Identification of suitable genotypes of lentil (Lens culinaris) for improved adaptation to rice fallow areas of Gangetic Alluvial Zone

Abstract views: 91 / PDF downloads: 154


  • SHAYREE CHATTERJEE Bidhan Chandra Krishi Vishwavidayalaya, Mohanpur, Nadia, West Bengal 741 252, India
  • ARPITA DAS Bidhan Chandra Krishi Vishwavidayalaya, Mohanpur, Nadia, West Bengal 741 252, India
  • SUDIP BHATTACHARYA Bidhan Chandra Krishi Vishwavidayalaya, Mohanpur, Nadia, West Bengal 741 252, India
  • JOYDEEP BANERJEE Indian Institute of Technology, Kharagpur, West Bengal
  • SANJEEV GUPTA ICAR, Krishi Bhawan, New Delhi
  • SHIV KUMAR International Centre for Agricultural Research in the Dry Areas (ICARDA), South Asia and China Regional Program, New Delhi


Desirability index, GE interaction, Lentil, Rice fallow, Stability


Rice fallows (RF) are the low lying kharif sown rice (Oryza sativa L.) areas that remain uncropped due to dearth of suitable cultivars of winter (rabi) pulses. A panel of 30 promising high yielding lentil (Lens culinaris Medik.) genotypes of diverse origin were assessed at both no till RF and with till condition at the Regional Research Substation (RRSS), Chakdah under the aegis of Bidhan Chandra Krishi Viswavidyalaya, Nadia, West Bengal for two years (2019–20 and 2020–21). Multi-trait performance (earliness, biomass and grain yield) of each genotype was considered during recommendation of suitable genotype for specific ecology deploying GGE biplot. The present
study recommended IC 560183 for no till RF ecology and Moitree, IC 559996, ILL 7978 and L 1112-19 for with till ecology having specific adaptation. Additionally, 2011S-56212-2 and ILL 8006 were identified as ideal and desirable genotypes for both the ecologies and therefore, recommended for commercial cultivation across the areas of Gangetic alluvial zone for augmenting lentil production and productivity.


Download data is not yet available.


Bhattacharya S, Das A, Banerjee J, Mandal S N, Kumar S and Gupta S. 2022. Elucidating genetic variability and genotype × environment interactions for grain iron and zinc content among diverse genotypes of lentil (Lens culinaris). Plant Breeding 141(6): 786–800.

Bhartiya A, Aditya J P, Kumari V, Kishore N, Purwar J P and Agrawal A. 2017. GGE biplot and AMMI analysis of yield stability in multi-environment trial of soybean [Glycine max (L.) Merrill] genotypes under rainfed condition of north western Himalayan hills. Journal of Animal and Plant Sciences 27(1): 227–38.

Das A, Parihar A K, Saxena D, Singh D, Singha K D and Kushwaha K P S. 2019. Deciphering genotype-by-environment interaction for targeting test environments and rust resistant genotypes in field pea (Pisum sativum L.). Frontiers in Plant Science 825.

Das A, Gupta S, Parihar A K, Singh D, Chand R and Pratap A. 2020. Delineating genotype × environment interactions towards durable resistance in mungbean against Cercospora leaf spot (Cercospora canescens) using GGE biplot. Plant Breeding 139(3): 639–50.

FAOSTAT. 2018. Retrieved 8 Mar 2018.

Gauch H G and Zobel R W. 1997. Identifying mega-environments and targeting genotypes. Crop Science 37: 311–26.

Gore P G, Das A, Bhardwaj R, Tripathi K, Pratap A and Dikshit H K. 2021. Understanding G × E interaction for nutritional and antinutritional factors in a diverse panel of Vigna stipulacea (Lam.) Kuntz germplasm tested over the locations. Frontiers in Plant Science 12: 766645.

Maji S, Das A, Nath R, Bandopadhyay P, Das R and Gupta S. 2019. Cool season food legumes in rice fallows: An indian perspective. Agronomic Crops, pp. 561–605. Springer, Singapore.

Pramanik K, Das A, Banerjee J, Das A, Chatterjee S, Sharma R, Kumar S and Gupta S. 2020. Metagenomic insights into rhizospheric microbiome profiling in lentil cultivars unveils differential microbial nitrogen and phosphorus metabolism under rice-fallow ecology. International Journal of Molecular Sciences 21(23): 8895.

Tamang S, Saha P, Bhattacharya S and Das A. 2021. Unveiling genotype × environment interactions towards identification of stable sources of resistance in chickpea-collar rot pathosystem exploiting GGE biplot technique. Australasian Plant Pathology 1: 1–12.

Rakshit S, Ganapathy K N, Gomashe S S, Rathore A, Ghorade R B, Kumar M V and Ganesmurthy K. 2012. GGE biplot analysis to evaluate genotype, environment and their interactions in sorghum multi-location data. Euphytica 185(3): 465–79.

R Development core team. 2018. R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria.

Singh B, Das A, Parihar A K, Bhagawati B, Singh D and Pathak K N. 2020. Delineation of genotype-by-environment interactions for identification and validation of resistant genotypes in mungbean to root-knot nematode (Meloidogyne incognita) using GGE biplot. Scientific Reports 10: 4108.

Yan W and Tinker N A. 2006. Biplot analysis of multi-environment trial data: Principles and applications. Canadian journal of plant science 86(3): 623–45.

Yan W. 2002. Singular-value partitioning in biplot analysis of multienvironment trial data. Agronomy Journal 94: 990–996.

Yan W, Kang M S, Ma B, Woods S and Cornelius P L. 2007. GGE biplot vs. AMMI analysis of genotype × environment data. Crop Science 47(2): 643–53.









How to Cite

CHATTERJEE, S., DAS, A., BHATTACHARYA, S., BANERJEE, J., GUPTA , S., & KUMAR, S. (2023). Identification of suitable genotypes of lentil (Lens culinaris) for improved adaptation to rice fallow areas of Gangetic Alluvial Zone. The Indian Journal of Agricultural Sciences, 93(8), 862–867.