| Autor: |
Gesesse CA; Center of Plant Sciences, Scuola Superiore Sant'Anna, Pisa 56127, Italy.; Amhara Regional Agricultural Research Institute, Bahir Dar 6000, Ethiopia., Nigir B; Center of Plant Sciences, Scuola Superiore Sant'Anna, Pisa 56127, Italy., de Sousa K; Digital Inclusion, Bioversity International, Parc Scientifique Agropolis II, Montpellier 34397, France.; Department of Agricultural Sciences, Inland Norway University of Applied Sciences, Hamar 2322, Norway., Gianfranceschi L; Department of Biosciences, University of Milan, Milan 20133, Italy., Gallo GR; Department of Biosciences, University of Milan, Milan 20133, Italy., Poland J; Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia., Kidane YG; Center of Plant Sciences, Scuola Superiore Sant'Anna, Pisa 56127, Italy.; Biodiversity for Food and Agriculture, Bioversity International, Addis Ababa 1000, Ethiopia; and., Abate Desta E; Amhara Regional Agricultural Research Institute, Bahir Dar 6000, Ethiopia., Fadda C; Biodiversity for Food and Agriculture, Bioversity International, Nairobi 00621, Kenya., Pè ME; Center of Plant Sciences, Scuola Superiore Sant'Anna, Pisa 56127, Italy., Dell'Acqua M; Center of Plant Sciences, Scuola Superiore Sant'Anna, Pisa 56127, Italy. |
| Abstrakt: |
In the smallholder, low-input farming systems widespread in sub-Saharan Africa, farmers select and propagate crop varieties based on their traditional knowledge and experience. A data-driven integration of their knowledge into breeding pipelines may support the sustainable intensification of local farming. Here, we combine genomics with participatory research to tap into traditional knowledge in smallholder farming systems, using durum wheat ( Triticum durum Desf.) in Ethiopia as a case study. We developed and genotyped a large multiparental population, called the Ethiopian NAM (EtNAM), that recombines an elite international breeding line with Ethiopian traditional varieties maintained by local farmers. A total of 1,200 EtNAM lines were evaluated for agronomic performance and farmers' appreciation in three locations in Ethiopia, finding that women and men farmers could skillfully identify the worth of wheat genotypes and their potential for local adaptation. We then trained a genomic selection (GS) model using farmer appreciation scores and found that its prediction accuracy over grain yield (GY) was higher than that of a benchmark GS model trained on GY. Finally, we used forward genetics approaches to identify marker-trait associations for agronomic traits and farmer appreciation scores. We produced genetic maps for individual EtNAM families and used them to support the characterization of genomic loci of breeding relevance with pleiotropic effects on phenology, yield, and farmer preference. Our data show that farmers' traditional knowledge can be integrated in genomics-driven breeding to support the selection of best allelic combinations for local adaptation. |
