The research institute in Martonvásár is one of the largest agricultural research institutes in Hungary and in Central Europe. For many years now, the accumulated data on the extensive wheat breeding stocks has been handled and analysed using programs developed in the institute. The information system that has been elaborated and constantly improved can be used for keeping records of breeding stock, for planning field and laboratory experiments, for site-plant performance evaluation, for automated data collection, for the rapid evaluation of the results and for effective management of the pedigree, seed exchange and the institute’s cereal gene bank.The demand for the storage of molecular data and their use in breeding has increased parallel with the development of new, PCR-based markers. For this reason, informatics tools (data structure and software) suited to the design of marker-assisted selection experiments and the interpretation of the results have been developed as part of the existing Martonvásár wheat breeding information system. The aim was to link molecular data to the phenotypic information already available in the database and to make the results available to wheat breeders and geneticists.The interpretation of molecular data related to specific genotypes is of assistance in clarifying the genetic background of economically important phenotypic traits, in identifying markers linked to the useful genes or agronomic traits to be found in the genomics database, and in the selection of satisfactory parental partners for breeding. Marker assisted selection coupled with traditional breeding activities enables the breeder to make plant selections based on the presence of target genes. Conventional wheat breeding with the integrated molecular component allows breeders to more accurately and efficiently select defined sets of genes in segregating generations.The molecular data are stored in a relational database, the central element of which is the [DNASource] entity. This is used to collect and store information on gene sources arising during breeding. It is therefore linked both to the phenotypic data stored in the traditional breeding system (measurements, observations, laboratory data) and to the component parts of the new, molecular data structure ([PrimerBank], [Marker], [Allele] and [Gene]).
Thinopyrum ponticum is particularly a valuable source of genes for wheat improvement. A novel wheat-Th. ponticum addition line, 1–27, was identified using cytology, SSR, ESTSSR, EST-STS and PCR-based landmark unique gene (PLUG) markers in this study. Cytological studies showed that 1–27 contained 44 chromosomes and formed 22 bivalents at meiotic metaphase I. Genomic in situ hybridization (GISH) analysis indicated that two chromosomes from Th. ponticum had been introduced into 1–27 and that these two chromosomes could form a bivalent in wheat background. Such results demonstrated that 1–27 was a disomic addition line with 42 wheat chromosomes and a pair of Th. ponticum chromosomes. One SSR marker (BARC235), one EST-STS marker (MAG3284) and 8 PLUG markers (TNAC1210, TNAC1787, TNAC1803, TNAC1805, TNAC1806, TNAC1821, TNAC1867 and TNAC1957), which were all from wheat chromosome group 7, produced the specific band in Th. ponticum and 1–27, indicating that the introduced Th. ponticum chromosomes belonging to the group 7 of wheat. Sequence analysis on specific bands from Th. ponticum and 1–27 amplified using the PLUG marker TNAC1867 further confirmed this result. The 1–27 addition line was also observed to be high resistant to powdery mildew though it is not clear if the resistance of 1–27 inherited from Th. ponticum. This study provided some useful information for effective exploitation of the source of genetic variability in wheat breeding.
The glutenin allele gene-pool, the distribution of the individual alleles on the 6 loci coding for glutenin subunits and their combinations were determined in a sample population containing 107 cultivars bred and grown in Martonvásár, Hungary at the Agricultural Research Institute of the Hungarian Academy of Sciences. The database is based on the results of three independent analytical procedures carried out using the traditional SDS-PAGE based allele identification, the state-of-art MALDI-TOF technology and the high throughput capillary electrophoresis based on the lab-on-a-chip technique. The usefulness of integrating the information on both HMW GS and LMW GS allelic composition for future genetic and technological improvement is discussed.
Among the factors which determine yield reliability an important role is played by disease resistance. One of the breeding aims in the Martonvásár institute is to develop wheat varieties with resistance to major diseases. The winter wheat varieties bred in Martonvásár are examined in artificially inoculated nurseries and greenhouses for resistance to economically important pathogens. The effectiveness of designated genes for resistance to powdery mildew and leaf rust has been monitored over a period of several decades. None of the designated major resistance genes examined in greenhouse tests is able to provide complete resistance to powdery mildew; however, a number of leaf rust resistance genes provide full protection against pathogen attack (Lr9, Lr19, Lr24, Lr25, Lr28 and Lr35). In the course of marker-assisted selection, efficient resistance genes (Lr9, Lr24, Lr25 and Lr29) have been incorporated into Martonvásár wheat varieties. The presence of Lr1, Lr10, Lr26, Lr34 and Lr37 in the Martonvásár gene pool was identified using molecular markers. New sources carrying alien genetic material have been tested for powdery mildew and leaf rust resistance. Valuable Fusarium head blight resistance sources have been identified in populations of old Hungarian wheat varieties. Species causing leaf spots (Pyrenophora tritici-repentis, Septoria tritici and Stagonospora nodorum) have gradually become more frequent over the last two decades. Tests on the resistance of the host plant were begun in Martonvásár four years ago and regular greenhouse tests on seedlings have also been initiated.
Six cropping populations, three variety mixtures and one diversity population were developed from winter wheat varieties and studied for physical, compositional and end-use quality traits for three years (2011–2013) under different European climatic and management conditions in order to study the stability of these traits resulted by the genetic diversity. The beneficial compositional and nutritional properties of the populations were assessed, while variation and stability of the traits were analysed statistically. No significant differences were found among the populations in low-input and organic management farming systems in the physical, compositional and processing properties, but there was a difference in the stability of these traits. Most of the populations showed higher stability than the control wheat variety, and populations developed earlier had higher stability than those developed later. Furthermore, some populations were found to be especially unstable for some traits at certain sites (mostly at Austrian, Swiss and UK organic sites). Protein content of the populations was high (13.0–14.7%) without significant difference among them, but there was significant variation in their gluten content (28–36%) and arabinoxylan content (14.6–20.3 mg/g). The most outstanding population for both protein and arabinoxylan content was a Hungarian cropping population named ELIT-CCP. It was concluded that the diversity found in the mixtures and CCPs have stabilizing effect on the quality parameters, but a higher stability was observed under low-input than under organic conditions. These results could be beneficial not only for breeders but also for the consumers in the long run.
Aboveground plant biomass is one of the most important features of ecosystems, and it is widely used in ecosystem research. Non-destructive biomass estimation methods provide an important toolkit, because the destructive harvesting method is in many cases not feasible. However, only few studies have compared the accuracy of these methods in grassland communities to date. We studied the accuracy of three widely used methods for estimation of aboveground biomass: the visual cover estimation method, the point intercept method, and field spectroscopy. We applied them in three independent series of field samplings in semi-arid sand grasslands in Central Hungary. For each sampling method, we applied linear regression to assess the strength of the relationship between biomass proxies and actual aboveground biomass, and used coefficient of determination to evaluate accuracy. We found no evidence that the visual cover estimation, which is generally considered as a subjective method, was less accurate than point intercept method or field spectroscopy in estimating biomass. Based on our three datasets, we found that accuracy was lower for the point intercept method compared to the other two methods, while field spectroscopy and visual cover estimation were similar to each other in the semi-arid sand grassland community. We conclude that visual cover estimation can be as accurate for estimating aboveground biomass as other approaches, thus the choice amongst the methods should be based on additional pros and cons associated with each of the method and related to the specific research objective.