Rice Science ›› 2019, Vol. 26 ›› Issue (1): 32-41.DOI: 10.1016/j.rsci.2018.04.006
• Orginal Article • Previous Articles Next Articles
Okechukwu Anyaoha Christian1,4(), Fofana Mamadou2, Gracen Vernon1,3, Tongoona Pangirayi1, Mande Semon2
Received:
2018-02-28
Accepted:
2018-04-27
Online:
2019-01-29
Published:
2018-10-22
Okechukwu Anyaoha Christian, Fofana Mamadou, Gracen Vernon, Tongoona Pangirayi, Mande Semon. Introgression of Two Drought QTLs into FUNAABOR-2 Early Generation Backcross Progenies Under Drought Stress at Reproductive Stage[J]. Rice Science, 2019, 26(1): 32-41.
Add to citation manager EndNote|Ris|BibTeX
QTL | Marker | Chr | Forward sequence (5′-3′) | Reverse sequence (5′-3′) | RT | AT (ºC) |
---|---|---|---|---|---|---|
qDTY12.1 | RM511 | 12 | CTTCGATCCGGTGACGAC | AACGAAAGCGAAGCTGTCTC | (GAC)7 | 55 |
RM28099 | 12 | TGTGCGGATGCGGGTAAGTCC | CCACCTGTCAACCACCGAAACC | (GAG)7 | 55 | |
RM1261 | 12 | GTCCATGCCCAAGACACAAC | GTTACATCATGGGTGACCCC | (AG)16 | 50 | |
RM28130 | 12 | CAGCAGACGTTCCGGTTCTACTCG | AGGACGGTGGTGGTGATCTGG | (GAG)7 | 50 | |
RM28166 | 12 | TGCTTGCAAACATTGCTTCTGG | ACTGATGTACTGAACACGGGAAGG | (CT)12 | 50 | |
qDTY2.3 | RM250 | 2 | GGTTCAAACCAAGCTGATCA | GATGAAGGCCTTCCACGCAG | (CT)17 | 55 |
Table 1 Details of markers associated with drought quantitative trait loci (QTLs) qDTY12.1 and qDTY2.3.
QTL | Marker | Chr | Forward sequence (5′-3′) | Reverse sequence (5′-3′) | RT | AT (ºC) |
---|---|---|---|---|---|---|
qDTY12.1 | RM511 | 12 | CTTCGATCCGGTGACGAC | AACGAAAGCGAAGCTGTCTC | (GAC)7 | 55 |
RM28099 | 12 | TGTGCGGATGCGGGTAAGTCC | CCACCTGTCAACCACCGAAACC | (GAG)7 | 55 | |
RM1261 | 12 | GTCCATGCCCAAGACACAAC | GTTACATCATGGGTGACCCC | (AG)16 | 50 | |
RM28130 | 12 | CAGCAGACGTTCCGGTTCTACTCG | AGGACGGTGGTGGTGATCTGG | (GAG)7 | 50 | |
RM28166 | 12 | TGCTTGCAAACATTGCTTCTGG | ACTGATGTACTGAACACGGGAAGG | (CT)12 | 50 | |
qDTY2.3 | RM250 | 2 | GGTTCAAACCAAGCTGATCA | GATGAAGGCCTTCCACGCAG | (CT)17 | 55 |
Fig. 2. Banding pattern of foreground selection for 70 FUNAABOR-2 BC1F1 progenies carrying qDTY12.1 using peak marker RM511 (A) and 20 selected FUNAABOR-2 introgresed lines carrying a combination of qDTY12.1 and qDTY2.3 using peak marker RM250 (B).M, Marker; P1, FUNAABOR-2; P2, IR84984-83-15-481-B; ‘1’, Susceptible allele; ‘2’, Resistant allele; ‘3’, Heterozygote.
Trait | Parameter | Drought stress | Non-stress |
---|---|---|---|
GY (g/m2) | FUNAABOR-2 | 11.16 | 262.33 |
IR84984-83-15-481-B | 56.43 | 359.3 | |
Population mean | 29.89 | 192.37 | |
Highest line | 86.67 | 586.36 | |
Lowest line | 0 | 29.54 | |
SED | 9.03 | 46.6 | |
LSD | 17.87 | 92.2 | |
Heritability (%) | 91 | 83 | |
P-value | 1.98 × 10-31 | 4.25 × 10-18 | |
DAF (d) | FUNAABOR-2 | 77.46 | 71.67 |
IR84984-83-15-481-B | 61.83 | 58 | |
Population mean | 70.92 | 65.07 | |
Highest line | 89 | 78 | |
Lowest line | 57 | 53 | |
SED | 4.49 | 2.71 | |
LSD | 8.87 | 5.36 | |
Heritability (%) | 70 | 79 | |
P-value | 2.08 × 10-9 | 1.34 × 10-14 | |
PH (cm) | FUNAABOR-2 | 122.11 | 139.33 |
IR84984-83-15-481-B | 121.43 | 126.44 | |
Population mean | 118.55 | 137.28 | |
Highest line | 149.33 | 163.33 | |
Lowest line | 84.33 | 113.33 | |
SED | 9.89 | 5.33 | |
LSD | 19.57 | 10.56 | |
Heritability (%) | 58 | 82 | |
P-value | 1.43 × 10-5 | 4.61 × 10-17 |
Table 2 Traits for parents and progenies under drought stress and non-stress conditions.
Trait | Parameter | Drought stress | Non-stress |
---|---|---|---|
GY (g/m2) | FUNAABOR-2 | 11.16 | 262.33 |
IR84984-83-15-481-B | 56.43 | 359.3 | |
Population mean | 29.89 | 192.37 | |
Highest line | 86.67 | 586.36 | |
Lowest line | 0 | 29.54 | |
SED | 9.03 | 46.6 | |
LSD | 17.87 | 92.2 | |
Heritability (%) | 91 | 83 | |
P-value | 1.98 × 10-31 | 4.25 × 10-18 | |
DAF (d) | FUNAABOR-2 | 77.46 | 71.67 |
IR84984-83-15-481-B | 61.83 | 58 | |
Population mean | 70.92 | 65.07 | |
Highest line | 89 | 78 | |
Lowest line | 57 | 53 | |
SED | 4.49 | 2.71 | |
LSD | 8.87 | 5.36 | |
Heritability (%) | 70 | 79 | |
P-value | 2.08 × 10-9 | 1.34 × 10-14 | |
PH (cm) | FUNAABOR-2 | 122.11 | 139.33 |
IR84984-83-15-481-B | 121.43 | 126.44 | |
Population mean | 118.55 | 137.28 | |
Highest line | 149.33 | 163.33 | |
Lowest line | 84.33 | 113.33 | |
SED | 9.89 | 5.33 | |
LSD | 19.57 | 10.56 | |
Heritability (%) | 58 | 82 | |
P-value | 1.43 × 10-5 | 4.61 × 10-17 |
Under non-stress condition | Backcross line | Under drought stress condition | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
GY | DAF | PH | qDTY12.1 | qDTY2.3 | GY | DAF | PH | qDTY12.1 | qDTY2.3 | |
(g/m2) | (d) | (cm) | (g/m2) | (d) | (cm) | |||||
417.73 | 66.33 | 136 | N | N | BC91-1 | 79.38 | 70.65 | 138.44 | Y | Y |
398.48 | 67.33 | 130 | Y | Y | BC50 | 76.12 | 73.02 | 110 | N | N |
345.45 | 67 | 132.56 | N | N | BC69 | 74.11 | 76.98 | 110.11 | Y | Y |
334.94 | 69.67 | 141.67 | N | N | BC43 | 72.91 | 73.49 | 116.56 | N | N |
318.18 | 67.67 | 141.11 | N | N | BC27-1 | 72.44 | 74.96 | 114.11 | Y | Y |
315.91 | 66 | 146 | Y | N | BC103 | 66.18 | 74.68 | 122.22 | N | N |
301.52 | 72 | 125.44 | Y | N | BC57 | 55.71 | 75.29 | 127.22 | Y | N |
284.85 | 68.33 | 138.89 | N | N | BC114 | 55.33 | 72.96 | 124.56 | N | N |
277.52 | 60.33 | 124.67 | Y | N | BC81 | 54.92 | 66.46 | 110.22 | N | N |
275 | 68.67 | 153.33 | N | N | BC75 | 54.16 | 73.52 | 122.19 | N | N |
264.09 | 65 | 142.33 | N | N | BC16 | 51.51 | 71.47 | 118.78 | Y | N |
254.54 | 69 | 128 | Y | Y | BC111 | 51.21 | 73.63 | 116.67 | N | N |
252.27 | 65.33 | 131.67 | N | N | BC14 | 47.94 | 62.79 | 112.33 | N | N |
251.09 | 68 | 151.33 | N | Y | BC59 | 47.41 | 75.35 | 130.11 | N | N |
250 | 64.67 | 143.67 | N | N | BC84 | 45.01 | 73.77 | 119.33 | Y | N |
247.73 | 69 | 133.89 | N | N | BC74 | 43.12 | 69.8 | 108.67 | N | N |
246.24 | 61.33 | 146.61 | Y | Y | BC173 | 42.96 | 70 | 130.44 | N | N |
244.7 | 66.67 | 136.67 | Y | N | BC4 | 42.64 | 73.14 | 122.56 | N | N |
262.3 | 71.67 | 139.33 | N | N | FUNAABOR-2 | 11.16 | 77.46 | 122.11 | N | N |
359.3 | 58 | 126.44 | Y | Y | IR84984-83-15-481-B | 56.42 | 61.83 | 121.43 | Y | Y |
46.6 | 2.71 | 5.33 | SED a | 9.03 | 4.49 | 9.89 | ||||
92.2 | 5.36 | 10.56 | LSD a | 17.87 | 8.87 | 19.57 |
Table 3 Quantitative trait locus and grain yield of 18 best yielding backcross progenies at BC1F2 generation.
Under non-stress condition | Backcross line | Under drought stress condition | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
GY | DAF | PH | qDTY12.1 | qDTY2.3 | GY | DAF | PH | qDTY12.1 | qDTY2.3 | |
(g/m2) | (d) | (cm) | (g/m2) | (d) | (cm) | |||||
417.73 | 66.33 | 136 | N | N | BC91-1 | 79.38 | 70.65 | 138.44 | Y | Y |
398.48 | 67.33 | 130 | Y | Y | BC50 | 76.12 | 73.02 | 110 | N | N |
345.45 | 67 | 132.56 | N | N | BC69 | 74.11 | 76.98 | 110.11 | Y | Y |
334.94 | 69.67 | 141.67 | N | N | BC43 | 72.91 | 73.49 | 116.56 | N | N |
318.18 | 67.67 | 141.11 | N | N | BC27-1 | 72.44 | 74.96 | 114.11 | Y | Y |
315.91 | 66 | 146 | Y | N | BC103 | 66.18 | 74.68 | 122.22 | N | N |
301.52 | 72 | 125.44 | Y | N | BC57 | 55.71 | 75.29 | 127.22 | Y | N |
284.85 | 68.33 | 138.89 | N | N | BC114 | 55.33 | 72.96 | 124.56 | N | N |
277.52 | 60.33 | 124.67 | Y | N | BC81 | 54.92 | 66.46 | 110.22 | N | N |
275 | 68.67 | 153.33 | N | N | BC75 | 54.16 | 73.52 | 122.19 | N | N |
264.09 | 65 | 142.33 | N | N | BC16 | 51.51 | 71.47 | 118.78 | Y | N |
254.54 | 69 | 128 | Y | Y | BC111 | 51.21 | 73.63 | 116.67 | N | N |
252.27 | 65.33 | 131.67 | N | N | BC14 | 47.94 | 62.79 | 112.33 | N | N |
251.09 | 68 | 151.33 | N | Y | BC59 | 47.41 | 75.35 | 130.11 | N | N |
250 | 64.67 | 143.67 | N | N | BC84 | 45.01 | 73.77 | 119.33 | Y | N |
247.73 | 69 | 133.89 | N | N | BC74 | 43.12 | 69.8 | 108.67 | N | N |
246.24 | 61.33 | 146.61 | Y | Y | BC173 | 42.96 | 70 | 130.44 | N | N |
244.7 | 66.67 | 136.67 | Y | N | BC4 | 42.64 | 73.14 | 122.56 | N | N |
262.3 | 71.67 | 139.33 | N | N | FUNAABOR-2 | 11.16 | 77.46 | 122.11 | N | N |
359.3 | 58 | 126.44 | Y | Y | IR84984-83-15-481-B | 56.42 | 61.83 | 121.43 | Y | Y |
46.6 | 2.71 | 5.33 | SED a | 9.03 | 4.49 | 9.89 | ||||
92.2 | 5.36 | 10.56 | LSD a | 17.87 | 8.87 | 19.57 |
qDTY | Under non-stress condition | qDTY | Under drought stress condition | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
GY | DAF | EF (g/m2) | MF | LF (g/m2) | GY (g/m2) | DAF | EF (g/m2) | MF (g/m2) | LF | ||
(g/m2) | (d) | (g/m2) | (d) | (g/m2) | |||||||
qDTY12.1 | 197.92 | 61.4 | 174.58 | 154.7 | 239.17 | qDTY12.1 | 31.13 | 71.6 | 5.3 | 17.3 | 38.67 |
qDTY12.1/qDTY2.3 | 206.76 | 73.95 | - | 175.96 | 237.5 | qDTY12.1/qDTY2.3 | 40.12 | 72.92 | - | - | 43.38 |
qDTY absent | 190.01 | 65.35 | 153.09 | 178.94 | 219 | qDTY absent | 29.2 | 69.14 | |||
FUNAABOR-2 | 262.3 | 71.67 | FUNAABOR-2 | 11.16 | 77.46 | ||||||
IR84984-83-15-481-B | 359.3 | 58 | IR84984-83-15-481-B | 56.42 | 61.83 |
Table 4 Effects of quantitative trait locus combinations on FUNAABOR-2 introgressed lines at BC1F2 generation.
qDTY | Under non-stress condition | qDTY | Under drought stress condition | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
GY | DAF | EF (g/m2) | MF | LF (g/m2) | GY (g/m2) | DAF | EF (g/m2) | MF (g/m2) | LF | ||
(g/m2) | (d) | (g/m2) | (d) | (g/m2) | |||||||
qDTY12.1 | 197.92 | 61.4 | 174.58 | 154.7 | 239.17 | qDTY12.1 | 31.13 | 71.6 | 5.3 | 17.3 | 38.67 |
qDTY12.1/qDTY2.3 | 206.76 | 73.95 | - | 175.96 | 237.5 | qDTY12.1/qDTY2.3 | 40.12 | 72.92 | - | - | 43.38 |
qDTY absent | 190.01 | 65.35 | 153.09 | 178.94 | 219 | qDTY absent | 29.2 | 69.14 | |||
FUNAABOR-2 | 262.3 | 71.67 | FUNAABOR-2 | 11.16 | 77.46 | ||||||
IR84984-83-15-481-B | 359.3 | 58 | IR84984-83-15-481-B | 56.42 | 61.83 |
[1] | Anyaoha C, Fofana, M, Gracen V, Tongoona P, Blay E, Semon M, Popoola B.2018. Yield potential of upland rice varieties under reproductive-stage drought and optimal water regimes in Nigeria.Plant Genet Res: Charact Util, 16(4): 378-385. |
[2] | Atlin G N, Cooper M, Bjørnstad A.2006. A comparison of formal and participatory breeding approaches using selection theory.Euphytica, 122(3): 463-475. |
[3] | Badu-Apraku B, Menkir A, Ajala S O, Akinwale R O, Oyekunle M, Obeng-Antwi K.2010. Performance of tropical early-maturing maize cultivars in multiple stress environments.Can J Plant Sci, 90(6): 831-852. |
[4] | Bernier J, Kumar A, Ramaiah V, Spaner D, Atlin G.2007. A large-effect QTL for grain yield under reproductive-stage drought stress in upland rice.Crop Sci, 47(2): 507-516. |
[5] | Bernier J, Atlin G N, Serraj R, Kumar A, Spaner D.2008. Breeding upland rice for drought resistance.J Sci Food Agr, 88(6): 927-939. |
[6] | Collard B C Y, Jahufer M Z Z, Brouwer J B, Pang E C K.2005. An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts.Euphytica, 142: 169-196. |
[7] | Collard B C Y, Mackill D J.2008. Marker-assisted selection: An approach for precision plant breeding in the twenty-first century.Philos T Roy Soc B, 363: 557-572. |
[8] | Dalton T J.2004. A household hedonic model of rice traits: Economic values from farmers in West Africa.Agric Econ, 31: 149-159. |
[9] | Dixit S, Swamy B P M, Vikram P, Ahmed H U, Sta Cruz M T, Amante M, Atri D, Leung H, Kumar A.2012. Fine mapping of QTLs for rice grain yield under drought reveals sub-QTLs conferring a response to variable drought severities.Theor Appl Genet, 125: 155-169. |
[10] | Dixit S, Singh A, Kumar A.2014. Rice breeding for high grain yield under drought: A strategic solution to a complex problem.Int J Agron, 2014: 1-15. |
[11] | Ghimire K H, Quiatchon L A, Vikram P, Swamy B P M, Dixit S, Ahmed H, Hemandez J E, Borromeo T H, Kumar A.2012. Identification and mapping of a QTL (qDTY1.1) with a consistent effect on grain yield under drought. Field Crops Res, 131: 88-96. |
[12] | Hospital F, Charcosset A.1997. Marker-assisted introgression of quantitative trait loci.Genetics, 147(3): 1469-1485. |
[13] | Imolehin E D.1991. Rice improvement and production in Nigeria. WARDA Upland Breeding Task Force Workshop, Bouake. |
[14] | Kumar A, Bernier J, Verulkar S, Lafitte H R, Atlin G N.2008. Breeding for drought tolerance: Direct selection for yield, response to selection and use of drought-tolerant donors in upland and lowland-adapted populations.Field Crops Res, 107(3): 221-231. |
[15] | Kumar A, Dixit S, Ram T, Yadaw R B, Mishra K K, Mandal N P.2014. Breeding high-yielding drought-tolerant rice: Genetic variations and conventional and molecular approaches.J Exp Bot, 65(21): 6265-6278. |
[16] | Lafitte R2003. Managing water for controlled drought in breeding plots. In: Fischer K S, Lafitte R, Fukai S, Atlin G, Hardy B. Breeding Rice for Drought-Prone Environments. Los Banos, the Philippines: International Rice Research Institute: 23-26. |
[17] | Lafitte H R, Price A H, Courtois B.2004. Yield response to water deficit in an upland rice mapping population: Associations among traits and genetic markers.Theor Appl Genet, 109(6): 1237-1246. |
[18] | Mishra K K, Vikram P, Yadaw R B, Swamy B P M, Dixit S, Sta Cruz M T G, Marker S, Kumar A.2013. qDTY12.1: A locus with a consistent effect on grain yield under drought in rice. BMC Genet, 14: 12. |
[19] | Neeraja C N, Maghirang-Rodriguez R, Pamplona A, Heuer S, Collard B C Y, Septiningsih E M, Vergara G, Sanchez D, Xu K, Ismail A M, Mackill D J.2007. A marker-assisted backcross approach for developing submergence-tolerant rice cultivars.Theor Appl Genet, 115(6): 767-776. |
[20] | Ologbon O A C, Ikheloa E E, Akerele E O.2012. Adoption of ‘Ofada’ rice variety and technical efficiency of rice-based production systems in Ogun State, Nigeria.World J Agric Sci, 8(6): 624-631. |
[21] | Palanog A D, Swamy B P M, Shamsudin N A A, Dixit S, Hernandez J E, Boromeo T H, Sta Cruz P C, Kumar A.2014. Grain yield QTLs with consistent-effect under reproductive- stage drought stress in rice.Field Crops Res, 161: 46-54. |
[22] | Saka J O, Lawal B O.2009. Determinants of adoption and productivity of improved rice varieties in southwestern Nigeria.Afr J Biotechnol, 8(19): 4923-4932. |
[23] | Shamsudin N A A, Swamy B P M, Ratnam W, Sta Cruz M T, Raman A, Kumar A.2016. Marker assisted pyramiding of drought yield QTLs into a popular Malaysian rice cultivar, MR219.BMC Genet, 17(1): 30. |
[24] | Showemimo F A, Gregorio G, Olowe V I O, Ukwungwu M N, Maji A, Adigbo S O, Olaoye O J, Akintokun P O, Bodunde J G, Idowu O T H, Awe C A.2011. Varietal release: Release of two dual purpose ofada rice varieties (Funaabor-1 and Funaabor-2) by federal university of agriculture, abeokuta (Funaab). J Agric Sci Environ, 11(2): 122-123. |
[25] | Sreewongchai T, Toojinda T, Thanintorn N, Kosawang C, Vanavichit A, Tharreau D, Sirithunya P.2010. Development of elite indica rice lines with wide spectrum of resistance to Thai blast isolates by pyramiding multiple resistance QTLs. Plant Breeding, 129(2): 176-180. |
[26] | Swamy B P M, Kumar A.2011. Sustainable rice yield in watershort drought-prone environments: Conventional and molecular approaches. In: Lee T S. Irrigation Systems and Practices in Challenging Environments. Rijeka, Croatia: InTech: 149-168. |
[27] | Swamy B P M, Kumar A.2012. Sustainable rice yield in water-short drought-prone environments: Conventional and molecular approaches. INTECH Open Access Publisher. |
[28] | Swamy B P M, Kumar A.2013. Genomics-based precision breeding approaches to improve drought tolerance in rice.Biotechnol Adv, 31(8): 1308-1318. |
[29] | Venuprasad R, Lafitte, H R, Atlin G N.2007. Response to direct selection for grain yield under drought stress in rice.Crop Sci, 47(1): 285-293. |
[30] | Vikram P, Swamy B P M, Dixit S, Singh R, Singh B P, Miro B, Kohli A, Henry A, Singh N K, Kumar A.2015. Drought susceptibility of modern rice varieties: An effect of linkage of drought tolerance with undesirable traits.Sci Rep, 5: 14799. |
[1] | Ayut Kongpun, Tonapha Pusadee, Pennapa Jaksomsak, Kawiporn Chinachanta, Patcharin Tuiwong, Phukjira Chan-In, Sawika Konsaeng, Wasu Pathom-Aree, Suchila Utasee, Benjamaporn Wangkaew, Chanakan Prom-U-Thai. Abiotic and Biotic Factors Controlling Grain Aroma along Value Chain of Fragrant Rice: A Review [J]. Rice Science, 2024, 31(2): 142-158. |
[2] | Sujeevan Rajendran, Hyeonseo Park, Jiyoung Kim, Soon Ju Park, Dongjin Shin, Jong-Hee Lee, Young Hun Song, Nam-Chon Paek, Chul Min Kim. Methane Emission from Rice Fields: Necessity for Molecular Approach for Mitigation [J]. Rice Science, 2024, 31(2): 159-178. |
[3] | Zhu Chengqi, Ye Yuxuan, Qiu Tian, Huang Yafan, Ying Jifeng, Shen Zhicheng. Drought-Tolerant Rice at Molecular Breeding Eras: An Emerging Reality [J]. Rice Science, 2024, 31(2): 179-189. |
[4] | Wu Lijuan, Han Cong, Wang Huimei, He Yuchang, Lin Hai, Wang Lei, Chen Chen, E Zhiguo. OsbZIP53 Negatively Regulates Immunity Response by Involving in Reactive Oxygen Species and Salicylic Acid Metabolism in Rice [J]. Rice Science, 2024, 31(2): 190-202. |
[5] | Xie Shuwei, Shi Huanbin, Wen Hui, Liu Zhiquan, Qiu Jiehua, Jiang Nan, Kou Yanjun. Carbon Catabolite Repressor UvCreA is Required for Development and Pathogenicity in Ustilaginoidea virens [J]. Rice Science, 2024, 31(2): 203-214. |
[6] | Zheng Shaoyan, Chen Junyu, Li Huatian, Liu Zhenlan, Li Jing, Zhuang Chuxiong. Analysis of RNA Recognition and Binding Characteristics of OsCPPR1 Protein in Rice [J]. Rice Science, 2024, 31(2): 215-225. |
[7] | Liu Dan, Zhao Huibo, Wang Zi’an, Xu Jing, Liu Yiting, Wang Jiajia, Chen Minmin, Liu Xiong, Zhang Zhihai, Cen Jiangsu, Zhu Li, Hu Jiang, Ren Deyong, Gao Zhenyu, Dong Guojun, Zhang Qiang, Shen Lan, Li Qing, Qian Qian, Hu Songping, Zhang Guangheng. Leaf Morphology Genes SRL1 and RENL1 Co-Regulate Cellulose Synthesis and Affect Rice Drought Tolerance [J]. Rice Science, 2024, 31(1): 103-117. |
[8] | Wei Huanhe, Geng Xiaoyu, Zhang Xiang, Zhu Wang, Zhang Xubin, Chen Yinglong, Huo Zhongyang, Zhou Guisheng, Meng Tianyao, Dai Qigen. Grain Yield, Biomass Accumulation, and Leaf Photosynthetic Characteristics of Rice under Combined Salinity-Drought Stress [J]. Rice Science, 2024, 31(1): 118-128. |
[9] | Masoumeh Kordi, Naser Farrokhi, Martin I. Pech-Canul, Asadollah Ahmadikhah. Rice Husk at a Glance: From Agro-Industrial to Modern Applications [J]. Rice Science, 2024, 31(1): 14-32. |
[10] | Tian Yu, Sun Jing, Li Jiaxin, Wang Aixia, Nie Mengzi, Gong Xue, Wang Lili, Liu Liya, Wang Fengzhong, Tong Litao. Effects of Milling Methods on Rice Flour Properties and Rice Product Quality: A Review [J]. Rice Science, 2024, 31(1): 33-46. |
[11] | Norhashila Hashim, Maimunah Mohd Ali, Muhammad Razif Mahadi, Ahmad Fikri Abdullah, Aimrun Wayayok, Muhamad Saufi Mohd Kassim, Askiah Jamaluddin. Smart Farming for Sustainable Rice Production: An Insight into Application, Challenge, and Future Prospect [J]. Rice Science, 2024, 31(1): 47-61. |
[12] | Gao Ningning, Ye Shuifeng, Zhang Yu, Zhou Liguo, Ma Xiaosong, Yu Hanxi, Li Tianfei, Han Jing, Liu Zaochang, Luo Lijun. A β-Carotene Ketolase Gene NfcrtO from Subaerial Cyanobacteria Confers Drought Tolerance in Rice [J]. Rice Science, 2024, 31(1): 62-76. |
[13] | Li Qianlong, Feng Qi, Wang Heqin, Kang Yunhai, Zhang Conghe, Du Ming, Zhang Yunhu, Wang Hui, Chen Jinjie, Han Bin, Fang Yu, Wang Ahong. Genome-Wide Dissection of Quan 9311A Breeding Process and Application Advantages [J]. Rice Science, 2023, 30(6): 552-565. |
[14] | Ji Dongling, Xiao Wenhui, Sun Zhiwei, Liu Lijun, Gu Junfei, Zhang Hao, Matthew Tom Harrison, Liu Ke, Wang Zhiqin, Wang Weilu. Translocation and Distribution of Carbon-Nitrogen in Relation to Rice Yield and Grain Quality as Affected by High Temperature at Early Panicle Initiation Stage [J]. Rice Science, 2023, 30(6): 598-612. |
[15] | Prathap V, Suresh Kumar, Nand Lal Meena, Chirag Maheshwari, Monika Dalal, Aruna Tyagi. Phosphorus Starvation Tolerance in Rice Through Combined Physiological, Biochemical, and Proteome Analyses [J]. Rice Science, 2023, 30(6): 613-631. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||