Agricultural data for Ethiopia, gathered from research institutes and government sources into one searchable catalogue.
831–840 of 1,112 datasets
CIMMYT Ethiopia
This dataset provides supplemental information related to an investigation of constrained multistage linear phenotypic selection indices.
CIMMYT Ethiopia
The Semi-Arid Wheat Yield Trial (SAWYT) is a replicated yield trial that contains spring bread wheat (Triticum aestivum) germplasm adapted to low rainfall, drought prone environments typically receiving less than 500 mm of water available during the cropping cycle. The combination of water-use efficiency and water responsive broad adaptation plus yield potential is important in drought environments where rainfall is frequently erratic across and within years. Stripe rust, leaf rust and stem rust, root rots, nematodes, and bunts are the key biotic constraints. Typical target environments include winter rain or Mediterranean-type drought associated with post-flowering moisture stress and heat stress such as those found at Aleppo (Syria), Settat (Morocco) and Marcos Juarez (Argentina), all classified by CIMMYT within Wheat Mega Environment 4 (Low rainfall, semi-arid environment; ME4: SA). It is distributed to 150 locations, and contains 50 entries.
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
Genomic prediction of hybrids unobserved in field evaluations is crucial. In this study, we used genomic G×E models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted for by environmental covariables.
CIMMYT Ethiopia
Genotyping of HiBAP using 35K Wheat breeders array, containing 9267 markers.
CIMMYT Ethiopia
Genomic, pedigree, grain yield and hyperspectral data for the manuscript
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
CIMMYT has Meteorological Records for each station office in Mexico. Many variables of weather are measured per day (rawdata) from which daily, monthly and yearly sumaries are generated. These climate variables are measured by meteorological station and some of them are shown daily or hourly. Some of those variables are: Temperature, Rainfall, Solar Radiation, Relative Humidity, Wind Speed, Evapotranspiration. These files include climate data from 1986 to 2017.
CIMMYT Ethiopia
CIMMYT has Meteorological Records for each station office in Mexico. Many variables of weather are measured per day (rawdata) from which daily, monthly and yearly sumaries are generated. These climate variables are measured by meteorological station and some of them are shown daily or hourly. Some of those variables are: Temperature, Rainfall, Solar Radiation, Relative Humidity, Wind Speed, Evapotranspiration. These files include climate data from 2012 to 2013.
CIMMYT Ethiopia
CIMMYT has Meteorological Records for each station office in Mexico. Many variables of weather are measured per day (rawdata) from which daily, monthly and yearly sumaries are generated. These climate variables are measured by meteorological station and some of them are shown daily or hourly. Some of those variables are: Temperature, Rainfall, Solar Radiation, Relative Humidity, Wind Speed, Evapotranspiration. These files include climate data from 2010 to 2017.