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Setting Up Decision-Making Tools toward a Quality-Oriented Participatory Maize Breeding Program

Overview of attention for article published in Frontiers in Plant Science, December 2017
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

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Title
Setting Up Decision-Making Tools toward a Quality-Oriented Participatory Maize Breeding Program
Published in
Frontiers in Plant Science, December 2017
DOI 10.3389/fpls.2017.02203
Pubmed ID
Authors

Mara L. Alves, Cláudia Brites, Manuel Paulo, Bruna Carbas, Maria Belo, Pedro M. R. Mendes-Moreira, Carla Brites, Maria do Rosário Bronze, Jerko Gunjača, Zlatko Šatović, Maria C. Vaz Patto

Abstract

Previous studies have reported promising differences in the quality of kernels from farmers' maize populations collected in a Portuguese region known to produce maize-based bread. However, several limitations have been identified in the previous characterizations of those populations, such as a limited set of quality traits accessed and a missing accurate agronomic performance evaluation. The objectives of this study were to perform a more detailed quality characterization of Portuguese farmers' maize populations; to estimate their agronomic performance in a broader range of environments; and to integrate quality, agronomic, and molecular data in the setting up of decision-making tools for the establishment of a quality-oriented participatory maize breeding program. Sixteen farmers' maize populations, together with 10 other maize populations chosen for comparison purposes, were multiplied in a common-garden experiment for quality evaluation. Flour obtained from each population was used to study kernel composition (protein, fat, fiber), flour's pasting behavior, and bioactive compound levels (carotenoids, tocopherols, phenolic compounds). These maize populations were evaluated for grain yield and ear weight in nine locations across Portugal; the populations' adaptability and stability were evaluated using additive main effects and multiplication interaction (AMMI) model analysis. The phenotypic characterization of each population was complemented with a molecular characterization, in which 30 individuals per population were genotyped with 20 microsatellites. Almost all farmers' populations were clustered into the same quality-group characterized by high levels of protein and fiber, low levels of carotenoids, volatile aldehydes, α- and δ-tocopherols, and breakdown viscosity. Within this quality-group, variability on particular quality traits (color and some bioactive compounds) could still be found. Regarding the agronomic performance, farmers' maize populations had low, but considerably stable, grain yields across the tested environments. As for their genetic diversity, each farmers' population was genetically heterogeneous; nonetheless, all farmers' populations were distinct from each other's. In conclusion, and taking into consideration different quality improvement objectives, the integration of the data generated within this study allowed the outline and exploration of alternative directions for future breeding activities. As a consequence, more informed choices will optimize the use of the resources available and improve the efficiency of participatory breeding activities.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 17%
Researcher 5 14%
Student > Ph. D. Student 5 14%
Student > Bachelor 2 6%
Student > Postgraduate 2 6%
Other 5 14%
Unknown 11 31%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 39%
Engineering 3 8%
Chemical Engineering 1 3%
Computer Science 1 3%
Nursing and Health Professions 1 3%
Other 2 6%
Unknown 14 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 14 January 2019.
All research outputs
#6,406,258
of 23,015,156 outputs
Outputs from Frontiers in Plant Science
#3,598
of 20,529 outputs
Outputs of similar age
#129,318
of 440,939 outputs
Outputs of similar age from Frontiers in Plant Science
#94
of 436 outputs
Altmetric has tracked 23,015,156 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 20,529 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 82% 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 440,939 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 436 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.