To achieve the food and energy security of an increasing World population likely to exceed nine billion by 2050 represents a major challenge for plant breeding. Our ability to measure traits under field conditions has improved little over the last decades and currently constitutes a major bottleneck in crop improvement. This work describes the development of a tractor-pulled multi-sensor phenotyping platform for small grain cereals with a focus on the technological development of the system. Various optical sensors like light curtain imaging, 3D Time-of-Flight cameras, laser distance sensors, hyperspectral imaging as well as color imaging are integrated into the system to collect spectral and morphological information of the plants. The study specifies: the mechanical design, the system architecture for data collection and data processing, the phenotyping procedure of the integrated system, results from field trials for data quality evaluation, as well as calibration results for plant height determination as a quantified example for a platform application. Repeated measurements were taken at three developmental stages of the plants in the years 2011 and 2012 employing triticale (×Triticosecale Wittmack L.) as a model species. The technical repeatability of measurement results was high for nearly all different types of sensors which confirmed the high suitability of the platform under field conditions. The developed platform constitutes a robust basis for the development and calibration of further sensor and multi-sensor fusion models to measure various agronomic traits like plant moisture content, lodging, tiller density or biomass yield, and thus, represents a major step towards widening the bottleneck of non-destructive phenotyping for crop improvement and plant genetic studies.

Key message: A simple and rapid speed breeding system was developed for short-day crops that enables up to five generations per year using LED lighting systems that allow very specific adjustments regarding light intensity and quality. Plant breeding is a key element for future agricultural production that needs to cope with a growing human population and climate change. However, the process of developing suitable cultivars is time-consuming, not least because of the long generation times of crops. Recently, speed breeding has been introduced for long-day crops, but a similar protocol for short-day crops is lacking to date. In this study, we present a speed breeding protocol based on light-emitting diodes (LEDs) that allow to modify light quality, and exemplarily demonstrate its effectiveness for the short-day crops soybean (Glycine max), rice (Oryza sativa) and amaranth (Amaranthus spp.). Adjusting the photoperiod to 10 h and using a blue-light enriched, far-red-deprived light spectrum facilitated the growth of short and sturdy soybean plants that flowered ~ 23 days after sowing and matured within 77 days, thus allowing up to five generations per year. In rice and amaranth, flowering was achieved ~ 60 and ~ 35 days after sowing, respectively. Interestingly, the use of far-red light advanced flowering by 10 and 20 days in some amaranth and rice genotypes, respectively, but had no impact on flowering in soybeans, highlighting the importance of light quality for speed breeding protocols. Taken together, our short-day crops’ speed breeding protocol enables several generations per year using crop-specific LED-based lighting regimes, without the need of tissue culture tools such as embryo rescue. Moreover, this approach can be readily applied to a multi-storey 96-cell tray-based system to integrate speed breeding with genomics, toward a higher improvement rate in breeding.

Background: Triticale is adapted to a wide range of abiotic stress conditions, is an important high-quality feed stock and produces similar grain yield but more biomass compared to other crops. Modern genomic approaches aimed at enhancing breeding progress in cereals require high-quality genetic linkage maps. Consensus maps are genetic maps that are created by a joint analysis of the data from several segregating populations and different approaches are available for their construction. The phenomenon that alleles at a locus deviate from the Mendelian expectation has been defined as segregation distortion. The study of segregation distortion is of particular interest in doubled haploid (DH) populations due to the selection pressure exerted on the plants during the process of their establishment. Results: The final consensus map, constructed out of six segregating populations derived from nine parental lines, incorporated 2555 DArT markers mapped to 2602 loci (1929 unique). The map spanned 2309.9 cM with an average number of 123.9 loci per chromosome and an average marker density of one unique locus every 1.2 cM. The R genome showed the highest marker coverage followed by the B genome and the A genome. In general, locus order was well maintained between the consensus linkage map and the component maps. However, we observed several groups of loci for which the colinearity was slightly uneven. Among the 2602 loci mapped on the consensus map, 886 showed distorted segregation in at least one of the individual mapping populations. In several DH populations derived by androgenesis, we found chromosomes (2B, 3B, 1R, 2R, 4R and 7R) containing regions where markers exhibited a distorted segregation pattern. In addition, we observed evidence for segregation distortion between pairs of loci caused either by a predominance of parental or recombinant genotypes. Conclusions: We have constructed a reliable, high-density DArT marker consensus genetic linkage map as a basis for genomic approaches in triticale research and breeding, for example for multiple-line cross QTL mapping experiments. The results of our study exemplify the tremendous impact of different DH production techniques on allele frequencies and segregation distortion covering whole chromosomes.

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.

The efficiency of breeding programs depends on the ability to screen large numbers of individuals. For complex traits like yield, this can be assisted by genomic selection, which is based on estimating breeding values with genome‐wide marker data. Here, we evaluate phenomic prediction, which, similar to its genomic counterpart, aims to predict the performance of untested individuals but using near‐infrared spectroscopy (NIRS) data. In a large panel of 944 soybean [ Glycine max (L.) Merr.] recombinant inbred lines phenotyped for seed yield, thousand‐seed weight, and plant height at three locations, we demonstrate that the phenomic predictive abilities are high and comparable with those obtained by genomic prediction. We found that ridge regression best linear unbiased prediction performs well for phenomic prediction and that the number of wavelengths can be reduced without a decrease in predictive ability. For prediction at different locations, NIRS data from a single location can be used. However, NIRS data from different environments, like years, should be connected by common genotypes in training and prediction sets. Phenomic prediction appears to be less susceptible to relatedness between individuals in training and prediction sets than genomic prediction, as generally half‐sib but also unrelated families achieved high predictive abilities. Moreover, for the same training set sizes phenomic prediction resulted in higher predictive abilities compared to genomic prediction. Phenomic prediction can be applied at different stages in a breeding program, and collectively our results highlight the potential of this approach to increase genetic gain in plant breeding.