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Genetic adaptation of soybean phenological stages to high‐latitude long‐day environments is the major pre‐requisite for enhancing regional plant‐based protein production. Both E‐genes controlling flowering and growing environment determine timing of flowering and maturity, and E‐gene composition might differ between Chinese and European soybeans bearing the potential for improving adaptability. Therefore, 140 early maturity elite soybean cultivars of either Chinese or European origin were genotyped for the E1 to E4 flowering loci, and genotypes were tested across 17 European environments spanning a latitude range from 45 to 52°N in order to determine effects of various E‐allele combinations. Differences in E‐allele composition between Chinese and European cultivars were largest for the loci E1 and E3. Wild‐type alleles significantly delayed flowering, and effects of particular E‐alleles were depending on geographic latitude. Consequently, photoperiod‐insensitive E‐haplotypes carrying several non‐functional alleles proved to be suitable for cultivation in higher latitudes, whereas photoperiod‐sensitive late‐maturity E‐haplotypes are adapted to lower latitudes only. Thus, breeding for new E‐haplotypes through combining Chinese and European alleles could enhance the potential for further soybean adaptation to northern growing regions. [ABSTRACT FROM AUTHOR]

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.

BackgroundSunflower belongs to the largest plant family on earth, the genomically poorly explored Compositae. Downy mildew Plasmopara halstedii (Farlow) Berlese & de Toni is one of the major diseases of cultivated sunflower (Helianthus annuus L.). In the search for new sources of downy mildew resistance, the locus Pl(ARG)on linkage group 1 (LG1) originating from H. argophyllus is promising since it confers resistance against all known races of the pathogen. However, the mapping resolution in the Pl(ARG) region is hampered by significantly suppressed recombination and by limited availability of polymorphic markers. Here we examined a strategy developed for the enrichment of molecular markers linked to this specific genomic region. We combined bulked segregant analysis (BSA) with next-generation sequencing (NGS) and de novo assembly of the sunflower transcriptome for single nucleotide polymorphism (SNP) discovery in a sequence resource combining reads originating from two sunflower species, H. annuus and H. argophyllus.ResultsA computational pipeline developed for SNP calling and pattern detection identified 219 candidate genes. For a proof of concept, 42 resistance gene-like sequences were subjected to experimental SNP validation. Using a high-resolution mapping population, 12 SNP markers were mapped to LG1. We successfully verified candidate sequences either co-segregating with or closely flanking Pl(ARG).ConclusionsThis study is the first successful example to improve bulked segregant analysis with de novo transcriptome assembly using next generation sequencing. The BSTA pipeline we developed provides a useful guide for similar studies in other non-model organisms. Our results demonstrate this method is an efficient way to enrich molecular markers and to identify candidate genes in a specific mapping interval.