Agricultural data for Ethiopia, gathered from research institutes and government sources into one searchable catalogue.
171–180 of 1,115 datasets
CIMMYT Ethiopia
Summary results and individual trial results from the International Late White Variety - ILWV, (Highland Late Normal and QPM Varieties - EVT17HLW) conducted in 2005.
CIMMYT Ethiopia
Summary results and individual trial results from the International Intermediate Yellow Hybrid - IIYH, (Subtropical Intermediate Yellow Normal and QPM Hybrid Trial - CHTSY) conducted in 2005.
CIMMYT Ethiopia
Summary results and individual trial results from the International Intermediate White Hybrid - IIWH, (Subtropical Intermediate White Normal and QPM Hybrid Trial - CHTSW) conducted in 2005.
CIMMYT Ethiopia
Summary results and individual trial results from the International Intermediate Yellow Variety Trial - IIYV, (Subtropical Intermediate Yellow Normal and QPM Varieties - EVT16A) conducted in 2005.
CIMMYT Ethiopia
Summary results and individual trial results from the International Intermediate White Variety - IIWV, (Subtropical Intermediate White Normal and QPM Varities - EVT16B) conducted in 2005.
CIMMYT Ethiopia
Summary results and individual trial results from the International Late Yellow Hybrid - ILYH, (Advanced Tropical Three-Way and Single CrossesYellow Normal and QPM Late - TTWSCYL) conducted in 2005.
CIMMYT Ethiopia
Summary results and individual trial results from the International Late White Hybrid - ILWH, (Advanced Tropical Three-Way and Single Crosses White Normal and QPM Late - TTWSCWL) conducted in 2005.
CIMMYT Ethiopia
Summary results and individual trial results from the International Late Yellow Hybrid - ILYH, (Elite Tropical Late Yellow Normal and QPM Hybrid Trial - CHTTY) conducted in 2005.
CIMMYT Ethiopia
The marker × environment interaction (M×E) decomposes the marker effects into main effects and interaction environmental-specific effects. The M×E interaction may be modeled through a linear kernel (Genomic Best Linear Unbiased Predictor, GBLUP) or with non-linear Gaussian kernels. In this paper we proposed to use two non-linear Gaussian kernels, one is the Reproducing Kernel Hilbert Space with Kernel Averaging (RKHS KA) and the other is the Gaussian kernel with the bandwidth estimated through an empirical Bayesian method (GKb). The three methods (GBLUP, RKHS KA, and GKb) were used to model single-environment and were extended to account for the M×E interaction (GBLUP-ME, RKHS KA-ME and GKb-ME) in wheat and maize data sets. Prediction accuracy was assessed by a c cross validation scheme that predicts the performance of lines in environments where the lines were not observed. For the single-environment analyses of wheat and maize data sets, GKb and RKHS KA had higher prediction accuracy than the GBLUP for all environments. For wheat data set, RKHS KA-ME and GKb-ME model did not show any advantage over the single-environment model for pair of environments with zero or negative correlations but up to 68% superiority for pairs of environments with positive correlation. For wheat data, the M×E interaction models with Gaussian kernels had accuracies up to 17% over that of the GBLUP-ME. Prediction accuracy of GKb-ME and RKHS KA-ME were up to 12% higher than those of the GBLUP-ME. The superiority of the Gaussian kernel models over the linear kernel couple with the M×E model is due to a more flexible kernels that allow to account for more complex small marker main effects and marker specific interaction effects.
CIMMYT Ethiopia
Non-parametric methods have been shown to be effective in genome-enabled prediction, in particular, the multi-layer perceptron (MLP) and the radial basis function neural network (RBFNN). In this study, we evaluated and compared the performance of MLP classifier versus the probabilistic neural network (PNN) classifier, which is a special case of RBFNN, to predict the probability of membership of one individual in a certain phenotypic class of interest, using genomic and phenotypic data. We used 16 maize and 17 wheat genomic and phenotypic datasets with different trait-environment combinations (sample sizes ranging from 290 to 300 individuals) with 1.4k and 55k SNPs. Classifiers were tested using continuous traits that were categorized into three classes (upper, middle and lower) based on the empirical distribution of each trait, constructed on the basis of two percentiles (15-85% and 30-70%). We focused on percentile 15% and 30% for the upper and lower classes for selecting the best individuals that are common in genomic selection. Wheat datasets were also used to compare the performance of the PNN with two and three classes. The criteria of predictive accuracy of two classifiers were the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCpr). Parameters of both classifiers were obtained by optimizing the AUC for the specific class of interest.