APPLICATION OF MOLECULAR METHODS IN SOYBEAN BREEDING PROGRAM AT THE AGRICULTURAL INSTITUTE OSIJEK (CROATIA)
Bibliographic record
Abstract
The soybean breeding work at the Agricultural Institute Osijek has focused on the permanently development of high-yielding cultivars with genetic yield potential of 5-6 t/ha, satisfactory grain quality (protein and oil content), high tolerance to the principal diseases (Peronospora manshurica, Sclerotinia sclerotiorum, Diaporthe/Phomopsis complex), high resistance to lodging, stress conditions over vegetation and pod shattering as well as satisfactory stability in level and quality of grain and wide adaptability. Results of this continued and intensive breeding work are 36 registered cultivars which significantly contributed and contribute to the development, improving and increasing of soybean production in Republic of Croatia. Further genetic improvement of soybean cultivars is based on the modern breeding strategies including combination of conventional breeding methods and recent chemical, biochemical, phytopathology and molecular analyses. Regarding to molecular analyses, in recent years, in the frame of the soybean breeding program has initiated by application of molecular markers technology as criterion for estimation genetic diversity for both soybean germplasm and pathogens from Diaporthe/Phomopsis complex on soybean, as well. The initial fingerprinting of several OS soybean genotypes has performed in collaboration with the University of Guelph (Canada) in their biomolecular laboratory using simple sequence repeats (SSR). The obtained results enabled new access in choosing parental pairs. Combining molecular markers technique with pedigree information, phenotypic markers and statistical procedure has provided a useful tool for more accurate and complete evaluation of genetic diversity and its more effective utilization into current soybean breeding program. The detection of pathogens from Diaporthe/Phomopsis complex on soybean on molecular level has performed in collaboration with the Istituto Sperimentale per la Patologia Vegetale (Rome, Italy) in their mycological and biomolecular laboratories using method of artificial infection and RFLP markers. Obtained data are incorporated in our breeding work for improving soybean genotypes tolerance on mentioned pathogens. In a whole, implementation of molecular marker technology into soybean breeding program at the Institute represents basis for significantly increasing of its quality, success, efficiency and competitiveness and enables further genetic improvement of cultivars. Each increasing of soybean production, resulting from genetic improved cultivars, is considerable national economical profit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".