Institut für Pflanzenzüchtung, Saatgutforschung und Populationsgenetik

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/13

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Browsing Institut für Pflanzenzüchtung, Saatgutforschung und Populationsgenetik by Journal "Plant breeding"

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    How can we breed for phosphate efficiency in maize (Zea mays)?
    (2022) Weiß, Thea M.; Li, Dongdong; Roller, Sandra; Liu, Wenxin; Hahn, Volker; Leiser, Willmar L.; Würschum, Tobias
    Future farming is required to produce high yields with reduced inputs. Increased fertilizer prices and policy goals underline the need to breed for nutrient‐efficient varieties. We therefore conducted a multienvironmental field trial comprising 400 maize genotypes, half elite lines and half doubled haploid lines from six European landraces and assessed yield parameters and corresponding phosphorus concentrations at two developmental stages. From these traits, we derived several measures for phosphate efficiency and evaluated them phenotypically and genetically. The results of this study revealed that ample variation for phosphate efficiency is present in maize. However, while elite material clearly outperformed all landraces with regard to yield‐related traits, some landrace genotypes indicated superior early development characteristics. The phosphate efficiency measures showed a complex genetic architecture, and hence, genomic selection appears best suited to assist their improvement. Taken together, breeding for phosphate efficiency is feasible but should be performed under the same conditions in which the crops are eventually grown because phosphate efficiency and what is deemed a sustainable P balance largely depends on the context.
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    Phenomic prediction can be improved by optimization of NIRS preprocessing
    (2025) Braun, Vincent; Zhu, Xintian; Meyenberg, Carina; Hahn, Volker; Maurer, Hans Peter; Würschum, Tobias; Thorwarth, Patrick
    In recent years, phenomic prediction has emerged as a new method in plant breeding that has been shown to have great potential. However, there are still many open questions regarding its practical application. For example, in the field of spectroscopy, it is standard practice to optimize the preprocessing of spectra, which so far has only been done to a limited extent for phenomic prediction. In this study, we therefore used three different data sets of soybean, triticale and maize to identify the best combinations of Savitzky–Golay filter parameters for preprocessing near‐infrared spectra for phenomic prediction. We tested 677 combinations of polynomial order, derivative and window size and evaluated them with Monte Carlo cross‐validation. Our results showed that the predictive ability can be improved with the right settings. However, there was no global optimum that gave the best results for all data sets. Even for different traits within the same data set, different combinations of parameters were necessary to achieve the highest predictive ability. Nevertheless, we show that some combinations generally result in a very low predictive ability and should not be used for preprocessing. In addition, we used the normalized discounted cumulative gain to assess whether preprocessing affected the ranking of individuals, which revealed no major changes in the top 1%, 10% or 20% of predicted individuals. Taken together, our results show the potential of preprocessing near‐infrared spectroscopy data to improve the phenomic predictive ability, but there appears to be no global optimum of parameter settings across data sets and traits.