Indicative traits of aluminium sensibility in rice. II. Canonical correlation with yield
As a step in the development of a method to access differential Al tolerance, data from grain yield of 20 rice genotypes growing in high and low Al saturation plots, were correlated with morphological characters of the same genotypes grown in nutrient solution. By the canonical correlation it was possible to obtain two linear combinations, among several possibilities. These combinations retained the root area and the maximal leaf length, combined with four Al levels (0, 10, 20 and 30 mg L-1 Al) as the first canonical variable. The second canonical variable considered the weighted difference between grain yield at low and high Al saturation level or the unweighted difference, with r 0.93 and 0.91, respectively. The results showed the possibility to correlate morphological traits, measured in nutrient solution, against field data, reducing uncertainty in choosening the most suitable indicator.
| Main Authors: | , , |
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| Format: | Digital revista |
| Language: | por |
| Published: |
Pesquisa Agropecuaria Brasileira
1998
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| Online Access: | https://seer.sct.embrapa.br/index.php/pab/article/view/4826 |
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| Summary: | As a step in the development of a method to access differential Al tolerance, data from grain yield of 20 rice genotypes growing in high and low Al saturation plots, were correlated with morphological characters of the same genotypes grown in nutrient solution. By the canonical correlation it was possible to obtain two linear combinations, among several possibilities. These combinations retained the root area and the maximal leaf length, combined with four Al levels (0, 10, 20 and 30 mg L-1 Al) as the first canonical variable. The second canonical variable considered the weighted difference between grain yield at low and high Al saturation level or the unweighted difference, with r 0.93 and 0.91, respectively. The results showed the possibility to correlate morphological traits, measured in nutrient solution, against field data, reducing uncertainty in choosening the most suitable indicator. |
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