A path analysis was performed to gain insights into the extent to which each underlying component relates to genotypic variations in RGR, from both C and N economy perspectives (Fig. 3.7). RGR was the response variable. NAR and LAR were explanatory variables from C economy perspective, while NP and PNC were explanatory variables from N economy perspective. Each explanatory variable and the response variable used in the path analysis represent the means of 10 genotypes. Path analysis was performed separately for each N treatment. Error terms were negligible; hence, they were not shown in the path diagrams. Single-arrowed lines along with path-coefficients indicate the direct influence of each explanatory variable on the response variable. The path coefficients measure how a change of one unit standard deviation of one variable affects another variable (expressed on same units) independent of
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other variables (Poorter and Remkes, 1990, Poorter, 1989). Double-arrowed lines accompanied by correlation coefficients indicate the association between explanatory variables (Chan-Navarrete et al., 2014).
First, I explored to what extent C economy traits contribute to variation in RGR across the 10 genotypes. Considering all genotypes together at 2 mM, the
Figure 3.7 Path diagrams showing the relationship between RGR and its underlying components of 10 genotypes of rice (averaged across six harvests) from both C and N economy perspective under two N levels. Mean RGR at each N level calculated by fitting a first order linear regression to the
ln transformed plant dry mass across six harvests. NAR at each N level calculated by dividing above mean RGR by LAR averaged values across six harvests as RGR is the product of NAR and LAR. NP at each N level calculated by dividing above calculated mean RGR by PNC averaged across six harvests as RGR is the product of NP and PNC. The variation in RGR explained by each underlying parameter is indicated by the path co-efficient as shown closer to the uni-directional arrow. Significance of relationship is shown as *p < 0.05, **p < 0.01, ***p < 0.001. The correlation co-efficients that represent the relationship among parameters are shown closer to the bi-directional arrows. See results (section 3.4.3) for further explanation.
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effect of NAR on RGR was 1.61 (p < 0.01) while, the effect of LAR was minor (0.98, non-significant) (Fig. 3.7). A significant negative correlation of -0.94 (p < 0.05) was found between NAR and LAR. NAR alone (without any covariance with other variables) was positively correlated with variations in RGR under high N supply. However, due to the strong negative correlation between NAR and LAR, a decrease in LAR suggests an increase in NAR. Due to the strong relationship between NAR and RGR and lack of statistically significant relationship between LAR and RGR, the effects of NAR dominate; thus RGR increases when increasing NAR. When considering 0.06 mM grown plants, the magnitudes of path-coefficients were smaller compared to 2 mM. NAR (0.96, p < 0.001) and LAR (0.46, p < 0.01) both positively influenced RGR at 0.06 mM. NAR negatively influenced LAR (-0.34); however, that effect was negligible. LAR alone (without any covariance with other variables) was positively correlated with RGR under low N. Although a decrease in LAR indicates an increase in NAR, the negative correlation between LAR and NAR was rather weak in the 0.06 mM grown plants. Due to the close relationship between NAR and RGR, the effects of NAR again dominate over low LAR in those plants grown on low N supply; consequently, RGR will increases as NAR increases under low N.
Having observed the significant positive contribution of LAR to variation in RGR at 0.06 mM, it is of interest to see how underlying components of LAR (i.e. SLA and LMR) along with NAR contribute to variation in RGR at 0.06 mM (Fig. 3.8).
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Including all genotypes collectively, all three factors NAR (1.04, p < 0.001), LMR (0.40, p < 0.001) and SLA (0.47, p < 0.001) have a positive effect on RGR. However, the effect of NAR on RGR was stronger than LMR and SLA. There were negative NAR-SLA and LMR-SLA correlations, as well as a positive NAR- LMR correlation. However, those were rather weak and statistically non- significant. NAR, LMR and SLA together explained 90.3% variation in RGR. Collectively, these results point to the importance of NAR for variations in RGR,
Figure 3.8 Path diagram showing the relationship between RGR and its underlying components (NAR, LMR and SLA) of 10 genotypes of rice (averaged across six harvests) from C economy perspective at 0.06 mM N level. Mean RGR at each N level calculated by fitting a first order linear regression to the ln transformed plant dry mass across six harvests. NAR at each N level calculated by dividing above mean RGR by LAR averaged values across six harvests as RGR is the product of NAR and LAR. The variation in RGR explained by each underlying parameter is indicated by the path co-efficient as shown closer to the uni-directional arrow. Significance of relationship is shown as *p < 0.05, **p < 0.01, ***p < 0.001. The correlation co-efficients that represent the relationship among parameters are shown closer to the bi-directional arrows. See results (section 3.4.3) for further explanation.
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and the way in which NAR is then linked to variations in leaf structure and/or biomass allocation to above and below ground organs.
Now I examine to what extent N economy traits contribute to variation in RGR (Fig. 3.7). Taking all genotypes together, both NP (1.41, p < 0.001) and PNC (0.85, p < 0.001) contributed positively to variation in RGR for 2 mM N grown plants. There was a negative correlation between NP and PNC for plants grown under high N supply, albeit with the relationship not being statistically significant. Not surprisingly, PNC alone (without any covariance with other variables) was positively correlated with RGR under high N. Due to the close relationship between NP and RGR, the effects of NP dominate over low PNC in determining variation in RGR in high-N grown plants. Consequently, RGR increases as NP increases under high N. Though the magnitude of path coefficients were lower in 0.06 mM N grown plants, both NP (1.81, p < 0.001) and PNC (0.35, p < 0.001) positively contributed to variation in RGR. In contrast to plants grown under 2 mM N, there was a positive (but not significant) correlation between NP and PNC in 0.06 mM N plants. Thus, while NP and PNC contribute to genotypic variations for plants grown on high and low N supply, variations in NP of greater importance.
3.5 Discussion
3.5.1 Did the factors that account for variations in RGR among the 10