Scientia Agricultura Sinica ›› 2023, Vol. 56 ›› Issue (22): 4359-4370.doi: 10.3864/j.issn.0578-1752.2023.22.001

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

The Effect of indica/Xian Pedigree Introgression in japonica/Geng Rice Breeding in China

XU HAI1(), LI XIUKUN1,2, LU JIAHAO1, JIANG KAI1, MA YUE1, XU ZHENGJIN1, XU QUAN1()   

  1. 1 Rice Research Institute, Shenyang Agricultural University, Shenyang 110866
    2 College of Agronomy, Hebei Agricultural University/State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory for Crop Germplasm Resources of Hebei, Baoding 071001, Hebei
  • Received:2023-05-19 Accepted:2023-06-16 Online:2023-11-16 Published:2023-11-17

Abstract:

【Objective】To demonstrate the impact of indica/Xian (XI) pedigree introgression on the yield and quality of japonica/Geng (GJ) rice varieties, providing a theoretical basis and genomic resources for optimizing XI pedigree introgression breeding programs in northern GJ rice.【Method】In this study, the whole genome sequence on Illumina platform was employed to elucidate the effects of XI pedigree introgression on the yield and quality of rice in Northeast China were analyzed using recombinant inbred lines (RIL) derived from the cross between XI and GJ varieties, and 74 major GJ varieties grown from Heilongjiang, Liaoning, Shandong, and Jiangsu provinces as test materials. Using CRISPR/Cas9 gene editing technology to knock out the unfavorable genes introduced by XI pedigree introgression. 【Result】Analysis of RIL revealed a significant positive correlation between XI pedigree introgression and panicle length, grain length, and a negative correlation with head rice ratio. XI pedigree introgression was significantly negatively correlated with Amylose content, and significantly positively correlated with protein content in Jiangsu. With the increase of latitude, the correlation efficiency between XI pedigree introgression and grain shape increased, while the correlation between XI pedigree introgression and panicle length and head rice ratio decreased. The genomic fragments of XI pedigree introgression are unevenly distributed across different chromosomes and are more abundantly present on chromosomes 1, 10, 11, and 12. The XI pedigree introgression of the major cultivars in Jiangsu and Liaoning provinces is significantly higher than that in Heilongjiang and Shandong provinces, and the XI pedigree introgression of the cultivars after 2000 is significantly higher than that before 2000. The XI pedigree introgression includes multiple resistance and fertility-related genes. The project identified an XI pedigree introgression fragment on chromosome 5 of YF47, including the XI type grain regulatory gene GS5 and XI type chalkiness regulatory gene Chalk5, which increased the 1000 grain weight of YF47 but affected its chalkiness-related traits. The project uses CRISPR/Cas9 technology to knock out the Chalk5 gene of YF47. The grain shape of the homozygous gene editing plants is similar to those of YF47, and its chalkiness character has been significantly improved. 【Conclusion】The XI pedigree introgression mainly increases the yield potential of GJ rice by increasing the number of grains per panicle, but has a negative impact on milling quality. Exploring the unfavorable alleles in varieties through high-throughput genome sequencing, combined with CRISPR/Cas9 gene editing, to break the genetic drag in breeding using the cross between XI and GJ, is an efficient breeding strategy that can quickly and accurately improve target traits.

Key words: rice, japonica/Geng breeding, indica/Xian pedigree introgression, yield, quality, molecular design breeding

Fig. 1

The yield and quality traits of RIL in four areas LN: Liaoning province; SC: Sichuan province; JS: Jiangsu province; GD: Guangdong province. Different letters denote significant differences at P=0.05. The same as below"

Fig. 2

The effects of XI pedigree introgression on yield and quality traits of RIL a: The distribution of XI pedigree introgression percentage of RIL; b: The correlation efficiency between XI pedigree introgression percentage and agronomic traits. The dotted line indicates significant differences at P=0.05. The same as below"

Fig. 3

The yield related trait of cultivars in different area and different released years A-E: The cultivars cultivated in four areas; F-J: The cultivars in different released year. HLJ: Heilongjiang province; SD: Shandong province. The same as below"

Fig. 4

The relationship between XI pedigree introgression and agronomic traits in major cultivars A: The distribution of XI pedigree introgression among 12 chromosomes in 74 major cultivars; B: The percentage of XI pedigree introgression of 74 major cultivars; C: The XI pedigree introgression of major cultivars which were released before 1980, 1980~1990, 1990~2000, and after 2000; D: The XI pedigree introgression of major cultivars in four areas; E: The correlation efficiency between XI pedigree introgression and yield related traits; F: The genotype of SN265, R99, Nipponbare, and LG31 at Gn1a locus; G: The functional loci located in the region of XI pedigree introgression"

Fig. 5

Break the genetic drag using CRISPR/Cas9 gene editing technology A: The Chalk5 and GS5 were located in Chr.5; B: The sequence analysis of Chalk5 and GS5 in YF47; C: The sgRNA and sequence comparison of gene editing lines; D: The grain shape of WT and gene editing lines; E: The chalkiness related traits of WT and gene editing lines; F: The chalkiness rice ratio of WT and gene editing lines"

[1]
GARRIS A J, TAI T H, COBURN J, KRESOVICH S, MCCOUCH S. Genetic structure and diversity in Oryza sativa L.. Genetics, 2005, 169(3): 1631-1638.

doi: 10.1534/genetics.104.035642
[2]
Huang X H, WEI X H, SANG T, ZHAO Q, FENG Q, ZHAO Y, LI C Y, ZHU C R, LU T T, ZHANG Z W, LI M, FAN D L, GUO Y L, WANG A H, WANG L, DENG L W, LI W J, LU Y Q, WENG Q J, LIU K Y, HUANG T, ZHOU T Y, JING Y F, LI W, LIN Z, BUCKLER E S, QIAN Q, ZHANG Q F, LI J Y, HAN B. Genome-wide association studies of 14 agronomic traits in rice landraces. Nature Genetics, 2010, 42(11): 961-967.

doi: 10.1038/ng.695 pmid: 20972439
[3]
Huang X H, ZHAO Y, WEI X H, LI C Y, WANG A H, ZHAO Q, LI W J, GUO Y L, DENG L W, ZHU C R, FAN D L, LU Y Q, WENG Q J, LIU K Y, ZHOU T Y, JING Y F, SI L Z, DONG G J, HUANG T, LU T T, FENG Q, QIAN Q, LI J Y, HAN B. Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nature Genetics, 2012, 44(1): 32-39.

doi: 10.1038/ng.1018
[4]
Yu H, Lin T, Meng X B, DU H L, ZHANG J K, LIU G F, CHEN M J, JING Y H, KOU L Q, LI X X, GAO Q, LIANG Y, LIU X D, FAN Z L, LIANG Y T, CHENG Z K, CHEN M S, TIAN Z X, WANG Y H, CHU C C, LI J Y. A route to de novo domestication of wild allotetraploid rice. Cell, 2021, 184(5): 1156-1170. e14.

doi: 10.1016/j.cell.2021.01.013
[5]
Wang W S, MAULEON R, HU Z Q, CHEBOTAROV D, TAI S S, WU Z C, LI M, ZHENG T Q, FUENTES R R, ZHANG F, MANSUETO L, COPETTI D, SANCIANGCO M, PALIS K C, XU J L, SUN C, FU B Y, ZHANG H L, GAO Y M, ZHAO X Q, SHEN F, CUI X, YU H, LI Z C, CHEN M L, DETRAS J, ZHOU Y L, ZHANG X Y, ZHAO Y, KUDRNA D, WANG C C, LI R, JIA B, LU J Y, HE X C, DONG Z T, XU J B, LI Y H, WANG M, SHI J X, LI J, ZHANG D B, LEE S, HU W S, POLIAKOV A, DUBCHAK I, ULAT V J, BORJA F N, MENDOZA J R, ALI J, LI J, GAO Q, NIU Y C, YUE Z, NAREDO M E B, TALAG J, WANG X Q, LI J J, FANG X D, YIN Y, GLASZMANN J C, ZHANG J W, LI J Y, HAMILTON R S, WING R A, RUAN J, ZHANG G Y, WEI C C, ALEXANDROV N, MCNALLY K L, LI Z K, LEUNG H. Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature, 2018, 557(7703): 43-49.

doi: 10.1038/s41586-018-0063-9
[6]
WEI X, QIU J, YONG K C, FAN J J, ZHANG Q, HUA H, LIU J, WANG Q, OLSEN K M, HAN B, HUANG X H. A quantitative genomics map of rice provides genetic insights and guides breeding. Nature Genetics, 2021, 53(2): 243-253.

doi: 10.1038/s41588-020-00769-9 pmid: 33526925
[7]
Liu Y Q, WANG H R, JIANG Z M, WANG W, XU R N, WANG Q H, ZHANG Z H, LI A F, LIANG Y, OU S J, LIU X J, CAO S Y, TONG H N, WANG Y H, ZHOU F, LIAO H, HU B, CHU C C. Genomic basis of geographical adaptation to soil nitrogen in rice. Nature, 2021, 590(7847): 600-605.

doi: 10.1038/s41586-020-03091-w
[8]
ZHOU G, CHEN Y, YAO W, ZHANG C J, XIE W B, HUA J P, XING Y Z, XIAO J H, ZHANG Q F. Genetic composition of yield heterosis in an elite rice hybrid. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(39): 15847-15852.
[9]
杨守仁, 赵纪书. 籼粳稻杂交问题之研究. 农业学报, 1959, 10 (4): 256-268.
YANG S R, ZHAO J S. A study on the problem of cross between XI and GJ rice. Acta Agriculture Sinica, 1959, 10(4): 256-268. (in Chinese)
[10]
徐正进, 陈温福. 中国北方粳型超级稻研究进展. 中国农业科学, 2016, 49(2): 239-250.

doi: 10.3864/j.issn.0578-1752.2016.02.005
XU Z J, CHEN W F. Research progress and related problems on japonica super rice in northern China. Scientia Agricultura Sinica, 2016, 49(2): 239-250. (in Chinese)
[11]
徐铨, 唐亮, 徐凡, 福嶌阳, 黄瑞冬, 陈温福, 徐正进. 粳稻食味品质改良研究现状与展望. 作物学报, 2013, 39(6): 961-968.
XU Q, TANG L, XU F, FU D Y, HUANG R D, CHEN W F, XU Z J. Research advances and prospects of eating and quality improvement in Japonica rice (Oryza sativa L.) Acta Agronomica Sinica, 2013, 39(6): 961-968. (in Chinese)

doi: 10.3724/SP.J.1006.2013.00961
[12]
LI X K, WU L, WANG J H, SUN J, XIA X H, GENG X, WANG X H, XU Z J, XU Q. Genome sequencing of rice subspecies and genetic analysis of recombinant lines reveals regional yield- and quality- associated loci. BMC Biology, 2018, 16(1): 102.

doi: 10.1186/s12915-018-0572-x
[13]
Ashikari M, Sakakibara H, Lin S Y, YAMAMOTO T, TAKASHI T, NISHIMURA A, ANGELES E R, QIAN Q, KITANO H, MATSUOKA M. Cytokinin oxidase regulates rice grain production. Science, 2005, 309(5735): 741-745.

doi: 10.1126/science.1113373 pmid: 15976269
[14]
Wang Y, Li F c, Zhang F, WU L, XU N, SUN Q, CHEN H, YU Z W, LU J H, JIANG K, WANG X C, WEN S Y, ZHOU Y, ZHAO H, JIANG Q, WANG J H, JIA R Z, SUN J, TANG L, XU H, HU W, XU Z J, CHEN W F, GUO A P, XU Q. Time-ordering japonica/geng genomes analysis indicates the importance of large structural variants in rice breeding. Plant Biotechnology Journal, 2023, 21(1): 202-218.

doi: 10.1111/pbi.v21.1
[15]
ZHANG F, XUE H Z, DONG X R, LI M, ZHENG X M, LI Z K, XU J L, WANG W S, WEI C C. Long-read sequencing of 111 rice genomes reveals significantly larger pan-genomes. Genome Research, 2022, 32(5): 853-863.

doi: 10.1101/gr.276015.121 pmid: 35396275
[16]
Qin P, Lu H W, DU H L, WANG H, CHEN W L, CHEN Z, HE Q, OU S J, ZHANG H Y, LI X Z, LI X X, LI Y, LIAO Y, GAO Q, TU B, YUAN H, MA B T, WANG Y P, QIAN Y W, FAN S J, LI S G. Pan-genome analysis of 33 genetically diverse rice accessions reveals hidden genomic variations. Cell, 2021, 184(13): 3542-3558. e16.

doi: 10.1016/j.cell.2021.04.046 pmid: 34051138
[17]
GHOSH A, PAREEK A, SOPORY S K, SINGLA-PAREEK S L. A glutathione responsive rice glyoxalase II, OsGLYII-2, functions in salinity adaptation by maintaining better photosynthesis efficiency and anti-oxidant pool. The Plant Journal, 2014, 80(1): 93-105.

doi: 10.1111/tpj.12621 pmid: 25039836
[18]
DU H, WANG N L, CUI F, LI X H, XIAO J H, XIONG L Z. Characterization of the beta-carotene hydroxylase gene DSM2 conferring drought and oxidative stress resistance by increasing xanthophylls and abscisic acid synthesis in rice. Plant Physiology, 2010, 154(3): 1304-1318.

doi: 10.1104/pp.110.163741 pmid: 20852032
[19]
ZOU J, LIU A L, CHEN X B, ZHOU X Y, GAO G F, WANG W F, ZHANG X W. Expression analysis of nine rice heat shock protein genes under abiotic stresses and ABA treatment. Journal of Plant Physiology, 2009, 166(8): 851-861.

doi: 10.1016/j.jplph.2008.11.007 pmid: 19135278
[20]
ZHOU L Y, NI E D, YANG J W, ZHOU H, LIANG H, LI J, JIANG D G, WANG Z H, LIU Z L, ZHUANG C X. Rice OsGL1-6 is involved in leaf cuticular wax accumulation and drought resistance. PLoS ONE, 2013, 8(5): e65139.

doi: 10.1371/journal.pone.0065139
[21]
Liu Y q, Wu H, Chen H, LIU Y L, HE J, KANG H Y, SUN Z G, PAN G, WANG Q, HU J L, ZHOU F, ZHOU K N, ZHENG X M, REN Y L, CHEN L M, WANG Y H, ZHAO Z G, LIN Q B, WU F Q, ZHANG X, GUO X P, CHENG X N, JIANG L, WU C Y, WANG H Y, WAN J M. A gene cluster encoding lectin receptor kinases confers broad-spectrum and durable insect resistance in rice. Nature Biotechnology, 2015, 33(3): 301-305.

doi: 10.1038/nbt.3069 pmid: 25485617
[22]
Zhao H J, WANG X Y, JIA Y L, MINKENBERG B, WHEATLEY M, FAN J B, JIA M H, FAMOSO A, EDWARDS J D, WAMISHE Y, VALENT B, WANG G L, YANG Y N. The rice blast resistance gene Ptr encodes an atypical protein required for broad-spectrum disease resistance. Nature Communications, 2018, 9: 2039.

doi: 10.1038/s41467-018-04369-4 pmid: 29795191
[23]
Zhou H, Zhou M, Yang Y Z, LI J, ZHU L Y, JIANG D G, DONG J F, LIU Q J, GU L F, ZHOU L Y, FENG M J, QIN P, HU X C, SONG C L, SHI J F, SONG X W, NI E D, WU X J, DENG Q Y, LIU Z L, CHEN M S, LIU Y G, CAO X F, ZHUANG C X. RNaseZS1 processes UbL40 mRNAs and controls thermosensitive genic male sterility in rice. Nature Communications, 2014, 5: 4884.

doi: 10.1038/ncomms5884 pmid: 25208476
[24]
Zhang P P, ZHANG Y X, SUN L P, SINUMPORN S, YANG Z F, SUN B, XUAN D D, LI Z H, YU P, WU W X, WANG K J, CAO L Y, CHENG S H. The rice AAA-ATPase OsFIGNL1 is essential for male meiosis. Frontiers in Plant Science, 2017, 8: 1639.

doi: 10.3389/fpls.2017.01639 pmid: 29021797
[25]
SONG S Y, CHEN Y, LIU L, SEE Y H B, MAO C Z, GAN Y B, YU H. OsFTIP7 determines auxin-mediated anther dehiscence in rice. Nature Plants, 2018, 4(7): 495-504.

doi: 10.1038/s41477-018-0175-0 pmid: 29915329
[26]
ZHOU L J, XIAO L T, XUE H W. Dynamic cytology and transcriptional regulation of rice Lamina joint development. Plant Physiology, 2017, 174(3): 1728-1746.

doi: 10.1104/pp.17.00413
[27]
ZHANG J, FAN X W, HU Y, ZHOU X C, HE Q, LIANG L W, XING Y Z. Global analysis of CCT family knockout mutants identifies four genes involved in regulating heading date in rice. Journal of Integrative Plant Biology, 2021, 63(5): 913-923.

doi: 10.1111/jipb.13013
[28]
LI Y B, FAN C C, XING Y Z, JIANG Y H, LUO L J, SUN L, SHAO D, XU C J, LI X H, XIAO J H, HE Y Q, ZHANG Q F. Natural variation in GS5 plays an important role in regulating grain size and yield in rice. Nature Genetics, 2011, 43(12): 1266-1269.

doi: 10.1038/ng.977 pmid: 22019783
[29]
LI Y B, FAN C C, XING Y Z, YUN P, LUO L J, YAN B, PENG B, XIE W B, WANG G W, LI X H, XIAO J H, XU C G, HE Y Q. Chalk5 encodes a vacuolar H+-translocating pyrophosphatase influencing grain chalkiness in rice. Nature Genetics, 2014, 46(4): 398-404.

doi: 10.1038/ng.2923
[30]
Huang X h, Kurata N, Wei X H, WANG Z X, WANG A H, ZHAO Q, ZHAO Y, LIU K Y, LU H Y, LI W J, GUO Y L, LU Y Q, ZHOU C C, FAN D L, WENG Q J, ZHU C R, HUANG T, ZHANG L, WANG Y C, FENG L, FURUUMI H, KUBO T, MIYABAYASHI T, YUAN X P, XU Q, DONG G J, ZHAN Q L, LI C Y, FUJIYAMA A, TOYODA A, LU T T, FENG Q, QIAN Q, LI J Y, HAN B. A map of rice genome variation reveals the origin of cultivated rice. Nature, 2012, 490(7421): 497-501.

doi: 10.1038/nature11532
[31]
Xie W B, WANG G W, YUAN M, YAO W, LYU K, ZHAO H, YANG M, LI P B, ZHANG X, YUAN J, WANG Q X, LIU F, DONG H X, ZHANG L J, LI X L, MENG X Z, ZHANG W, XIONG L Z, HE Y Q, WANG S P, YU S B, XU C G, LUO J, LI X H, XIAO J H, LIAN X M, ZHANG Q F. Breeding signatures of rice improvement revealed by a genomic variation map from a large germplasm collection. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(39): E5411-E5419.
[32]
Zhao K Y, TUNG C W, EIZENGA G C, WRIGHT M H, ALI M L, PRICE A H, NORTON G J, ISLAM M R, REYNOLDS A, MEZEY J, MCCLUNG A M, BUSTAMANTE C D, MCCOUCH S R. Genome- wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nature Communications, 2011, 2: 467.

doi: 10.1038/ncomms1467
[33]
CUI D, ZHOU H, MA X D, LIN Z C, SUN L H, HAN B, LI M M, SUN J C, LIU J, JIN G X, WANG X J, CAO G L, DENG X W, HE H, HAN L Z. Genomic insights on the contribution of introgressions from Xian/Indica to the genetic improvement of Geng/Japonica rice cultivars. Plant Communication, 2022, 3(3): 100325.

doi: 10.1016/j.xplc.2022.100325
[34]
Chen Z, Bu Q Y, LIU G F, WANG M Q, WANG H R, LIU H Z, LI X F, LI H, FANG J, LIANG Y, TENG Z F, KANG S, YU H, CHENG Z K, XUE Y B, LIANG C Z, TANG J Y, LI J Y, CHU C C. Genomic decoding of breeding history to guide breeding-by-design in rice. National Science Review, 2023, 10(5): nwad029.

doi: 10.1093/nsr/nwad029
[35]
FEI C, XU Q, XU Z J, CHEN W F. Effect of rice breeding process on improvement of yield and quality in China. Rice Science, 2020, 27(5): 363-367.

doi: 10.1016/j.rsci.2019.12.009
[36]
SHAN Q W, WANG Y P, LI J, ZHANG Y, CHEN K L, LIANG Z, ZHANG K, LIU J X, XI J J, QIU J L, GAO C X. Targeted genome modification of crop plants using a CRISPR-Cas system. Nature Biotechnology, 2013, 31(8): 686-688.

doi: 10.1038/nbt.2650 pmid: 23929338
[37]
CUI Y, ZHU M M, XU Z J, XU Q. Assessment of the effect of ten heading time genes on reproductive transition and yield components in rice using a CRISPR/Cas9 system. Theoretical and Applied Genetics, 2019, 132(6): 1887-1896.

doi: 10.1007/s00122-019-03324-1 pmid: 30887096
[38]
CUI Y, JIANG N, XU Z J, XU Q. Heterotrimeric G protein are involved in the regulation of multiple agronomic traits and stress tolerance in rice. BMC Plant Biology, 2020, 20(1): 90.

doi: 10.1186/s12870-020-2289-6 pmid: 32111163
[39]
LU Y M, YE X, GUO R M, HUANG J, WANG W, TANG J Y, TAN L T, ZHU J K, CHU C C, QIAN Y W. Genome-wide targeted mutagenesis in rice using the CRISPR/Cas9 system. Molecular Plant, 2017, 10(9): 1242-1245.

doi: S1674-2052(17)30173-9 pmid: 28645638
[40]
ZENG D C, LIU T L, MA X L, WANG B, ZHENG Z Y, ZHANG Y L, XIE X R, YANG B W, ZHAO Z, ZHU Q L, LIU Y G. Quantitative regulation of Waxy expression by CRISPR/Cas9-based promoter and 5'UTR-intron editing improves grain quality in rice. Plant Biotechnology Journal, 2020, 18(12): 2385-2387.

doi: 10.1111/pbi.v18.12
[41]
HUANG L C, LI Q F, ZHANG C Q, CHU R, GU Z W, TAN H Y, ZHAO D S, FAN X L, LIU Q Q. Creating novel Wx alleles with fine-tuned amylose levels and improved grain quality in rice by promoter editing using CRISPR/Cas9 system. Plant Biotechnology Journal, 2020, 18(11): 2164-2166.

doi: 10.1111/pbi.v18.11
[42]
SONG X G, MENG X B, GUO H Y, CHENG Q, JING Y H, CHEN M J, LIU G F, WANG B, WANG Y H, LI J Y, YU H. Targeting a gene regulatory element enhances rice grain yield by decoupling panicle number and size. Nature Biotechnology, 2022, 40(9): 1403-1411.

doi: 10.1038/s41587-022-01281-7 pmid: 35449414
[1] WEI YaNan, BO QiFei, TANG An, GAO JiaRui, MA Tian, WEI XiongXiong, ZHANG FangFang, ZHOU XiangLi, YUE ShanChao, LI ShiQing. Effects of Long-Term Film Mulching and Application of Organic Fertilizer on Yield and Quality of Spring Maize on the Loess Plateau [J]. Scientia Agricultura Sinica, 2023, 56(9): 1708-1717.
[2] REN ZhiQiang, WANG ChenYang, KOU ZhongYun, CAI Rui, YANG GongShe, PANG WeiJun. In Vivo Estimation of Lean Percentage, Fat Percentage, and Intramuscular Fat Content of Boars by Computed Tomography [J]. Scientia Agricultura Sinica, 2023, 56(9): 1787-1799.
[3] WEN YuanYuan, LI Yan, LI JianGuo, WANG MeiMei, YU ChangHui, SHEN YiZhao, GAO YanXia, LI QiuFeng, CAO YuFeng. Effects of Holstein Bulls Fed Mixed Silage of Potato Chips Processing by Product with Rice Straw on Fattening Performance and Blood Biochemical Indexes [J]. Scientia Agricultura Sinica, 2023, 56(9): 1800-1812.
[4] JU XiaoJun, ZHANG Ming, SHAN YanJu, JI GaiGe, TU YunJie, LIU YiFan, ZOU JianMin, SHU JingTing. Chicken Quality Analysis and Screening of Key Flavor Substances and Genes [J]. Scientia Agricultura Sinica, 2023, 56(9): 1813-1826.
[5] SUN QiBin, WANG JianNan, LI YiNian, HE RuiYin, DING QiShuo. Study on the Dynamics of Root Length Density in Soil Layers of Single Plant Wheat Under Controlled Seed-to-Seed Distance [J]. Scientia Agricultura Sinica, 2023, 56(8): 1456-1470.
[6] HAN ZiXuan, FANG JingJing, WU XuePing, JIANG Yu, SONG XiaoJun, LIU XiaoTong. Synergistic Effects of Organic Carbon and Nitrogen Content in Water-Stable Aggregates as well as Microbial Biomass on Crop Yield Under Long-Term Straw Combined Chemical Fertilizers Application [J]. Scientia Agricultura Sinica, 2023, 56(8): 1503-1514.
[7] LIU MengJie, LIANG Fei, LI QuanSheng, TIAN YuXin, WANG GuoDong, JIA HongTao. Effects of Drip Irrigation Under Film and Trickle Furrow Irrigation on Maize Growth and Yield [J]. Scientia Agricultura Sinica, 2023, 56(8): 1515-1530.
[8] WANG Ning, FENG KeYun, NAN HongYu, CONG AnQi, ZHANG TongHui. Effects of Combined Application of Organic Manure and Chemical Fertilizer Ratio on Water and Nitrogen Use Efficiency of Cotton Under Water Deficit [J]. Scientia Agricultura Sinica, 2023, 56(8): 1531-1546.
[9] WANG PengFei, YU AiZhong, WANG YuLong, SU XiangXiang, LI Yue, LÜ HanQiang, CHAI Jian, YANG HongWei. Effects of Returning Green Manure to Field Combined with Reducing Nitrogen Application on the Dry Matter Accumulation, Distribution and Yield of Maize [J]. Scientia Agricultura Sinica, 2023, 56(7): 1283-1294.
[10] WEN YiBo, CHEN ShuTing, XU ZhengJin, SUN Jian, XU Quan. Combination of DEP1, Gn1a, and qSW5 Regulates the Panicle Architecture in Rice [J]. Scientia Agricultura Sinica, 2023, 56(7): 1218-1227.
[11] LI RuXiang, ZHOU Kai, WANG DaChuan, LI QiaoLong, XIANG AoNi, LI Lu, LI MiaoMiao, XIANG SiQian, LING YingHua, HE GuangHua, ZHAO FangMing. Analysis of QTLs and Breeding of Secondary Substitution Lines for Panicle Traits Based on Rice Chromosome Segment Substitution Line CSSL-Z481 [J]. Scientia Agricultura Sinica, 2023, 56(7): 1228-1247.
[12] ZHAO ZiJun, WU RuHui, WANG Shuo, ZHANG Jun, YOU Jing, DUAN QianNan, TANG Jun, ZHANG XinFang, WEI Mi, LIU JinYan, LI YunFeng, HE GuangHua, ZHANG Ting. Mutation of PDL2 Gene Causes Degeneration of Lemma in the Spikelet of Rice [J]. Scientia Agricultura Sinica, 2023, 56(7): 1248-1259.
[13] ZHU HongHui, LI YingZi, GAO YuanZhuo, LIN Hong, WANG ChengYang, YAN ZiYi, PENG HanPing, LI TianYe, XIONG Mao, LI YunFeng. Map-Based Cloning of the SHORT AND WIDEN GRAIN 1 Gene in Rice (Oryza sativa L.) [J]. Scientia Agricultura Sinica, 2023, 56(7): 1260-1274.
[14] ZHANG Ji, ZHOU ShangLing, HE Fa, LIU LiSha, ZHANG YuJuan, HE JinYu, DU XiaoQiu. Expression Pattern of the Rice α-Amylase Genes Related with the Process of Floret Opening [J]. Scientia Agricultura Sinica, 2023, 56(7): 1275-1282.
[15] NAN Rui, YANG YuCun, SHI FangHui, ZHANG LiNing, MI TongXi, ZHANG LiQiang, LI ChunYan, SUN FengLi, XI YaJun, ZHANG Chao. Identification of Excellent Wheat Germplasms and Classification of Source-Sink Types [J]. Scientia Agricultura Sinica, 2023, 56(6): 1019-1034.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!