Fusarium head blight (FHB) is one of the economically important diseases of wheat as it causes severe yield loss and reduces grain quality. In winter wheat, due to its vernalization requirement, it takes an exceptionally long time for plants to reach the heading stage, thereby prolonging the time it takes for characterizing germplasm for FHB resistance. Therefore, in this work, we developed a protocol to evaluate winter wheat germplasm for FHB resistance under accelerated growth conditions. The protocol reduces the time required for plants to begin heading while avoiding any visible symptoms of stress on plants. The protocol was tested on 432 genotypes obtained from a breeding program and a genebank. The mean area under disease progress curve for FHB was 225.13 in the breeding set and 195.53 in the genebank set, indicating that the germplasm from the genebank set had higher resistance to FHB. In total, 10 quantitative trait loci (QTL) for FHB severity were identified by association mapping. Of these, nine QTL were identified in the combined set comprising both genebank and breeding sets, while two QTL each were identified in the breeding set and genebank set, respectively, when analyzed separately. Some QTLs overlapped between the three datasets. The results reveal that the protocol for FHB evaluation integrating accelerated growth conditions is an efficient approach for FHB resistance breeding in winter wheat and can be even applied to spring wheat after minor modifications.

Long amplicon metabarcoding has opened the door for phylogenetic analysis of the largely unknown communities of microeukaryotes in soil. Here, we amplified and sequenced the ITS and LSU regions of the rDNA operon (around 1500 bp) from grassland soils using PacBio SMRT sequencing. We tested how three different methods for generation of operational taxonomic units (OTUs) effected estimated richness and identified taxa, and how well large‐scale ecological patterns associated with shifting environmental conditions were recovered in data from the three methods. The field site at Kungsängen Nature Reserve has drawn frequent visitors since Linnaeus's time, and its species rich vegetation includes the largest population of Fritillaria meleagris in Sweden. To test the effect of different OTU generation methods, we sampled soils across an abrupt moisture transition that divides the meadow community into a Carex acuta dominated plant community with low species richness in the wetter part, which is visually distinct from the mesic‐dry part that has a species rich grass‐dominated plant community including a high frequency of F. meleagris. We used the moisture and plant community transition as a framework to investigate how detected belowground microeukaryotic community composition was influenced by OTU generation methods. Soil communities in both moisture regimes were dominated by protists, a large fraction of which were taxonomically assigned to Ciliophora (Alveolata) while 30%–40% of all reads were assigned to kingdom Fungi. Ecological patterns were consistently recovered irrespective of OTU generation method used. However, different methods strongly affect richness estimates and the taxonomic and phylogenetic resolution of the characterized community with implications for how well members of the microeukaryotic communities can be recognized in the data.

Fusarium head blight (FHB) is an economically important disease affecting wheat and thus poses a major threat to wheat production. Several studies have evaluated the effectiveness of image analysis methods to predict FHB using disease-infected grains; however, few have looked at the final application, considering the relationship between cost and benefit, resolution, and accuracy. The conventional screening of FHB resistance of large-scale samples is still dependent on low-throughput visual inspections. This study aims to compare the performance of two cost–benefit seed image analysis methods, the free software “SmartGrain” and the fully automated commercially available instrument “Cgrain Value™” by assessing 16 seed morphological traits of winter wheat to predict FHB. The analysis was carried out on a seed set of FHB which was visually assessed as to the severity. The dataset is composed of 432 winter wheat genotypes that were greenhouse-inoculated. The predictions from each method, in addition to the predictions combined from the results of both methods, were compared with the disease visual scores. The results showed that Cgrain Value™ had a higher prediction accuracy of R2 = 0.52 compared with SmartGrain for which R2 = 0.30 for all morphological traits. However, the results combined from both methods showed the greatest prediction performance of R2 = 0.58. Additionally, a subpart of the morphological traits, namely, width, length, thickness, and color features, showed a higher correlation with the visual scores compared with the other traits. Overall, both methods were related to the visual scores. This study shows that these affordable imaging methods could be effective to predict FHB in seeds and enable us to distinguish minor differences in seed morphology, which could lead to a precise performance selection of disease-free seeds/grains.

Breeding for disease resistance in winter wheat is a critical task in the agricultural industry, as plant diseases can significantly impact crop yield and quality. Traditional breeding methods are time-consuming, and disease resistance screenings are often cost and labour-demanding. Therefore, novel breeding tools are being developed to speed up winter wheat genetic gain and increase its genetic diversity. A protocol to characterize winter wheat germplasm for resistance to Fusarium head blight (FHB) under accelerated growth conditions was carried out. The results showed that it is possible to reduce the time necessary to characterize germplasm for FHB resistance by growing up to three generations per year. In a genome-wide association study (GWAS), several markers were identified that were significantly associated with FHB resistance. These markers overlapped with previously known markers contributing to FHB resistance. Novel phenomic methods, the low throughput and affordable SmartGrain and the high throughput Cgrain ValueTM were implemented to predict FHB severity in the tested germplasm. Both methods showed good correlation to visual scoring, suggesting a potential alternative for the traditional visual assessment methods with machine-based methods that offer higher throughput and lower cost. The study also investigated seedling resistance to Septoria tritici blotch (STB) using association mapping and genomic prediction (GP). The study identified 20 QTL for STB seedling resistance of which nine were potentially novel QTL for STB seedling resistance and four overlapped with previously identified genomic regions at the adult stage. The identified QTL could be exploited in winter wheat marker-assisted selection (MAS) against STB and promote the seedling stage for early selection instead of the adult stage. Furthermore, the study investigated the genotypic responses of winter wheat seedlings infected with STB to the fungal biocontrol agent Clonostachys rosea. SNP markers associated with C. rosea biocontrol efficacy and disease resistance were identified, laying the groundwork for further research in genotype-specific-biocontrol compatibility in disease resistance breeding. The thesis provides useful insights into developing novel breeding tools for disease resistance in winter wheat and emphasizes the importance of industry collaboration to transfer knowledge from research to application.