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Common bunt is a seed borne disease of wheat whose importance is anticipated to increase with a growing organic seed market which, in addition to seed phytosanitary measures, relies on genetic resistances towards common bunt. Genome wide association studies have been proven a useful tool in the detection of genetic polymorphisms underlying phenotypic trait variation in wheat. We screened 248 wheat accessions for two years for their resistance reactions towards common bunt. The majority of lines exhibited high levels of susceptibility towards common bunt, but 25 accessions had less than 10 % infection. Using Diversity Array Technology (DArT) markers for genotyping and correcting for population stratification by using a compressed mixed linear model, we identified two significant marker trait associations for common bunt resistance, designated Q Cbt-cph-2 B and Q Cbt-cph-7 A, located on wheat chromosomes 2 B and 7 A, respectively. We show that genome wide association studies are applicable in the search for genetic polymorphisms for resistance towards rare plant diseases in the context of an under-representation of resistant lines.

Organic farming is increasing in Europe, and it is a key sector in the policies that promote the transition towards more sustainable farming systems in the European Union (Farm to Fork, Biodiversity Strategy). However, most organic production depends on organic quality seeds of cultivars which have been bred under conventional conditions or even sometimes are non-chemically treated conventional seeds. As a result, there is a remarkable lack of organic seeds of cultivars specifically adapted to organic farming.

Agrarlandschaften beherbergen einen erheblichen Teil der biologischen Vielfalt Deutschlands. Der Schutz und die Förderung dieser Vielfalt ist ein gesellschaftlich anerkanntes Ziel. Denn erstens hat sich Deutschland national und international verpflichtet, die biologische Vielfalt im Sinne des Naturschutzes zu erhalten. Zweitens sichert die biologische Vielfalt Genpools, die für die Ökosystemfunktionen (Produktion, Klima-, Boden- und Gewässerschutz) in genutzten Landschaften unter sich ändernden Bedingungen, etwa des Klimawandels, wichtig werden können. Drittens können genutzte (Kulturarten, Sorten) und begleitende Vielfalt (z.B. Beikräuter, Bodenorganismen, oberirdische Insekten) dazu beitragen, die Leistungsfähigkeit von Agrarlandschaften unter extremen Bedingungen länger bzw. in einem höheren Maße zu erhalten. In den zurückliegenden Dekaden hat es einen erheblichen und bis heute anhaltenden Verlust an biologischer Vielfalt in den Agrarlandschaften gegeben. Die Ursachen liegen maßgeblich in den Änderungen der Landnutzung und der Landschaftsstruktur. Der fortschreitende Klimawandel stellt ein weiteres, bislang noch wenig beachtetes Risiko für die biologische Vielfalt dar. Der Wissenschaftliche Beirat für Biodiversität und Genetische Ressourcen sieht daher einen dringenden Bedarf, dieses Risiko wirksam in die Gestaltung der Agrarpolitik und insbesondere der Agrarumweltpolitik einzubeziehen. Allerdings ist die Wirkung der sich ändernden Klimafaktoren auf die biologische Vielfalt und auf die davon abhängigen Ökosystemleistungen der Agrarlandschaft sehr komplex. Präzise Voraussagen zu zukünftigen klimawandelbedingten Veränderungen sind daher mit erheblichen Unsicherheiten behaftet. Das bedeutet, dass die Wirksamkeit physischer und politischer Gegenmaßnahmen derzeit nicht sicher prognostiziert werden kann. Sicher ist dagegen, dass der Klimawandel die biologische Vielfalt und deren Funktionen zusätzlich zu der aktuellen Art der Landnutzung gefährdet, und dass es Möglichkeiten gibt, dieses Gefährdungsrisiko zu senken. Daher empfiehlt der Beirat, die Agrarpolitik so weiterzuentwickeln, dass die klimabedingten Risiken für die biologische Vielfalt in Agrarlandschaften deutlich verringert werden. Der Beirat fasst den politischen Handlungsbedarf in zehn Leitprinzipien zusammen, die als Orientierung für die künftige Agrar- und Agrarumweltpolitik dienen sollen. Sie adressieren fünf Themenbereiche: (i) Deutlich verstärkte Anstrengungen in Forschung und Monitoring sowie Foresight-Prozesse sollen dazu beitragen, den Gestaltungs- und Anpassungsspielraum zu erhöhen und die Wirksamkeit von Maßnahmen mit größerer Zuverlässigkeit zu prognostizieren. (ii) Stärkere Ausrichtung der Agrarpolitik auf die Verbesserung der Resilienz der vielfältigen Funktionen der Agrarlandschaft. Dazu bedarf es einer ‚lernenden Politik‘, die durch Feedback-Schleifen bei Entscheidungsprozessen gekennzeichnet ist, die partizipativ ist, und die Erkenntnisfortschritte kontinuierlich einbezieht. (iii) Agrarpolitische und agrarumweltpolitische Instrumente sollten einem konsequenten ‚Klimawandelcheck‘ unterzogen werden. Es muss also sichergestellt werden, dass deren Wirkung der notwendigen Anpassung an den Klimawandel nicht entgegensteht, sondern diese unterstützt. (iv) Prioritäre Handlungsfelder für den Erhalt der biologischen Vielfalt im Klimawandel sind ein Aufhalten der Fragmentierung und Homogenisierung der Agrarlandschaft durch die Förderung vielfältiger Agrarlandschaften und die wirksame Stärkung artenreicher Agrarsysteme. (v) Handlungsoptionen, die der derzeitige Regelungsrahmen der Gemeinsamen Europäischen Agrarpolitik schon jetzt bietet, sollten konsequent genutzt werden. Maßnahmen mit multiplen positiven Wirkungen (win-win-win-Lösungen) sollten priorisiert werden., Berichte über Landwirtschaft - Zeitschrift für Agrarpolitik und Landwirtschaft, Sonderheft 232, Dezember 2020

Das Diskussionspapier „Ackerbaustrategie 2035 – Perspektiven für einen produktiven und vielfältigen Pflanzenbau“ des BMEL zeigt anhand von sechs Leitlinien und zwölf dazugehörigen Handlungsfeldern Optionen für die Verbesserung des deutschen Ackerbaus auf. Es enthält viele sinnvolle Ansätze. Allerdings greift das Papier bezüglich des Erhalts und der Förderung der Biodiversität in der deutschen Landwirtschaft zu kurz. Der WBBGR formuliert und erläutert daher 10 Empfehlungen für eine ganzheitliche Betrachtung von landwirtschaftlicher Produktion und biologischer Vielfalt sowie für die Förderung der Biodiversität in der Agrarlandschaft: 1. Biodiversität in der Landwirtschaft mit Zielbildern und zügig umzusetzenden Maßnahmen ganzheitlich betrachten 2. Mehr biodiversitätsfördernde Ziele konkret hinsichtlich Inhalt und Umfang sowie Zeitraum benennen sowie den Grad der Zielerreichung zu späteren Zeitpunkten messbar machen 3. Biodiversität beeinträchtigende Zielkonflikte der Maßnahmen benennen und Lösungsvorschläge entwickeln 4. Mehr biodiversitätsfördernde Maßnahmen ausgestalten 5. Inhaltliche und zeitliche Umsetzung biodiversitätsfördernder Maßnahmen präzisieren 6. Klares Bekenntnis zu mehr Fördermitteln für Gemeinwohlleistungen (durch den Ackerbau) bei der zukünftigen GAP abgeben 7. Inwertsetzung von Biodiversität und Ökosystemleistungen fördernden Maßnahmen der Landwirte1 über den Markt vorantreiben 8. Ressort- und strategieübergreifende Ziele und Maßnahmen identifizieren sowie Zuständigkeiten frühzeitig abstimmen und festlegen 9. Mehr Weiterbildungsmaßnahmen zur Biodiversitätsförderung für alle an der Aus- und Weiterbildung von Landwirten beteiligten Personen verpflichtend machen 10. Forschungsaktivitäten zur Biodiversität verstärken und Innovationen fördern. Biodiversität und eine ebenso effiziente wie effektive landwirtschaftliche Produktion sollten bei der Umsetzung der Ackerbaustrategie nicht als Gegensätze („Produktion oder Biodiversität“), sondern als Teile eines sich gegenseitig fördernden Systems („Produktion und Biodiversität“) behandelt werden., Berichte über Landwirtschaft - Zeitschrift für Agrarpolitik und Landwirtschaft, Sonderheft 231, November 2020

Background Genome-wide association studies (GWAS) attempt to identify links between gene loci and trait expressions. In order to avoid false positives, GWAS methods use information about population structure, which might have disadvantageous effects in association studies. Several methods are used to describe and integrate this additional information in GWAS. However, structures might feature discrete as well as continuous patterns of variation which cannot be identified sufficiently by current (linear) analysis approaches (Diaz-Papkovich et al. 2019). Therefore, GWAS models using non-linear methods (msMDS, NMDS and UMAP) were compared with those using linear methods (PCA, PCoA, iMDS) by calculating the pairwise correlation coefficient of the p-values yielded from GWAS models and the resulting relationships were visualised by UMAP. Material and Methods DNA was isolated from 184 CCP lines derived from two winter wheat CCPs and genotyped using a 20k wheat SNP array (TraitGenetics). The genotyping data, together with the phenotypic data are being used for the GWAS to link allelic changes to trait expressions. GAPIT-related GWAS: general linear model (GLM), mixed linear model (MLM), multi-locus mixed model (MLMM), fixed and random models circulating probability unification (FarmCPU) included K and Q matrix (Wang and Zhang 2019). Covariates were calculated using GAPIT-based PCA on 5822 selected SNPs. Additionally, principal coordinate analysis (PCoA), interval, M-spline, and ordinal (non-metric) multi-dimensional scaling (MDS) using MM algorithm initialised by Torgerson configuration (de Leeuw and Mair 2009), as well as uniform manifold approximation and projection (UMAP) initialised by spectral embedding (McInnes et al. 2018) were calculated using 583 SNPs selected by clumping. Altogether, 76 combinations were compared by calculating Pearson correlation coefficient of the p-values yielded from the GWAS models, converted to Euclidian distances (\(\mathsf{δ_r = \sqrt{1 - ρ}}\)). Results The results of GLM-, MLM, and MLMM-based models tend to cluster together, whereas FarmCPU shows different outcomes. UMAP yielded the best results for correcting PS used in GLM for the plant height. PCA outperformed MDS-based PS methods, and little differences were observed between PS configurations for MLM- and MLMM-based models. In contrast, FarmCPU-based models tend to be conservative: the correction for PS with PCA tends to be too strong. Conclusions The preliminary results are promising and show a potential to use additional covariate methods for GWAS when analysing data derived from diverse CCP lines of wheat. Therefore, further tests and comparison with different environments, GWAS methods, and settings are needed, especially for the fine-tuning of UMAP-based methods., {"references":["de Leeuw, J., & Mair, P. (2009). Multidimensional scaling using majorization: SMACOF in R. J. Stat. Softw., 31(3), 1–30.","Diaz-Papkovich, A., Anderson-Trocmé, L., Ben-Eghan, C., & Gravel, S. (2019). UMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts. PLOS Genetics, 15, 1–24.","McInnes, L., Healy, J., & Melville, J. (2018). UMAP: Uniform manifold approximation and projection for dimension reduction, arXiv: 1802.03426.","Wang, J., & Zhang, Z. (2019). GAPIT version 3: An interactive analytical tool for genomic association and prediction. Retrieved February 19, 2019, from https://github.com/jiabowang/GAPIT3."]}

Background Biodiversity at all levels from microbial populations up to inter- and intra-specific diversity of plants is a major component in self-regulating systems. Aims and methods for breeding of crops adapted to self-regulating systems, such as heterogeneous populations, are different from most current breeding programs in several aspects. This study attempts to use wheat lines selected from composite cross populations (CCPs) in order to test the applicability as diverse, but still restricted population for genome-wide association studies (GWAS). Material and Methods Out of 200 CCP lines, derived from different winter wheat CCPs, DNA was isolated from 184, selected for contrasting phenotypes and homozygosity and genotyped using a 20K (15K+5K) SNP micro array chip. The genotyping data, together with the phenotypic data are being used for the GWAS to link allelic changes to trait expressions. GAPIT-related GWAS: GLM, MLM, MLMM, and FarmCPU included K and Q matrix. Covariate (Q matrix) was calculated using principal component analysis (PCA) based on 8961 selected SNPs. Network-based GWAS: using Gaussian copula graphical model for reconstructing conditional independence networks. Results Preliminary GWAS results were obtained from up to ten phenotypic traits at the organically and conventional managed trial farms of the University of Kassel (KU) and the Technical University of Munich (TUM; Germany) for the season 2017/18. GAPIT-related GWAS resulted in up to 13 candidate for up 6 of the 10 traits and 31--65 potential candidates for all traits. In contrast, the network-based GWAS resulted in 50--52 candidates for 9 of the 10 traits. Conclusions Further investigations and comparison with different environments and methods are needed for a more complex understanding of the phenotypic traits in the development of diverse populations and mixtures in organic plant breeding., {"references":["Behrouzi, P., Arends, D., & Wit, E. C. (2018). Netgwas: An R package for network-based genome-wide association studies. arXiv:1710.01236v4 [stat.AP].","Behrouzi, P., & Wit, E. C. (2019). Detecting epistatic selection with partially observed genotype data by using copula graphical models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68(1):141–160.","Döring, T. F., Annicchiarico, P., Clarke, S., Haigh, Z., Jones, H. E., Pearce, H., Snape, J., Zhan, J., & Wolfe, M. S. (2015). Comparative analysis of performance and stability among composite cross populations, variety mixtures and pure lines of winter wheat in organic and conventional cropping systems. Field Crops Research, 183:235–245.","Döring, T. F., Pautasso, M., Finckh, M. R., & Wolfe, M. S. (2011). Concepts of plant health—reviewing and challenging the foundations of plant protection. Plant Pathology, 61(1):1–15.","Lipka, A. E., Tian, F., Wang, Q., Peiffer, J., Li, M., Bradbury, P. J., Gore, M. A., Buckler, E. S., & Zhang, Z. (2012). Gapit: Genome association and prediction integrated tool. Bioinformatics, 28(18):2397–2399.","Loreau, M. (2010). Linking biodiversity and ecosystems: Towards a unifying ecological theory. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 365(1537):49–60.","Tang, Y., Liu, X., Wang, J., Li, M., Wang, Q., Tian, F., Su, Z., Pan, Y., Liu, D., Lipka, A. E., Buckler, E. S., & Zhang, Z. (2016). Gapit version 2: An enhanced integrated tool for genomic association and prediction. The Plant Genome Journal, 9(2):1–9.","Wang, J., & Zhang, Z. (2019). GAPIT version 3: An interactive analytical tool for genomic association and prediction. Retrieved February 19, 2019, from https://github.com/jiabowang/GAPIT3."]}

When assesssing races of common bunt for virulens pattern within a region, it is important to take into account that collected spores may represent a diverse population of different virulence races. When screening spores on a differential set of wheat lines with known resistance genes, a low infection rate on a resistant wheat variety does not necessarily demonstrate that virulence is absent in the spore collection, but could be a sign that virulence is present, but only present in a low frequency among the spores. If just a few spores within a spore sample are indeed virulent, they may infect some plants and from there multiply the virulence quite rapidly next years. Previous studies have shown that virulence against most resistance genes were present in Denmark after purifying races of common bunt (Tilletia caries) on resistant varieties. So far, only wheat differential varieties with Bt4, Bt6, Bt9, Bt11 and Bt12 cannot be infected with bunt races purified from Danish collections [1, and later own unpublished data]. Virulence against Bt4, Bt6 and Bt9 has been found in other European studies [2], and Bt11 may not be only one gene but a combination of at least two genes [3]. Therefore, Bt12 seems to be the only gene for which virulence have not been found in European population of common bunt. This leads to the conclusion that if resistance breeding shall safely control common bunt in wheat, we need not only one effective gene, but a combination of pyramided genes. Since it is very difficult to test if a resistant line has only one gene or more genes, the most effective tool to achieve this at present are genetic markers. Using Genome Wide Association Studies (GWAS) to find QTLs and markers for the major resistance genes in wheat have so far led to only few commercial useful markers. Till now, only markers for Bt9 [6] and Bt10 are used in practice, but a marker for Bt12 [4] and Blizzard [7] have also been found. One of the problems in developing markers for bunt resistance have been that spores used in GWAS trials have been divers or unknown in virulence, and that phenotypic results not distinguishes between different resistance genes. Therefore, the most successful studies have used segregating populations of single crosses where the resistance gene is known on before hand [5]. In the LIVESEED project, we have the ambition to develop genetic markers on several different resistance genes at the same time. We will do so by testing segregating populations of several different crosses between varieties with 7 different resistance genes, and infect them with 7-11 different virulence races of common bunt able to distinguish between the resistance genes. A total of 300 varieties will be pheno- and genotyped. Using this experimental design, we attempt during 2018 and ‘19 to develop markers for Bt1, Bt2, Bt5, Bt7, Bt13, BtZ and Quebon-resistance, and hopefully also a couple of minor QTLs.

Plant associated soil microbes are known to play an important role in the expression and stability of certain plant traits such as nutrient use efficiency and disease resistance. Arbuscular mycorrhizal fungi (AMF) form one of the primary mutualistic plant-microbe symbioses. Besides known benefits such as improved nutrient mobilisation (mainly phosphorus and zinc) and tolerance against abiotic stresses (mainly drought), an increasing number of studies highlight a significant role of AMF in the mediation of disease resistances and priming mechanisms. Individual reports have shown enhanced levels of defence-related compounds (such as glucanases, chitinases and phenolics) in mycorrhizal plants, and there is first evidence of certain phytohormone pathways (in particular jasmonate signalling) to be involved in mycorrhiza-mediated disease resistance. The level of mycorrhisation (formation of mycorrhizae on the roots) and mycorrhizal responsiveness (response to AMF) can vary widely between plant species and also among genotypes within the same species, indicating a genetic basis for the regulation of this symbiosis. Genotypic differences in mycorrhizal responsiveness have been observed in various crops and quantitative trait loci (QTL) that govern plant growth responses to AMF have been reported for maize, barley and onion. However, little is known about the heritability of mycorrhiza-mediated disease resistance. Mycorrhizal responsiveness (when based on biomass) is negatively correlated with available soil P content. Breeding under high P conditions might therefore indirectly select for poor AMF hosts. We hypothesise that a reduced mycorrhizal dependency also affects other benefits elicited by AMF such as disease resistance. We therefore pledge to include factors other than biomass to estimate mycorrhizal responsiveness (i.e. disease resistance, PUE and drought tolerance) to obtain a more comprehensive differentiation of the plant-AMF interaction. The authors also propose to complement mycorrhizal responsiveness with an additional measure called mycorrhizal efficiency since mycorrhization and mycorrhizal responsiveness on their own might not indicate an optimum cost-benefit ratio of this symbiosis. We will present initial results on genotypic variation in mycorrhization, mycorrhizal responsiveness and mycorrhizal efficiency of SNP-genotyped accessions of pea (Pisum sativum L.). Eventually, these SNP-genotyped accessions can be used to identify QTL that govern mycorrhiza-mediated disease resistance and exploit genotypic differences, e.g., via marker-assisted selection. Another research project has been initiated to investigate the role of flavonoids in defence mechanisms of pea and their possible function in microbe-mediated disease resistance. Variation in microbial composition has been attributed to a differential exudation of compounds that stimulate or suppress particular members/groups of the microbial community. The complex group of flavonoids has been shown to play a signalling and/or direct role in plant defence mechanisms, but also to influence the interaction with symbionts including mycorrhizal fungi (and also plant-symbiotic rhizobia). Overall, current and future research activities of our group aim to better understand and make use of plant-microbe interactions in plant breeding for an improved expression and stability of important plant traits.

Common bunt, caused by Tilletia caries and T. foetida, is a fungal disease of wheat world wide. Infection, occurring via seed borne teliospores, is generally controlled by the application of seed treatments prior to sowing. Farming systems like organic agriculture with a very limited range of organic seed treatments available rely heavily on common bunt resistance genes within wheat. In the framework of the BIOBREED project an association study in winter wheat was conducted, aiming at the identification of genetic loci linked to resistance towards common bunt in wheat. 152 European wheat cultivars were phenotyped for their resistance reaction for the two consecutive years 2011/12 at Agrologica research station at Mariager. Infection was scored as percent infected ears. The scorings were log-transformed to fit a disease scoring scale ranging from 1 to 9. The association analysis was performed for each year separately as well as for the mean scoring of the two years. The wheat cultivars were genotyped with DArT markers, yielding 1832 polymorphic loci. The association analysis was conducted using the computer program Genstat, with the ASReml module. Minimun allele frequency for the association analysis was set to 0.07. 13 out of the total of1832 marker in our study were linked to common bunt resistance in wheat (-log10(P) >3). These marker are located on 8 out of the 21 wheat chromosomes. Comparisons of these findings with other published results are difficult since only very little is known about the chromosomal location of common bunt resistance genes/QTL in wheat. Chromosome 2B was previously reported to carry gene(s) for common bunt resistance. Findings of our analysis are in accordance with this: 4 of the linked marker resided on this chromosome. Further, another two linked marker were found on chromosome 2D, another chromosome previously reported to carry common bunt resistance genes. Our study shows the possibilities of finding makers linked to common bunt resistance in wheat, and of using these markers for marker assisted selection of wheat cultivars tailored for the needs of organic agriculture.

According to a widely accepted theory on barley domestication, wild barley (Hordeum vulgare ssp. spontaneum) from the Fertile Crescent is the progenitor of all cultivated barley (Hordeum vulgare ssp. vulgare). In order to determine whether barley has undergone According to a widely accepted theory on barley domestication, wild barley (Hordeum vulgare ssp. spontaneum) from the Fertile Crescent is the progenitor of all cultivated barley (Hordeum vulgare ssp. vulgare). To determine whether barley has undergone one or more domestication events, barley accessions from three continents have been studied (a) using 38 nuclear SSR (nuSSRs) markers, (b) using five chloroplast SSR (cpSSR) markers yielding 5 polymorphic loci and (c) by detecting the differences in a 468 bp fragment from the non-coding region of chloroplast DNA. A clear separation was found between Eritrean/Ethiopian barley and barley from West Asia and North Africa (WANA) as well as from Europe. The data from chloroplast DNA clearly indicate that the wild barley (Hordeum vulgare ssp. spontaneum) as it is found today in the “Fertile Crescent” might not be the progenitor of the barley cultivated in Eritrea (and Ethiopia). Consequently, an independent domestication might have taken place at the Horn of Africa.

Genetic markers have been used since the beginnings of plant breeding, but the concept of linkage and recently the availability of molecular markers have offered new and powerful tools that can help to perform the traditional tasks of selection or that can change the traditional breeding process. Markers can either be used in a descriptive manner to identify varieties, to study the ‘micro-evolution’ of composite crosses or variety mixtures or to analyse the breeding progress retrospectively in order to learn from the past. The operative use of markers in plant breeding is connected to the selection of parental lines and progeny lines. The possible implementation of these processes stretches from the introgression of specific chromosome fragments to ‘marker-based idiotype breeding’.

Gennemgang af hvad en god vårbygsort er for økologisk jordbrug. Sortsvalg, udbytte, sortsblandinger, næringsstofoptagelse, ukrudtskonkurrenceevne, sygdomsrobusthed, molekulære markører.