Determination of Selection Criteria and Salinity Tolerance Indices for Screening of Rice Genotypes
Y. Z. El-Refaee
Rice Research Department, Field Crops Research Institute, ARC, Egypt.
S. M. Sakr
Rice Research Department, Field Crops Research Institute, ARC, Egypt.
R. F. El-Mantawy *
Crop Physiology Research Department, Field Crops Research Institute, ARC, Egypt.
R. Y. El-Agoury
Rice Research Department, Field Crops Research Institute, ARC, Egypt.
*Author to whom correspondence should be addressed.
Abstract
Genetic diversity is a valuable asset for crop improvement. In this study, a total of twenty rice genotypes were screened for salinity tolerance at the reproductive stage under artificial selection environments (Lysimeter conditions). Different morpho-agronomic, physiological parameters and tolerance indices were used to classify tolerant and sensitive genotypes. Our results showed high genetic variability in response of rice genotypes to salinity at non-saline and saline conditions. The environmental and genetic variances and heritability showed highly significant for all studied traits under non-saline and salinity conditions. High heritability coupled with high genetic advance as percent of the mean was observed for most studied traits under non-saline and salinity conditions. Hence, these genetic parameters can be used as direct selection criteria for rice improvement under salinity stress conditions. This study proved that the artificial salinization environment (Lyzimeter conditions) is reflect the normal saline field environment while geometric mean productivity, stress tolerance index and yield index are the tolerance indices that can be classified as better predictors of salinity tolerance considering the yield potentials of the genotypes. The genotypes A69-1, IR16T1009, SAL010 and MTU1010 can be used for breeding in the future through low Na+: K+ ratio while CSR28 and IR18T1007 for breeding salt-tolerant cultivars with higher yield potentials.
Keywords: Genetic parameters, salinity tolerance indices, multivariate analysis, Oryza sativa