Registration of ‘YZ081609’ Sugarcane

PeiFang, Zhao; Xia, Hongming; Liu, Jiayong; CaiWen, Wu; Zhao, Jietang; Yang, Kun; FengGang, Zan; Chen, Xuekuan; Li, Jun; Yao, Li; Yang, Hong; Qin, Wei; Zhao, Liping; Wu, Zhuandi

doi:10.3198/jpr2018.10.0068crc

Cited by 10 publications

(11 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experiment was conducted from 2017 to 2018 using sugarcane cultivar YZ081609 [ 24 ]. Planting was carried out on 22 April 2016 at a conventional planting density of 120,000 buds ha -1 (two-bud cane setts).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Effect of cutting depth during sugarcane (Saccharum spp. hybrid) harvest on root characteristics and yield

et al. 2021

View full text Add to dashboard Cite

Ratooning is an important cultivation practice in sugarcane production around the world, with underground buds on the remaining stalk acting as the source for establishment of a subsequent ratoon crop. However, the optimal depth of cutting during harvest in terms of yield and root growth remains unknown. We carried out a two-year field study to determine the effects of three cutting depths (0, 5 and 10 cm below the surface) ratoon cane root and yield. Results showed that cutting to a depth of 5 cm increased the root fresh weight and root volume by 21–59% and 41–127%, respectively, compared to cutting depths of 0 and 10 cm. Remarkably, cutting to a depth of 5 cm also had a significant effect on the development of fine roots, which is closely linked to cane yield. The effect was particularly noticeable in terms of two root traits, root volume and the surface area of roots with a diameter of 1.0–2.0mm, and root length and the number of root tips in roots with a diameter of 0–0.5mm. As a result, a cutting depth of 5 cm below the surface increased cane yield by 43 and 28% compared to depths of 0 and 10 cm below the surface, respectively. Overall, these findings suggest that a cutting depth of 5 cm is optimal in terms of sugarcane yield, largely due to the enhanced effect on root traits, especially the development of fine roots. These findings will help optimize sugarcane ratoon management and improve the ratoon cycle.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Since underground buds are important in establishing ratoon crops [ 24 ], proper ratoon management aimed at promoting underground germination is important. The activity of underground buds was found to decrease, while the number of buds increases with soil depth [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of cutting depth during sugarcane (Saccharum spp. hybrid) harvest on root characteristics and yield

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The hairs are few, defoliation is good, and ratooning ability is strong. It is highly resistant to sugarcane mosaic disease, and highly tolerance to drought stress, but moderately susceptible to sugarcane smut disease (Figure 1B) (Xia et al, 2018;Zhao et al, 2019;Zhang et al, 2022Zhang et al, , 2024. Notably, YZ08-1609 exhibits lower resistance to smut disease during the seedling stage and it is prone to falling over once it reaches a height of over 2.0 meters.…”

Section: Genetic Characteristics Of Yz08-1609mentioning

confidence: 99%

“…Besides, the combination configuration is random and the penetration of modern technology is limited. As for the breeding process, it still relies heavily on empirical methods, well known as "pulling and matching for parental combination, and bumping for variety selection" (Zhao et al, 2019;Zhang et al, 2022Zhang et al, , 2024.…”

Section: Conclusion and Future Perspectivementioning

confidence: 99%

Theory to practice: a success in breeding sugarcane variety YZ08–1609 known as the King of Sugar

Wu,

Li,

Zhao

et al. 2024

Front. Plant Sci.

Self Cite

View full text Add to dashboard Cite

Sugarcane, a significant cash crop in tropical and subtropical regions, contributes to 80% of sugar production and 40% of bioethanol production in the world. It is a key sugar crop, accounting for 85% of sugar production in China. Developing new varieties with high yield, high sugar, and better stress resistance is crucial for the sustainable growth of sugar industry. Hybrid breeding is the most widely used and effective method, with over 98% of Chinese sugarcane varieties resulting from this approach. Over the past two decades, Chinese breeders have developed the theory of high-heterogeneous composite high-sugar breeding, leading to the successful breeding of the fifth-generation sugarcane varieties. Among them, YZ08–1609, a complex hybrid of Saccharum spp., was developed by Sugarcane Research Institute (YSRI) of Yunnan Academy of Agricultural Sciences. The average cane yield of YZ08–1609 was 14.4% higher than ROC22. It is highly resistant to mosaic disease, and highly tolerant to drought stress, but moderately susceptible to smut disease. Notably, YZ08–1609 stands out with a sucrose content of 20.3%, setting an international record, earning the reputation as “King of Sugar”. To summarize experience and inspire breeding, we provided here the detailed insights into the selection of parents, breeding process, and characteristics of YZ08–1609. Besides, the biological mechanisms underlying its high yield and high sugar was excavated at both transcriptional and metabolic levels. The challenges and prospects in breeding sugarcane varieties especially with high sugar were also discussed, offering a foundation for the future development of high-sugar varieties.

show abstract

“…The multidimensional vector form requires more computational resources but provides a higher accuracy. (23) Therefore, in this study, the multidimensional vector form is utilized.…”

Section: Similarity Calculation Of Spatiotemporal Informationmentioning

confidence: 99%

Fuzzy Spatiotemporal Representation Modelfor Human Trajectory Classification

Chen,

Jin,

et al. 2023

Sensors and Materials

View full text Add to dashboard Cite

Effective trajectory selection and classification are pivotal in user tracking systems utilizing spatiotemporal data collected from city sensors. However, the inherent limitations in sensor technologies and data collection point distributions often result in low-quality spatiotemporal data. Real-life trajectory classification encounters challenges due to the following: (1) high-order and sparse activity data encompassing both temporal and spatial contexts, and (2) inherent vagueness in the semantics of visited locations, making it difficult to represent behavioral intentions. Traditional statistics-based or trajectory-based feature approaches prove ineffective with non-discriminate features. In response to these challenges, we introduce a novel classification method that integrates fuzzy spatiotemporal features and crowd habit features. This approach involves feature extraction using the Time-Geo Hash (TGH) and User Transit Pattern and Similarity (UTPS) models, followed by the training of a machine learning classification model. On the basis of the performance indicators of classification models, we identify two classification algorithms, incorporate the Bagging algorithm from ensemble learning to enhance the UTPS classification model, and combine the TGH and UTPS models through specified rules. Extensive experiments demonstrate that our proposed model significantly outperforms other classification baselines when applied to a labeled real-life dataset, emphasizing its effectiveness in handling noisy and challenging spatiotemporal data for trajectory classification in user tracking systems.

show abstract

Registration of ‘YZ081609’ Sugarcane

Cited by 10 publications

References 2 publications

Effect of cutting depth during sugarcane (Saccharum spp. hybrid) harvest on root characteristics and yield

Effect of cutting depth during sugarcane (Saccharum spp. hybrid) harvest on root characteristics and yield

Theory to practice: a success in breeding sugarcane variety YZ08–1609 known as the King of Sugar

Fuzzy Spatiotemporal Representation Modelfor Human Trajectory Classification

Contact Info

Product

Resources

About