Agricultural data for Ethiopia, gathered from research institutes and government sources into one searchable catalogue.
201–210 of 275 datasets
CIMMYT Ethiopia
Replicated crop-cuts and related agronomy data for maize from 142 farmers' fields in Southern, Eastern and Northern Zones in 2014/2015 season. The soils data from these fields can be found here.
CIMMYT Ethiopia
A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.
CIMMYT Ethiopia
This dataset provides supplemental information related to an investigation of constrained multistage linear phenotypic selection indices.
CIMMYT Ethiopia
This study provides supplemental data to support an investigation of the power of multi-trait deep learning (MTDL) models in terms of genomic-enabled prediction accuracy.
CIMMYT Ethiopia
One of the major issues in plant breeding is the occurrence of genotype by environment (GE) interaction. Several models have been created to understand this phenomenon and explore it by selecting the most stable genotypes. In the genomic era, several models were employed to simultaneously improve selection by using markers and account for GE interaction. Some of these models use special genetic covariance matrices. In addition, multi-environment trials scales are getting larger, and this increases the computational challenges. In this context, we propose an R package that, in general, allows building GE genomic covariance matrices and fitting linear mixed models, in particular, to a few genome GE models. Here we propose a function to create the genomic kernels needed to fit these models. This function makes genome predictions through a Bayesian linear mixed model approach. A particular treatment is given for structured dispersed covariance matrices; in particular, those structured as a block diagonal that are present in some GE models in order to decrease the computational demand. In empirical comparisons with Bayesian Genomic Linear Regression (BGLR), accuracies and the mean squared error were similar; however, the computational time was up to five times lower than when using the classic approach. Bayesian Genomic Genotype × Environment Interaction (BGGE) is a fast, efficient option to create genome GE kernels and make genomic predictions.
CIMMYT Ethiopia
SASHAYDIALL is a code written in SAS to perform diallel analysis according to the method of Hayman (1954). The SASHAYDIALL program was written in SAS/IML and runs in SAS version 9.0 or higher. The SASHAYDIALL program requires an input ‘csv’ format data file that has replication, parent 1, parent 2, and the trait of interest for a single site experiment. For data obtained from multiple environments, the user should include a variable for the locations. The locations or environments should be numbered sequentially in the ‘csv’ file. The file with data to be analyzed can be saved in any directory on the computer. The user is only required to specify the location and name of the data file to be analyzed in the SASHAYDIALL program. The program automatically detects the number of parents and presence or absence of reciprocals in the diallel cross data set. Before analysis can proceed, the user is required to provide responses in two input windows: one window for variable information and the other to indicate whether the data to be analyzed is from single or multiple environments. The SASHAYDIALL program will then ran ANOVA for the diallel table, create variances and covariances, calculate second degree statistics, test adequacy of the additive-dominance model, and estimate various genetic parameters including heritability. The program also plots a Wr–Vr graph and is not computationally intensive. More details can be found in a paper in Crop Science 58:1605–1615: https://dx.doi.org/10.2135/cropsci2018.01.0047 .
CIMMYT Ethiopia
APS consists of household- and plot-level data on 336 households in maize-growing areas of Jimma and Bako zones. This dataset consists of information about characteristics, agronomic history, input and management of the focal plot. dataset variables observations focal_plot_merged 65 336 input_focal 18 822 other_management_focal 13 4032 planting_focal 11 339 crop_residue_focal 10 3696 agronomic history 18 3360 There are two linked household datasets, on Belg and Meher production Note that dates are in the Ethiopian calendar. see, https://en.wikipedia.org/wiki/Ethiopian_calendar"
CIMMYT Ethiopia
APS consists of household- and plot-level data on 336 households in maize-growing areas of Jimma and Bako zones. This dataset consists of information about Belg production and inputs in 2016. dataset variables observations belg_production_2016 30 357 belg_input_cost 9 696 belg_family_labor 22 357 belg_hired_services 15 338 Data for Meher season and Focal Plot are also available Note that dates are in the Ethiopian calendar. see, https://en.wikipedia.org/wiki/Ethiopian_calendar"
CIMMYT Ethiopia
Summary results and individual trial results from the International Late Yellow Hybrid - ILYH, (Highland Late Yellow Hybrids - CHTHLY) conducted in 2007.
CIMMYT Ethiopia
Summary results and individual trial results from the International Intermediate White Hybrid - IIWH, (Transition Zone Intermediate White Hybrid - CHTHTZIW) conducted in 2007.