RESUMO
Beneficial effects of silicon (Si) on growth have been observed in some plant species, reportedly due to stoichiometric changes of C, N, and P. However, little is known about the effects on the stoichiometric relationships between C, N, and P when silicon is supplied via different modes in sorghum and sunflower plants under salt stress conditions. Therefore, the current study was performed to investigate the impact of differing modes of Si supply on shoot biomass production and C:N:P stoichiometry in sorghum and sunflower plants under salt stress. Two experiments were performed in a glass greenhouse using the strong Si-accumulator plant sorghum, as well as the intermediate type Si-accumulator sunflower, both of which were grown in pots filled with washed sand. Plant species were cultivated for 30 days in the absence or presence of salt stress (0 or 100 mM) and supplemented with one of four Si treatments: control plants (without Si), 28.6 mmol Si L-1 via foliar application, 2.0 mmol Si L-1 via nutrient solution, and combined application of foliar and nutrient solution, each group with five replications. The results revealed that supplied Si modified the C, N, and P concentrations, thereby enhancing the C:N:P stoichiometry and shoot dry matter of sorghum and sunflower plants under salt stress. Both application of Si via nutrient solution, as well as combined application via foliar and nutrient solution, increased the C:N ratio in both plant species under salt stress, but in sorghum plants decreased the C:P and N:P ratios and increased the shoot biomass production by 39%, while in sunflower plants increased the C:P and N:P ratios and increased the shoot biomass production by 24%. Our findings suggest that salt stress alleviation by Si impacts C:N:P stoichiometric relationships in a variable manner depending on the ability of the species to accumulate Si, as well as the route of Si administration.
RESUMO
In the context of multi-environment trials, where a series of experiments is conducted across different environmental conditions, the analysis of the structure of genotype-by-environment interaction is an important topic. This paper presents a generalization of the joint regression analysis for the cases where the response (e.g. yield) is not linear across environments and can be written as a second (or higher) order polynomial or another non-linear function. After identifying the common form regression function for all genotypes, we propose a selection procedure based on the adaptation of two tests: (i) a test for parallelism of regression curves; and (ii) a test of coincidence for those regressions. When the hypothesis of parallelism is rejected, subgroups of genotypes where the responses are parallel (or coincident) should be identified. The use of the Scheffé multiple comparison method for regression coefficients in second-order polynomials allows to group the genotypes in two types of groups: one with upward-facing concavity (i.e. potential yield growth), and the other with downward-facing concavity (i.e. the yield approaches saturation). Theoretical results for genotype comparison and genotype selection are illustrated with an example of yield from a non-orthogonal series of experiments with winter rye (Secalecereale L.). We have deleted 10 % of that data at random to show that our meteorology is fully applicable to incomplete data sets, often observed in multi-environment trials.
RESUMO
In the context of multi-environment trials, where a series of experiments is conducted across different environmental conditions, the analysis of the structure of genotype-by-environment interaction is an important topic. This paper presents a generalization of the joint regression analysis for the cases where the response (e.g. yield) is not linear across environments and can be written as a second (or higher) order polynomial or another non-linear function. After identifying the common form regression function for all genotypes, we propose a selection procedure based on the adaptation of two tests: (i) a test for parallelism of regression curves; and (ii) a test of coincidence for those regressions. When the hypothesis of parallelism is rejected, subgroups of genotypes where the responses are parallel (or coincident) should be identified. The use of the Scheffé multiple comparison method for regression coefficients in second-order polynomials allows to group the genotypes in two types of groups: one with upward-facing concavity (i.e. potential yield growth), and the other with downward-facing concavity (i.e. the yield approaches saturation). Theoretical results for genotype comparison and genotype selection are illustrated with an example of yield from a non-orthogonal series of experiments with winter rye (Secalecereale L.). We have deleted 10 % of that data at random to show that our meteorology is fully applicable to incomplete data sets, often observed in multi-environment trials.