RESUMO
Although the impact of sulfur (S) availability on the seed yield and nutritional quality of seeds has been demonstrated, its impact coupled with nitrogen (N) availability remains poorly studied in oilseed rape. A deeper knowledge of S and N interactions on seed yield components and seed quality could improve S and N fertilization management in a sustainable manner. To address this question, our goals were to determine the effects of nine different S fertilization management strategies (i) in interaction with different levels of N fertilization and (ii) according to the timing of application (by delaying and fractionating the S inputs) on agronomic performances and components of seed yield. The impact of these various managements of S and N fertilizations was also investigated on the seed quality with a focus on the composition of SSPs (mainly represented by napins and cruciferins). Our results highlighted synergetic effects on S and N use efficiencies at optimum rates of S and N inputs and antagonistic effects at excessive rates of one of the two elements. The data indicated that adjustment of S and N fertilization may lead to high seed yield and seed protein quality in a sustainable manner, especially in the context of reductions in N inputs. Delaying S inputs improved the seed protein quality by significantly increasing the relative abundance of napin (a SSP rich in S-containing amino acids) and decreasing the level of a cruciferin at 30 kDa (a SSP with low content of S-amino acids). These observations suggest that fractionated or delayed S fertilizer inputs could provide additional insights into the development of N and S management strategies to maintain or improve seed yield and protein quality. Our results also demonstrated that the S% in seeds and the napin:30 kDa-cruciferin ratio are highly dependent on S/N fertilization in relation to S supply. In addition, we observed a strong relationship between S% in seeds and the abundance of napin as well as the napin:30 kDa-cruciferin ratio, suggesting that S% may be used as a relevant index for the determination of protein quality in seeds in terms of S-containing amino acids.
RESUMO
BACKGROUND AND AIMS: Germination and heterotrophic growth are crucial steps for stand establishment. Numerical experiments based on the modelling of these early stages in relation to major environmental factors at sowing were used as a powerful tool to browse the effects of the genetic diversity of Medicago truncatula, one of the model legume species, under a range of agronomic scenarios, and to highlight the most important plant parameters for emergence. To this end, the emergence of several genotypes of M. truncatula was simulated under a range of sowing conditions with a germination and emergence simulation model. METHODS: After testing the predictive quality of the model by comparing simulations to field observations of several genotypes of M. truncatula, numerical experiments were performed under a wide range of environmental conditions (sowing dates × years × seedbed structure). Germination and emergence was simulated for a set of five genotypes previously parameterized and for two virtual genotypes engineered to maximize the potential effects of genetic diversity. KEY RESULTS: The simulation results gave an average value of 5-10 % difference in final emergence between genotypes, which was low, but the analysis underlined considerable inter-annual variation. The effects of parameters describing germination and emergence processes were quantified and ranked according to their contribution to the variation in emergence. Seedling non-emergence was mainly related to mechanical obstacles (40-50 %). More generally, plant parameters that accelerated the emergence time course significantly contributed to limiting the risk of soil surface crusting occurring before seedling emergence. CONCLUSIONS: The model-assisted analysis of the effects of genetic diversity demonstrated its usefulness in helping to identify the parameters which have most influence that could be improved by breeding programmes. These results should also enable a deeper analysis of the genetic determinism of the main plant parameters influencing emergence, using the genomic tools available for this model plant.
