and describing the physiologically relevant dynamic features of field photosynthesis in sufficient detail. Such traits are highly complex, hence we dub this the 'photosynthome'. This review sets out the state of play and describes some approaches that could be made to address this challenge with reference to the relevant biological processes involved.

Adaptive selection of founder segments and epistatic control of plant height in the MAGIC winter wheat population WM-800

BACKGROUND

Multi-parent advanced generation intercross (MAGIC) populations are a newly established tool to dissect quantitative traits. We developed the high resolution MAGIC wheat population WM-800, consisting of 910 F4:6 lines derived from intercrossing eight recently released European winter wheat cultivars.

RESULTS

Genotyping WM-800 with 7849 SNPs revealed a low mean genetic similarity of 59.7% between MAGIC lines. WM-800 harbours distinct genomic regions exposed to segregation distortion. These are mainly located on chromosomes 2 to 6 of the wheat B genome where founder specific DNA segments were positively or negatively selected. This suggests adaptive selection of individual founder alleles during population development. The application of a genome-wide association study identified 14 quantitative trait loci (QTL) controlling plant height in WM-800, including the known semi-dwarf genes Rht-B1 and Rht-D1 and a potentially novel QTL on chromosome 5A. Additionally, epistatic effects controlled plant height. For example, two loci on chromosomes 2B and 7B gave rise to an additive epistatic effect of 13.7 cm.

CONCLUSION

The present study demonstrates that plant height in the MAGIC-WHEAT population WM-800 is mainly determined by large-effect QTL and di-genic epistatic interactions. As a proof of concept, our study confirms that WM-800 is a valuable tool to dissect the genetic architecture of important agronomic traits.

Plant Phenomics, From Sensors to Knowledge

Major improvements in crop yield are needed to keep pace with population growth and climate change. While plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e., the structure and function of plants) associated with allelic variants and environments remains a major technical bottleneck. Here, we review the conceptual and technical challenges facing plant phenomics. We first discuss how, given plants' high levels of morphological plasticity, crop phenomics presents distinct challenges compared with studies in animals. Next, we present strategies for multi-scale phenomics, and describe how major improvements in imaging, sensor technologies and data analysis are now making high-throughput root, shoot, whole-plant and canopy phenomic studies possible. We then suggest that research in this area is entering a new stage of development, in which phenomic pipelines can help researchers transform large numbers of images and sensor data into knowledge, necessitating novel methods of data handling and modelling. Collectively, these innovations are helping accelerate the selection of the next generation of crops more sustainable and resilient to climate change, and whose benefits promise to scale from physiology to breeding and to deliver real world impact for ongoing global food security efforts.

Effect of Ppd-A1 and Ppd-B1 Allelic Variants on Grain Number and Thousand Kernel Weight of Durum Wheat and Their Impact on Final Grain Yield

The main yield components in durum wheat are grain number per unit area (GN) and thousand kernel weight (TKW), both of which are affected by environmental conditions. The most critical developmental stage for their determination is flowering time, which partly depends on photoperiod sensitivity genes at Ppd-1 loci. Fifteen field experiments, involving 23 spring durum wheat genotypes containing all known allelic variants at the PHOTOPERIOD RESPONSE LOCUS (Ppd-A1 and Ppd-B1) were carried out at three sites at latitudes ranging from 41° to 27° N (Spain, Mexico-north, and Mexico-south, the latter in spring planting). Allele GS100 at Ppd-A1, which causes photoperiod insensitivity and results in early-flowering genotypes, tended to increase TKW and yield, albeit not substantially. Allele Ppd-B1a, also causing photoperiod insensitivity, did not affect flowering time or grain yield. Genotypes carrying the Ppd-B1b allele conferring photoperiod sensitivity had consistently higher GN, which did not translate into higher yield due to under-compensation in TKW. This increased GN was due to a greater number of grains spike-1 as a result of a higher number of spikelets spike-1. Daylength from double ridge to terminal spikelet stage was strongly and positively associated with the number of spikelets spike-1 in Spain. This association was not found in the Mexico sites, thereby indicating that Ppd-B1b had an intrinsic effect on spikelets spike-1 independently of environmental cues. Our results suggest that, in environments where yield is limited by the incapacity to produce a high GN, selecting for Ppd-B1b may be advisable.

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.

Simultaneous improvement of grain yield and protein content in durum wheat by different phenotypic indices and genomic selection

Simultaneous improvement of protein content and grain yield by index selection is possible but its efficiency largely depends on the weighting of the single traits. The genetic architecture of these indices is similar to that of the primary traits. Grain yield and protein content are of major importance in durum wheat breeding, but their negative correlation has hampered their simultaneous improvement. To account for this in wheat breeding, the grain protein deviation (GPD) and the protein yield were proposed as targets for selection. The aim of this work was to investigate the potential of different indices to simultaneously improve grain yield and protein content in durum wheat and to evaluate their genetic architecture towards genomics-assisted breeding. To this end, we investigated two different durum wheat panels comprising 159 and 189 genotypes, which were tested in multiple field locations across Europe and genotyped by a genotyping-by-sequencing approach. The phenotypic analyses revealed significant genetic variances for all traits and heritabilities of the phenotypic indices that were in a similar range as those of grain yield and protein content. The GPD showed a high and positive correlation with protein content, whereas protein yield was highly and positively correlated with grain yield. Thus, selecting for a high GPD would mainly increase the protein content whereas a selection based on protein yield would mainly improve grain yield, but a combination of both indices allows to balance this selection. The genome-wide association mapping revealed a complex genetic architecture for all traits with most QTL having small effects and being detected only in one germplasm set, thus limiting the potential of marker-assisted selection for trait improvement. By contrast, genome-wide prediction appeared promising but its performance strongly depends on the relatedness between training and prediction sets.

Land-based crop phenotyping by image analysis: consistent canopy characterization from inconsistent field illumination

BACKGROUND

One of the main challenges associated with image-based field phenotyping is the variability of illumination. During a single day's imaging session, or between different sessions on different days, the sun moves in and out of cloud cover and has varying intensity. How is one to know from consecutive images alone if a plant has become darker over time, or if the weather conditions have simply changed from clear to overcast? This is a significant problem to address as colour is an important phenotypic trait that can be measured automatically from images.

RESULTS

In this work we use an industry standard colour checker to balance the colour in images within and across every day of a field trial conducted over four months in 2016. By ensuring that the colour checker is present in every image we are afforded a 'ground truth' to correct for varying illumination conditions across images. We employ a least squares approach to fit a quadratic model for correcting RGB values of an image in such a way that the observed values of the colour checker tiles align with their true values after the transformation.

CONCLUSIONS

The proposed method is successful in reducing the error between observed and reference colour chart values in all images. Furthermore, the standard deviation of mean canopy colour across multiple days is reduced significantly after colour correction is applied. Finally, we use a number of examples to demonstrate the usefulness of accurate colour measurements in recording phenotypic traits and analysing variation among varieties and treatments.

A return to the genetic heritage of durum wheat to cope with drought heightened by climate change

The objective of this work was to perform a comparative analysis of the physiological, biochemical and agronomical parameters of recent and heritage durum wheat cultivars (Triticum durum Desf.) under water-deficit conditions. Five cultivars were grown under irrigated (control) and rainfall (stressed) conditions. Different agro-physiological and biochemical parameters were studied: electrolyte leakage, relative water content, chlorophyll fluorescence, proline, soluble sugars, specific peroxidase activity, yield and drought stress indices. It was revealed that a water deficit increased proline content, electrolyte leakage, soluble sugars and specific peroxidase activity and decreased relative water content, fluorescence and grain yield. According to these parameters and drought stress indices, our investigation indicated that old cultivars are the best-adapted to local conditions and showed characteristics of drought tolerance, while recent cultivars showed more drought susceptibility. Therefore, local cultivars of each country should be kept by farmers and plant breeders to preserve their genetic heritage.

The Physiological Basis of Drought Tolerance in Crop Plants: A Scenario-Dependent Probabilistic Approach

Drought tolerance involves mechanisms operating at different spatial and temporal scales, from rapid stomatal closure to maintenance of crop yield. We review how short-term mechanisms are controlled for stabilizing shoot water potential and how long-term processes have been constrained by evolution or breeding to fit into acclimation strategies for specific drought scenarios. These short- or long-term feedback processes participate in trade-offs between carbon accumulation and the risk of deleterious soil water depletion. Corresponding traits and alleles may therefore have positive or negative effects on crop yield depending on drought scenarios. We propose an approach that analyzes the genetic architecture of traits in phenotyping platforms and of yield in tens of field experiments. A combination of modeling and genomic prediction is then used to estimate the comparative interests of combinations of alleles depending on drought scenarios. Hence, drought tolerance is understood probabilistically by estimating the benefit and risk of each combination of alleles.

High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR

Crop improvement efforts are targeting increased above-ground biomass and radiation-use efficiency as drivers for greater yield. Early ground cover and canopy height contribute to biomass production, but manual measurements of these traits, and in particular above-ground biomass, are slow and labor-intensive, more so when made at multiple developmental stages. These constraints limit the ability to capture these data in a temporal fashion, hampering insights that could be gained from multi-dimensional data. Here we demonstrate the capacity of Light Detection and Ranging (LiDAR), mounted on a lightweight, mobile, ground-based platform, for rapid multi-temporal and non-destructive estimation of canopy height, ground cover and above-ground biomass. Field validation of LiDAR measurements is presented. For canopy height, strong relationships with LiDAR (r2 of 0.99 and root mean square error of 0.017 m) were obtained. Ground cover was estimated from LiDAR using two methodologies: red reflectance image and canopy height. In contrast to NDVI, LiDAR was not affected by saturation at high ground cover, and the comparison of both LiDAR methodologies showed strong association (r2 = 0.92 and slope = 1.02) at ground cover above 0.8. For above-ground biomass, a dedicated field experiment was performed with destructive biomass sampled eight times across different developmental stages. Two methodologies are presented for the estimation of biomass from LiDAR: 3D voxel index (3DVI) and 3D profile index (3DPI). The parameters involved in the calculation of 3DVI and 3DPI were optimized for each sample event from tillering to maturity, as well as generalized for any developmental stage. Individual sample point predictions were strong while predictions across all eight sample events, provided the strongest association with biomass (r2 = 0.93 and r2 = 0.92) for 3DPI and 3DVI, respectively. Given these results, we believe that application of this system will provide new opportunities to deliver improved genotypes and agronomic interventions via more efficient and reliable phenotyping of these important traits in large experiments.

Phenotyping Conservation Agriculture Management Effects on Ground and Aerial Remote Sensing Assessments of Maize Hybrids Performance in Zimbabwe

In the coming decades, Sub-Saharan Africa (SSA) faces challenges to sustainably increase food production while keeping pace with continued population growth. Conservation agriculture (CA) has been proposed to enhance soil health and productivity to respond to this situation. Maize is the main staple food in SSA. To increase maize yields, the selection of suitable genotypes and management practices for CA conditions has been explored using remote sensing tools. They may play a fundamental role towards overcoming the traditional limitations of data collection and processing in large scale phenotyping studies. We present the result of a study in which Red-Green-Blue (RGB) and multispectral indexes were evaluated for assessing maize performance under conventional ploughing (CP) and CA practices. Eight hybrids under different planting densities and tillage practices were tested. The measurements were conducted on seedlings at ground level (0.8 m) and from an unmanned aerial vehicle (UAV) platform (30 m), causing a platform proximity effect on the images resolution that did not have any negative impact on the performance of the indexes. Most of the calculated indexes (Green Area (GA) and Normalized Difference Vegetation Index (NDVI)) were significantly affected by tillage conditions increasing their values from CP to CA. Indexes derived from the RGB-images related to canopy greenness performed better at assessing yield differences, potentially due to the greater resolution of the RGB compared with the multispectral data, although this performance was more precise for CP than CA. The correlations of the multispectral indexes with yield were improved by applying a soil-mask derived from a NDVI threshold with the aim of corresponding pixels with vegetation. The results of this study highlight the applicability of remote sensing approaches based on RGB images to the assessment of crop performance and hybrid choice.

Genome-Wide Association Analyses Identify QTL Hotspots for Yield and Component Traits in Durum Wheat Grown under Yield Potential, Drought, and Heat Stress Environments

Understanding the genetic bases of economically important traits is fundamentally important in enhancing genetic gains in durum wheat. In this study, a durum panel of 208 lines (comprised of elite materials and exotics from the International Maize and Wheat Improvement Center gene bank) were subjected to genome wide association study (GWAS) using 6,211 DArTseq single nucleotide polymorphisms (SNPs). The panel was phenotyped under yield potential (YP), drought stress (DT), and heat stress (HT) conditions for 2 years. Mean yield of the panel was reduced by 72% (to 1.64 t/ha) under HT and by 60% (to 2.33 t/ha) under DT, compared to YP (5.79 t/ha). Whereas, the mean yield of the panel under HT was 30% less than under DT. GWAS identified the largest number of significant marker-trait associations on chromosomes 2A and 2B with p-values 10-06 to 10-03 and the markers from the whole study explained 7-25% variation in the traits. Common markers were identified for stress tolerance indices: stress susceptibility index, stress tolerance, and stress tolerance index estimated for the traits under DT (82 cM on 2B) and HT (68 and 83 cM on 3B; 25 cM on 7A). GWAS of irrigated (YP and HT combined), stressed (DT and HT combined), combined analysis of three environments (YP + DT + HT), and its comparison with trait per se and stress indices identified QTL hotspots on chromosomes 2A (54-70 cM) and 2B (75-82 cM). This study enhances our knowledge about the molecular markers associated with grain yield and its components under different stress conditions. It identifies several marker-trait associations for further exploration and validation for marker-assisted breeding.

Genetic dissection of the relationships between grain yield components by genome-wide association mapping in a collection of tetraploid wheats

Increasing grain yield potential in wheat has been a major target of most breeding programs. Genetic advance has been frequently hindered by negative correlations among yield components that have been often observed in segregant populations and germplasm collections. A tetraploid wheat collection was evaluated in seven environments and genotyped with a 90K SNP assay to identify major and stable quantitative trait loci (QTL) for grain yield per spike (GYS), kernel number per spike (KNS) and thousand-kernel weight (TKW), and to analyse the genetic relationships between the yield components at QTL level. The genome-wide association analysis detected eight, eleven and ten QTL for KNS, TKW and GYS, respectively, significant in at least three environments or two environments and the mean across environments. Most of the QTL for TKW and KNS were found located in different marker intervals, indicating that they are genetically controlled independently by each other. Out of eight KNS QTL, three were associated to significant increases of GYS, while the increased grain number of five additional QTL was completely or partially compensated by decreases in grain weight, thus producing no or reduced effects on GYS. Similarly, four consistent and five suggestive TKW QTL resulted in visible increase of GYS, while seven additional QTL were associated to reduced effects in grain number and no effects on GYS. Our results showed that QTL analysis for detecting TKW or KNS alleles useful for improving grain yield potential should consider the pleiotropic effects of the QTL or the association to other QTLs.

Comparative Performance of Ground vs. Aerially Assessed RGB and Multispectral Indices for Early-Growth Evaluation of Maize Performance under Phosphorus Fertilization

Low soil fertility is one of the factors most limiting agricultural production, with phosphorus deficiency being among the main factors, particularly in developing countries. To deal with such environmental constraints, remote sensing measurements can be used to rapidly assess crop performance and to phenotype a large number of plots in a rapid and cost-effective way. We evaluated the performance of a set of remote sensing indices derived from Red-Green-Blue (RGB) images and multispectral (visible and infrared) data as phenotypic traits and crop monitoring tools for early assessment of maize performance under phosphorus fertilization. Thus, a set of 26 maize hybrids grown under field conditions in Zimbabwe was assayed under contrasting phosphorus fertilization conditions. Remote sensing measurements were conducted in seedlings at two different levels: at the ground and from an aerial platform. Within a particular phosphorus level, some of the RGB indices strongly correlated with grain yield. In general, RGB indices assessed at both ground and aerial levels correlated in a comparable way with grain yield except for indices a* and u*, which correlated better when assessed at the aerial level than at ground level and Greener Area (GGA) which had the opposite correlation. The Normalized Difference Vegetation Index (NDVI) evaluated at ground level with an active sensor also correlated better with grain yield than the NDVI derived from the multispectral camera mounted in the aerial platform. Other multispectral indices like the Soil Adjusted Vegetation Index (SAVI) performed very similarly to NDVI assessed at the aerial level but overall, they correlated in a weaker manner with grain yield than the best RGB indices. This study clearly illustrates the advantage of RGB-derived indices over the more costly and time-consuming multispectral indices. Moreover, the indices best correlated with GY were in general those best correlated with leaf phosphorous content. However, these correlations were clearly weaker than against grain yield and only under low phosphorous conditions. This work reinforces the effectiveness of canopy remote sensing for plant phenotyping and crop management of maize under different phosphorus nutrient conditions and suggests that the RGB indices are the best option.

Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping

BACKGROUND

Accurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments.

RESULTS

In this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images. All methods are compared and evaluated with different environmental conditions and the following criteria: (1) comparison with ground-truth images, (2) variation along a day with changes in ambient illumination, (3) comparison with manual measurements and (4) an estimation of performance along the full life cycle of a wheat canopy.

CONCLUSION

The method described is capable of coping with the environmental challenges faced in field conditions, with high levels of adaptiveness and without the need for adjusting a threshold for each digital image. The proposed method is also an ideal candidate to process a time series of phenotypic information throughout the crop growth acquired in the field. Moreover, the introduced method has an advantage that it is not limited to growth measurements only but can be applied on other applications such as identifying weeds, diseases, stress, etc.

Comparative UAV and Field Phenotyping to Assess Yield and Nitrogen Use Efficiency in Hybrid and Conventional Barley

With the commercialization and increasing availability of Unmanned Aerial Vehicles (UAVs) multiple rotor copters have expanded rapidly in plant phenotyping studies with their ability to provide clear, high resolution images. As such, the traditional bottleneck of plant phenotyping has shifted from data collection to data processing. Fortunately, the necessarily controlled and repetitive design of plant phenotyping allows for the development of semi-automatic computer processing tools that may sufficiently reduce the time spent in data extraction. Here we present a comparison of UAV and field based high throughput plant phenotyping (HTPP) using the free, open-source image analysis software FIJI (Fiji is just ImageJ) using RGB (conventional digital cameras), multispectral and thermal aerial imagery in combination with a matching suite of ground sensors in a study of two hybrids and one conventional barely variety with ten different nitrogen treatments, combining different fertilization levels and application schedules. A detailed correlation network for physiological traits and exploration of the data comparing between treatments and varieties provided insights into crop performance under different management scenarios. Multivariate regression models explained 77.8, 71.6, and 82.7% of the variance in yield from aerial, ground, and combined data sets, respectively.

Linkage mapping aided by de novo genome and transcriptome assembly in Portunus trituberculatus: applications in growth-related QTL and gene identification

A high-resolution genetic linkage map is an essential tool for decoding genetics and genomics in non-model organisms. In this study, a linkage map was constructed for the swimming crab (Portunus trituberculatus) with 10,963 markers; as far as we know, this number of markers has never been achieved in any other crustacean. The linkage map covered 98.85% of the whole genome with a mean marker interval of 0.51 cM. The de novo assembly based on genome and transcriptome sequencing data enabled 2,378 explicit annotated markers to be anchored to the map. Quantitative trait locus (QTL) mapping revealed 10 growth-related QTLs with a phenotypic variance explained (PVE) range of 12.0-35.9. Eight genes identified from the growth-related QTL regions, in particular, RE1-silencing transcription factor and RNA-directed DNA polymerase genes with nonsynonymous substitutions, were considered important growth-related candidate genes. We have demonstrated that linkage mapping aided by de novo assembly of genome and transcriptome sequencing could serve as an important platform for QTL mapping and the identification of trait-related genes.

QTL mapping for nine drought-responsive agronomic traits in bread wheat under irrigated and rain-fed environments

In bread wheat, QTL interval mapping was conducted for nine important drought responsive agronomic traits. For this purpose, a doubled haploid (DH) mapping population derived from Kukri/Excalibur was grown over three years at four separate locations in India, both under irrigated and rain-fed environments. Single locus analysis using composite interval mapping (CIM) allowed detection of 98 QTL, which included 66 QTL for nine individual agronomic traits and 32 QTL, which affected drought sensitivity index (DSI) for the same nine traits. Two-locus analysis allowed detection of 19 main effect QTL (M-QTL) for four traits (days to anthesis, days to maturity, grain filling duration and thousand grain weight) and 19 pairs of epistatic QTL (E-QTL) for two traits (days to anthesis and thousand grain weight). Eight QTL were common in single locus analysis and two locus analysis. These QTL (identified both in single- and two-locus analysis) were distributed on 20 different chromosomes (except 4D). Important genomic regions on chromosomes 5A and 7A were also identified (5A carried QTL for seven traits and 7A carried QTL for six traits). Marker-assisted recurrent selection (MARS) involving pyramiding of important QTL reported in the present study, together with important QTL reported earlier, may be used for improvement of drought tolerance in wheat. In future, more closely linked markers for the QTL reported here may be developed through fine mapping, and the candidate genes may be identified and used for developing a better understanding of the genetic basis of drought tolerance in wheat.

Mapping QTL for Root and Shoot Morphological Traits in a Durum Wheat × T. dicoccum Segregating Population at Seedling Stage

A segregating population of 136 recombinant inbred lines derived from a cross between the durum wheat cv. "Simeto" and the T. dicoccum accession "Molise Colli" was grown in soil and evaluated for a number of shoot and root morphological traits. A total of 17 quantitative trait loci (QTL) were identified for shoot dry weight, number of culms, and plant height and for root dry weight, volume, length, surface area, and number of forks and tips, on chromosomes 1B, 2A, 3A, 4B, 5B, 6A, 6B, and 7B. LODs were 2.1 to 21.6, with percent of explained phenotypic variability between 0.07 and 52. Three QTL were mapped to chromosome 4B, one of which corresponds to the Rht-B1 locus and has a large impact on both shoot and root traits (LOD 21.6). Other QTL that have specific effects on root morphological traits were also identified. Moreover, meta-QTL analysis was performed to compare the QTL identified in the "Simeto" × "Molise Colli" segregating population with those described in previous studies in wheat, with three novel QTL defined. Due to the complexity of phenotyping for root traits, further studies will be helpful to validate these regions as targets for breeding programs for optimization of root function for field performance.

Genome-wide association study for 13 agronomic traits reveals distribution of superior alleles in bread wheat from the Yellow and Huai Valley of China

Bread wheat is a leading cereal crop worldwide. Limited amount of superior allele loci restricted the progress of molecular improvement in wheat breeding. Here, we revealed new allelic variation distribution for 13 yield-related traits in series of genome-wide association studies (GWAS) using the wheat 90K genotyping assay, characterized in 163 bread wheat cultivars. Agronomic traits were investigated in 14 environments at three locations over 3 years. After filtering SNP data sets, GWAS using 20 689 high-quality SNPs associated 1769 significant loci that explained, on average, ~20% of the phenotypic variation, both detected already reported loci and new promising genomic regions. Of these, repetitive and pleiotropic SNPs on chromosomes 6AS, 6AL, 6BS, 5BL and 7AS were significantly linked to thousand kernel weight, for example BS00021705_51 on 6BS and wsnp_Ex_c32624_41252144 on 6AS, with phenotypic variation explained (PVE) of ~24%, consistently identified in 12 and 13 of the 14 environments, respectively. Kernel length-related SNPs were mainly identified on chromosomes 7BS, 6AS, 5AL and 5BL. Plant height-related SNPs on chromosomes 4DS, 6DL, 2DS and 1BL were, respectively, identified in more than 11 environments, with averaged PVE of ~55%. Four SNPs were confirmed to be important genetic loci in two RIL populations. Based on repetivity and PVE, a total of 41 SNP loci possibly played the key role in modulating yield-related traits of the cultivars surveyed. Distribution of superior alleles at the 41 SNP loci indicated that superior alleles were getting popular with time and modern cultivars had integrated many superior alleles, especially for peduncle length- and plant height-related superior alleles. However, there were still 19 SNP loci showing less than percentages of 50% in modern cultivars, suggesting they should be paid more attention to improve yield-related traits of cultivars in the Yellow and Huai wheat region. This study could provide useful information for dissection of yield-related traits and valuable genetic loci for marker-assisted selection in Chinese wheat breeding programme.

Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example

A comprehensive linkage atlas for seed yield in rapeseed. Most agronomic traits of interest for crop improvement (including seed yield) are highly complex quantitative traits controlled by numerous genetic loci, which brings challenges for comprehensively capturing associated markers/genes. We propose that multiple trait interactions underlie complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual quantitative trait loci (QTL) effects more effectively and improve yield predictions. Using a segregating rapeseed (Brassica napus) population, we analyzed a large set of trait data generated in 19 independent experiments to investigate correlations between seed yield and other complex traits, and further identified QTL in this population with a SNP-based genetic bin map. A total of 1904 consensus QTL accounting for 22 traits, including 80 QTL directly affecting seed yield, were anchored to the B. napus reference sequence. Through trait association analysis and QTL meta-analysis, we identified a total of 525 indivisible QTL that either directly or indirectly contributed to seed yield, of which 295 QTL were detected across multiple environments. A majority (81.5%) of the 525 QTL were pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting genetic effects, as well as 31 QTL with minor complementary effects. Implementation of the 525 QTL in genomic prediction models improved seed yield prediction accuracy. Dissecting the genetic and phenotypic interrelationships underlying complex quantitative traits using this method will provide valuable insights for genomics-based crop improvement.

Genetic diversity, population structure and marker-trait associations for agronomic and grain traits in wild diploid wheat Triticum urartu

BACKGROUND

Wild diploid wheat, Triticum urartu (T. urartu) is the progenitor of bread wheat, and understanding its genetic diversity and genome function will provide considerable reference for dissecting genomic information of common wheat.

RESULTS

In this study, we investigated the morphological and genetic diversity and population structure of 238 T. urartu accessions collected from different geographic regions. This collection had 19.37 alleles per SSR locus and its polymorphic information content (PIC) value was 0.76, and the PIC and Nei's gene diversity (GD) of high-molecular-weight glutenin subunits (HMW-GSs) were 0.86 and 0.88, respectively. UPGMA clustering analysis indicated that the 238 T. urartu accessions could be classified into two subpopulations, of which Cluster I contained accessions from Eastern Mediterranean coast and those from Mesopotamia and Transcaucasia belonged to Cluster II. The wide range of genetic diversity along with the manageable number of accessions makes it one of the best collections for mining valuable genes based on marker-trait association. Significant associations were observed between simple sequence repeats (SSR) or HMW-GSs and six morphological traits: heading date (HD), plant height (PH), spike length (SPL), spikelet number per spike (SPLN), tiller angle (TA) and grain length (GL).

CONCLUSIONS

Our data demonstrated that SSRs and HMW-GSs were useful markers for identification of beneficial genes controlling important traits in T. urartu, and subsequently for their conservation and future utilization, which may be useful for genetic improvement of the cultivated hexaploid wheat.

Predictable 'meta-mechanisms' emerge from feedbacks between transpiration and plant growth and cannot be simply deduced from short-term mechanisms

Growth under water deficit is controlled by short-term mechanisms but, because of numerous feedbacks, the combination of these mechanisms over time often results in outputs that cannot be deduced from the simple inspection of individual mechanisms. It can be analysed with dynamic models in which causal relationships between variables are considered at each time-step, allowing calculation of outputs that are routed back to inputs for the next time-step and that can change the system itself. We first review physiological mechanisms involved in seven feedbacks of transpiration on plant growth, involving changes in tissue hydraulic conductance, stomatal conductance, plant architecture and underlying factors such as hormones or aquaporins. The combination of these mechanisms over time can result in non-straightforward conclusions as shown by examples of simulation outputs: 'over production of abscisic acid (ABA) can cause a lower concentration of ABA in the xylem sap ', 'decreasing root hydraulic conductance when evaporative demand is maximum can improve plant performance' and 'rapid root growth can decrease yield'. Systems of equations simulating feedbacks over numerous time-steps result in logical and reproducible emergent properties that can be viewed as 'meta-mechanisms' at plant level, which have similar roles as mechanisms at cell level.

Dissecting the old Mediterranean durum wheat genetic architecture for phenology, biomass and yield formation by association mapping and QTL meta-analysis

Association mapping was used to identify genome regions affecting yield formation, crop phenology and crop biomass in a collection of 172 durum wheat landraces representative of the genetic diversity of ancient local durum varieties from the Mediterranean Basin. The collection was genotyped with 1,149 DArT markers and phenotyped in Spanish northern and southern locations during three years. A total of 245 significant marker trait associations (MTAs) (P<0.01) were detected. Some of these associations confirmed previously identified quantitative trait loci (QTL) and/or candidate genes, and others are reported for the first time here. Eighty-six MTAs corresponded with yield and yield component traits, 70 to phenology and 89 to biomass production. Twelve genomic regions harbouring stable MTAs (significant in three or more environments) were identified, while five and two regions showed specific MTAs for northern and southern environments, respectively. Sixty per cent of MTAs were located on the B genome and 29% on the A genome. The marker wPt-9859 was detected in 12 MTAs, associated with six traits in four environments and the mean across years. To refine QTL positions, a meta-analysis was performed. A total of 477 unique QTLs were projected onto a durum wheat consensus map and were condensed to 71 meta-QTLs and left 13 QTLs as singletons. Sixty-one percent of QTLs explained less than 10% of the phenotypic variance confirming the high genetic complexity of the traits analysed.

Grain quality characteristics and dough rheological properties in Langdon durum-wild emmer wheat chromosome substitution lines under nitrogen and water deficits

BACKGROUND

Wild emmer wheat could serve as a source of novel variation in grain quality and stress resistance for wheat breeding. A set of Triticum durum-T. dicoccoides chromosome substitution lines [LDN(DIC)] and the parental recipient cv. Langdon grown under contrasting water and nitrogen availability in the soil was examined in this study to identify differences in grain quality traits and dough rheological properties.

RESULTS

Significant genotypic variation was found among the materials for studied traits. This variation was also considerably affected by soil treatments and G × E interactions. The substitutions LDN(DIC-1A) and LDN(DIC-1B) showed separate differentiation in the composition of glutenin sub-units. The results indicated that primarily chromosome DIC-6B is stable source of an enhanced grain protein content and advantageous dough rheological properties. Similar features seem to be shown by the substitutions with the DIC-1A, DIC-2A and DIC-6A, but not under nitrogen shortage, when generally a considerable decrease was noticed in the range of genotypic variation in grain quality.

CONCLUSIONS

The substitution lines, particularly those with DIC-6B and DIC-6A and to a lesser extent DIC-1A and DIC-2A, were distinguished by advantageous grain quality traits, mixing properties and dough functionality and appear to be the most promising sources of innovative genes for wheat breeding. © 2016 Society of Chemical Industry.

Identification of Early Represented Gluten Proteins during Durum Wheat Grain Development

The time course of biosynthesis and accumulation of storage proteins in developing grains of durum wheat (Triticum turgidum ssp. durum (Desf.) Husn.) pasta-quality reference cv. Svevo was investigated at the protein level for the first time. Seeds were harvested at key kernel developmental stages, namely, 3 (seed increase 3-fold in size), 5 (kernel development, water-ripe stage), 11 (kernel development, water-ripe stage), 16 (kernel full development, water-ripe stage), 21 (milk-ripe stage), and 30 (dough stage) days postanthesis (dpa). Gliadins and glutenins were fractionated according to their different solubility and individually analyzed after fractionation by reversed-phase high performance liquid chromatography and sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Proteins were identified by liquid chromatography-tandem mass spectrometry of proteolytic peptides. The α- and γ-gliadin were already detected at 3 dpa. The biosynthesis of high molecular mass glutenin Bx7 was slightly delayed (11 dpa). Most of the gluten proteins accumulated rapidly between 11 and 21 dpa, with a minor further increase up to 30 dpa. The expression pattern of gluten proteins in Triticum durum at the early stages of synthesis provides reference data sets for future applications in crop breeding and growth monitoring.

EasyPCC: Benchmark Datasets and Tools for High-Throughput Measurement of the Plant Canopy Coverage Ratio under Field Conditions

Understanding interactions of genotype, environment, and management under field conditions is vital for selecting new cultivars and farming systems. Image analysis is considered a robust technique in high-throughput phenotyping with non-destructive sampling. However, analysis of digital field-derived images remains challenging because of the variety of light intensities, growth environments, and developmental stages. The plant canopy coverage (PCC) ratio is an important index of crop growth and development. Here, we present a tool, EasyPCC, for effective and accurate evaluation of the ground coverage ratio from a large number of images under variable field conditions. The core algorithm of EasyPCC is based on a pixel-based segmentation method using a decision-tree-based segmentation model (DTSM). EasyPCC was developed under the MATLAB^® and R languages; thus, it could be implemented in high-performance computing to handle large numbers of images following just a single model training process. This study used an experimental set of images from a paddy field to demonstrate EasyPCC, and to show the accuracy improvement possible by adjusting key points (e.g., outlier deletion and model retraining). The accuracy (R² = 0.99) of the calculated coverage ratio was validated against a corresponding benchmark dataset. The EasyPCC source code is released under GPL license with benchmark datasets of several different crop types for algorithm development and for evaluating ground coverage ratios.

Genome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to Central Europe

Genome-wide association mapping as well as marker- and haplotype-based genome-wide selection unraveled a complex genetic architecture of grain yield with absence of large effect QTL and presence of local epistatic effects. The genetic architecture of grain yield determines to a large extent the optimum design of genomic-assisted wheat breeding programs. The main goal of our study was to examine the potential and limitations to dissect the genetic architecture of grain yield in wheat using a large experimental data set. Our study was based on phenotypic information and genomic data of 13,901 SNPs of a diverse set of 3816 elite wheat lines adapted to Central Europe. We applied genome-wide association mapping based on experimental and simulated data sets and performed marker- and haplotype-based genomic prediction. Computer simulations revealed for our mapping population a high power to detect QTL, even if they individually explained only 2.5% of the genetic variation. Despite this, we found no stable marker-trait associations when validating in independent subsets. A two-dimensional scan for marker-marker interactions indicated presence of local epistasis which was further supported by improved prediction abilities when shifting from marker- to haplotype-based genome-wide prediction approaches. We observed that marker effects estimated using genome-wide prediction approaches strongly varied across years albeit resulting in high prediction abilities. Thus, our results suggested that the prediction accuracy of genomic selection in wheat is mainly driven by relatedness rather than by exploiting knowledge of the genetic architecture.

Genetic Dissection of Root Morphological Traits Related to Nitrogen Use Efficiency in Brassica napus L. under Two Contrasting Nitrogen Conditions

As the major determinant for nutrient uptake, root system architecture (RSA) has a massive impact on nitrogen use efficiency (NUE). However, little is known the molecular control of RSA as related to NUE in rapeseed. Here, a rapeseed recombinant inbred line population (BnaZNRIL) was used to investigate root morphology (RM, an important component for RSA) and NUE-related traits under high-nitrogen (HN) and low-nitrogen (LN) conditions by hydroponics. Data analysis suggested that RM-related traits, particularly root size had significantly phenotypic correlations with plant dry biomass and N uptake irrespective of N levels, but no or little correlation with N utilization efficiency (NUtE), providing the potential to identify QTLs with pleiotropy or specificity for RM- and NUE-related traits. A total of 129 QTLs (including 23 stable QTLs, which were repeatedly detected at least two environments or different N levels) were identified and 83 of them were integrated into 22 pleiotropic QTL clusters. Five RM-NUE, ten RM-specific and three NUE-specific QTL clusters with same directions of additive-effect implied two NUE-improving approaches (RM-based and N utilization-based directly) and provided valuable genomic regions for NUE improvement in rapeseed. Importantly, all of four major QTLs and most of stable QTLs (20 out of 23) detected here were related to RM traits under HN and/or LN levels, suggested that regulating RM to improve NUE would be more feasible than regulating N efficiency directly. These results provided the promising genomic regions for marker-assisted selection on RM-based NUE improvement in rapeseed.

Phenomic Approaches and Tools for Phytopathologists

Plant phenomics approaches aim to measure traits such as growth, performance, and composition of plants using a suite of noninvasive technologies. The goal is to link phenotypic traits to the genetic information for particular genotypes, thus creating the bridge between the phenome and genome. Application of sensing technologies for detecting specific phenotypic reactions occurring during plant-pathogen interaction offers new opportunities for elucidating the physiological mechanisms that link pathogen infection and disease symptoms in the host, and also provides a faster approach in the selection of genetic material that is resistant to specific pathogens or strains. Appropriate phenomics methods and tools may also allow presymptomatic detection of disease-related changes in plants or to identify changes that are not visually apparent. This review focuses on the use of sensor-based phenomics tools in plant pathology such as those related to digital imaging, chlorophyll fluorescence imaging, spectral imaging, and thermal imaging. A brief introduction is provided for less used approaches like magnetic resonance, soft x-ray imaging, ultrasound, and detection of volatile compounds. We hope that this concise review will stimulate further development and use of tools for automatic, nondestructive, and high-throughput phenotyping of plant-pathogen interaction.

Genetic Diversity and Association Mapping for Agromorphological and Grain Quality Traits of a Structured Collection of Durum Wheat Landraces Including subsp. durum, turgidum and diccocon

Association mapping was performed for 18 agromorphological and grain quality traits in a set of 183 Spanish landraces, including subspecies durum, turgidum and dicoccon, genotyped with 749 DArT (Diversity Array Technology) markers. Large genetic and phenotypic variability was detected, being the level of diversity among the chromosomes and genomes heterogeneous, and sometimes complementary, among subspecies. Overall, 356 were monomorphic in at least one subspecies, mainly in dicoccon, and some of them coincidental between subspecies, especially between turgidum and dicoccon. Several of those fixed markers were associated to plant responses to environmental stresses or linked to genes subjected to selection during tetraploid wheat domestication process. A total of 85 stable MTAs (marker–trait associations) have been identified for the agromorphological and quality parameters, some of them common among subspecies and others subspecies-specific. For all the traits, we have found MTAs explaining more than 10% of the phenotypic variation in any of the three subspecies. The number of MTAs on the B genome exceeded that on the A genome in subsp. durum, equalled in turgidum and was below in dicoccon. The validation of several adaptive and quality trait MTAs by combining the association mapping with an analysis of the signature of selection, identifying the putative gene function of the marker, or by coincidences with previous reports, showed that our approach was successful for the detection of MTAs and the high potential of the collection to identify marker–trait associations. Novel MTAs not previously reported, some of them subspecies specific, have been described and provide new information about the genetic control of complex traits.