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Rana, Vijay
- Progeny Performance of Plus Trees of Toona ciliata M. Roem. under Nursery and Field Conditions
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Indian Forester, Vol 135, No 1 (2009), Pagination: 92-98Abstract
Progenies of 25 seed sources (plus trees) of Toona ciliata (Toon) collected from different seed zones of Himachal Pradesh were evaluated under nursery and field conditions. Analysis of variance for seedling height, collar diameter and number of leaves indicated significant mean squares for all the characters studied under nursery conditions. Seedling height and collar diameter after 120 days of sowing exhibited high heritability (broad sense) coupled with high genetic advance. Under field evaluation, seed sources viz. S1, S2, S5 and S6 exhibited faster growth rate based on the values of growth parameters.Keywords
Toona ciliata (Toon), Plus Tree, Progeny Performance, Genetic Variation, AnnualGrowth
- A Survey on Paraphrase Detection and Generation Techniques
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Authors
Affiliations
1 Department of Computer Science and Applications, Sant Baba Bhag Singh University, Jalandhar, IN
2 Department of Computer Applications, DAV University, Jalandhar, IN
1 Department of Computer Science and Applications, Sant Baba Bhag Singh University, Jalandhar, IN
2 Department of Computer Applications, DAV University, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 26 (2017), Pagination: 76-83Abstract
Whenever “the same thing,” need to be expressed using different ways or by various alternatives an automated paraphrase generation mechanism would be useful. One reason why paraphrase generation systems have been difficult to build is because paraphrases are hard to define. Although the strict interpretation of the term “paraphrase” is quite narrow because it requires exactly identical meaning, in linguistics literature paraphrases are most often characterized by an approximate equivalence of semantics across sentences or phrases. This paper presents a survey of paraphrase generation techniques for Indian and foreign languages.Keywords
Paraphrasing, Sentence Simplification, Sentence Fusion, Sentence Compression.References
- . Kevin Knight and Daniel Marcu. 2000. Statisticsbased summarization-step one: Sentence compression. In Proceedings of AAAI-IAAI.
- . Trevor Cohn and Mirella Lapata. 2008. Sentence compression beyond word deletion. In Proceedings of COLING.
- . Katja Filippova and Michael Strube. 2008. Dependency tree based sentence compression. In Proceedings of INLG
- . Emily Pitler. 2010. Methods for sentence compression. Technical report, University of Pennsylvania.
- . Katja Filippova, Enrique Alfonseca, Carlos Colmenares, Lukasz Kaiser, and Oriol Vinyals. 2015. Sentence compression by deletion with LSTMs. In Proceedings of EMNLP.
- . Kristina Toutanova, Chris Brockett, Ke M. Tran, and Saleema Amershi. 2016. A dataset and evaluation metrics for abstractive compression of sentences and short paragraphs. In Proceedings of EMNLP.
- . Kathleen McKeown, Sara Rosenthal, Kapil Thadani, and Coleman Moore. 2010. Time-efficient creation of an accurate sentence fusion corpus. In Proceedings of NAACL-HLT.
- . Katja Filippova. 2010. Multi-sentence compression: Finding shortest paths in word graphs. In Proceedings of COLING.
- . Kathleen McKeown, Sara Rosenthal, Kapil Thadani, and Coleman Moore. 2010. Time-efficient creation of an accurate sentence fusion corpus. In Proceedings of NAACL-HLT.
- . Mark Dras. 1999. Tree adjoining grammar and the reluctant paraphrasing of text. Ph.D. thesis, Macquarie University, Australia
- . Regina Barzilay and Kathleen R McKeown. 2001. Extracting paraphrases from a parallel corpus. In Proceedings of ACL.
- . Colin Bannard and Chris Callison-Burch. 2005. Paraphrasing with bilingual parallel corpora. In Proceedings of ACL.
- . Sander Wubben, Antal Van Den Bosch, and Emiel Krahmer. 2010. Paraphrase generation as monolingual translation: Data and evaluation. In Proceedings of INLG.
- . Jonathan Mallinson, Rico Sennrich, and Mirella Lapata. 2017. Paraphrasing revisited with neural machine translation. In Proceedings of EACL.
- . Advaith Siddharthan. 2010. Complex lexico-syntactic reformulation of sentences using typed dependency representations. In Proceedings of INLG.
- . Zhemin Zhu, Delphine Bernhard, and Iryna Gurevych. 2010. A monolingual tree-based translation model for sentence simplification. In Proceedings of COLING.
- . Kristian Woodsend and Mirella Lapata. 2011. Learning to simplify sentences with quasi-synchronous grammar and integer programming. In Proceedings of EMNLP.
- . Sander Wubben, Antal van den Bosch, and Emiel Krahmer. 2012. Sentence simplification by monolingual machine translation. In Proceedings of ACL.
- . Shashi Narayan and Claire Gardent. 2014. Hybrid simplification using deep semantics and machine translation. In Proceedings of ACL.
- . Wei Xu, Chris Callison-Burch, and Courtney Napoles. 2015. Problems in current text simplification research: New data can help. Transactions of the Association for Computational Linguistics, 3:283–297.
- . Kristina Toutanova, Chris Brockett, Ke M. Tran, and Saleema Amershi. 2016. A dataset and evaluation metrics for abstractive compression of sentences and short paragraphs. In Proceedings of EMNLP
- . Xingxing Zhang and Mirella Lapata. 2017. Sentence simplification with deep reinforcement learning. In Proceedings of EMNLP.
- . Bautista, Susana, et al. "An approach to treat numerical information in the text simplification process." Universal Access in the Information Society 16.1 (2017): 85-102.
- . Stajner, Sanja, Biljana Drndarevic, and Horacio Saggion. "Corpus-based sentence deletion and split decisions for Spanish text simplification." Computación y Sistemas 17.2 (2013).
- . CENTAL, ILC. "Syntactic sentence simplification for French." Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR)@ EACL. 2014.
- . Inui, Kentaro, et al. "Text simplification for reading assistance: a project note." Proceedings of the second international workshop on Paraphrasing-Volume 16. Association for Computational Linguistics, 2003.
- . Ma, Shuming, and Xu Sun. "A semantic relevance based neural network for text summarization and text simplification." arXiv preprint arXiv:1710.02318 (2017).
- . Zhang, Xingxing, and Mirella Lapata. "Sentence Simplification with Deep Reinforcement Learning." arXiv preprint arXiv:1703.10931 (2017).
- . Lee, John, and J. Buddhika K. Pathirage Don. "Splitting Complex English Sentences." Proceedings of the 15th International Conference on Parsing Technologies. 2017.
- . Petersen, Sarah E., and Mari Ostendorf. "Text simplification for language learners: a corpus analysis." Workshop on Speech and Language Technology in Education. 2007.
- . Sethi, Nandini, et al. "A novel Approach to Paraphrase Hindi sentences using Natural language Processing." Indian Journal of Science and Technology 9.28 (2016).
- . Narayan, Shashi, et al. "Split and rephrase." arXiv preprint arXiv:1707.06971 (2017).
- . Wubben, Sander, Antal Van Den Bosch, and Emiel Krahmer. "Paraphrase generation as monolingual translation: Data and evaluation." Proceedings of the 6th International Natural Language Generation Conference. Association for Computational Linguistics, 2010.
- . Callison-Burch, C., and C. Bannard. "Paraphrasing with bilingual parallel corpora." Proceedings of 43th Annual Meeting of the Association for Computational Linguistics. 2005.
- . Bingel, Joachim, and Anders Søgaard. "Text simplification as tree labeling." The 54th Annual Meeting of the Association for Computational Linguistics. 2016.
- . Narayan, Shashi, and Claire Gardent. "Unsupervised sentence simplification using deep semantics." arXiv preprint arXiv:1507.08452 (2015).
- . Knight, Kevin, and Daniel Marcu. "Summarization beyond sentence extraction: A probabilistic approach to sentence compression." Artificial Intelligence 139.1 (2002): 91-107.
- . Cheung, Andrew KF. "Paraphrasing exercises and training for Chinese to English consecutive interpreting." FORUM. Revue internationaled’interprétation et de traduction/International Journal of Interpretation and Translation. Vol. 14. No. 1. John Benjamins Publishing Company, 2016.Roig, Miguel. "Plagiarism and paraphrasing criteria of college and university professors." Ethics & Behavior 11.3 (2001): 307-323.
- . Roig, Miguel. "Plagiarism and paraphrasing criteria of college and university professors." Ethics & Behavior 11.3 (2001): 307-323.
- . Rogerson, Ann M., and Grace McCarthy. "Using Internet based paraphrasing tools: Original work, patchwriting or facilitated plagiarism?." International Journal for Educational Integrity 13.1 (2017): 2.
- . Hyytinen, Heidi, Erika Löfström, and Sari Lindblom-Ylänne. "Challenges in argumentation and paraphrasing among beginning students in educational sciences." Scandinavian Journal of Educational Research 61.4 (2017): 411-429.
- . Bokharaeian, B., and A. Diaz. "Extraction of Drug-Drug Interaction from Literature through Detecting Linguistic-based Negation and Clause Dependency." Journal of AI and Data Mining 4.2 (2016): 203-212.
- . Kim, Mi-Young, et al. "Legal Question Answering Using Paraphrasing and Entailment Analysis." Tenth International Workshop on Juris-informatics (JURISIN). 2016.
- . Sethi, Nandini, et al. "A novel Approach to Paraphrase Hindi sentences using Natural language Processing." Indian Journal of Science and Technology 9.28 (2016).
- . Hagaman, Jessica L., Kathryn J. Casey, and Robert Reid. "Paraphrasing strategy instruction for struggling readers." Preventing School Failure: Alternative Education for Children and Youth 60.1 (2016): 43-52.
- . Garg, Urvashi, and Vishal Goyal. "Maulik: A Plagiarism Detection Tool for Hindi Documents." Indian Journal of Science and Technology 9.12 (2016).
- . Mrabet, Yassine, et al. "Aligning texts and knowledge bases with semantic sentence simplification." (2016): 29-36.
- Genetic Diversity Analysis for Various Agromorphological, Yield and Yield Related Traits in Wheat (Triticum aestivum L.)
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Authors
Affiliations
1 Department of Genetics and Plant Breeding CSK Himachal Pradesh Krishi Vishvavidyalaya, Palampur-176 062, IN
2 CSKHPKV, Rice and Wheat Research Centre, Malan, District Kangra-176 047, IN
3 Former Dean, College of Agriculture, CSK HPKV, Palampur-176 062, IN
1 Department of Genetics and Plant Breeding CSK Himachal Pradesh Krishi Vishvavidyalaya, Palampur-176 062, IN
2 CSKHPKV, Rice and Wheat Research Centre, Malan, District Kangra-176 047, IN
3 Former Dean, College of Agriculture, CSK HPKV, Palampur-176 062, IN
Source
Himachal Journal of Agricultural Research, Vol 46, No 2 (2020), Pagination: 136-144Abstract
Thirty diverse wheat genotypes were used to assess the genetic diversity for various agromorphological, grain yield and yield related traits. The analysis of variance showed highly significant differences among the test genotypes for grain yield and its contributing components viz., days to 50% flowering, days to 75% maturity, number of tillers per plant, plant height (cm), biological yield (g), harvest index (%), grain yield per plant (g) and 1000 grain weight (g). High heritability along with high genetic advance and high phenotypic coefficient of variation (PCV) were recorded for biological yield per plant (g) and 1000 grain weight (g). It indicated substantial contribution of additive gene action in the expression and thus selection would be effective for genetic improvement of these traits. On the basis of multivariate analysis, 30 genotypes were grouped into '13' clusters based on genetic divergence (D2) value. The compositions of clusters revealed that the Cluster II and Cluster IV had the highest number of genotypes (9) followed by Cluster I (2). The highest intra-cluster distance was observed in cluster IV (2.05) followed by cluster II (1.98) and cluster I (1.12) and in the remaining clusters, there was only one genotype each, thereby the intra-cluster distance was zero. Cluster I (HS 507 and HPW 368) showed maximum values for biological yield per plant. Cluster III (E 9) showed minimum values for days to 75% maturity and cluster XIII showed minimum value for days to 50% flowering. Cluster VI (Roelfs F 2007) showed maximum values for tillers per plant. Cluster XI (HPW 373) showed maximum values for harvest index and cluster XII having variety Baj#1 showed maximum value for grain yield per plant and 1000-grain weight. The highest inter-cluster distance of 5.18 was observed between cluster VIII (TC1-7) and X (TC 1-24) followed by cluster VI (Roelfs F 2007) and X (TC 1-24) with a distance of 4.92 indicating that genotypes in these clusters have wide genetic diversity and thus can be used in hybridization programme for improving grain yield.Keywords
Wheat Genotypes, Genetic Diversity, Genetic Parameters, Yield Traits.References
- Arya VK , Singh J, Kumar L, Kumar R, Kumar P and Chand P. 2017. Indian Journal of Agricultural Research 51 (2): 128-134. Genetic variability and diversity analysis for yield and its components in wheat (Triticum aestivum L.).
- Anonymous. 2016. Progress Report, All India Coordinated Wheat and Barley Improvement Project, 1-5 pp. G. P. Singh (Ed). Indian Institute of Wheat and Barley Research, Karnal.
- Burton GW and Vane de EH 1953. Estimating heritability in tall fescue (Festuca arundinacea L.) from replicated clonal material. Agronomy Journal 45: 478-481.
- Johnson HW, Robinson HF and Comstock RE. 1955. Estimates of genetic and environmental variability in soybeans. Agronomy Journal 47: 314-318.
- Kumar Pradeep, Singh Gyanendra, Kumar Sarvan, Kumar Anuj and Ojha Ashish. 2016a. Genetic analysis of grain yield and its contributing traits for their implications in improvement of bread wheat cultivars. Journal of Applied and Natural Science 8: 350-357.
- Kumar Sandeep, Pradeep Kumar and Kerkhi, SA. 2017. Genetic analysis for various yield components and gluten content in bread wheat (Triticum aestivum L.). Journal of Applied and Natural Science 9(2): 879-882.
- Kumar J, Kumar A, Singh, SK, Singh L, Kumar A,Chaudhary M, Kumar S and Singh SK. 2016b. Principal component analysis for yield and its contributing traits in bread wheat (Triticum aestivum) genotypes under late. Current Advances in Agricultural Sciences 8: 55-57.
- Lal BK, Ruchig M and Upadhyay A. 2009. Genetic variability, diversity and association of quantitative traits with grain yield in bread wheat (Triticum aestivum L.). Asian Journal of Agricultural Sciences 1(1):4-6.
- Mahalanobis PC. 1936. On the generalized distance on statistics, a statistical study of Chinese head measurement. Journal of the Asiatic Society of Bengal 25: 301-307.
- Meena HS, Kumar, D and Prasad SR. 2014. Genetic variability and character association in bread wheat (Triticum aestivum). Indian Journal of Agricultural Sciences 84 (4): 487-91.
- Mondal s, Dutta S, Herrera LC, Espino JH, Braun HJ and Singh RP. 2020. Fifty years of semi-dwarf spring wheat breeding at CIMMYT: Grain yield progress in optimum, drought and heat stress environments. Field Crops Research 250: 107757.
- Panse VG and Sukhatme PV. 1985. Statistical Methods for Agricultural Workers. Indian Council of Agricultural Research Publication, 87-89.
- Rao CR. 1952. Advance Statistical Methods in Biometrical Research. John Wiley and Sons Inc. New York, p. 383.
- Saini Manisha and Shweta. 2017. Genetic variability, heritability, correlation co-efficient and of yield and yield contributing traits in bread wheat Triticum aestivum L. International Journal of Plant Sciences Muzaffarnagar 12(2):173-180.
- Saini PK, Kumar S, and Singh SV.2019. Heritability and genetic advance for yield and its contributing traits in bread wheat (Triticum aestivum L.). International Journal of Chemical Studies 7(3): 3078-3081.
- Sendhil Ramadas, T.M. Kiran Kumar and Gyanendra Pratap Singh. 2019. Wheat Production in India: Trends and Prospects, Recent Advances in Grain Crops Research, Farooq Shah, Zafar Khan, Amjad Iqbal, Metin Turan and Murat Olgun, Intech Open, DOI: 10.5772/intechopen.86341. Available from: https://www.intechopen.com/books/recent-advances-ingrain crops-research/wheat-production-in-india-trendsand prospects (July 12th 2019).
- Sharma I, Shoran J, Singh G and Tyagi BS. 2011. Wheat Improvement in India. Souvenir of 50th All India Wheat and Barley Research Workers, Meet, New Delhi, p 11.
- Singh Gyanendra, Kulshreshtha N, Singh BN, Setter TL, Singh MK, Saharan MS, Tyagi BS, Verma Ajay and Sharma I. 2014. Germplasm characterization, association and clustering for salinity and water logging tolerance in bread wheat (Triticum aestivum). Indian Journal of Agricultural Sciences 84: 1102-10.
- Singh MK, Sharma PK, Tyagi BS and Singh Gyanendra.2013. Genetic analysis for morphological traits and protein content in bread wheat (Triticum aestivum L.) under normal and heat stress environments. Indian Journal of Genetics and Plant Breeding 73:320 324.
- Taneva K, Bozhanova V and Petrova Ivanka. 2019. Variability, heritability and genetic advance of some grain quality traits and grain yield in durum wheat genotypes. Bulgarian Journal of Agricultural Science 25 (2): 288 295.
- Tewari R, Jaiswal JP, Gangwar RP and Singh PK. 2015. Genetic diversity analysis in Exotic germplasm accessions of Wheat (Triticum aestivum L.) by cluster analysis. Electronic Journal of Plant Breeding 6: 1111 1117.
- Thapa RS, Sharma PK, Pratap D, Singh T and Kumar A. 2019. Assessment of genetic variability, heritability and genetic advance in wheat (Triticum aestivum L.) genotypes under normal and heat stress environment. Indian Journal of Agricultural Research 53(1): 51-56.
- Verma PN, Singh BN, Singh G, Singh MK and Setter TL. 2014. Genetic diversity analysis for yield and other agronomic traits in bread wheat under water logged sodic soil condition. Journal of Wheat Research 6:51-58.
- Vora ZN, Patel JB, Pansuriya AG and Yusufzai SA. 2017. Genetic divergence analysis in bread wheat (Triticum aestivum L.). Research in Environments and Life Sciences 10:291-292.
- Evaluation of Wheat Genotypes for Adult Plant Resistance Against Powdery Mildew Caused by Blumeria graminis tritici
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Authors
Affiliations
1 Department of Genetics and Plant Breeding, CSK Himachal Pradesh Krishi Vishvavidyalaya, Palampur-176062, IN
2 CSKHPKV, Rice & Wheat Research Centre, Malan-176 047, IN
3 Department of Plant Pathology, CSK HPKV, Palampur-176062, IN
4 Former Vice-Chancellor, CSK Himachal Pradesh Krishi Vishvavidyalaya, Palampur-176 062, IN
1 Department of Genetics and Plant Breeding, CSK Himachal Pradesh Krishi Vishvavidyalaya, Palampur-176062, IN
2 CSKHPKV, Rice & Wheat Research Centre, Malan-176 047, IN
3 Department of Plant Pathology, CSK HPKV, Palampur-176062, IN
4 Former Vice-Chancellor, CSK Himachal Pradesh Krishi Vishvavidyalaya, Palampur-176 062, IN
Source
Himachal Journal of Agricultural Research, Vol 47, No 2 (2021), Pagination: 149-155Abstract
Powdery mildew, caused by Blumeria graminis f.sp. tritici (Bgt) has emerged as a devastating diseases of wheat (Triticum aestivum L.) worldwide. The disease is widely prevalent and causes severe losses in the north and southern hills and north western plain zone of India. It can be effectively managed by cultivation of resistant varieties, however majority of the varieties grown in epidemiologically important areas are susceptible. A successful breeding programme requires stable resistant donors and in this context, thirty-six diverse promising wheat germplasm lines were evaluated at multi hotspot locations i.e. Rice and Wheat Research Centre, Malan and IIWBR, summer nursery at Dalang Maidan (Lahaul & Spiti) under natural epiphytotic and controlled (net house) conditions for three consecutive years. Four lines (ONS 29, ONS 27, Pollemer and PMC 1) were free from the disease whereas, three lines (EIGN 33, TL 2995 and TL 2999) were resistant (score 1-3) at both the locations. These stable and durable resistant donors may be used in the breeding programme to diversify the powdery mildew resistance base of future wheat varieties.Keywords
Wheat, Powdery Mildew, Resistance, Blumeria graminis F.sp. tritici, Adult Plant Resistance.References
- Asad S, Fayyaz M, Munir A and Rattu A. 2014. Screening of wheat commercial varieties for resistance against powdery mildew (Blumeria graminis f. sp. tritici) at Kaghan valley, Pakistan. Pakistan Journal of Phytopathology 26 (01): 07-13.
- Basandrai AK and Basandrai D. 2017. Powdery mildew of wheat and its management. In: Management of wheat and barley diseases (Devender Pal Singh ed.). Apple Academic Press, Canada pp 173-181.
- Campbell CL and Madden LV. 1990. Introduction to Plant Disease Epidemiology. John Wiley & Sons, New York, NY, USA.
- Cheng B, Ding YQ, Gao X, Cao N, Xin ZH and Zhang LY. 2020. The diversity of powdery mildew resistance gene loci among wheat germplasm in Southwest China. Cereal Research Communications 48: 65-70. https://doi.org/10.1007/s42976-020-00015-2.
- Dean R, van Kan JAL, Pretorius ZA, Hammond-kosack KE, Di pietro A, Spanu PD et al. 2012. The top 10 fungal pathogens in molecular plant pathology. Molecular Plant Pathology 13: 414-430.
- Gupta V, Kumar S, Mishra CN, Selvakumar R, Tiwari V and Sharma I. 2014. Evaluation of wheat germplasm for powdery mildew and stripe rust resistance. In: Proceedings of National symposium on Crop Improvement for Inclusive Sustainable Development at Punjab Agricultural University Ludhiana from 7-9 Nov. 2014. pp 905-907.
- Gupta V, Selvakumar R, Kumar S, Mishra CN, Tiwari V and Sharma I. 2016. Evaluation and identification of resistance to powdery mildew in Indian wheat varieties under artificially created epiphytotic. Journal of Applied and Natural Science 8 (2): 565 -569.
- Huang XQ, Hsam SLK, Mohler V, Roder MS and Zeller FJ. 2004. Genetic mapping of three alleles at the Pm3 locus conferring powdery mildew resistance in common wheat (Triticum aestivum L.). Genome 47: 1130-1136.
- ICAR-IIWBR. 2020. Progress Report of AICRP on Wheat and Barley 2019-20, Crop Improvement. eds: Gyanendra Singh, Bhudeva Singh Tyagi, Arun Gupta, Sanjay Kumar Singh, Hanif Khan, Satish Kumar, Charan Singh, Chandra Nath Mishra, Karnam Venkatesh, Vikas Gupta, Gopalareddy Krishnappa, Sindhu Sareen, Mamrutha HM, Amit Kumar Sharma, Raj Kumar, Bhumesh Kumar, Rinki, Ratan Tiwari, Ajay Verma and Gyanendra Pratap Singh. ICAR-Indian Institute of Wheat and Barley Research, Karnal, Haryana, India. p.197
- Kaur R. 2017. Studies on variability for some agromorphological traits and inheritance of powdery mildew resistance in bread wheat (Triticum aestivum L.), M Sc Thesis, CSK HPKV, Palampur, H.P.
- Rana SK, Sharma BK and Basandrai AK. 2006. Estimation of losses due to powdery mildew of wheat in Himachal Pradesh. Indian Phytopathology 59 (1): 112-114.
- Saari EE and Prescott JM. 1975. A scale for appraising the foliar intensity of wheat disease. Plant Disease Reporter 59: 377-380.
- Savary S, Willocquet L, Pethybridge SJ, Esker P, McRoberts N and Nelson A. 2019. The global burden of pathogens and pests on major food crops. Nature Ecology and Evolution 3: 430-439. https://doi.org/10.1038/s41559-018-0793-y.
- Shamanin V, Shepeleva S, Pozherukovaa V, Gultyaevab E, Kolomietsc T, Pakholkovac E and Morgounovd A. 2019. Primary hexaploid synthetics: Novel sources of wheat disease resistance. Crop Protection 121: 7-10.
- Singh DP, Sharma AK, Nagarajan S, Kumar J, Saharan MS, Shoran J. et al. 2005. Powdery mildew resistant genotypes in wheat and Triticale. Indian Phytopathology 58: 124.
- Singh DP, Sharma AK, Sharma I, Singh D, Rana SK et al. 2016. Identification of resistance sources against powdery mildew (Blumeria graminis) of wheat. Indian Phytopathology 69 (4): 413-415.
- Sivasamy M, Jayaprakash P, Vikas VK, Nallathambi P, Umamaheswari C, Nanjundan J, Berliner J, Manjunatha C, Meena M, Bojan S and Sivan K. 2016. Multiple disease resistant wheat varieties developed thro’ pyramiding of rust and powdery mildew resistance genes employing conventional breeding approach. Nilgiri Wheat News 8 (2) May-Aug. ICAR-IARI, Regional Station, Wellington, Tamil Nadu. P 3-5.
- Sood T, Basandrai D, Basandrai AK, Sohu VS, Rana V, Mehta A, Sharma BK, Mavi GS, Kaur J and Bains NS. 2020. Stable sources of resistance to yellow rust and powdery mildew in Indian and exotic wheat germplasm. Journal of Cereal Research 12 (1): 23-28. http://doi.org/10.25174/2582-2675/2020/100835
- Vikas VK, Kumar S, Archak S et al. 2020. Screening of 19,460 genotypes of wheat species for resistance to powdery mildew and identification of potential candidates using focused identification of germplasm strategy (FIGS). Crop Science 60: 2857-2866. https://doi.org/10.1002/csc2.20196
- Genetic variability for yield and yield related traits in barley (Hordeum vulgare L.)
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Authors
Affiliations
1 Department of Genetics and Plant Breeding, College of Agriculture CSK Himachal Pradesh Krishi Vishvavidyalaya, Palampur–176 062,, IN
1 Department of Genetics and Plant Breeding, College of Agriculture CSK Himachal Pradesh Krishi Vishvavidyalaya, Palampur–176 062,, IN
Source
Himachal Journal of Agricultural Research, Vol 48, No 2 (2022), Pagination: 272-275Abstract
The study was carried out during Rabi season of 2019-20 at HAREC, Bajaura Farm, to evaluate genetic variability for yield and yield related traits in two hundred ten barley germplasm lines (144 exotic and 66 indigenous) and six standard check varieties [HBL 113 (Vimal), HBL 713 (Him Palam Jau 1), HBL 804 (Him Palam Jau 2), BHS 400 (Pusa Sheetal), BHS 352 (Himadri) and VLB 118 (VLJau 118)] in Augmented Design. The analysis of variance indicated significant difference among entries (ignoring blocks), checks, varieties and checks v/s varieties for all quantitative characters except peduncle length in case of checks vs. varieties. The mean squares due to blocks were non-significant for most of the characters under study except for peduncle length (cm), plant height (cm) and days to 75% maturity. High values of PCV and GCV (>20%) were observed for grain yield/plant, number of effective tillers/plant, biological yield/plant and number of grains/spike in this set of experimental barley genotypes indicating high response to selection. High heritability coupled with high genetic advance as per cent of mean was observed for number of grains/spike, biological yield/plant and grain yield/plant, indicated their importance for grain yield improvement in barley.Keywords
Barley, PCV, GCV, heritability, genetic advance, selection.References
- Adhikari BN, Joshi BP, Shrestha J and Bhatta NB. 2018. Genetic variability, heritability, genetic advance and correlation among yield and yield components of rice (Oryza sativa L.). Journal of Agriculture and Natural Resources 1 (1): 149-160.
- Al Tabbal JA and Al Fraihat AH. 2012. Genetic variation, heritability, phenotypic and genotypic correlation studies for yield and yield components in promising barley genotypes. Journal of Agricultural Sciences 4: 193-210.
- Anonymous.2018-2019. Annual Progress Report, Indian Institute of Wheat and Barley Research, Karnal. Burton WG and Devane EH. 1953 Estimating heritability in tall fescue (Festuca arundinacea) from replicated clonal material. Agronomy Journal 45: 478-481.
- Falconer DS. 1981. An Introduction to Quantitative nd Genetics, 2 edition. Longman. New York.
- Johnson HW, Robinson HE and Comstock RE. 1955. Estimates of genetic and environmental variability in soybean. Agronomy Journal 47: 314-318.
- Kumar P, Pratap S, Verma RPS, Tikle AN and Malik R. 2018. Diversity assessment of hulled barley (Hordeum vulgare L.) accessions of ICARDA in Indian condition using cluster analysis. Indian Journal of Agricultural Research 52 (4): 429-433.
- Lodhi R, Prasad LC, Madakemohekar AH, Bornare SS and Prasad R. 2015. Study of genetic parameters for yield and yield contributing trait of elite genotypes of barley (Hordeum vulgare L.). Indian Research Journal of Genetics and Biotechnology 7 (1): 17-21.
- Malik P, Singh SK, Singh L, Gupta PK, Kumar S, Yadav RK, Amardeep and Kumar A. 2018. Studies on genetic heritability and genetic advance for seed yield and its component in barley (Hordeum vulgare L.). International Journal of Pure and Applied Bioscience 6 (6): 810-813.
- Matin MQI, Amiruzzaman M, Billah MM, Banu MB, Naher N and Choudhary DA. 2019. Genetic variability and path analysis studies in barley (Hordeum vulgare L.). International Journal of Applied Sciences and Biotechnology 7(2): 243-247.
- Prasad GSK and Singh RS. 1980. Genotypic correlation and path coefficient analysis in barley under saline alkaline condition. Plant Breeding Abstract 2: 147-158.
- Samah AM, Farid MA and. Karima AR. 2018. Morphological and molecular characterization of some Egyptian barley cultivars under calcareous soil conditions. Middle East Journal of Agriculture Research 7 (2): 408-420.
- Ullrich SE. 2010. Significance, adaptation, production and trade of barley. In: Ullrich SE (Eds.) Barley: production, improvement and uses. Wiley-Blackwell, Chichester. P 3-13.
- Verma I and Verma SR. 2011. Genotypic variability and correlations among morpho-physiological traits affecting grain yield in barley (Hordeum vulgare L.). Journal of Wheat Research 3(1): 37-42.
- Yadav RK, Gautam S, Palikhey E, Joshi BK, Ghimire KH, Gurung R, Adhikari AR, Pudasaini N and Dhakal R. 2018. Agro morphological diversity of Nepalese naked barley landraces. Agriculture & Food Security 7:86