Significantly improved crop varieties are urgently needed to feed the rapidly growing human population under changing climates. While genome sequence information and excellent genomic tools are in place for major crop species, the systematic quantification of phenotypic traits or components thereof in a high-throughput fashion remains an enormous challenge. In order to help bridge the genotype to phenotype gap, we developed a comprehensive framework for highthroughput phenotype data analysis in plants, which enables the extraction of an extensive list of phenotypic traits from nondestructive plant imaging over time. As a proof of concept, we investigated the phenotypic components of the drought responses of 18 different barley (Hordeum vulgare) cultivars during vegetative growth. We analyzed dynamic properties of trait expression over growth time based on 54 representative phenotypic features. The data are highly valuable to understand plant development and to further quantify growth and crop performance features. We tested various growth models to predict plant biomass accumulation and identified several relevant parameters that support biological interpretation of plant growth and stress tolerance. These image-based traits and model-derived parameters are promising for subsequent genetic mapping to uncover the genetic basis of complex agronomic traits. Taken together, we anticipate that the analytical framework and analysis results presented here will be useful to advance our views of phenotypic trait components underlying plant development and their responses to environmental cues.

Association-based trait mapping is an innovative methodology based on linkage disequilibrium. Studies in plants, especially in cereals, are rare. A genome-wide association study of wheat is reported, in which a large number of diversity array technology markers was used to genotype a winter wheat core collection of 96 accessions. The germplasm was structured into two sub-populations. Twenty agronomic traits were measured in field trials conducted over up to eight growing seasons. Association analysis was performed with two different approaches, the general linear model incorporating the Q-matrix only and the mixed linear model including also the kinship-matrix. In total, 385 marker-trait associations significant in both models were detected. The intrachromosomal location of many of these coincided with those of known major genes or quantitative trait loci, but others were detected in regions where no known genes have been located to date. These latter presumptive loci provide opportunities for further wheat improvement, based on a marker approach.

No abstract

The genetic architecture of plant height was investigated in a set of 358 recent European winter wheat varieties plus 14 spring wheat varieties based on field data in eight environments. Genotyping of diagnostic markers revealed the Rht-D1b mutant allele in 58% of the investigated varieties, while the Rht-B1b mutant was only present in 7% of the varieties. Rht-D1 was significantly associated with plant height by using a mixed linear model and employing a kinship matrix to correct for population stratification. Further genotyping data included 732 microsatellite markers, resulting in 770 loci, of which 635 markers were placed on the ITMI map plus a set of 7769 mapped SNP markers genotyped with the 90 k iSELECT chip. When Bonferroni correction was applied, a total of 153 significant marker-trait associations (MTAs) were observed for plant height and the SSR markers (−log10 (P-value) ≥4.82) and 280 (−log10 (P-value) ≥5.89) for the SNPs. Linear regression between the most effective markers and the BLUEs for plant height indicated additive effects for the MTAs of different chromosomal regions. Analysis of syntenic regions in the rice genome revealed closely linked rice genes related to gibberellin acid (GA) metabolism and perception, i.e. GA20 and GA2 oxidases orthologous to wheat chromosomes 1A, 2A, 3A, 3B, 5B, 5D and 7B, ent-kaurenoic acid oxidase orthologous to wheat chromosome 7A, ent-kaurene synthase on wheat chromosome 2B, as well as GA-receptors like DELLA genes orthologous to wheat chromosomes 4B, 4D and 7A and genes of the GID family orthologous to chromosomes 2B and 5B. The data indicated that besides the widely used GA-insensitive dwarfing genes Rht-B1 and Rht-D1 there is a wide spectrum of loci available that could be used for modulating plant height in variety development.

Globally, over 7.4 million accessions of crop seeds are stored in gene banks, and conservation of genotypic variation is pivotal for breeding. We combined genetic and biochemical approaches to obtain a broad overview of factors that influence seed storability and ageing in barley (Hordeum vulgare). Seeds from a germplasm collection of 175 genotypes from four continents grown in field plots with different nutrient supply were subjected to two artificial ageing regimes. Genome-wide association mapping revealed 107 marker trait associations, and hence, genotypic effects on seed ageing. Abiotic and biotic stresses were found to affect seed longevity. To address aspects of abiotic, including oxidative, stress, two major antioxidant groups were analysed. No correlation was found between seed deterioration and the lipid-soluble tocochromanols, nor with oil, starch and protein contents. Conversely, the water-soluble glutathione and related thiols were converted to disulphides, indicating a strong shift towards more oxidizing intracellular conditions, in seeds subjected to long-term dry storage at two temperatures or to two artificial ageing treatments. The data suggest that intracellular pH and (bio)chemical processes leading to seed deterioration were influenced by the type of ageing or storage. Moreover, seed response to ageing or storage treatment appears to be significantly influenced by both maternal environment and genetic background.

Phenotyping large numbers of genotypes still represents the rate-limiting step in many plant genetic experiments and in breeding. To address this issue, novel automated phenotyping technologies have been developed. We investigated for a core set of barley cultivars if high-throughput image analysis can help to dissect vegetative biomass accumulation in response to two different watering regimes under semi-controlled greenhouse conditions. We found that experiments, treatments, genotypes and genotype by environment interaction (G × E) can be characterized at any time point by certain digital traits. Biomass accumulation under control and stress conditions was highly heritable. Growth model-derived maximum vegetative biomass (K max), inflection point (I) and regrowth rate (k) were identified as promising candidate traits for genome-wide association studies. Drought stress symptoms can be visualized, dissected and modelled. Especially the highly heritable regrowth rate, which had the biggest influence on biomass accumulation in stress treatment, seems promising for future studies to improve drought tolerance in different crop species. A proof of concept study revealed potential correlations between digital traits obtained from pot experiments under greenhouse conditions and agronomic traits from field experiments. Overall, non-invasive, imaging-based phenotyping platforms under greenhouse conditions offer excellent possibilities for trait discovery, trait development and industrial applications.

Plain language summary This article argues that the rapid evolution of expectations and influence capacity by corporate stakeholders requires managers in MNCs not only to adapt operating activities, but to rethink the purpose and overarching goal of the business enterprise, adapting it from a shareholder‐primacy to a multi‐stakeholder enterprise model. This complex adaptation process, however, requires the development of a specific form of competence to be developed and honed, different in kind from the typical capabilities focused on organizational change. With the help of concrete examples of sustainability‐driven change initiatives, we discuss how the two enterprise models differ and how the development of this new type of competence facilitates the transition from one to the other. Technical summary This article argues that the dynamic capabilities framework can be useful to study the evolutionary change processes that MNCs go through as they strive to innovate and adapt to societal pressures related to corporate sustainability. These pressures require MNCs to rethink and adapt core organizational elements and we argue that current dynamic capabilities theory is not sufficiently developed to explain these adaptive efforts. We suggest refining and extending theory in two directions: (1) distinguishing between behavioral, cognitive, and relational organizational elements as objects of the innovation, change, and learning dynamics; and (2) distinguishing between capabilities specific to generative variation and selection of innovative change and adaptation ideas from those specific to their diffusion and retention. Copyright © 2016 Strategic Management Society

Breeding new crop cultivars with efficient root systems carries great potential to enhance resource use efficiency and plant adaptation to unstable climates. Here, we evaluated the natural variation of root system architectural traits in a diverse spring barley association panel and conducted genome-wide association mapping to identify genomic regions associated with root traits. For six studied traits, root system depth, root spreading angle, seminal root number, total seminal root length, and average seminal root length 1.9- to 4.2-fold variations were recorded. Using a mixed linear model, 55 QTLs were identified cumulatively explaining between 12.1% of the phenotypic variance for seminal root number to 48.1% of the variance for root system depth. Three major QTLs controlling root system depth, root spreading angle and total seminal root length were found on Chr 2H (56.52 cM), Chr 3H (67.92 cM), and Chr 2H (76.20 cM) and explained 12.4%, 18.4%, and 22.2% of the phenotypic variation, respectively. Meta-analysis and allele combination analysis indicated that root system depth and root spreading angle are valuable candidate traits for improving grain yield by pyramiding of favorable alleles.