Given the lack of comprehensive research on seed vigour of sweet corn (Zea mays saccharata) and different genetic backgrounds of super sweet and sugar-enhanced sweet corn, it is necessary to improve their seed vigour assessment. Seed vigour status of eight super sweet and eight sugar-enhanced sweet corn hybrids was examined by standard germination, stress resistance, physiological and biochemical tests, and field emergence tests. Analysis of variance showed significant differences between sweet corn genotype and seed vigour. However, vigour assessment was different with different tests. Correlation analysis revealed that volatile aldehyde was the optimal index for seed vigour assessment of super sweet corn and dehydrogenase activity was the optimal for sugar-enhanced sweet corn. On the basis of correlation analysis, regression equations were established for predicting field emergence performance. Verification experimentation suggested the established regression equations were very reliable.
Oxygen sensing technology was employed to study the rapid methods for seed vigor assessment of Chinese fir (Cunninghamia lanceolata) and Masson pine (Pinus massoniana). Firstly, seeds of five lots were performed using accelerated aging (AA) into three vigor levels. Then, four oxygen sensing indices, including increased metabolism time (IMT), oxygen metabolism rate (OMR), critical oxygen pressure (COP), relative germination time (RGT) and the control indices such as laboratory germination indices, dehydrogenase activity (DA), and electrical conductivity (EC) were analyzed by the tests of 15 samples. The results of correlation analysis between these indices and field emergence performances based on two-year and two-spot data showed that RGT and OMR should be indicated as the optimal oxygen sensing indices to rapidly and automatically evaluate seed vigor of Chinese fir and Masson pine, respectively. On the basis, one-variable linear regression equations were built to forecast their field emergence performances by the two oxygen sensing indices.
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