Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs

Rice is the staple food for more than half of the world’s population. Although rice production has doubled in the last 30 years as a result of the development of high-yield, widely adaptable, resource-responsive, semi-dwarf varieties, the threat of a food crisis remains as severe as it was 60 years ago due to the ever-increasing population, water scarcity, labor scarcity, shifting climatic conditions, pest/diseases, loss of productive land to housing, industries, rising sea levels, increasing incidences of drought, flood, urbanization, soil erosion, reduction in soil nutrient status, and environmental issues associated with high-input agriculture. Among these, drought is predicted to be the most severe stress that reduces rice yield. Systematic research on drought over the last 10 years has been conducted across institutes on physiology, breeding, molecular genetics, biotechnology, and cellular and molecular biology. This has provided a better understanding of plant drought mechanisms and has helped scientists to devise better strategies to reduce rice yield losses under drought stress. These include the identification of quantitative trait loci (QTLs) for grain yield under drought as well as many agronomically important traits related to drought tolerance, marker-assisted pyramiding of genetic regions that increase yield under drought, development of efficient techniques for genetic transformation, complete sequencing and annotation of rice genomes, and synteny studies of rice and other cereal genomes. Conventional and marker-assisted breeding rice lines containing useful introgressed genes or loci have been field tested and released as varieties. Still, there is a long way to go towards developing drought-tolerant rice varieties by exploiting existing genetic diversity, identifying superior alleles for drought tolerance, understanding interactions among alleles for drought tolerance and their interaction with genetic backgrounds, and pyramiding the best combination of alleles. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Nitika Sandhu [verfasserIn] Arvind Kumar [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	drought marker pyramiding QTLs rice genomics

Übergeordnetes Werk:	In: Agronomy - MDPI AG, 2012, 7(2017), 2, p 27
Übergeordnetes Werk:	volume:7 ; year:2017 ; number:2, p 27

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/agronomy7020027

Katalog-ID:	DOAJ07195354X

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ07195354X
003	DE-627
005	20230503064256.0
007	cr uuu---uuuuu
008	230228s2017 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3390/agronomy7020027 \|2 doi
035			\|a (DE-627)DOAJ07195354X
035			\|a (DE-599)DOAJad7de0aee0dd4616ab0018b2069a9028
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	0		\|a Nitika Sandhu \|e verfasserin \|4 aut
245	1	0	\|a Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs
264		1	\|c 2017
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Rice is the staple food for more than half of the world’s population. Although rice production has doubled in the last 30 years as a result of the development of high-yield, widely adaptable, resource-responsive, semi-dwarf varieties, the threat of a food crisis remains as severe as it was 60 years ago due to the ever-increasing population, water scarcity, labor scarcity, shifting climatic conditions, pest/diseases, loss of productive land to housing, industries, rising sea levels, increasing incidences of drought, flood, urbanization, soil erosion, reduction in soil nutrient status, and environmental issues associated with high-input agriculture. Among these, drought is predicted to be the most severe stress that reduces rice yield. Systematic research on drought over the last 10 years has been conducted across institutes on physiology, breeding, molecular genetics, biotechnology, and cellular and molecular biology. This has provided a better understanding of plant drought mechanisms and has helped scientists to devise better strategies to reduce rice yield losses under drought stress. These include the identification of quantitative trait loci (QTLs) for grain yield under drought as well as many agronomically important traits related to drought tolerance, marker-assisted pyramiding of genetic regions that increase yield under drought, development of efficient techniques for genetic transformation, complete sequencing and annotation of rice genomes, and synteny studies of rice and other cereal genomes. Conventional and marker-assisted breeding rice lines containing useful introgressed genes or loci have been field tested and released as varieties. Still, there is a long way to go towards developing drought-tolerant rice varieties by exploiting existing genetic diversity, identifying superior alleles for drought tolerance, understanding interactions among alleles for drought tolerance and their interaction with genetic backgrounds, and pyramiding the best combination of alleles.
650		4	\|a drought
650		4	\|a marker
650		4	\|a pyramiding
650		4	\|a QTLs
650		4	\|a rice
650		4	\|a genomics
653		0	\|a Agriculture
653		0	\|a S
700	0		\|a Arvind Kumar \|e verfasserin \|4 aut
773	0	8	\|i In \|t Agronomy \|d MDPI AG, 2012 \|g 7(2017), 2, p 27 \|w (DE-627)658000543 \|w (DE-600)2607043-1 \|x 20734395 \|7 nnns
773	1	8	\|g volume:7 \|g year:2017 \|g number:2, p 27
856	4	0	\|u https://doi.org/10.3390/agronomy7020027 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/ad7de0aee0dd4616ab0018b2069a9028 \|z kostenfrei
856	4	0	\|u http://www.mdpi.com/2073-4395/7/2/27 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2073-4395 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 7 \|j 2017 \|e 2, p 27

Indexfelder

author_variant	n s ns a k ak
matchkey_str	article:20734395:2017----::rdighrcyedasnedogttseeadhiu
hierarchy_sort_str	2017
publishDate	2017
allfields	10.3390/agronomy7020027 doi (DE-627)DOAJ07195354X (DE-599)DOAJad7de0aee0dd4616ab0018b2069a9028 DE-627 ger DE-627 rakwb eng Nitika Sandhu verfasserin aut Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rice is the staple food for more than half of the world’s population. Although rice production has doubled in the last 30 years as a result of the development of high-yield, widely adaptable, resource-responsive, semi-dwarf varieties, the threat of a food crisis remains as severe as it was 60 years ago due to the ever-increasing population, water scarcity, labor scarcity, shifting climatic conditions, pest/diseases, loss of productive land to housing, industries, rising sea levels, increasing incidences of drought, flood, urbanization, soil erosion, reduction in soil nutrient status, and environmental issues associated with high-input agriculture. Among these, drought is predicted to be the most severe stress that reduces rice yield. Systematic research on drought over the last 10 years has been conducted across institutes on physiology, breeding, molecular genetics, biotechnology, and cellular and molecular biology. This has provided a better understanding of plant drought mechanisms and has helped scientists to devise better strategies to reduce rice yield losses under drought stress. These include the identification of quantitative trait loci (QTLs) for grain yield under drought as well as many agronomically important traits related to drought tolerance, marker-assisted pyramiding of genetic regions that increase yield under drought, development of efficient techniques for genetic transformation, complete sequencing and annotation of rice genomes, and synteny studies of rice and other cereal genomes. Conventional and marker-assisted breeding rice lines containing useful introgressed genes or loci have been field tested and released as varieties. Still, there is a long way to go towards developing drought-tolerant rice varieties by exploiting existing genetic diversity, identifying superior alleles for drought tolerance, understanding interactions among alleles for drought tolerance and their interaction with genetic backgrounds, and pyramiding the best combination of alleles. drought marker pyramiding QTLs rice genomics Agriculture S Arvind Kumar verfasserin aut In Agronomy MDPI AG, 2012 7(2017), 2, p 27 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:7 year:2017 number:2, p 27 https://doi.org/10.3390/agronomy7020027 kostenfrei https://doaj.org/article/ad7de0aee0dd4616ab0018b2069a9028 kostenfrei http://www.mdpi.com/2073-4395/7/2/27 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2017 2, p 27
spelling	10.3390/agronomy7020027 doi (DE-627)DOAJ07195354X (DE-599)DOAJad7de0aee0dd4616ab0018b2069a9028 DE-627 ger DE-627 rakwb eng Nitika Sandhu verfasserin aut Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rice is the staple food for more than half of the world’s population. Although rice production has doubled in the last 30 years as a result of the development of high-yield, widely adaptable, resource-responsive, semi-dwarf varieties, the threat of a food crisis remains as severe as it was 60 years ago due to the ever-increasing population, water scarcity, labor scarcity, shifting climatic conditions, pest/diseases, loss of productive land to housing, industries, rising sea levels, increasing incidences of drought, flood, urbanization, soil erosion, reduction in soil nutrient status, and environmental issues associated with high-input agriculture. Among these, drought is predicted to be the most severe stress that reduces rice yield. Systematic research on drought over the last 10 years has been conducted across institutes on physiology, breeding, molecular genetics, biotechnology, and cellular and molecular biology. This has provided a better understanding of plant drought mechanisms and has helped scientists to devise better strategies to reduce rice yield losses under drought stress. These include the identification of quantitative trait loci (QTLs) for grain yield under drought as well as many agronomically important traits related to drought tolerance, marker-assisted pyramiding of genetic regions that increase yield under drought, development of efficient techniques for genetic transformation, complete sequencing and annotation of rice genomes, and synteny studies of rice and other cereal genomes. Conventional and marker-assisted breeding rice lines containing useful introgressed genes or loci have been field tested and released as varieties. Still, there is a long way to go towards developing drought-tolerant rice varieties by exploiting existing genetic diversity, identifying superior alleles for drought tolerance, understanding interactions among alleles for drought tolerance and their interaction with genetic backgrounds, and pyramiding the best combination of alleles. drought marker pyramiding QTLs rice genomics Agriculture S Arvind Kumar verfasserin aut In Agronomy MDPI AG, 2012 7(2017), 2, p 27 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:7 year:2017 number:2, p 27 https://doi.org/10.3390/agronomy7020027 kostenfrei https://doaj.org/article/ad7de0aee0dd4616ab0018b2069a9028 kostenfrei http://www.mdpi.com/2073-4395/7/2/27 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2017 2, p 27
allfields_unstemmed	10.3390/agronomy7020027 doi (DE-627)DOAJ07195354X (DE-599)DOAJad7de0aee0dd4616ab0018b2069a9028 DE-627 ger DE-627 rakwb eng Nitika Sandhu verfasserin aut Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rice is the staple food for more than half of the world’s population. Although rice production has doubled in the last 30 years as a result of the development of high-yield, widely adaptable, resource-responsive, semi-dwarf varieties, the threat of a food crisis remains as severe as it was 60 years ago due to the ever-increasing population, water scarcity, labor scarcity, shifting climatic conditions, pest/diseases, loss of productive land to housing, industries, rising sea levels, increasing incidences of drought, flood, urbanization, soil erosion, reduction in soil nutrient status, and environmental issues associated with high-input agriculture. Among these, drought is predicted to be the most severe stress that reduces rice yield. Systematic research on drought over the last 10 years has been conducted across institutes on physiology, breeding, molecular genetics, biotechnology, and cellular and molecular biology. This has provided a better understanding of plant drought mechanisms and has helped scientists to devise better strategies to reduce rice yield losses under drought stress. These include the identification of quantitative trait loci (QTLs) for grain yield under drought as well as many agronomically important traits related to drought tolerance, marker-assisted pyramiding of genetic regions that increase yield under drought, development of efficient techniques for genetic transformation, complete sequencing and annotation of rice genomes, and synteny studies of rice and other cereal genomes. Conventional and marker-assisted breeding rice lines containing useful introgressed genes or loci have been field tested and released as varieties. Still, there is a long way to go towards developing drought-tolerant rice varieties by exploiting existing genetic diversity, identifying superior alleles for drought tolerance, understanding interactions among alleles for drought tolerance and their interaction with genetic backgrounds, and pyramiding the best combination of alleles. drought marker pyramiding QTLs rice genomics Agriculture S Arvind Kumar verfasserin aut In Agronomy MDPI AG, 2012 7(2017), 2, p 27 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:7 year:2017 number:2, p 27 https://doi.org/10.3390/agronomy7020027 kostenfrei https://doaj.org/article/ad7de0aee0dd4616ab0018b2069a9028 kostenfrei http://www.mdpi.com/2073-4395/7/2/27 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2017 2, p 27
allfieldsGer	10.3390/agronomy7020027 doi (DE-627)DOAJ07195354X (DE-599)DOAJad7de0aee0dd4616ab0018b2069a9028 DE-627 ger DE-627 rakwb eng Nitika Sandhu verfasserin aut Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rice is the staple food for more than half of the world’s population. Although rice production has doubled in the last 30 years as a result of the development of high-yield, widely adaptable, resource-responsive, semi-dwarf varieties, the threat of a food crisis remains as severe as it was 60 years ago due to the ever-increasing population, water scarcity, labor scarcity, shifting climatic conditions, pest/diseases, loss of productive land to housing, industries, rising sea levels, increasing incidences of drought, flood, urbanization, soil erosion, reduction in soil nutrient status, and environmental issues associated with high-input agriculture. Among these, drought is predicted to be the most severe stress that reduces rice yield. Systematic research on drought over the last 10 years has been conducted across institutes on physiology, breeding, molecular genetics, biotechnology, and cellular and molecular biology. This has provided a better understanding of plant drought mechanisms and has helped scientists to devise better strategies to reduce rice yield losses under drought stress. These include the identification of quantitative trait loci (QTLs) for grain yield under drought as well as many agronomically important traits related to drought tolerance, marker-assisted pyramiding of genetic regions that increase yield under drought, development of efficient techniques for genetic transformation, complete sequencing and annotation of rice genomes, and synteny studies of rice and other cereal genomes. Conventional and marker-assisted breeding rice lines containing useful introgressed genes or loci have been field tested and released as varieties. Still, there is a long way to go towards developing drought-tolerant rice varieties by exploiting existing genetic diversity, identifying superior alleles for drought tolerance, understanding interactions among alleles for drought tolerance and their interaction with genetic backgrounds, and pyramiding the best combination of alleles. drought marker pyramiding QTLs rice genomics Agriculture S Arvind Kumar verfasserin aut In Agronomy MDPI AG, 2012 7(2017), 2, p 27 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:7 year:2017 number:2, p 27 https://doi.org/10.3390/agronomy7020027 kostenfrei https://doaj.org/article/ad7de0aee0dd4616ab0018b2069a9028 kostenfrei http://www.mdpi.com/2073-4395/7/2/27 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2017 2, p 27
allfieldsSound	10.3390/agronomy7020027 doi (DE-627)DOAJ07195354X (DE-599)DOAJad7de0aee0dd4616ab0018b2069a9028 DE-627 ger DE-627 rakwb eng Nitika Sandhu verfasserin aut Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rice is the staple food for more than half of the world’s population. Although rice production has doubled in the last 30 years as a result of the development of high-yield, widely adaptable, resource-responsive, semi-dwarf varieties, the threat of a food crisis remains as severe as it was 60 years ago due to the ever-increasing population, water scarcity, labor scarcity, shifting climatic conditions, pest/diseases, loss of productive land to housing, industries, rising sea levels, increasing incidences of drought, flood, urbanization, soil erosion, reduction in soil nutrient status, and environmental issues associated with high-input agriculture. Among these, drought is predicted to be the most severe stress that reduces rice yield. Systematic research on drought over the last 10 years has been conducted across institutes on physiology, breeding, molecular genetics, biotechnology, and cellular and molecular biology. This has provided a better understanding of plant drought mechanisms and has helped scientists to devise better strategies to reduce rice yield losses under drought stress. These include the identification of quantitative trait loci (QTLs) for grain yield under drought as well as many agronomically important traits related to drought tolerance, marker-assisted pyramiding of genetic regions that increase yield under drought, development of efficient techniques for genetic transformation, complete sequencing and annotation of rice genomes, and synteny studies of rice and other cereal genomes. Conventional and marker-assisted breeding rice lines containing useful introgressed genes or loci have been field tested and released as varieties. Still, there is a long way to go towards developing drought-tolerant rice varieties by exploiting existing genetic diversity, identifying superior alleles for drought tolerance, understanding interactions among alleles for drought tolerance and their interaction with genetic backgrounds, and pyramiding the best combination of alleles. drought marker pyramiding QTLs rice genomics Agriculture S Arvind Kumar verfasserin aut In Agronomy MDPI AG, 2012 7(2017), 2, p 27 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:7 year:2017 number:2, p 27 https://doi.org/10.3390/agronomy7020027 kostenfrei https://doaj.org/article/ad7de0aee0dd4616ab0018b2069a9028 kostenfrei http://www.mdpi.com/2073-4395/7/2/27 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2017 2, p 27
language	English
source	In Agronomy 7(2017), 2, p 27 volume:7 year:2017 number:2, p 27
sourceStr	In Agronomy 7(2017), 2, p 27 volume:7 year:2017 number:2, p 27
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	drought marker pyramiding QTLs rice genomics Agriculture S
isfreeaccess_bool	true
container_title	Agronomy
authorswithroles_txt_mv	Nitika Sandhu @@aut@@ Arvind Kumar @@aut@@
publishDateDaySort_date	2017-01-01T00:00:00Z
hierarchy_top_id	658000543
id	DOAJ07195354X
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ07195354X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503064256.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2017 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/agronomy7020027</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ07195354X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJad7de0aee0dd4616ab0018b2069a9028</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Nitika Sandhu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Rice is the staple food for more than half of the world’s population. Although rice production has doubled in the last 30 years as a result of the development of high-yield, widely adaptable, resource-responsive, semi-dwarf varieties, the threat of a food crisis remains as severe as it was 60 years ago due to the ever-increasing population, water scarcity, labor scarcity, shifting climatic conditions, pest/diseases, loss of productive land to housing, industries, rising sea levels, increasing incidences of drought, flood, urbanization, soil erosion, reduction in soil nutrient status, and environmental issues associated with high-input agriculture. Among these, drought is predicted to be the most severe stress that reduces rice yield. Systematic research on drought over the last 10 years has been conducted across institutes on physiology, breeding, molecular genetics, biotechnology, and cellular and molecular biology. This has provided a better understanding of plant drought mechanisms and has helped scientists to devise better strategies to reduce rice yield losses under drought stress. These include the identification of quantitative trait loci (QTLs) for grain yield under drought as well as many agronomically important traits related to drought tolerance, marker-assisted pyramiding of genetic regions that increase yield under drought, development of efficient techniques for genetic transformation, complete sequencing and annotation of rice genomes, and synteny studies of rice and other cereal genomes. Conventional and marker-assisted breeding rice lines containing useful introgressed genes or loci have been field tested and released as varieties. Still, there is a long way to go towards developing drought-tolerant rice varieties by exploiting existing genetic diversity, identifying superior alleles for drought tolerance, understanding interactions among alleles for drought tolerance and their interaction with genetic backgrounds, and pyramiding the best combination of alleles.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">drought</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">marker</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">pyramiding</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">QTLs</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">rice</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">genomics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Agriculture</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">S</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Arvind Kumar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Agronomy</subfield><subfield code="d">MDPI AG, 2012</subfield><subfield code="g">7(2017), 2, p 27</subfield><subfield code="w">(DE-627)658000543</subfield><subfield code="w">(DE-600)2607043-1</subfield><subfield code="x">20734395</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:7</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:2, p 27</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/agronomy7020027</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/ad7de0aee0dd4616ab0018b2069a9028</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.mdpi.com/2073-4395/7/2/27</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2073-4395</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">7</subfield><subfield code="j">2017</subfield><subfield code="e">2, p 27</subfield></datafield></record></collection>
author	Nitika Sandhu
spellingShingle	Nitika Sandhu misc drought misc marker misc pyramiding misc QTLs misc rice misc genomics misc Agriculture misc S Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs
authorStr	Nitika Sandhu
ppnlink_with_tag_str_mv	@@773@@(DE-627)658000543
format	electronic Article
delete_txt_mv	keep
author_role	aut aut
collection	DOAJ
remote_str	true
illustrated	Not Illustrated
issn	20734395
topic_title	Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs drought marker pyramiding QTLs rice genomics
topic	misc drought misc marker misc pyramiding misc QTLs misc rice misc genomics misc Agriculture misc S
topic_unstemmed	misc drought misc marker misc pyramiding misc QTLs misc rice misc genomics misc Agriculture misc S
topic_browse	misc drought misc marker misc pyramiding misc QTLs misc rice misc genomics misc Agriculture misc S
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Agronomy
hierarchy_parent_id	658000543
hierarchy_top_title	Agronomy
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)658000543 (DE-600)2607043-1
title	Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs
ctrlnum	(DE-627)DOAJ07195354X (DE-599)DOAJad7de0aee0dd4616ab0018b2069a9028
title_full	Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs
author_sort	Nitika Sandhu
journal	Agronomy
journalStr	Agronomy
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2017
contenttype_str_mv	txt
author_browse	Nitika Sandhu Arvind Kumar
container_volume	7
format_se	Elektronische Aufsätze
author-letter	Nitika Sandhu
doi_str_mv	10.3390/agronomy7020027
author2-role	verfasserin
title_sort	bridging the rice yield gaps under drought: qtls, genes, and their use in breeding programs
title_auth	Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs
abstract	Rice is the staple food for more than half of the world’s population. Although rice production has doubled in the last 30 years as a result of the development of high-yield, widely adaptable, resource-responsive, semi-dwarf varieties, the threat of a food crisis remains as severe as it was 60 years ago due to the ever-increasing population, water scarcity, labor scarcity, shifting climatic conditions, pest/diseases, loss of productive land to housing, industries, rising sea levels, increasing incidences of drought, flood, urbanization, soil erosion, reduction in soil nutrient status, and environmental issues associated with high-input agriculture. Among these, drought is predicted to be the most severe stress that reduces rice yield. Systematic research on drought over the last 10 years has been conducted across institutes on physiology, breeding, molecular genetics, biotechnology, and cellular and molecular biology. This has provided a better understanding of plant drought mechanisms and has helped scientists to devise better strategies to reduce rice yield losses under drought stress. These include the identification of quantitative trait loci (QTLs) for grain yield under drought as well as many agronomically important traits related to drought tolerance, marker-assisted pyramiding of genetic regions that increase yield under drought, development of efficient techniques for genetic transformation, complete sequencing and annotation of rice genomes, and synteny studies of rice and other cereal genomes. Conventional and marker-assisted breeding rice lines containing useful introgressed genes or loci have been field tested and released as varieties. Still, there is a long way to go towards developing drought-tolerant rice varieties by exploiting existing genetic diversity, identifying superior alleles for drought tolerance, understanding interactions among alleles for drought tolerance and their interaction with genetic backgrounds, and pyramiding the best combination of alleles.
abstractGer	Rice is the staple food for more than half of the world’s population. Although rice production has doubled in the last 30 years as a result of the development of high-yield, widely adaptable, resource-responsive, semi-dwarf varieties, the threat of a food crisis remains as severe as it was 60 years ago due to the ever-increasing population, water scarcity, labor scarcity, shifting climatic conditions, pest/diseases, loss of productive land to housing, industries, rising sea levels, increasing incidences of drought, flood, urbanization, soil erosion, reduction in soil nutrient status, and environmental issues associated with high-input agriculture. Among these, drought is predicted to be the most severe stress that reduces rice yield. Systematic research on drought over the last 10 years has been conducted across institutes on physiology, breeding, molecular genetics, biotechnology, and cellular and molecular biology. This has provided a better understanding of plant drought mechanisms and has helped scientists to devise better strategies to reduce rice yield losses under drought stress. These include the identification of quantitative trait loci (QTLs) for grain yield under drought as well as many agronomically important traits related to drought tolerance, marker-assisted pyramiding of genetic regions that increase yield under drought, development of efficient techniques for genetic transformation, complete sequencing and annotation of rice genomes, and synteny studies of rice and other cereal genomes. Conventional and marker-assisted breeding rice lines containing useful introgressed genes or loci have been field tested and released as varieties. Still, there is a long way to go towards developing drought-tolerant rice varieties by exploiting existing genetic diversity, identifying superior alleles for drought tolerance, understanding interactions among alleles for drought tolerance and their interaction with genetic backgrounds, and pyramiding the best combination of alleles.
abstract_unstemmed	Rice is the staple food for more than half of the world’s population. Although rice production has doubled in the last 30 years as a result of the development of high-yield, widely adaptable, resource-responsive, semi-dwarf varieties, the threat of a food crisis remains as severe as it was 60 years ago due to the ever-increasing population, water scarcity, labor scarcity, shifting climatic conditions, pest/diseases, loss of productive land to housing, industries, rising sea levels, increasing incidences of drought, flood, urbanization, soil erosion, reduction in soil nutrient status, and environmental issues associated with high-input agriculture. Among these, drought is predicted to be the most severe stress that reduces rice yield. Systematic research on drought over the last 10 years has been conducted across institutes on physiology, breeding, molecular genetics, biotechnology, and cellular and molecular biology. This has provided a better understanding of plant drought mechanisms and has helped scientists to devise better strategies to reduce rice yield losses under drought stress. These include the identification of quantitative trait loci (QTLs) for grain yield under drought as well as many agronomically important traits related to drought tolerance, marker-assisted pyramiding of genetic regions that increase yield under drought, development of efficient techniques for genetic transformation, complete sequencing and annotation of rice genomes, and synteny studies of rice and other cereal genomes. Conventional and marker-assisted breeding rice lines containing useful introgressed genes or loci have been field tested and released as varieties. Still, there is a long way to go towards developing drought-tolerant rice varieties by exploiting existing genetic diversity, identifying superior alleles for drought tolerance, understanding interactions among alleles for drought tolerance and their interaction with genetic backgrounds, and pyramiding the best combination of alleles.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	2, p 27
title_short	Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs
url	https://doi.org/10.3390/agronomy7020027 https://doaj.org/article/ad7de0aee0dd4616ab0018b2069a9028 http://www.mdpi.com/2073-4395/7/2/27 https://doaj.org/toc/2073-4395
remote_bool	true
author2	Arvind Kumar
author2Str	Arvind Kumar
ppnlink	658000543
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3390/agronomy7020027
up_date	2024-07-03T23:02:47.523Z
_version_	1803600807879245824
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ07195354X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503064256.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2017 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/agronomy7020027</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ07195354X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJad7de0aee0dd4616ab0018b2069a9028</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Nitika Sandhu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Rice is the staple food for more than half of the world’s population. Although rice production has doubled in the last 30 years as a result of the development of high-yield, widely adaptable, resource-responsive, semi-dwarf varieties, the threat of a food crisis remains as severe as it was 60 years ago due to the ever-increasing population, water scarcity, labor scarcity, shifting climatic conditions, pest/diseases, loss of productive land to housing, industries, rising sea levels, increasing incidences of drought, flood, urbanization, soil erosion, reduction in soil nutrient status, and environmental issues associated with high-input agriculture. Among these, drought is predicted to be the most severe stress that reduces rice yield. Systematic research on drought over the last 10 years has been conducted across institutes on physiology, breeding, molecular genetics, biotechnology, and cellular and molecular biology. This has provided a better understanding of plant drought mechanisms and has helped scientists to devise better strategies to reduce rice yield losses under drought stress. These include the identification of quantitative trait loci (QTLs) for grain yield under drought as well as many agronomically important traits related to drought tolerance, marker-assisted pyramiding of genetic regions that increase yield under drought, development of efficient techniques for genetic transformation, complete sequencing and annotation of rice genomes, and synteny studies of rice and other cereal genomes. Conventional and marker-assisted breeding rice lines containing useful introgressed genes or loci have been field tested and released as varieties. Still, there is a long way to go towards developing drought-tolerant rice varieties by exploiting existing genetic diversity, identifying superior alleles for drought tolerance, understanding interactions among alleles for drought tolerance and their interaction with genetic backgrounds, and pyramiding the best combination of alleles.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">drought</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">marker</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">pyramiding</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">QTLs</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">rice</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">genomics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Agriculture</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">S</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Arvind Kumar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Agronomy</subfield><subfield code="d">MDPI AG, 2012</subfield><subfield code="g">7(2017), 2, p 27</subfield><subfield code="w">(DE-627)658000543</subfield><subfield code="w">(DE-600)2607043-1</subfield><subfield code="x">20734395</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:7</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:2, p 27</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/agronomy7020027</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/ad7de0aee0dd4616ab0018b2069a9028</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.mdpi.com/2073-4395/7/2/27</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2073-4395</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">7</subfield><subfield code="j">2017</subfield><subfield code="e">2, p 27</subfield></datafield></record></collection>
score	7.40226

Nicht das Richtige dabei?

Schreiben Sie uns!

Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and their Use in Breeding Programs

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?