Herbert Bistrich
Research Subjects
Herbert Bistrich has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Christian Ametz
Saatzucht Donau GesmbH & CoKG, Saatzuchtstrasse 11, 2301 Probstdorf, Austria
3 shared publications - Hermann Buerstmayr
Institute of Biotechnology in Plant Production, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430 Tulln, Austria
3 shared publications - Sebastian Michel
Institute of Biotechnology in Plant Production, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430 Tulln, Austria
3 shared publications - Franziska Löschenberger
Saatzucht Donau GesmbH & CoKG, Saatzuchtstrasse 11, 2301 Probstdorf, Austria
2 shared publications - Franziska Löschenberger
Saatzucht Donau GesmbH & CoKG, Saatzuchtstrasse 11, 2301 Probstdorf, Austria
1 shared publication
Latest Publications
- Open Access
Predicting Plant Breeder Decisions Across Multiple Selection Stages in a Wheat Breeding Program
Selection decisions in plant breeding programs are complex, and breeders aim to integrate phenotypic impressions, genotypic data, and agronomic performance across multiple selection stages to develop successful varieties. This study investigates whether such decisions can be predicted in a commercial winter wheat (Triticum aestivum L.) breeding program using elastic net models trained on genome-wide distributed markers and genomic estimated breeding values. For this purpose, a dataset of several thousand lines tested between 2015 and 2019 in preliminary, advanced, and elite multi-environment yield trials was analyzed across three decision-making scenarios. The predictive models achieved a higher precision than random selection in all scenarios, with an increased performance when genomic estimated breeding values were included as predictors. Comparisons of breeder selections and model recommendations in terms of selection differentials for key agronomic traits showed a substantial overlap in breeding objectives, while both the breeder’s decisions and the model’s suggestions maintained similar levels of genetic diversity. Although the precision of the elastic net model was of moderate magnitude, divergent model recommendations often identified promising alternative lines, highlighting the potential of artificial intelligence to support decision-making in plant breeding.
Open AccessPredicting Plant Breeder Decisions Across Multiple Selection Stages in a Wheat Breeding Program
Selection decisions in plant breeding programs are complex, and breeders aim to integrate phenotypic impressions, genotypic data, and agronomic performance across multiple selection stages to develop successful varieties. This study investigates whether such decisions can be predicted in a commercial winter wheat (Triticum aestivum L.) breeding program using elastic net models trained on genome-wide distributed markers and genomic estimated breeding values. For this purpose, a dataset of several thousand lines tested between 2015 and 2019 in preliminary, advanced, and elite multi-environment yield trials was analyzed across three decision-making scenarios. The predictive models achieved a higher precision than random selection in all scenarios, with an increased performance when genomic estimated breeding values were included as predictors. Comparisons of breeder selections and model recommendations in terms of selection differentials for key agronomic traits showed a substantial overlap in breeding objectives, while both the breeder’s decisions and the model’s suggestions maintained similar levels of genetic diversity. Although the precision of the elastic net model was of moderate magnitude, divergent model recommendations often identified promising alternative lines, highlighting the potential of artificial intelligence to support decision-making in plant breeding.
- Open Access
Can We Teach Machines to Select Like a Plant Breeder? A Recommender System Approach to Support Early Generation Selection Decisions Based on Breeders’ Preferences
Plant breeding is considered to be the science and art of genetically improving plants according to human needs. Breeders in this context oftentimes face the difficult task of selecting among thousands of genotypes for dozens of traits simultaneously. Using a breeder’s selection decisions from a commercial wheat breeding program as a case study, this study investigated the possibility of implementing a recommender system based on the breeder’s preferences to support early-generation selection decisions in plant breeding. The target trait was the retrospective binary classification of selected versus non-selected breeding lines during a period of five years, while the selection decisions of the breeder were predicted by various machine learning models. The explained variance of these selection decisions was of moderate magnitude ( = 0.45), and the models’ precision suggested that the breeder’s selection decisions were to some extent predictable (~20%), especially when some of the pending selection candidates were part of the training population (~30%). Training machine learning algorithms with breeders’ selection decisions can thus aid breeders in their decision-making processes, particularly when integrating human and artificial intelligence in the form a recommender system to potentially reduce a breeder’s effort and the required time to find interesting selection candidates.Open AccessCan We Teach Machines to Select Like a Plant Breeder? A Recommender System Approach to Support Early Generation Selection Decisions Based on Breeders’ Preferences
Plant breeding is considered to be the science and art of genetically improving plants according to human needs. Breeders in this context oftentimes face the difficult task of selecting among thousands of genotypes for dozens of traits simultaneously. Using a breeder’s selection decisions from a commercial wheat breeding program as a case study, this study investigated the possibility of implementing a recommender system based on the breeder’s preferences to support early-generation selection decisions in plant breeding. The target trait was the retrospective binary classification of selected versus non-selected breeding lines during a period of five years, while the selection decisions of the breeder were predicted by various machine learning models. The explained variance of these selection decisions was of moderate magnitude ( = 0.45), and the models’ precision suggested that the breeder’s selection decisions were to some extent predictable (~20%), especially when some of the pending selection candidates were part of the training population (~30%). Training machine learning algorithms with breeders’ selection decisions can thus aid breeders in their decision-making processes, particularly when integrating human and artificial intelligence in the form a recommender system to potentially reduce a breeder’s effort and the required time to find interesting selection candidates. - Open Access
Towards Streamlining the Choice of Crossing Combinations in Plant Breeding by Integrating Model-Based Recommendations and Plant Breeder’s Preferences
Selecting crossing combinations crucial for successfully developing new improved crop varieties and genomic data from DNA markers have become invaluable for guiding plant breeders in evaluating and choosing promising crosses between potential parents. However, navigating the vast array of thousands of possible parental combinations, even with extensive genomic information, can be challenging, even for experienced breeders with deep knowledge of their crop’s gene pool. This case study aimed to evaluate the effectiveness of a recommender system to support plant breeders in this complex decision-making process. It took a retrospective approach, analyzing selection decisions made by an experienced breeder across several thousand potential crossing combinations over six years. The results indicated that a recommender system could significantly reduce the time and effort needed to identify promising crosses aligned with the breeder’s preferences. However, active feedback from the breeder to the recommender system appeared to be essential for achieving a satisfactory prediction. Integrating model-based recommendations and plant breeder’s preferences in a recommender system featuring such a reciprocal fine-tuning scheme, where the breeder actively provides feedback to the machine in the style of hybrid human–artificial intelligence, represents one step towards streamlining the choice of crossing combinations in plant breeding programs.
Open AccessTowards Streamlining the Choice of Crossing Combinations in Plant Breeding by Integrating Model-Based Recommendations and Plant Breeder’s Preferences
Selecting crossing combinations crucial for successfully developing new improved crop varieties and genomic data from DNA markers have become invaluable for guiding plant breeders in evaluating and choosing promising crosses between potential parents. However, navigating the vast array of thousands of possible parental combinations, even with extensive genomic information, can be challenging, even for experienced breeders with deep knowledge of their crop’s gene pool. This case study aimed to evaluate the effectiveness of a recommender system to support plant breeders in this complex decision-making process. It took a retrospective approach, analyzing selection decisions made by an experienced breeder across several thousand potential crossing combinations over six years. The results indicated that a recommender system could significantly reduce the time and effort needed to identify promising crosses aligned with the breeder’s preferences. However, active feedback from the breeder to the recommender system appeared to be essential for achieving a satisfactory prediction. Integrating model-based recommendations and plant breeder’s preferences in a recommender system featuring such a reciprocal fine-tuning scheme, where the breeder actively provides feedback to the machine in the style of hybrid human–artificial intelligence, represents one step towards streamlining the choice of crossing combinations in plant breeding programs.