Using multivariate analyses to evaluate the impact of proline on sugar and root yield traits in sugar beet under saline conditions
Abstract Background Using vital amino acids, such as proline, helps the sugar beet and benefits plants that grow in harsh and saline soil. Saline soil stress has a negative effect on both root and sugar yield. Methods To lessen the negative impacts of salinity, six monogerm sugar beet varieties were...
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2025-07-01
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| Series: | Bulletin of the National Research Centre |
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| Online Access: | https://doi.org/10.1186/s42269-025-01338-y |
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| author | Farrag F. B. Abu-Ellail Noran A. M. Bassiony Ramy N. F. Abdelkawy |
| author_facet | Farrag F. B. Abu-Ellail Noran A. M. Bassiony Ramy N. F. Abdelkawy |
| author_sort | Farrag F. B. Abu-Ellail |
| collection | DOAJ |
| description | Abstract Background Using vital amino acids, such as proline, helps the sugar beet and benefits plants that grow in harsh and saline soil. Saline soil stress has a negative effect on both root and sugar yield. Methods To lessen the negative impacts of salinity, six monogerm sugar beet varieties were assessed with varying proline concentrations during the 2022–2023 and 2023–2024 seasons. Across two growing seasons, six sugar beet varieties— Smart Meyra, Smart Seza, BTS3740, BTS3880, Wombat Smart, and SV2003—were cultivated in subplots, while proline levels (zero, 50, 100, and 200 ppm) were administered in the main plots. Results BTS3880 and Smart Seza exhibited the highest mean values for quality attributes, whereas Smart Meyra and Smart Seza emerged as elite varieties with superior growth and yield traits. Sugar beet root yield showed a positive association with root length, root diameter, root fresh weight/plant, top fresh weight, sucrose, extractable sugar, and total sugar yield; in contrast, the sugar yield was negatively correlated with potassium (K), sodium (Na), alpha-amino (N), and sucrose loss to molasses (SLM). The findings showed that only three of the twelve principal components (PCs) demonstrated 95.6% variability between the characteristics under study. Selecting key traits that enhance sugar yield under PC1 may be beneficial, as PC1 showed the most significant variation. Conclusion It is possible to obtain high yield and quality sugar characteristics by spraying sugar beet varieties with a 200 ppm proline concentration. Smart Meyra and Smart Seza were the top-performing varieties, exhibiting high values (as a mean) for growth, yield, and quality traits. |
| format | Article |
| id | doaj-art-00821e530d8741eca8a77208d1290848 |
| institution | Kabale University |
| issn | 2522-8307 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Bulletin of the National Research Centre |
| spelling | doaj-art-00821e530d8741eca8a77208d12908482025-08-20T04:01:46ZengSpringerOpenBulletin of the National Research Centre2522-83072025-07-0149111410.1186/s42269-025-01338-yUsing multivariate analyses to evaluate the impact of proline on sugar and root yield traits in sugar beet under saline conditionsFarrag F. B. Abu-Ellail0Noran A. M. Bassiony1Ramy N. F. Abdelkawy2Breeding & Genetic Departmen, Sugar Crops Research Institute, Agricultural Research CenterVariety Maintenance Research Department, Sugar Crops Research Institute, Agricultural Research CenterCentral Laboratory for Design and Statistical Analysis Research, Agricultural Research CenterAbstract Background Using vital amino acids, such as proline, helps the sugar beet and benefits plants that grow in harsh and saline soil. Saline soil stress has a negative effect on both root and sugar yield. Methods To lessen the negative impacts of salinity, six monogerm sugar beet varieties were assessed with varying proline concentrations during the 2022–2023 and 2023–2024 seasons. Across two growing seasons, six sugar beet varieties— Smart Meyra, Smart Seza, BTS3740, BTS3880, Wombat Smart, and SV2003—were cultivated in subplots, while proline levels (zero, 50, 100, and 200 ppm) were administered in the main plots. Results BTS3880 and Smart Seza exhibited the highest mean values for quality attributes, whereas Smart Meyra and Smart Seza emerged as elite varieties with superior growth and yield traits. Sugar beet root yield showed a positive association with root length, root diameter, root fresh weight/plant, top fresh weight, sucrose, extractable sugar, and total sugar yield; in contrast, the sugar yield was negatively correlated with potassium (K), sodium (Na), alpha-amino (N), and sucrose loss to molasses (SLM). The findings showed that only three of the twelve principal components (PCs) demonstrated 95.6% variability between the characteristics under study. Selecting key traits that enhance sugar yield under PC1 may be beneficial, as PC1 showed the most significant variation. Conclusion It is possible to obtain high yield and quality sugar characteristics by spraying sugar beet varieties with a 200 ppm proline concentration. Smart Meyra and Smart Seza were the top-performing varieties, exhibiting high values (as a mean) for growth, yield, and quality traits.https://doi.org/10.1186/s42269-025-01338-yClusterCorrelationHeat mapProlineRegression and yield |
| spellingShingle | Farrag F. B. Abu-Ellail Noran A. M. Bassiony Ramy N. F. Abdelkawy Using multivariate analyses to evaluate the impact of proline on sugar and root yield traits in sugar beet under saline conditions Bulletin of the National Research Centre Cluster Correlation Heat map Proline Regression and yield |
| title | Using multivariate analyses to evaluate the impact of proline on sugar and root yield traits in sugar beet under saline conditions |
| title_full | Using multivariate analyses to evaluate the impact of proline on sugar and root yield traits in sugar beet under saline conditions |
| title_fullStr | Using multivariate analyses to evaluate the impact of proline on sugar and root yield traits in sugar beet under saline conditions |
| title_full_unstemmed | Using multivariate analyses to evaluate the impact of proline on sugar and root yield traits in sugar beet under saline conditions |
| title_short | Using multivariate analyses to evaluate the impact of proline on sugar and root yield traits in sugar beet under saline conditions |
| title_sort | using multivariate analyses to evaluate the impact of proline on sugar and root yield traits in sugar beet under saline conditions |
| topic | Cluster Correlation Heat map Proline Regression and yield |
| url | https://doi.org/10.1186/s42269-025-01338-y |
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