Dataset Information

Accounting for soil moisture improves prediction of flowering time in chickpea and wheat.

ABSTRACT: Matching crop phenology to environment is essential to improve yield and reduce risk of losses due to extreme temperatures, hence the importance of accurate prediction of flowering time. Empirical evidence suggests that soil water can influence flowering time in chickpea and wheat, but simulation models rarely account for this effect. Adjusting daily thermal time accumulation with fractional available soil water in the 0-60 cm soil layer improved the prediction of flowering time for both chickpea and wheat in comparison to the model simulating flowering time with only temperature and photoperiod. The number of post-flowering frost events accounted for 24% of the variation in observed chickpea yield using a temperature-photoperiod model, and 66% of the variation in yield with a model accounting for top-soil water content. Integrating the effect of soil water content in crop simulation models could improve prediction of flowering time and abiotic stress risk assessment.

SUBMITTER: Chauhan YS

PROVIDER: S-EPMC6525173 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Accounting for soil moisture improves prediction of flowering time in chickpea and wheat.

Chauhan Yashvir S YS Ryan Merrill M Chandra Subhash S Sadras Victor O VO

Scientific reports 20190517 1

Matching crop phenology to environment is essential to improve yield and reduce risk of losses due to extreme temperatures, hence the importance of accurate prediction of flowering time. Empirical evidence suggests that soil water can influence flowering time in chickpea and wheat, but simulation models rarely account for this effect. Adjusting daily thermal time accumulation with fractional available soil water in the 0-60 cm soil layer improved the prediction of flowering time for both chickpe ...[more]

PMID: 31101844

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Project description:Identification of functionally relevant potential genomic loci using an economical, simpler and user-friendly genomics-assisted breeding strategy is vital for rapid genetic dissection of complex flowering time quantitative trait in chickpea. A high-throughput multiple QTL-seq strategy was employed in two inter (Cicer arietinum desi accession ICC 4958 × C reticulatum wild accession ICC 17160)- and intra (ICC 4958 × C. arietinum kabuli accession ICC 8261)-specific RIL mapping populations to identify the major QTL genomic regions governing flowering time in chickpea. The whole genome resequencing discovered 1635117 and 592486 SNPs exhibiting differentiation between early- and late-flowering mapping parents and bulks, constituted by pooling the homozygous individuals of extreme flowering time phenotypic trait from each of two aforesaid RIL populations. The multiple QTL-seq analysis using these mined SNPs in two RIL mapping populations narrowed-down two longer (907.1 kb and 1.99 Mb) major flowering time QTL genomic regions into the high-resolution shorter (757.7 kb and 1.39 Mb) QTL intervals on chickpea chromosome 4. This essentially identified regulatory as well as coding (non-synonymous/synonymous) novel SNP allelic variants from two efl1 (early flowering 1) and GI (GIGANTEA) genes regulating flowering time in chickpea. Interestingly, strong natural allelic diversity reduction (88-91%) of two known flowering genes especially mapped at major QTL intervals as compared to that of background genomic regions (where no flowering time QTLs were mapped; 61.8%) in cultivated vis-à-vis wild Cicer gene pools was evident inferring the significant impact of evolutionary bottlenecks on these loci during chickpea domestication. Higher association potential of coding non-synonymous and regulatory SNP alleles mined from efl1 (36-49%) and GI (33-42%) flowering genes for early and late flowering time differentiation among chickpea accessions was evident. The robustness and validity of two functional allelic variants-containing genes localized at major flowering time QTLs was apparent by their identification from multiple intra-/inter-specific mapping populations of chickpea. The functionally relevant molecular tags delineated can be of immense use for deciphering the natural allelic diversity-based domestication pattern of flowering time and expediting genomics-aided crop improvement to develop early flowering cultivars of chickpea.

Dataset Information

Accounting for soil moisture improves prediction of flowering time in chickpea and wheat.

Publications

Accounting for soil moisture improves prediction of flowering time in chickpea and wheat.

Similar Datasets

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