| Literature DB >> 28330205 |
Subramanyam Ragupathy1, Shanmughanandhan Dhivya2,3, Kirit Patel4, Abiran Sritharan2, Kathirvelu Sambandan5,6, Hom Gartaula7, Ramalingam Sathishkumar3, Kamal Khadka8, Balasubramanian C Nirmala9,6, A Nirmala Kumari10, Steven G Newmaster11.
Abstract
Despite the extensive use of small millet landraces as an important source of nutrition for people living in semi-arid regions, they are presently marginalized and their diversity and distribution are threatened at a global scale. Local farmers have developed ancient breeding programs entrenched in traditional knowledge (TK) that has sustained rural cultures for thousands of years. The convention on biological diversity seeks fair and equitable sharing of genetic resources arising from local knowledge and requires signatory nations to provide appropriate policy and legal framework to farmers' rights over plant genetic resources and associated TK. DNA barcoding employed in this study is proposed as a model for conservation of genetic diversity and an essential step towards documenting and protecting farmers' rights and TK. Our study focuses on 32 landraces of small millets that are still used by indigenous farmers located in the rain fed areas of rural India and Nepal. Traditional knowledge of traits and utility was gathered using participatory methods and semi-structured interviews with key informants. DNA was extracted and sequenced (rbcL, trnH-psbA and ITS2) from 160 samples. Both multivariate analysis of traits and phylogenetic analyses were used to assess diversity among small millet landraces. Our research revealed considerable variation in traits and DNA sequences among the 32 small millet landraces. We utilized a tiered approach using ITS2 DNA barcode to make 100 % accurate landrace (32 landraces) and species (six species) assignments for all 160 blind samples in our study. We have also recorded precious TK of nutritional value, ecological and agricultural traits used by local farmers for each of these traditional landraces. This research demonstrates the potential of DNA barcoding as a reliable identification tool and for use in evaluating and conserving genetic diversity of small millets. We suggest ways in which DNA barcodes could be used in the Protection of Plant Varieties and Farmers' Rights in India and Nepal.Entities:
Keywords: DNA barcoding; PPVFR; Small millets; Tiered approach; Traditional knowledge
Year: 2016 PMID: 28330205 PMCID: PMC4903100 DOI: 10.1007/s13205-016-0450-6
Source DB: PubMed Journal: 3 Biotech ISSN: 2190-5738 Impact factor: 2.406
Pictorial DNA barcodes are serving a commodity identification tool to ensure authentication and traceability of millet landraces main traits of 32 small millets
Voucher accession numbers are provided for deposits at the Centre for Biocultural Diversity, Chennai, India
Fig. 1Neighbor-joining (NJ) tree based on the p-distance of the nuclear barcoding loci ITS2 and trnH-psbA. Branch color represents scientific species (red) and landraces (green) based on farmers traditional knowledge (TK) of small millets. The branches in blue represent the major millet Sorghum. Neighbor-joining (NJ) tree of Finger millet (Eleusine coracana) landraces based on the p-distance of the chloroplast barcoding region trnH-psbA
Pearson correlation and significance (p < 0.05) with each of the Nonmetric Multidimensional Scaling (NMS) axis for 50 agricultural traits among 32 landraces based on native farmer’s traditional knowledge
| Agricultural trait | NMS axis 1 | NMS axis 2 | NMS axis 3 | |||
|---|---|---|---|---|---|---|
| Pearson correlation | Significance ( | Pearson correlation | Significance ( | Pearson correlation | Significance ( | |
| Plant duration | −0.233 | 0.223 | 0.194 | 0.313 |
|
|
| Plant habit | 0.218 | 0.256 |
|
| 0.065 | 0.737 |
| Plant height | 0.205 | 0.286 |
|
| 0.257 | 0.178 |
| Plant physique |
|
| 0.294 | 0.121 | 0.19 | 0.324 |
| Plant biomass | 0.344 | 0.068 |
|
| −0.107 | 0.582 |
| Stem diameter | 0.216 | 0.261 |
|
| 0.277 | 0.145 |
| Stem length | 0.24 | 0.209 |
|
| 0.383 | 0.041 |
| Stem color |
|
|
|
|
|
|
| Stem juice | 0.236 | 0.218 | −0.141 | 0.466 | 0.188 | 0.328 |
| Stem hollow or heavy | 0.252 | 0.187 | 0.234 | 0.221 | −0.334 | 0.076 |
| Stem Internode diameter |
|
| 0.311 | 0.1 | 0.219 | 0.253 |
| Stem Internode length |
|
|
|
| 0.256 | 0.18 |
| Stem Internode color |
|
|
|
| −0.031 | 0.874 |
| Stem Internode juice | −0.172 | 0.373 | −0.103 | 0.594 | 0.205 | 0.287 |
| Stem Internode hollow or heavy | 0.008 | 0.966 |
|
|
|
|
| Stem sheath length |
|
| 0.046 | 0.813 | 0.192 | 0.318 |
| Stem sheath color | 0.672 | 0 | −0.462 | 0.012 | 0.343 | 0.069 |
| Stem sheath blade | −0.632 | 0 | −0.486 | 0.007 | 0.008 | 0.966 |
| Collar length | 0.493 | 0.007 | 0.329 | 0.081 | −0.215 | 0.264 |
| Collar color | 0.787 | 0 | −0.482 | 0.008 | 0.164 | 0.394 |
| Ligule length | 0.771 | 0 | 0.348 | 0.064 | 0.344 | 0.068 |
| Ligule color | 0.784 | 0 | −0.45 | 0.014 | 0.147 | 0.445 |
| Leaves position | −0.012 | 0.949 | −0.118 | 0.541 | −0.075 | 0.7 |
| Leaf sheaths margins | −0.866 | 0 | 0.403 | 0.03 | 0.18 | 0.349 |
| Leaf sheaths length | 0.402 | 0.031 | 0.589 | 0.001 | 0.277 | 0.145 |
| Grain color | −0.385 | 0.039 | −0.159 | 0.409 | 0.52 | 0.004 |
| Grain length | 0.497 | 0.006 | 0.159 | 0.411 | −0.702 | 0 |
| Grain size | −0.032 | 0.87 | 0.642 | 0 | 0.256 | 0.181 |
| Grain apex | −0.709 | 0 | −0.109 | 0.572 | 0.088 | 0.651 |
| Seed storage endurance | −0.555 | 0.002 | 0.273 | 0.152 | −0.015 | 0.938 |
| Seed storage without treatment | −0.529 | 0.003 | 0.375 | 0.045 | −0.104 | 0.59 |
| Seed storage with treatment | −0.251 | 0.189 | 0.439 | 0.017 | −0.241 | 0.209 |
| Seed preparation for storing | 0.789 | 0 | −0.246 | 0.198 | −0.454 | 0.013 |
| Seed preparation for sowing | 0.799 | 0 | −0.064 | 0.74 | −0.172 | 0.371 |
| Productive tillers | 0.603 | 0.001 | 0.567 | 0.001 | 0.018 | 0.926 |
| Population germination rate | 0.1 | 0.606 | −0.446 | 0.015 | 0.642 | 0 |
| Population establishment | 0.752 | 0 | 0.218 | 0.255 | 0.458 | 0.013 |
| Population harvest time | 0.748 | 0 | 0.368 | 0.049 | 0.087 | 0.654 |
| Flowering time | 0.36 | 0.055 | 0.596 | 0.001 | 0.191 | 0.32 |
| Panicle density | 0.101 | 0.602 | 0.221 | 0.25 | 0.348 | 0.064 |
| Panicle yield | 0.185 | 0.335 | 0.22 | 0.252 | 0.152 | 0.43 |
| Grain Yield | 0.143 | 0.461 | 0.101 | 0.602 | 0.332 | 0.078 |
| Straw Yield | 0.158 | 0.414 | 0.755 | 0 | −0.11 | 0.57 |
| Grain yield by season | 0.406 | 0.029 | 0.429 | 0.02 | −0.089 | 0.646 |
| Grazing | 0.424 | 0.022 | −0.283 | 0.137 | 0.49 | 0.007 |
| Best season | −0.34 | 0.072 | 0.144 | 0.457 | −0.15 | 0.437 |
| Water needs | 0.888 | 0 | 0.044 | 0.822 | −0.005 | 0.98 |
| Ploughing needs | 0.432 | 0.019 | 0.415 | 0.025 | 0.352 | 0.061 |
| Hay utility commercial | −0.313 | 0.098 | 0.715 | 0 | −0.098 | 0.614 |
| Disease resistant | −0.833 | 0 | 0.268 | 0.16 | 0.06 | 0.759 |
Strong Pearson correlation and p values are in italics
Fig. 2Non-Metric Multi-Dimensional Scaling was used to identify variation among 32 landraces and 50 agricultural traits (stress = 0.09)
Fig. 3Industrialized millets cookies consumed by high end people for their brake fast
Fig. 4Farmers preferred Kalourcho ragi—Save this ragi for their own use (either bread making or local wine making)