Analysis Of Fruit Quantitative Characters Of Apricot Varieties

Apricot, belonging to Armennica within Rosaceae, has been one of the most important economically-exploited plants for its nutrient-rich fruits and apricot kernels with some chemical values in China. On the basis of production practice, breeding improved varieties is an important step to breed the high yield and quality of apricot varieties. Thirty-seven of ten-year apricot cultivars cultivated in Guangzhong of Shaanxi was used as materials in our research. Nine indices of apricot quantitative characters(AQC) like fruit weight(FW) were adopted to study the relationships among all indices using correlation analysis and regression analysis. All indices were graded by probability grading method to establish a reasonable grading standard. The representative indices were identified comprehensively using principal component analysis and cluster analysis. The main results were as follows:1. There was a relatively variation among nine fruit quantitative characters among apricot varieties. The variation coefficients of fruit shape index(FSI), fruit weight(FW), nucleus shape index(NSI), nucleus dry weight(NDW), kernel shape index(KSI), kernel dry weight(KDW), kernel rate(KR), water ratio(WR), and fruit weight of each plants(EFW) were 9.91%, 49.68%, 7.97%, 28.17%, 9.82%, 34.01%, 19.07%, 22.41%, 64.12%, respectively. Of those, the characters of EFW, FW and KDW presented a relatively wide variation, indicating these quantitative characters possessed abundant genetic diversity. It is an enormous potential to select the improved varieties.2. FW, NSI, NDW, KSI, KDW, KR and WR presented the normal distribution, while FST and EFW showed the asymmetrical distribution. All indices of fruit quantitative characters were divided into five grades with normal distribution, which were very low level, low level, medium level, high level and very high level, of which 10.85% were very low level, 18.76% were low level and 18.72% were high level. The highest number of varieties was medium level(42.01%); however, the very high level was only 9.66%. The results were consistent with theoretical probability distribution. The established classification standard could provide a reference for breeding improved varieties and formulating the DUS testing guidelines of apricot.3. All indices of fruit quantitative characters were simplified into 6 indices: kernel shape index(KSI), kernel rate(KR), water ratio(WR), fruit shape index(FSI), fruit weight(FW) and fruit weight of each plants(EFW).4. The five multivariate linear regression equations were obtained based on discussing the qualitative and quantitative relationships among nine indices of apricot quantitative characters(AQC), in order to accurately predict the relative quantitative characters. The dual linear regressive equation was discovered among NDW, KDW and KR for their respective prediction, while FSI and KSI could also be predicted by other indices. The regression equations were as follows: NDW y4=1.531+3.301 x6-0.050 x7, KDW y6=-0.442+0.288 x4+0.015 x7, KR y7=29.178+59.785 x6-17.142 x4, FSI y1=0.741-0.004 x2+0.319 x3, KSI y3=0.533+0.010 x7+0.301 x5.