Agricultural data for Ethiopia, gathered from research institutes and government sources into one searchable catalogue.
1–5 of 5 datasets
CIMMYT Ethiopia
GWAS identifies common loci under stressed and non-stressed environments.
CIMMYT Ethiopia
Genome-wide association study (GWAS) was conducted for grain yield (YLD) and yield components on a wheat association mapping initiative (WAMI) population of 287 elite, spring wheat lines grown under temperate irrigated high-yield potential condition in Ciudad Obregón, Mexico, during four crop cycles (from 2009–2010 to 2012–2013). Raw data for grain yield, yield components and physiological traits are provided.
CIMMYT Ethiopia
Increases in genetic gains in grain yield can be accelerated through genomic selection (GS). In the present study seven genomic prediction models under two cross validation scenarios were evaluated on the Wheat Association Mapping Initiative population of 287 advanced elite lines phenotyped for grain yield (GY), thousand grain weight (GW), grain number (GN), and thermal time for flowering (TTF) in 18 environments (year location combinations) in major wheat producing countries in 2010 and 2011. The seven genomic prediction models tested herein: four of them (model 1 (L+E), model 2 (L+E+G), model 3 (L+E+A) , and model 4 (L+E+A+G )) with main effects (lines (L), environme nts (E), genetic relationship matrix (G), and pedigree derived matrix (A) and three of them (model 5 (L+E+A+AE), model 6 (L+E+G+GE), and model 7 (L+E+G+A+AE+GE)) with interaction effects between A×E, G×E, and both together with main effects. Moreover, two cross validation (CV) schemes were applied: (1) predicting lines’ performance at untested sites (CV1) and (2) predicting the lines’ performance at some sites with the performance from other sites (CV2). The genomic prediction models with interaction terms, models 6 and 7 had the highest prediction accuracy on average for CV1 for GY (0.31), GN (0.30), and model 5 for TTF (0.26). Models 3 and 7 2, were the best model for GW (0.45 each) under CV1 scenario. For CV2, the prediction accuracy was generally high for the model with interaction terms models 5, 6, and 7 for GY (0.39), model 5 and 7 for GN (0.43. For GW and TTF models prediction accuracy were similar. Results indicated genomic selection can be used to predict genotype by environment (G×E) interaction in multi environment trials to select varieties for release as well as for accelerated breeding.
CIMMYT Ethiopia
In plant breeding research, several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes. To increase the GP accuracy of unobserved phenotypes while simultaneously accounting for the complexity of genotype × environment interaction (GE), deep learning (DL) neural networks have been developed.These analyses can potentially include phenomics data obtained through imaging. The two datasets included in this study contain phenomic, phenotypic, and genotypic data for a set of wheat materials. They have been used to compare a novel DL method with conventional GP models.The results of these analyses are reported in the accompanying journal article.
CIMMYT Ethiopia
Gene-based marker data from international screening nurseries for marker-assisted selection.