Drought escape can provide high grain yields under early drought in lentils
- 25 Downloads
In the current context of climate change, evaluation of intraspecific genetic and phenotypic variability of lentil (Lens culinaris Medik) in response to early droughts is fundamental to gain novel insight on the adaptive potential of lentil to water deficit. Here we studied in the field the variability of functional traits, leaf biochemical composition and yield components of three Spanish lentil cultivars submitted to an atypically dry spring. The genetic variability was also studied using amplified fragment length polymorphisms based on 156 polymorphic bands. The vulnerability of the cultivars to future drier scenarios was assessed and we identified potential traits related with lentil’s tolerance to early water deficit. Inter-cultivar variability was found in the response pattern of stomatal conductance to water availability but also in other functional traits and yield components. The small-seeded commercial cultivar PAR had the highest harvest index, grain yield, maximum stomatal conductance, stomatal density and specific leaf area while the lowest root-to-shoot ratio. PAR also had early flowering and the shortest life cycle as well as high leaf polyphenol and carotenoid contents. Therefore, the drought escape strategy exhibited by PAR could be beneficial under dry springs in Mediterranean environments. In contrast, despite the big-seeded non-commercial traditional cultivar MAN displayed drought tolerant traits and high genetic and phenotypic variability, it had the lowest grain yield, suggesting that MAN is less valuable than PAR as a source of early drought resistance for the genetic improvement of lentils.
KeywordsAFLP analysis Drought tolerance Carotenoids Grain yield Polyphenols Stomatal conductance
This research was supported by the European Social Fund (ESF) co-funding a grant of the INIA subprogramme DOC-INIA to D.S-G. We thank Dr. Marcelino de los Mozos, manager and curator of the Bank of Plant Germplasm of Cuenca (ESP124) for providing us with the seeds. We also thank Eugenio Salamanca, Amparo Calvo and Rafael Carrascosa for their technical assistance.
- Ahmed B, Alam MJ, Hossain MA et al (2016) Study of selected lentil genotypes against drought. Int J Appl Res 2:95–99Google Scholar
- Chakherchaman SA, Mostafaei H, Imanparast L, Eivazian MR (2009) Evaluation of drought tolerance in lentil advanced genotypes in Ardabil region, Iran. J Food Agric Environ 7:283–288Google Scholar
- Cristóbal MD, Pando V, Herrero B (2014) Morphological characterization of lentil (Lens Culinaris Medik.) landraces from Castilla Y León, Spain. Pak J Bot 46:1373–1380Google Scholar
- FAO (2010) The contribution of plant genetic resources for food and agriculture to food security and sustainable agricultural development. In: The second report on the state of the world’s plant genetic resources for food and agriculture. Rome, pp. 182–201Google Scholar
- FAOSTAT (2017) Food and agriculture organisation of the United Nations statistics division. http://fao.org/faostat/en. Accessed 12 Sep 2017
- Farooq M, Hussain M, Wahid A, Siddique KHM (2012) Drought stress in plants: an overview. In: Aroca R (ed) Plant responses to drought stress. Springer, Berlin, pp 37–61Google Scholar
- Idrissi O, Udupa MS, De Keyser E et al (2016) Functional genetic diversity analysis and identification of associated simple sequence repeats and amplified fragment length polymorphism markers to drought tolerance in Lentil (Lens culinaris ssp culinaris Medicus) landraces. Plant Mol Biol Report 34:659–680. https://doi.org/10.1007/s11105-015-0940-4 CrossRefGoogle Scholar
- Pandey P, Irulappan V, Bagavathiannan MV, Senthil-Kumar M (2017) Impact of combined abiotic and biotic stresses on plant growth and avenues for crop improvement by exploiting physio-morphological traits. Front Plant Sci 8:1–15. https://doi.org/10.3389/fpls.2017.00537 CrossRefPubMedPubMedCentralGoogle Scholar
- R_Core_Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
- Wei T, Simko V (2017) R package “corrplot”: visualization of a correlation matrix (version 0.84). https://github.com/taiyun/corrplot