Background: Due to the diversity of rice varieties and cropping systems in China, the limitation of seeding density and seedling quality makes it hard to improve machine-transplanted efficiency. Previous studies have shown that indica and japonica varieties varied in machine transplanting efficiency and optimal seeding density. In this study, a RIL population derived from ‘9311’ and ‘Nipponbare’ were performed to explore the seedling traits variations and the genetic mechanism under three seeding densities. Results: The parents and RIL population exhibited similar trends as the seeding density increased, including seedling height and first leaf sheath length increases, shoot dry weight and root dry weight decreases. Among the 37 QTLs for six traits detected under the three seeding densities, 12 QTLs were detected in both three seeding densities. Five QTL hotspots identified clustered within genomic regions on chromosomes 1, 2, 4, 6 and 11. Specific QTLs such as qRDW1.1 and qFLSL5.1 were detected under low and high seeding densities, respectively. Detailed analysis the QTL regions identified under specific seeding densities revealed several candidate genes involved in phytohormones signals and abiotic stress responses. Whole-genome additive effects showed that ‘9311’ contributed more loci enhancing trait performances than ‘Nipponbare’, indicating ‘9311’ was more sensitive to the seeding density than ‘Nipponbare’. The prevalence of negative epistasis effects indicated that the complementary two-locus homozygotes may not have marginal advantages over the means of the two parental genotypes. Conclusions: Our results revealed the differences between indica rice and japonica rice seedling traits in response to seeding density. Several QTL hotspots involved in different traits and specific QTLs (such as qRDW1.1 and qFLSL5.1) in diverse seeding densities had been detected. Genome-wide additive and two-locus epistasis suggested a dynamic of the genetic control underlying different seeding densities. It was concluded that novel QTLs, additive and epistasis effects under specific seeding density would provide adequate information for rice seedling improvement during machine transplanting.
Background: Due to the diversity of rice varieties and cropping systems in China, the limitation of seeding density and seedling quality makes it hard to improve machine-transplanted efficiency. Previous studies have shown that indica and japonica varieties varied in machine transplanting efficiency and optimal seeding density . In this study, a RIL population derived from ‘9311’ and ‘ Nipponbare ’ were performed to explore the seedling traits variations and the genetic mechanism under three seeding densities. Results: The parents and RIL population exhibited similar trends as the seeding density increased, including seedling height and first leaf sheath length increases, shoot dry weight and root dry weight decreases. Among the 37 QTLs for six traits detected under the three seeding densities, 12 QTLs were detected in both three seeding densities. Five QTL hotspots identified clustered within genomic regions on chromosomes 1, 2, 4, 6 and 11. Specific QTLs such as qRDW 1.1 and qFLSL 5.1 were detected under low and high seeding densities, respectively. Detailed analysis the QTL regions identified under specific seeding densities revealed several candidate genes involved in phytohormones signals and abiotic stress responses. Whole-genome additive effects showed that ‘9311’ contributed more loci enhancing trait performances than ‘Nipponbare’, indicating ‘9311’ was more sensitive to the seeding density than ‘Nipponbare’. The prevalence of negative epistasis effects indicated that the complementary two-locus homozygotes may not have marginal advantages over the means of the two parental genotypes. Conclusions: Our results revealed the differences between indica rice and japonica rice seedling traits in response to seeding density. Several QTL hotspots involved in different traits and specific QTLs (such as qRDW 1.1 and qFLSL 5.1 ) in diverse seeding densities had been detected. Genome-wide additive and two-locus epistasis suggested a dynamic of the genetic control underlying different seeding densities. It was concluded that novel QTLs, additive and epistasis effects under specific seeding density would provide adequate information for rice seedling improvement during machine transplanting.
We studied the effects of leaf surface characteristics on canopy droplet behaviour using two rice cultivars with similar leaf shapes but significantly different leaf surface characteristics: Jia58 (glabrous rice; smooth leaf surface and no burrs) and Yongyou12 (hairy-leaved rice; rough leaf surface covered with burrs). The plants were subjected to spray tests with different spray pressures and nozzle apertures. The results show that the deposition amount per unit leaf area was significantly higher in the Yongyou12 canopy than in the Jia58 canopy. The diameter, volume median diameter, number median diameter, and coverage of droplets were significantly higher in Yongyou12 than in Jia58, while the coverage density of droplets was significantly lower. The proportion of small droplets of Jia58 is higher than that of Yongyou12. Thus, a larger amount of large-sized droplets could retain on the leaf surface of hairy-leaved rice, and a larger number of small-sized droplets were retained on the leaf surface of glabrous rice. Smaller pressure and larger flow nozzle were conducive to the retention of the Jia58, while Yongyou12 required larger pressure and larger flow nozzles. Ultrastructural analyses revealed that the leaf surface of glabrous rice had no trichomes and more wax than hairy-leaved rice, and the critical surface tension was lower, resulting in the retention of mainly small droplets on its leaf surface and a lower deposition amount. Therefore, in order to increase the deposition of pesticide droplets on the leaf surface in production, glabrous rice should choose nozzles with smaller spray pressure and large flow rate.
Background: Due to the diversity of rice varieties and cropping systems in China, the limitation of seeding density and seedling quality makes it hard to improve machine-transplanted efficiency. Previous studies have shown that indica and japonica varieties varied in machine transplanting efficiency and optimal seeding density. In this study, a RIL population derived from ‘9311’ and ‘Nipponbare’ were performed to explore the seedling traits variations and the genetic mechanism under three seeding densities. Results: The parents and RIL population exhibited similar trends as the seeding density increased, including seedling height and first leaf sheath length increases, shoot dry weight and root dry weight decreases. Among the 37 QTLs for six traits detected under the three seeding densities, 12 QTLs were detected in both three seeding densities. Five QTL hotspots identified clustered within genomic regions on chromosomes 1, 2, 4, 6 and 11. Specific QTLs such as qRDW1.1 and qFLSL5.1 were detected under low and high seeding densities, respectively. Detailed analysis the QTL regions identified under specific seeding densities revealed several candidate genes involved in phytohormones signals and abiotic stress responses. Whole-genome additive effects showed that ‘9311’ contributed more loci enhancing trait performances than ‘Nipponbare’, indicating ‘9311’ was more sensitive to the seeding density than ‘Nipponbare’. The prevalence of negative epistasis effects indicated that the complementary two-locus homozygotes may not have marginal advantages over the means of the two parental genotypes. Conclusions: Our results revealed the differences between indica rice and japonica rice seedling traits in response to seeding density. Several QTL hotspots involved in different traits and specific QTLs (such as qRDW1.1 and qFLSL5.1) in diverse seeding densities had been detected. Genome-wide additive and two-locus epistasis suggested a dynamic of the genetic control underlying different seeding densities. It was concluded that novel QTLs, additive and epistasis effects under specific seeding density would provide adequate information for rice seedling improvement during machine transplanting.
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