Multiple-line cross QTL mapping for biomass yield and plant height in triticale (× Triticosecale Wittmack)

Exploiting Rye in Wheat Quality Breeding: The Case of Arabinoxylan Content

Rye (Secale cereale subsp. cereale L.) has long been exploited as a valuable alternative genetic resource in wheat (Triticum aestivum L.) breeding. Indeed, the introgression of rye genetic material led to significant breakthroughs in the improvement of disease and pest resistance of wheat, as well as a few agronomic traits. While such traits remain a high priority in cereal breeding, nutritional aspects of grain crops are coming under the spotlight as consumers become more conscious about their dietary choices and the food industry strives to offer food options that meet their demands. To address this new challenge, wheat breeding can once again turn to rye to look for additional genetic variation. A nutritional aspect that can potentially greatly benefit from the introgression of rye genetic material is the dietary fibre content of flour. In fact, rye is richer in dietary fibre than wheat, especially in terms of arabinoxylan content. Arabinoxylan is a major dietary fibre component in wheat and rye endosperm flours, and it is associated with a variety of health benefits, including normalisation of glycaemic levels and promotion of the gut microbiota. Thus, it is a valuable addition to the human diet, and it can represent a novel target for wheat-rye introgression breeding.

The Genome Regions Associated with Abiotic and Biotic Stress Tolerance, as Well as Other Important Breeding Traits in Triticale

This review article presents the greatest challenges in modern triticale breeding. Genetic maps that were developed and described thus far, together with the quantitative trait loci and candidate genes linked to important traits are also described. The most important part of this review is dedicated to a winter triticale mapping population based on doubled haploid lines obtained from a cross of the cultivars 'Hewo' and 'Magnat'. Many research studies on this population have focused on the analysis of quantitative trait loci regions associated with abiotic (drought and freezing) and biotic (pink snow mold and powdery mildew) stress tolerance as well as related to other important breeding traits such as stem length, plant height, spike length, number of the productive spikelets per spike, number of grains per spike, and thousand kernel weight. In addition, candidate genes located among these regions are described in detail. A comparison analysis of all of these results revealed the location of common quantitative trait loci regions on the rye chromosomes 4R, 5R, and 6R, with a particular emphasis on chromosome 5R. Described here are the candidate genes identified in the above genome regions that may potentially play an important role in the analysis of trait expression. Nevertheless, these results should guide further research using molecular methods of gene identification and it is worth extending the research to other mapping populations. The article is also a review of research led by other authors on the triticale tolerance to the most current stress factors appearing in the breeding.

Genotyping-by-sequencing and genome-wide association study reveal genetic diversity and loci controlling agronomic traits in triticale

The genetic diversity and loci underlying agronomic traits were analyzed by the reads coverage and genome-wide association study based genotyping-by-sequencing in a diverse population consisting of 199 accessions. Triticale (× Triticosecale Wittmack) is an economically important grain forage and energy crop planted worldwide for its high biomass. Little is known about the genetic diversity and loci underlying agronomic traits in triticale. We performed genotyping-by-sequencing of 199 cultivars and mapped reads to the A, B, D, and R genomes for karyotype analysis. These cultivars could mostly be grouped into five types. Some chromosome abnormalities occurred with high frequency, such as 2D (2R) substitution, deletion of the long arm of chromosome 2D or the short arm of 5R, and translocation of the long arms of 7D/7A, the short arms of 6D/6A, or the long arms of 1D/1A. We chose only widely planted hexaploid triticale cultivars (153) for genome-wide association study. These cultivars could be divided into nine distinct groups, and the linkage disequilibrium decay was 25.4 kb in this population. We identified 253 significant marker-trait associations (MTAs) on 20 chromosomes, except 7R. Twenty-one reliable MTAs were identified repeatedly over two environments. We predicted 16 putative candidate genes involved in plant growth and development using the genome sequences of wheat and rye. These results provide a basis for understanding the genetic mechanisms of agronomic traits and will benefit the breeding of improved hexaploid triticale.

Identification and Fine-Mapping of Quantitative Trait Loci Controlling Plant Height in Central European Winter Triticale (×Triticosecale Wittmack)

The quantitatively inherited trait plant height is routinely evaluated in triticale breeding programs as it substantially influences lodging and disease susceptibility, is a main contributor to biomass yield, and is required to improve hybrid seed production by fine-tuning plant height in the female and male parental pools in hybrid breeding programs. In this study, we evaluated a panel of 846 diverse Central European triticale genotypes to dissect the genetic architecture underlying plant height by genome-wide association mapping. This revealed three medium- to large-effect QTL on chromosomes 5A, 4B, and 5R. Genetic and physical fine-mapping of the putative QTL revealed that the QTL on chromosome 5R most likely corresponds to Ddw1 and that the QTL on chromosome 5A is likely to be Rht12. Furthermore, we observed a temporal trend in registered cultivars with a decreasing plant height during the past decades, accompanied by an increasing use of the height-reducing alleles at the identified QTL. In summary, our results shed new light on the genetic control of plant height in triticale and open new avenues for future improvement by breeding.

Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods

BACKGROUND

To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture.

METHODS

The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models.

RESULTS

The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model.

CONCLUSIONS

The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.

Applications of UAS in Crop Biomass Monitoring: A Review

Biomass is an important indicator for evaluating crops. The rapid, accurate and nondestructive monitoring of biomass is the key to smart agriculture and precision agriculture. Traditional detection methods are based on destructive measurements. Although satellite remote sensing, manned airborne equipment, and vehicle-mounted equipment can nondestructively collect measurements, they are limited by low accuracy, poor flexibility, and high cost. As nondestructive remote sensing equipment with high precision, high flexibility, and low-cost, unmanned aerial systems (UAS) have been widely used to monitor crop biomass. In this review, UAS platforms and sensors, biomass indices, and data analysis methods are presented. The improvements of UAS in monitoring crop biomass in recent years are introduced, and multisensor fusion, multi-index fusion, the consideration of features not directly related to monitoring biomass, the adoption of advanced algorithms and the use of low-cost sensors are reviewed to highlight the potential for monitoring crop biomass with UAS. Considering the progress made to solve this type of problem, we also suggest some directions for future research. Furthermore, it is expected that the challenge of UAS promotion will be overcome in the future, which is conducive to the realization of smart agriculture and precision agriculture.

Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing

Yield evaluation of breeding lines is the key to successful release of cultivars, which is becoming a serious issue due to soil heterogeneity in enlarged field tests. This study aimed at establishing plot-yield prediction models using unmanned aerial vehicle (UAV)-based hyperspectral remote sensing for yield-selection in large-scale soybean breeding programs. Three sets of soybean breeding lines (1103 in total) were tested in blocks-in-replication experiments for plot yield and canopy spectral reflectance on 454~950 nm bands at different growth stages using a UAV-based hyperspectral spectrometer (Cubert UHD185 Firefly). The four elements for plot-yield prediction model construction were studied respectively and concluded as: the suitable reflectance-sampling unit-size in a plot was its 20%–80% central part; normalized difference vegetation index (NDVI) and ration vegetation index (RVI) were the best combination of vegetation indices; the initial seed-filling stage (R5) was the best for a single stage prediction, while another was the best combination for a two growth-stage prediction; and multi-variate linear regression was suitable for plot-yield prediction. In model establishment for each material-set, a random half was used for modelling and another half for verification. Twenty-one two growth-stage two vegetation-index prediction models were established and compared for their modelling coefficient of determination (RM2) and root mean square error of the model (RMSEM), verification RV2 and RMSEV, and their sum RS2 and RMSES. Integrated with the coincidence rate between the model predicted and the practical yield-selection results, the models, MA1-2, MA4-2 and MA6-2, with coincidence rates of 56.8%, 58.5% and 52.4%, respectively, were chosen for yield-prediction in yield-test nurseries. The established model construction elements and methods can be used as local models for pre-harvest yield-selection and post-harvest integrated yield-selection in advanced breeding nurseries as well as yield potential prediction in plant-derived-line nurseries. Furthermore, multiple models can be used jointly for plot-yield prediction in soybean breeding programs.

Gene Expression Profiling and Fine Mapping Identifies a Gibberellin 2-Oxidase Gene Co-segregating With the Dominant Dwarfing Gene Ddw1 in Rye (Secale cereale L.)

The gibberellin (GA)-sensitive dwarfing gene Ddw1 provides an opportunity to genetically reduce plant height in rye. Genetic analysis in a population of recombinant inbred lines confirmed a monogenetic dominant inheritance of Ddw1. Significant phenotypic differences in PH between homo- and heterozygotic genotypes indicate an incomplete dominance of Ddw1. De novo transcriptome sequencing of Ddw1 mutant as well as tall genotypes resulted in 113,547 contigs with an average length of 318 bp covering 36.18 Mbp rye DNA. A hierarchical cluster analysis based on individual groups of rye homologs of functionally characterized rice genes controlling morphological or physiological traits including plant height, flowering time, and source activity identified the gene expression profile of stems at the begin of heading to most comprehensively mirror effects of Ddw1. Genome-wide expression profiling identified 186 transcripts differentially expressed between semi-dwarf and tall genotypes in stems. In total, 29 novel markers have been established and mapped to a 27.2 cM segment in the distal part of the long arm of chromosome 5R. Ddw1 could be mapped within a 0.4 cM interval co-segregating with a marker representing the C20-GA2-oxidase gene ScGA2ox12, that is up-regulated in stems of Ddw1 genotypes. The increased expression of ScGA2ox12 observed in semi-dwarf rye as well as structural alterations in transcript sequences associated with the ScGA2ox12 gene implicate, that Ddw1 is a dominant gain-of-function mutant. Integration of the target interval in the wheat reference genome sequence indicated perfect micro-colinearity between the Ddw1 locus and a 831 kb segment on chromosome 5A, which resides inside of a 11.21 Mb interval carrying the GA-sensitive dwarfing gene Rht12 in wheat. The potential of Ddw1 as a breeder's option to improve lodging tolerance in rye is discussed.

Effect of Dwarfing Gene Ddw1 on Height and Agronomic Traits in Spring Triticale in Greenhouse and Field Experiments in a Non-Black Earth Region of Russia

Triticale is a relatively new crop which still possesses serious drawbacks that can be significantly improved by breeding. The dwarfing genes proved to be very useful in the development of new lodging resistant and productive cultivars of winter triticale. The aim of our research was to assess the effect of the Ddw1 dwarfing gene from rye on the agronomic valuable traits in spring triticale. The Ddw1 effect was studied in the greenhouse experiment in segregating the F_2:3 population and in the field of F_3:4 and F_4:5 families derived from crossing winter triticale ‘Hongor’ (Ddw1Ddw1) and spring triticale ‘Dublet’ (ddw1ddw1). As a result, in all three generations, a strong decrease in plant height was demonstrated that was accompanied by a decrease in grain weight per spike and 1000-grain weight. In field experiments, a decrease in spike length and increase in spike density and delay in flowering and heading were observed. As a result of decrease in culm vegetative weight due to Ddw1, the harvest index measured in F_4:5 increased. The spike fertility and number of grains were not affected by Ddw1. The comparison of Ddw1 in rye, winter, and spring triticale, and the possible role of Ddw1 in improving spring triticale are discussed.

Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data

BACKGROUND

Above-ground biomass (AGB) is a basic agronomic parameter for field investigation and is frequently used to indicate crop growth status, the effects of agricultural management practices, and the ability to sequester carbon above and below ground. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, which makes large-area, long-term measurements challenging and time consuming. However, with the diversity of platforms and sensors and the improvements in spatial and spectral resolution, remote sensing is now regarded as the best technical means for monitoring and estimating AGB over large areas.

RESULTS

In this study, we used structural and spectral information provided by remote sensing from an unmanned aerial vehicle (UAV) in combination with machine learning to estimate maize biomass. Of the 14 predictor variables, six were selected to create a model by using a recursive feature elimination algorithm. Four machine-learning regression algorithms (multiple linear regression, support vector machine, artificial neural network, and random forest) were evaluated and compared to create a suitable model, following which we tested whether the two sampling methods influence the training model. To estimate the AGB of maize, we propose an improved method for extracting plant height from UAV images and a volumetric indicator (i.e., BIOVP). The results show that (1) the random forest model gave the most balanced results, with low error and a high ratio of the explained variance for both the training set and the test set. (2) BIOVP can retain the largest strength effect on the AGB estimate in four different machine learning models by using importance analysis of predictors. (3) Comparing the plant heights calculated by the three methods with manual ground-based measurements shows that the proposed method increased the ratio of the explained variance and reduced errors.

CONCLUSIONS

These results lead us to conclude that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB. This work suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.

High-Throughput Phenotyping of Bioethanol Potential in Cereals Using UAV-Based Multi-Spectral Imagery

Bioethanol production obtained from cereal straw has aroused great interest in recent years, which has led to the development of breeding programs to improve the quality of lignocellulosic material in terms of the biomass and sugar content. This process requires the analysis of genotype-phenotype relationships, and although genotyping tools are very advanced, phenotypic tools are not usually capable of satisfying the massive evaluation that is required to identify potential characters for bioethanol production in field trials. However, unmanned aerial vehicle (UAV) platforms have demonstrated their capacity for efficient and non-destructive acquisition of crop data with an application in high-throughput phenotyping. This work shows the first evaluation of UAV-based multi-spectral images for estimating bioethanol-related variables (total biomass dry weight, sugar release, and theoretical ethanol yield) of several accessions of wheat, barley, and triticale (234 cereal plots). The full procedure involved several stages: (1) the acquisition of multi-temporal UAV images by a six-band camera along different crop phenology stages (94, 104, 119, 130, 143, 161, and 175 days after sowing), (2) the generation of ortho-mosaicked images of the full field experiment, (3) the image analysis with an object-based (OBIA) algorithm and the calculation of vegetation indices (VIs), (4) the statistical analysis of spectral data and bioethanol-related variables to predict a UAV-based ranking of cereal accessions in terms of theoretical ethanol yield. The UAV-based system captured the high variability observed in the field trials over time. Three VIs created with visible wavebands and four VIs that incorporated the near-infrared (NIR) waveband were studied, obtaining that the NIR-based VIs were the best at estimating the crop biomass, while the visible-based VIs were suitable for estimating crop sugar release. The temporal factor was very helpful in achieving better estimations. The results that were obtained from single dates [i.e., temporal scenario 1 (TS-1)] were always less accurate for estimating the sugar release than those obtained in TS-2 (i.e., averaging the values of each VI obtained during plant anthesis) and less accurate for estimating the crop biomass and theoretical ethanol yield than those obtained in TS-3 (i.e., averaging the values of each VI obtained during full crop development). The highest correlation to theoretical ethanol yield was obtained with the normalized difference vegetation index (R ² = 0.66), which allowed to rank the cereal accessions in terms of potential for bioethanol production.

Triticale Improvement for Forage and Cover Crop Uses in the Southern Great Plains of the United States

Triticale (×Triticosecale Wittmack) is a man-made species developed by crossing wheat (Triticum spp.) and rye (Secale cereale L.). It incorporates favorable alleles from both progenitor species (wheat and rye), enabling adaptation to environments that are less favorable for wheat yet providing better biomass yield and forage quality. Triticale has huge potential for both grain and forage production, though research to improve the crop for better adaptation and grain quality is lagging behind that of other small grains. It is also gaining popularity as a cover crop to improve soil health and reduce nutrient leaching. Because of its genetic and flower structure, triticale is suitable for both line and hybrid breeding methods. Advances in the areas of molecular biology and the wealth of genomic resources from both wheat and rye can be exploited for triticale improvement. Gene mapping and genomic selection will facilitate triticale breeding by increasing selection precision and reducing time and cost. The objectives of this review are to summarize current triticale production status, breeding, and genetics research achievements and to highlight gaps for future research.

Populations of doubled haploids for genetic mapping in hexaploid winter triticale

To create a framework for genetic dissection of hexaploid triticale, six populations of doubled haploid (DH) lines were developed from pairwise hybrids of high-yielding winter triticale cultivars. The six populations comprise between 97 and 231 genotyped DH lines each, totaling 957 DH lines. A consensus genetic map spans 4593.9 cM is composed of 1576 unique DArT markers. The maps reveal several structural rearrangements in triticale genomes. In preliminary tests of the populations and maps, markers specific to wheat segments of the engineered rye chromosome 1R (RM1B) were identified. Example QTL mapping of days to heading in cv. Krakowiak revealed loci on chromosomes 2BL and 2R responsible for extended vernalization requirement, and candidate genes were identified. The material is available to all parties interested in triticale genetics.

Adult plant development in triticale (× triticosecale wittmack) is controlled by dynamic genetic patterns of regulation

Many biologically and agronomically important traits are dynamic and show temporal variation. In this study, we used triticale (× Triticosecale Wittmack) as a model crop to assess the genetic dynamics underlying phenotypic plasticity of adult plant development. To this end, a large mapping population with 647 doubled haploid lines derived from four partially connected families from crosses among six parents was scored for developmental stage at three different time points. Using genome-wide association mapping, we identified main effect and epistatic quantitative trait loci (QTL) at all three time points. Interestingly, some of these QTL were identified at all time points, whereas others appear to only contribute to the genetic architecture at certain developmental stages. Our results illustrate the temporal contribution of QTL to the genetic control of adult plant development and more generally, the temporal genetic patterns of regulation that underlie dynamic traits.

Genetic architecture of winter hardiness and frost tolerance in triticale

Abiotic stress experienced by autumn-sown crops during winter is of great economic importance as it can have a severe negative impact on yield. In this study, we investigated the genetic architecture of winter hardiness and frost tolerance in triticale. To this end, we used a large mapping population of 647 DH lines phenotyped for both traits in combination with genome-wide marker data. Employing multiple-line cross QTL mapping, we identified nine main effect QTL for winter hardiness and frost tolerance of which six were overlapping between both traits. Three major QTL were identified on chromosomes 5A, 1B and 5R. In addition, an epistasis scan revealed the contribution of epistasis to the genetic architecture of winter hardiness and frost tolerance in triticale. Taken together, our results show that winter hardiness and frost tolerance are complex traits that can be improved by phenotypic selection, but also that genomic approaches hold potential for a knowledge-based improvement of these important traits in elite triticale germplasm.

Genetic dynamics underlying phenotypic development of biomass yield in triticale

BACKGROUND

The nature of dynamic traits with their phenotypic plasticity suggests that they are under the control of a dynamic genetic regulation. We employed a precision phenotyping platform to non-invasively assess biomass yield in a large mapping population of triticale at three developmental stages.

RESULTS

Using multiple-line cross QTL mapping we identified QTL for each of these developmental stages which explained a considerable proportion of the genotypic variance. Some QTL were identified at each developmental stage and thus contribute to biomass yield throughout the studied developmental phases. Interestingly, we also observed QTL that were only identified for one or two of the developmental stages illustrating a temporal contribution of these QTL to the trait. In addition, epistatic QTL were detected and the epistatic interaction landscape was shown to dynamically change with developmental progression.

CONCLUSIONS

In summary, our results reveal the temporal dynamics of the genetic architecture underlying biomass accumulation in triticale and emphasize the need for a temporal assessment of dynamic traits.

Mapping dynamic QTL for plant height in triticale

BACKGROUND

Plant height is a prime example of a dynamic trait that changes constantly throughout adult development. In this study we utilised a large triticale mapping population, comprising 647 doubled haploid lines derived from 4 families, to phenotype for plant height by a precision phenotyping platform at multiple time points.

RESULTS

Using multiple-line cross QTL mapping we identified main effect and epistatic QTL for plant height for each of the time points. Interestingly, some QTL were detected at all time points whereas others were specific to particular developmental stages. Furthermore, the contribution of the QTL to the genotypic variance of plant height also varied with time as exemplified by a major QTL identified on chromosome 6A.

CONCLUSIONS

Taken together, our results in the small grain cereal triticale reveal the importance of considering temporal genetic patterns in the regulation of complex traits such as plant height.