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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Genome Wide Association Study to Identify the Genetic Base of Smallholder Farmer Preferences of Durum Wheat Traits
|
|---|---|
| Published in |
Frontiers in Plant Science, July 2017
|
| DOI | 10.3389/fpls.2017.01230 |
| Pubmed ID | |
| Authors |
Yosef G. Kidane, Chiara Mancini, Dejene K. Mengistu, Elisabetta Frascaroli, Carlo Fadda, Mario Enrico Pè, Matteo Dell'Acqua |
| Abstract |
Smallholder agriculture involves millions of farmers worldwide. A methodical utilization of their traditional knowledge in modern breeding efforts may help the production of locally adapted varieties better addressing their needs. In this study, a combination of participatory approaches, genomics, and quantitative genetics is used to trace the genetic basis of smallholder farmer preferences of durum wheat traits. Two smallholder communities evaluated 400 Ethiopian wheat varieties, mostly landraces, for traits of local interest in two locations in the Ethiopian highlands. For each wheat variety, farmers provided quantitative evaluations of their preference for flowering time, spike morphology, tillering capacity, and overall quality. Ten agronomic and phenology traits were simultaneously measured on the same varieties, providing the means to compare them with farmer traits. The analysis of farmer traits showed that they were partially influenced by gender and location but were repeatable and heritable, in some cases more than metric traits. The durum wheat varieties were genotyped for more than 80,000 SNP markers, and the resulting data was used in a genome wide association (GWA) study providing the molecular dissection of smallholder farmers' choice criteria. We found 124 putative quantitative trait loci (QTL) controlling farmer traits and 30 putative QTL controlling metric traits. Twenty of such QTL were jointly identified by farmer and metric traits. QTL derived from farmer traits were in some cases dependent on gender and location, but were consistent throughout. The results of the GWA study show that smallholder farmers' traditional knowledge can yield QTL eluding metric measurements of phenotypes. We discuss the potential of including farmer evaluations based on traditional knowledge in crop breeding, arguing for the utilization of this untapped resource to develop better adapted genetic materials for local agriculture. |
X Demographics
The data shown below were collected from the profiles of 35 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 7 | 20% |
| Germany | 2 | 6% |
| Kenya | 1 | 3% |
| Brazil | 1 | 3% |
| Switzerland | 1 | 3% |
| Uganda | 1 | 3% |
| Spain | 1 | 3% |
| Bolivia, Plurinational State of | 1 | 3% |
| Ethiopia | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 18 | 51% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 32 | 91% |
| Science communicators (journalists, bloggers, editors) | 2 | 6% |
| Scientists | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 100 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 25 | 25% |
| Student > Ph. D. Student | 17 | 17% |
| Student > Doctoral Student | 7 | 7% |
| Student > Master | 6 | 6% |
| Professor > Associate Professor | 5 | 5% |
| Other | 11 | 11% |
| Unknown | 29 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 49 | 49% |
| Biochemistry, Genetics and Molecular Biology | 7 | 7% |
| Social Sciences | 6 | 6% |
| Economics, Econometrics and Finance | 4 | 4% |
| Unspecified | 2 | 2% |
| Other | 3 | 3% |
| Unknown | 29 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 95. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 May 2022.
All research outputs
#457,918
of 25,795,662 outputs
Outputs from Frontiers in Plant Science
#89
of 24,936 outputs
Outputs of similar age
#9,044
of 308,402 outputs
Outputs of similar age from Frontiers in Plant Science
#4
of 521 outputs
Altmetric has tracked 25,795,662 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 24,936 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 308,402 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 521 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.