Scholarlink

<p>In Brazil, nearly half of the country’s eucalyptus plantations are located in regions where seasonal dry periods lasting three to five months bring monthly rainfall below 50 mm. Thus, the main goal of this study was to evaluate the potential benefits of incorporating multi-year and multi-environmental data, as well as to investigate the impact of modeling different covariance structures on the selection of drought-tolerant families, and thus compare the results with the standard analysis model framework used in forest breeding. For this purpose, we evaluated the diameter at breast height (DBH, cm) in 232 <i>Eucalyptus</i> families and six commercial clones, installed in different experimental trials distributed in three distinct locations in Brazil. Three measurements were performed at 18, 30, and 42 months after planting. Finally, we identified promising families for cultivation under drought conditions. Our results show that spatial row–column corrections improved model fit (as indicated by lower AIC values) in single-environment analyses and were therefore adopted for subsequent multi-environment and multi-age analyses. Among the selected best-fitting models, the heterogeneous compound symmetry covariance structure effectively accommodated the data complexity for predicting interaction effects, while diagonal <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\otimes\)</EquationSource> </InlineEquation> first-order heterogeneous autoregressive structures were more suitable for modeling residual effects. In this context, the selected model exhibited significant variability among families in drought tolerance. Models that incorporated multi-year and multi-environmental data and adjustments in the variance-covariance structures for genetic and residual effects allowed for a better fit to the data, resulting in more consistent variance component estimates and increasing the accuracy of genetic and spatial interaction assessments. Furthermore, the selected families promise to continue regional breeding programs to develop drought-tolerant eucalyptus. Our findings underscore the potential of Brazilian eucalyptus germplasm for drought-tolerance improvement and highlight the importance of robust data integration in analyzing trials under abiotic stress.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Longitudinal data improves selection of drought-tolerant Eucalyptus germplasm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月10日

查看全文
  • Guilherme B. Canal,
  • Glêison A. dos Santos,
  • Filipe M. Ferreira,
  • Leonardo L. Bhering,
  • Thales A. P. C. Nogueira,
  • Camila F. Paixão,
  • Saulo Chaves,
  • Kaio Olimpio G. Dias

摘要

In Brazil, nearly half of the country’s eucalyptus plantations are located in regions where seasonal dry periods lasting three to five months bring monthly rainfall below 50 mm. Thus, the main goal of this study was to evaluate the potential benefits of incorporating multi-year and multi-environmental data, as well as to investigate the impact of modeling different covariance structures on the selection of drought-tolerant families, and thus compare the results with the standard analysis model framework used in forest breeding. For this purpose, we evaluated the diameter at breast height (DBH, cm) in 232 Eucalyptus families and six commercial clones, installed in different experimental trials distributed in three distinct locations in Brazil. Three measurements were performed at 18, 30, and 42 months after planting. Finally, we identified promising families for cultivation under drought conditions. Our results show that spatial row–column corrections improved model fit (as indicated by lower AIC values) in single-environment analyses and were therefore adopted for subsequent multi-environment and multi-age analyses. Among the selected best-fitting models, the heterogeneous compound symmetry covariance structure effectively accommodated the data complexity for predicting interaction effects, while diagonal \(\otimes\) first-order heterogeneous autoregressive structures were more suitable for modeling residual effects. In this context, the selected model exhibited significant variability among families in drought tolerance. Models that incorporated multi-year and multi-environmental data and adjustments in the variance-covariance structures for genetic and residual effects allowed for a better fit to the data, resulting in more consistent variance component estimates and increasing the accuracy of genetic and spatial interaction assessments. Furthermore, the selected families promise to continue regional breeding programs to develop drought-tolerant eucalyptus. Our findings underscore the potential of Brazilian eucalyptus germplasm for drought-tolerance improvement and highlight the importance of robust data integration in analyzing trials under abiotic stress.