Mapping QTLs Conferring Seed Yield Per Spike and Spike Axis Dry Weight in Castor

doi:10.7668/hbnxb.20195009

Abstract

Abstract:

The seed yield per spike and spike axis dry weight affect yield per plant.In order to reveal the genetic basis,QTLs were mapped with populations F₂ and BC₁ (F₁×P₂) derived from the cross combination 9048×16-201 using CIM and ICIM-ADD methods.A total of 7/10 (CIM/ICIM-ADD) QTLs were detected in the F₂ population,including 3/6,3/3 and 1/1 QTLs conferring PSSY,PSADW and PBSADW respectively,with a contribution rate of 15.81%/24.09%,2.84%/8.65% and 6.49%/6.56%,respectively.And 1/3 QTLs were identified in the BC₁ population,including 0/2 and 1/1 QTLs underlying PSSY and PBSADW,with a contribution rate of 0/10.28% and 11.60%/10.22% respectively.Among all the detected QTLs,qPSSY3.1 and qPBSADW3.1 were stable QTLs,and the latter was also the main-effect QTL,with contribution rates ranging from 3.76%—7.00% and 6.49%—11.60%,respectively.They overlapped in the marker interval RCM920—RCM950,forming a QTL cluster (i.e.,QTL-cluster4).Additionally,a dozen pairs of epistatic QTLs were detected with the epistatic effect of these traits accounting for more than 19% of the total contribution rate,indicating that the epistatic effect was an important genetic component of seed yield per spike and spike axis dry weight in castor.The results not only provided a reference for the understanding of the genetic structure of seed yield per spike and spike axis dry weight but also offered available molecular markers for marker-assisted selection.

Key words: Castor, Seed yield per spike, Spike axis dry weight, QTL mapping, Main-effect QTL

LIN Haihong, LU Jiannong, YIN Xuegui, HUANG Guanrong, ZHANG Liuqin, ZHANG Xiaoxiao, LIU Chaoyu, ZUO Jinying. Mapping QTLs Conferring Seed Yield Per Spike and Spike Axis Dry Weight in Castor[J]. Acta Agriculturae Boreali-Sinica, 2024, 39(6): 94-105. doi: 10.7668/hbnxb.20195009.

/ / Recommend / Download Citations

URL: http://www.hbnxb.net/EN/10.7668/hbnxb.20195009

http://www.hbnxb.net/EN/Y2024/V39/I6/94

Figures/Tables 9

References 39

[1]	Nitbani F O, Tjitda P J P, Wogo H E, Detha A I R. Preparation of ricinoleic acid from castor oil:a review[J]. Journal of Oleo Science, 2022, 71(6):781-793.doi:10.5650/jos.ess21226. pmid: 35661063
[2]	Carrino L, Visconti D, Fiorentino N, Fagnano M. Biofuel production with castor bean:a win win strategy for marginal land[J]. Agronomy, 2020, 10(11):1690.doi:10.3390/agronomy10111690.
[3]	Osorio-González C S, Gómez-Falcon N, Sandoval-Salas F, Saini R, Brar S K, Ramírez A A. Production of biodiesel from castor oil:a review[J]. Energies, 2020, 13(10):2467.doi:10.3390/en13102467.
[4]	Zhi H, He Q, Tang S, Yang J J, Zhang W, Liu H F, Jia Y C, Jia G Q, Zhang A Y, Li Y H, Guo E H, Gao M, Li S J, Li J X, Qin N, Zhu C C, Ma C Y, Zhang H J, Chen G Q, Zhang W F, Wang H G, Qiao Z J, Li S G, Cheng R H, Xing L, Wang S Y, Liu J R, Liu J, Diao X M. Genetic control and phenotypic characterization of panicle architecture and grain yield-related traits in foxtail millet (Setaria italica)[J]. Theoretical and Applied Genetics, 2021, 134(9):3023-3036.doi:10.1007/s00122-021-03875-2. pmid: 34081150
[5]	Zhou J G, Liu Q, Tian R, Chen H X, Wang J, Yang Y Y, Zhao C H, Liu Y L, Tang H P, Deng M, Xu Q, Jiang Q T, Chen G Y, Qi P F, Jiang Y F, Chen G D, Tang L W, Ren Y, Zheng Z, Liu C J, Zheng Y L, He Y J, Wei Y M, Ma J. A co-located QTL for seven spike architecture-related traits shows promising breeding use potential in common wheat (Triticum aestivum L.)[J]. Theoretical and Applied Genetics, 2024, 137(1):31.doi:10.1007/s00122-023-04536-2.
[6]	Lephuthing M C, Khumalo T P, Tolmay V L, Dube E, Tsilo T J. Genetic mapping of quantitative trait loci associated with plant height and yield component traits in a wheat (Triticum aestivum L.) doubled haploid population derived from tugela-DN×elands[J]. Agronomy, 2022, 12(10):2283.doi:10.3390/agronomy12102283.
[7]	Wang J B, Sun G L, Ren X F, Li C D, Liu L P, Wang Q F, Du B B, Sun D F. QTL underlying some agronomic traits in barley detected by SNP markers[J]. BMC Genetics, 2016, 17(1):103.doi:10.1186/s12863-016-0409-y. pmid: 27388211
[8]	Venu R C, Ma J B, Jia Y L, Liu G J, Jia M H, Nobuta K, Sreerekha M V, Moldenhauer K, McClung A M, Meyers B C, Wang G L. Identification of candidate genes associated with positive and negative heterosis in rice[J]. PLoS One, 2014, 9(4):e95178.doi:10.1371/journal.pone.0095178.
[9]	Shen B, Yu W D, Zhu Y J, Fan Y Y, Zhuang J Y. Fine mapping of a major quantitative trait locus,qFLL6.2,controlling flag leaf length and yield traits in rice (Oryza sativa L.)[J]. Euphytica, 2012, 184(1):57-64.doi:10.1007/s10681-011-0539-2.
[10]	Liu X, Zhao Z G, Liu L L, Xiao Y H, Tian Y L, Liu S J, Chen L M, Wang Y H, Liu Y Q, Chen S H, Zhang W W, Wang C M, Jiang L, Wan J M. Construction of chromosomal segment substitution lines and genetic dissection of introgressed segments associated with yield determination in the parents of a super-hybrid rice[J]. Plant Breeding, 2016, 135(1):63-72.doi:10.1111/pbr.12329.
[11]	Yang M Y, Yang X, Yan Z, Chao Q, Shen J, Shui G H, Guo P M, Wang B C. OsTST1,a key tonoplast sugar transporter from source to sink,plays essential roles in affecting yields and height of rice (Oryza sativa L.)[J]. Planta, 2023, 258(1):4.doi:10.1007/s00425-023-04160-w.
[12]	Zhai L Y, Wang F, Yan A, Liang C W, Wang S, Wang Y, Xu J L. Pleiotropic effect of GNP1 underlying grain number per panicle on sink,source and flow in rice[J]. Frontiers in Plant Science, 2020,11:933.doi:10.3389/fpls.2020.00933.
[13]	Chiluwal A, Perumal R, Poudel H P, Muleta K, Ostmeyer T, Fedenia L, Pokharel M, Bean S R, Sebela D, Bheemanahalli R, Oumarou H, Klein P, Rooney W L, Jagadish S V K. Genetic control of source-sink relationships in grain sorghum[J]. Planta, 2022, 255(2):1-16.doi:10.1007/s00425-022-03822-5.
[14]	Makino Y, Hirooka Y, Homma K, Kondo R, Liu T S, Tang L, Nakazaki T, Xu Z J, Shiraiwa T. Effect of flag leaf length of erect panicle rice on the canopy structure and biomass production after heading[J]. Plant Production Science, 2022, 25(1):1-10.doi:10.1080/1343943x.2021.1908152.
[15]	Liang X G, Gao Z, Fu X X, Chen X M, Shen S, Zhou S L. Coordination of carbon assimilation,allocation,and utilization for systemic improvement of cereal yield[J]. Frontiers in Plant Science, 2023,14:1206829.doi:10.3389/fpls.2023.1206829.
[16]	Mohammad A, Muhammad A, Muhammad S. Genetic improvement in physiological traits of rice yield[M]// Genetic Improvement of Field Crops, The United States: CRC Press,2021:413-455.doi:10.1201/9781003210238-9.
[17]	Zhai L Y, Yan A, Shao K T, Wang S, Wang Y, Chen Z H, Xu J L. Large vascular bundle phloem area 4 enhances grain yield and quality in rice via source-sink-flow[J]. Plant Physiology, 2023, 191(1):317-334.doi:10.1093/plphys/kiac461.
[18]	Liao S Y, Yan J, Xing H K, Tu Y, Zhao H, Wang G W. Genetic basis of vascular bundle variations in rice revealed by genome-wide association study[J]. Plant Science, 2021,302:110715.doi:10.1016/j.plantsci.2020.110715.
[19]	吕军, 王伯伦, 孟维韧, 赵凤艳. 不同穗型粳稻的光合作用与物质生产特性[J]. 中国农业科学, 2007, 40(5):902-908.doi:10.3321/j.issn:0578-1752.2007.05.006.
	Lü J, Wang B L, Meng W R, Zhao F Y. The characteristics of photosynthesis and dry matter production in japonica rice cultivars with different type panicles[J]. Scientia Agricultura Sinica, 2007, 40(5):902-908.
[20]	王芳, 王云, 朱国立, 刘海臣, 张云华, 庞晓斌. 蓖麻主要农艺性状及单株产量通径分析[J]. 内蒙古民族大学学报(自然科学版), 2002, 17(1):29-32.doi:10.14045/j.cnki.15-1220.2002.01.011.
	Wang F, Wang Y, Zhu G L, Liu H C, Zhang Y H, Pang X B. The path analysis between the agricultural characters and kernels yield of per plant in castor[J]. Journal of Inner Mongolia University for Nationalities(Nataral Sciences), 2002, 17(1):29-32.
[21]	冉隆贵, 梁宗锁, 毕建琦, 单长卷. 蓖麻主穗产量构成因素及生物量分配规律研究[J]. 西北农业学报, 2005, 14(5):41-44.doi:10.3969/j.issn.1004-1389.2005.05.009.
	Ran L G, Liang Z S, Bi J Q, Shan C J. Relationship between factors of output constitution and length of main ear and distribution of biomass for castor[J]. Acta Agriculturae Boreali-occidentalis Sinica, 2005, 14(5):41-44.
[22]	张锡顺, 杨建国, 杨若菡, 徐宁生, 刘旭云, 杜刚. 蓖麻数量性状遗传距离与杂种优势关系的研究[J]. 中国农业科学, 2006, 39(3):633-640.doi:10.3321/j.issn:0578-1752.2006.03.029.
	Zhang X S, Yang J G, Yang R H, Xu N S, Liu X Y, Du G. Study of the relationship between genetic distance and heterosis in castor[J]. Scientia Agricultura Sinica, 2006, 39(3):633-640.
[23]	Norris R F. Relationship between inflorescence size and seed production in barnyardgrass (Echinochloa crus-galli)[J]. Weed Science, 1992, 40(1):74-78.doi:10.1017/S0043174500056988.
[24]	Pannwitt H, Westerman P R, De Mol F D, Gerowitt B. Using panicle dry weight to estimate seed production in Echinochloa crus-galli[J]. Weed Research, 2019, 59(6):437-445.doi:10.1111/wre.12381.
[25]	Akbar H, Idrees M, Ahmad M F, Arif M, Zakirullah M. Dry weight of spike at anthesis determines grain weight of spike at maturity[J]. Journal of Agricultural & Biological Science, 2006, 1(3):55-61.
[26]	Fischer, R A. Number of kernels in wheat crops and the influence of solar radiation and temperature[J]. The Journal of Agricultural Science, 1985, 105(2):447-461.doi:10.1017/S0021859600056495.
[27]	Savin R, Slafer G A. Shading effects on the yield of an Argentinian wheat cultivar[J]. The Journal of Agricultural Science, 1991, 116(1):1-7.doi:10.1017/s0021859600076085.
[28]	Abbate P E, Andrade F H, Culot J P, Bindraban P S. Grain yield in wheat:effects of radiation during spike growth period[J]. Field Crops Research, 1997, 54(2/3):245-257.doi:10.1016/s0378-4290(97)00059-2.
[29]	Baye A, Berihun B, Bantayehu M, Derebe B. Genotypic and phenotypic correlation and path coefficient analysis for yield and yield-related traits in advanced bread wheat (Triticum aestivum L.) lines[J]. Cogent Food & Agriculture, 2020, 6(1):1752603.doi:10.1080/23311932.2020.1752603.
[30]	Masoomi-Aladizgeh F, Jabbari L, Khayam N R, Aalami A, Atwell B J, Haynes P A. A universal protocol for high-quality DNA and RNA isolation from diverse plant species[J]. PLoS One, 2023, 18(12):e0295852.doi:10.1371/journal.pone.0295852.
[31]	Yeboah A, Lu J N, Yang T, Karikari B, Gu S L, Xie Y, Liu H Y, Yin X G. Genome-wide association study identifies loci,beneficial alleles,and candidate genes for cadmium tolerance in castor (Ricinus communis L.)[J]. Industrial Crops and Products, 2021,171:113842.doi:10.1016/j.indcrop.2021.113842.
[32]	黄冠荣, 殷学贵, 陆建农, 张柳琴, 刘朝裕, 张肖肖, 林海虹, 左金鹰. 蓖麻有效穗数QTL定位及候选基因鉴定[J]. 华北农学报, 2023, 38(6):36-44.doi:10.7668/hbnxb.20194396.
	Huang G R, Yin X G, Lu J N, Zhang L Q, Liu C Y, Zhang X X, Lin H H, Zuo J Y. QTL mapping and candidate gene identification of effective spike number in Ricinusc communis L.[J]. Acta Agriculturae Boreali-Sinica, 2023, 38(6):36-44.
[33]	Yadav P, Saxena K B, Hingane A, Kumar C V S, Kandalkar V S, Varshney R K, Saxena R K. An "Axiom Cajanus SNP Array" based high density genetic map and QTL mapping for high-selfing flower and seed quality traits in pigeonpea[J]. BMC Genomics, 2019, 20(1):235.doi:10.1186/s12864-019-5595-3. pmid: 30898108
[34]	Wu J, Mao L L, Tao J C, Wang X X, Zhang H J, Xin M, Shang Y Q, Zhang Y N, Zhang G H, Zhao Z T, Wang Y M, Cui M S, Wei L M, Song X L, Sun X Z. Dynamic quantitative trait loci mapping for plant height in recombinant inbred line population of upland cotton[J]. Frontiers in Plant Science, 2022,13:914140.doi:10.3389/fpls.2022.914140.
[35]	Wang H, Jia J, Cai Z D, Duan M M, Jiang Z, Xia Q J, Ma Q B, Lian T X, Nian H. Identification of quantitative trait loci (QTLs) and candidate genes of seed iron and zinc content in soybean (Glycine max L.Merr.)[J]. BMC Genomics, 2022, 23(1):146.doi:10.1186/s12864-022-08313-1.
[36]	McCouch S R, Chen X L, Panaud O, Temnykh S, Xu Y B, Cho Y G, Huang N, Ishii T, Blair M. Microsatellite marker development,mapping and applications in rice genetics and breeding[J]. Plant Molecular Biology, 1997, 35(1/2):89-99.doi:10.1023/A:1005711431474.
[37]	Huang L L, Tang J C, Zhu B H, Chen G D, Chen L Y, Bu S H, Zhu H T, Liu Z P, Li Z, Meng L J, Liu G F, Wang S K. QTL epistasis plays a role of homeostasis on heading date in rice[J]. Scientific Reports, 2024, 14(1):373.doi:10.1038/s41598-023-50786-x.
[38]	Ranjan R, Yadav R, Jain N, Sinha N, Bainsla N K, Gaikwad K B, Kumar M. Epistatic QTLs play a major role in nitrogen use efficiency and its component traits in indian spring wheat[J]. Agriculture-Basel, 2021, 11(11):1149.doi:10.3390/agriculture11111149.
[39]	Liu P Y, Zhu J, Lou X Y, Lu Y. A method for marker-assisted selection based on QTLs with epistatic effects[J]. Genetica, 2003, 119(1):75-86.doi:10.1023/a:1024439008631. pmid: 12903749

群体 Population	性状 Trait	相关系数 Correlation coefficient	通径系数 Path coefficient				决策系数 Decision coefficient
群体 Population	性状 Trait	相关系数 Correlation coefficient	PSADW	PSPDW	PSHGW	PSSN	决策系数 Decision coefficient
F₂	PSADW	0.399	0.070	—	0.037	0.292	0.329	0.050 960
	PSPDW	—	—	—	—	—	—	—
	PSHGW	-0.020	0.006	—	0.451	-0.477	-0.472	-0.221 441
	PSSN	0.870	0.019	—	-0.204	1.054	-0.185	0.723 044
BC₁	PSADW	0.628	0.161	-0.000 05	0.148	0.128	0.276	0.176 295
	PSPDW	-0.072	0.008	-0.001 00	0.145	-0.090	0.063	0.000 143
	PSHGW	0.246	0.055	-0.000 33	0.434	-0.097	-0.043	0.025 172
	PSSN	0.832	0.058	0.000 25	-0.118	0.358	-0.060	0.688 624

群体 Population	性状 Trait	相关系数 Correlation coefficient	通径系数 Path coefficient				决策系数 Decision coefficient
群体 Population	性状 Trait	相关系数 Correlation coefficient	PSADW	PSPDW	PSHGW	PSSN	决策系数 Decision coefficient
F₂	PSADW	0.399	0.070	—	0.037	0.292	0.329	0.050 960
	PSPDW	—	—	—	—	—	—	—
	PSHGW	-0.020	0.006	—	0.451	-0.477	-0.472	-0.221 441
	PSSN	0.870	0.019	—	-0.204	1.054	-0.185	0.723 044
BC₁	PSADW	0.628	0.161	-0.000 05	0.148	0.128	0.276	0.176 295
	PSPDW	-0.072	0.008	-0.001 00	0.145	-0.090	0.063	0.000 143
	PSHGW	0.246	0.055	-0.000 33	0.434	-0.097	-0.043	0.025 172
	PSSN	0.832	0.058	0.000 25	-0.118	0.358	-0.060	0.688 624

性状 Trait	世代 Generation	群体规模/株 Population size	极差 Range	均值±标准差 Mean±s	变异系数/% CV	偏度 Skewness	峰度 Kurtosis
主穗籽粒产量	P₁	25	84.20~108.57	92.61±7.73A	8.17	-0.02	-1.93
Seed yield of	P₂	25	11.14~16.67	13.28±1.48B	11.11	0.93	0.97
primary spike	F₁	25	30.74~39.22	34.75±2.11	6.07	0.08	1.82
	F₂	282	1.83~121.50	41.81±21.17	50.63	0.92	0.81
	BC₁	250	2.34~165.89	33.29±19.86	59.64	2.35	10.88
一级分枝穗籽粒产量	P₁	25	30.22~42.50	37.59±3.68A	9.80	-0.75	0.00
Seed yield of primary	P₂	25	4.21~9.86	6.89±1.66B	24.15	0.34	-0.11
branch spike	F₁	25	10.04~16.96	13.18±2.32	17.61	-0.06	-1.26
	F₂	282	1.74~255.95	67.38±41.80	62.04	0.87	1.04
	BC₁	250	0.30~217.89	93.01±43.00	46.24	0.17	-0.07
主穗穗轴干质量	P₁	25	10.00~15.00	13.18±2.52A	19.14	-0.66	-1.96
Dry weight of primary	P₂	25	5.00~5.00	5.00±0.00B	0.01
spike axis	F₁	25	5.00~10.00	8.33±2.46	29.54	-0.81	-1.65
	F₂	282	5.00~55.00	7.22±6.19	85.80	5.77	39.24
	BC₁	250	5.00~25.00	5.91±2.66	44.96	4.03	21.17
一级分枝穗穗轴干质量	P₁	25	7.50~11.67	8.86±1.84A	20.71	1.05	-0.90
Dry weight of primary	P₂	25	1.25~2.00	1.70±0.29B	16.86	-0.49	-0.89
branch spike axis	F₁	25	3.33~4.17	3.88±0.30	7.85	-0.93	-0.21
	F₂	282	0.56~12.00	2.58±1.64	63.50	1.92	5.73
	BC₁	250	0.63~5.00	1.93±0.89	45.92	0.99	0.94

性状 Trait	世代 Generation	群体规模/株 Population size	极差 Range	均值±标准差 Mean±s	变异系数/% CV	偏度 Skewness	峰度 Kurtosis
主穗籽粒产量	P₁	25	84.20~108.57	92.61±7.73A	8.17	-0.02	-1.93
Seed yield of	P₂	25	11.14~16.67	13.28±1.48B	11.11	0.93	0.97
primary spike	F₁	25	30.74~39.22	34.75±2.11	6.07	0.08	1.82
	F₂	282	1.83~121.50	41.81±21.17	50.63	0.92	0.81
	BC₁	250	2.34~165.89	33.29±19.86	59.64	2.35	10.88
一级分枝穗籽粒产量	P₁	25	30.22~42.50	37.59±3.68A	9.80	-0.75	0.00
Seed yield of primary	P₂	25	4.21~9.86	6.89±1.66B	24.15	0.34	-0.11
branch spike	F₁	25	10.04~16.96	13.18±2.32	17.61	-0.06	-1.26
	F₂	282	1.74~255.95	67.38±41.80	62.04	0.87	1.04
	BC₁	250	0.30~217.89	93.01±43.00	46.24	0.17	-0.07
主穗穗轴干质量	P₁	25	10.00~15.00	13.18±2.52A	19.14	-0.66	-1.96
Dry weight of primary	P₂	25	5.00~5.00	5.00±0.00B	0.01
spike axis	F₁	25	5.00~10.00	8.33±2.46	29.54	-0.81	-1.65
	F₂	282	5.00~55.00	7.22±6.19	85.80	5.77	39.24
	BC₁	250	5.00~25.00	5.91±2.66	44.96	4.03	21.17
一级分枝穗穗轴干质量	P₁	25	7.50~11.67	8.86±1.84A	20.71	1.05	-0.90
Dry weight of primary	P₂	25	1.25~2.00	1.70±0.29B	16.86	-0.49	-0.89
branch spike axis	F₁	25	3.33~4.17	3.88±0.30	7.85	-0.93	-0.21
	F₂	282	0.56~12.00	2.58±1.64	63.50	1.92	5.73
	BC₁	250	0.63~5.00	1.93±0.89	45.92	0.99	0.94

群体 Population	方法 Method	性状 Trait	QTL	染色体 Chromosome	连锁群 LG	位置 Position	LOD	加性 Add	显性 Dom	贡献率/% PVE	置信区间 CI	标记区间 MI
F₂	CIM	PSSY	FqPSSY9.1	9	3	8.01	37.00	75.00	-3.06	6.29	1.7~38.0	RCM820~RCM523
			FqPSSY10.1	10	4	22.61	3.21	6.76	-3.75	2.52	4.5~27.6	RCM938~RCM279
			qPSSY3.1	3	10	71.11	3.34	8.87	-0.52	7.00	62.0~80.1	RCM931~RCM922
		PSADW	FqPSADW7.1	7	1	2.01	40.47	-23.02	-22.20	1.74	1.0~2.4	RCM1335~RCM1336
			FqPSADW10.1	10	4	8.01	44.96	22.11	-22.86	0.01	5.5~11.1	RCM938~RCM872
			FqPSADW10.2	10	4	73.91	3.01	-4.39	-3.40	1.09	72.2~80.3	RCM226~RCM945
		PBSADW	qPBSADW3.1	3	10	75.11	2.01	0.24	-0.83	6.49	47.6~80.1	RCM933~RCM922
	ICIM-ADD	PSSY	FqPSSY7.1	7	1	18.00	5.28	-8.76	-1.18	5.71	16.5~18.0	RCM1336~RCM1338
			FqPSSY9.1	9	3	29.00	4.00	8.96	-2.28	5.36	25.5~37.5	RCM1212~RCM523
			FqPSSY9.2	9	3	81.00	2.01	1.61	7.02	2.00	76.5~92.5	RCM1582~RCM67
			FqPSSY10.1	10	4	32.00	2.66	-4.57	-6.52	3.08	18.5~53.5	RCM279~RCM1567
			FqPSSY10.2	10	4	79.00	3.04	-5.84	-7.23	4.18	70.5~87.0	RCM226~RCM945
			qPSSY3.1	3	10	70.00	2.62	7.81	-2.67	3.76	52.5~81.0	RCM931~RCM922
		PSADW	FqPSADW7.1	7	1	3.00	3.20	-3.19	-2.41	0.93	0.0~15.5	RCM1335~RCM1336
			FqPSADW10.1	10	4	9.00	46.19	2.44	-22.96	7.25	5.5~11.5	RCM938~RCM872
			FqPSADW2.1	2	8	1.00	2.23	1.60	-1.51	0.47	0.0~15.0	RCM150~RCM1824
		PBSADW	qPBSADW3.1	3	10	81.00	2.38	0.33	-0.66	6.56	66.5~81.0	RCM931~RCM922
BC₁	CIM	PBSADW	qPBSADW3.1	3	4	14.41	5.33	0.61		11.60	10.8~17.8	RCM950~RCM920
	ICIM-ADD	PSSY	BqPSSY4.1	4	3	23.00	2.27	9.29		4.85	0.0~43.5	RCM904~RCM523
			qPSSY3.1	3	4	1.00	2.79	9.61		5.43	33.5~53.0	RCM931~RCM922
		PBSADW	qPBSADW3.1	3	4	4.00	5.40	0.63		10.22	10.5~17.5	RCM950~RCM920

Please choose a citation manager

Content to export