Xiaohong Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xiaohong Liu has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Yun Long
College of Life Science, China West Normal University, Nanchong 637009, China
2 shared publications - Yun Yang
Nanchong Academy of Agricultural Sciences, Nanchong 637000, China
2 shared publications - Chuan He
Nanchong Academy of Agricultural Sciences, Nanchong 637000, China
1 shared publication - Guoqi Li
Key Laboratory of Pathogenic, Fungi and Mycotoxins of Fujian Province, School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
1 shared publication - Guoqin Wen
College of Life Science, China West Normal University, Nanchong 637002, Sichuan, China
1 shared publication - Rui Wang
Key Laboratory of Southwest China Wildlife Resource Conservation (Ministry of Education), College of Life Science, China West Normal University, Nanchong 637000, China
1 shared publication
Latest Publications
- Open Access
- 3 November 2025
- Published by MDPI AG in Agriculture
- Vol. 15 (21) , 2294
Influence of Meteorological Factors and Sowing Dates on Growth and Yield Traits of Summer Maize in Northeastern Sichuan, China
This study investigates meteorological factors’ effects on summer maize growth, agronomic traits and yield in northeastern Sichuan, China, under different sowing dates. A five-gradient sowing date experiment was conducted with three varieties from 2023 to 2024. The results showed delayed sowing prolonged total growth period mainly in the joint–tasseling and silking–maturity stages. Early sowing (5th May and 20th May) significantly improved key agronomic traits and increased grain yield, with Xianyu 1171 achieving the highest yield of 9.77 t ha−1 under early sowing. Meteorological factors had limited influence during vegetative growth but strongly affected reproductive growth. Among them, average temperature (AT) and growing degree days (GDDs) were critical throughout the growth cycle, though their effects varied by stage. These findings suggest that adjusting sowing dates to align key growth stages with favorable weather—particularly by avoiding high-temperature stress during flowering and ensuring sufficient warmth during grain filling—can enhance yield stability. This study provides a basis for constructing a climate-resilient cultivation system and promoting stable and high summer maize yields in the hilly areas of northeastern Sichuan.
- 3 November 2025
- Published by MDPI AG in Agriculture
- Vol. 15 (21) , 2294
Open AccessInfluence of Meteorological Factors and Sowing Dates on Growth and Yield Traits of Summer Maize in Northeastern Sichuan, China
This study investigates meteorological factors’ effects on summer maize growth, agronomic traits and yield in northeastern Sichuan, China, under different sowing dates. A five-gradient sowing date experiment was conducted with three varieties from 2023 to 2024. The results showed delayed sowing prolonged total growth period mainly in the joint–tasseling and silking–maturity stages. Early sowing (5th May and 20th May) significantly improved key agronomic traits and increased grain yield, with Xianyu 1171 achieving the highest yield of 9.77 t ha−1 under early sowing. Meteorological factors had limited influence during vegetative growth but strongly affected reproductive growth. Among them, average temperature (AT) and growing degree days (GDDs) were critical throughout the growth cycle, though their effects varied by stage. These findings suggest that adjusting sowing dates to align key growth stages with favorable weather—particularly by avoiding high-temperature stress during flowering and ensuring sufficient warmth during grain filling—can enhance yield stability. This study provides a basis for constructing a climate-resilient cultivation system and promoting stable and high summer maize yields in the hilly areas of northeastern Sichuan.
- Open Access
High-Throughput Sequencing Reveals the Mycoviral Diversity of the Pathogenic Grape Fungus Penicillium astrolabium During Postharvest
Penicillium astrolabium is a primary pathogenic fungus that causes grape blue mold during postharvest, leading to substantial losses in the grape industry. Nevertheless, hypovirulence-associated mycoviruses can attenuate the virulence of postharvest grape-rot pathogens, thereby offering a promising biocontrol tool. Characterizing the mycovirus repertoire of P. astrolabium is imperative for grape protection, yet remains largely unexplored. Here, we screened six strains harboring viruses in 13 P. astrolabium isolates from rotted grapes. Using high-throughput sequencing, four novel dsRNA viruses and two +ssRNA viruses were identified from the six P. astrolabium strains. The dsRNA viruses belonged to two families—Chrysoviridae and Partitiviridae—and were designated to Penicillium astrolabium chrysovirus 1 (PaCV1), Penicillum astrolabium partitivirus 1′ (PaPV1′), Penicillum astrolabium partitivirus 2 (PaPV2), and Penicillum astrolabium partitivirus 3 (PaPV3). For the +ssRNA viruses, one was clustered into the Alphaflexiviridae family, while the other one was clustered into the Narnaviridae family. The two +ssRNA viruses were named Penicillium astrolabium alphaflexivirus 1 (PaAFV1) and Penicillium astrolabium narnavirus 1 (PaNV1), respectively. Moreover, several viral genomic contigs with non-overlapping and discontinuous sequences were identified in this study, which were probably representatives of five viruses from four families, including Discoviridae, Peribunyaviridae, Botourmiaviridae, and Picobirnaviridae. Taken together, our findings could expand the diversity of mycoviruses, advance the understanding of mycovirus evolution in P. astrolabium, and provide both potential biocontrol resources and a research system for dissecting virus–fungus–plant interactions.
Open AccessHigh-Throughput Sequencing Reveals the Mycoviral Diversity of the Pathogenic Grape Fungus Penicillium astrolabium During Postharvest
Penicillium astrolabium is a primary pathogenic fungus that causes grape blue mold during postharvest, leading to substantial losses in the grape industry. Nevertheless, hypovirulence-associated mycoviruses can attenuate the virulence of postharvest grape-rot pathogens, thereby offering a promising biocontrol tool. Characterizing the mycovirus repertoire of P. astrolabium is imperative for grape protection, yet remains largely unexplored. Here, we screened six strains harboring viruses in 13 P. astrolabium isolates from rotted grapes. Using high-throughput sequencing, four novel dsRNA viruses and two +ssRNA viruses were identified from the six P. astrolabium strains. The dsRNA viruses belonged to two families—Chrysoviridae and Partitiviridae—and were designated to Penicillium astrolabium chrysovirus 1 (PaCV1), Penicillum astrolabium partitivirus 1′ (PaPV1′), Penicillum astrolabium partitivirus 2 (PaPV2), and Penicillum astrolabium partitivirus 3 (PaPV3). For the +ssRNA viruses, one was clustered into the Alphaflexiviridae family, while the other one was clustered into the Narnaviridae family. The two +ssRNA viruses were named Penicillium astrolabium alphaflexivirus 1 (PaAFV1) and Penicillium astrolabium narnavirus 1 (PaNV1), respectively. Moreover, several viral genomic contigs with non-overlapping and discontinuous sequences were identified in this study, which were probably representatives of five viruses from four families, including Discoviridae, Peribunyaviridae, Botourmiaviridae, and Picobirnaviridae. Taken together, our findings could expand the diversity of mycoviruses, advance the understanding of mycovirus evolution in P. astrolabium, and provide both potential biocontrol resources and a research system for dissecting virus–fungus–plant interactions.
- Open Access
- 28 September 2024
- Published by MDPI AG in Agriculture
- Vol. 14 (10) , 1703
Multivariate Analysis of Grain Yield and Main Agronomic Traits in Different Maize Hybrids Grown in Mountainous Areas
Inconsistent reports exist on the relationships between key agronomic traits and maize yield. We performed a multivariate analysis of yield and 10 agronomic traits in 59 hybrids to explore maize yields in mountainous areas. The yield per plant (YP) was significantly and positively correlated with kernel weight (KW), growth period (GP), and kernel row number (KRN). KW and KRN had positive effects on YP, whereas kernel rows per ear (KRE) had a negative effect. GP indirectly affected YP. GP, KW, KRN, and ear length (EL) showed the highest grey relational degree with YP. The first four principal components cumulatively accounted for 73.36% of variation. EL, KW, plant height (PH), ear height (EH), GP, KRN, and YP contributed positively to the variation, whereas KRE, shelling percentage (SP), bald-tip length (BTL), and ear girth (EG) contributed negatively. Based on trait similarity, the 59 maize hybrids were classified into two clusters, Clusters I and II. A total of 11 traits were grouped into four clusters, Clusters A–D. Cluster D included KW, GP, KRN, EL, EH, PH, and YP, and the 22 maize hybrids in Cluster I performed better in these traits. These results provide a theoretical basis for the breeding of high-yield maize varieties in mountainous areas.
- 28 September 2024
- Published by MDPI AG in Agriculture
- Vol. 14 (10) , 1703
Open AccessMultivariate Analysis of Grain Yield and Main Agronomic Traits in Different Maize Hybrids Grown in Mountainous Areas
Inconsistent reports exist on the relationships between key agronomic traits and maize yield. We performed a multivariate analysis of yield and 10 agronomic traits in 59 hybrids to explore maize yields in mountainous areas. The yield per plant (YP) was significantly and positively correlated with kernel weight (KW), growth period (GP), and kernel row number (KRN). KW and KRN had positive effects on YP, whereas kernel rows per ear (KRE) had a negative effect. GP indirectly affected YP. GP, KW, KRN, and ear length (EL) showed the highest grey relational degree with YP. The first four principal components cumulatively accounted for 73.36% of variation. EL, KW, plant height (PH), ear height (EH), GP, KRN, and YP contributed positively to the variation, whereas KRE, shelling percentage (SP), bald-tip length (BTL), and ear girth (EG) contributed negatively. Based on trait similarity, the 59 maize hybrids were classified into two clusters, Clusters I and II. A total of 11 traits were grouped into four clusters, Clusters A–D. Cluster D included KW, GP, KRN, EL, EH, PH, and YP, and the 22 maize hybrids in Cluster I performed better in these traits. These results provide a theoretical basis for the breeding of high-yield maize varieties in mountainous areas.