Agricultural data for Ethiopia, gathered from research institutes and government sources into one searchable catalogue.
341–350 of 417 datasets
CIMMYT Ethiopia
CIMMYT annually distributes improved germplasm developed by its researchers and partners in international nurseries trials and experiments. The High Rainfall Wheat Yield Trial (HRWYT) contains very top-yielding advance lines of spring bread wheat (Triticum aestivum) germplasm adapted to high rainfall, Wheat Mega-environment 2 (ME2HR).
CIMMYT Ethiopia
This dataset contains supplemental files related to the GigaScience journal article 'Integrating genomic resources to present full gene and putative promoter capture probe sets for bread wheat' (Volume 8, Issue 4, April 2019) with DOI: 10.1093/gigascience/giz018.
CIMMYT Ethiopia
CIMMYT annually distributes improved germplasm developed by its researchers and partners in international nurseries trials and experiments. The High Rainfall Wheat Yield Trial (HRWYT) contains very top-yielding advance lines of spring bread wheat (Triticum aestivum) germplasm adapted to high rainfall, Wheat Mega-environment 2 (ME2HR).
CIMMYT Ethiopia
CIMMYT annually distributes improved germplasm developed by its researchers and partners in international nurseries trials and experiments. The High Temperature Wheat Yield Trial (HTWYT) is a replicated yield trial that contains spring bread wheat (Triticum aestivum) germplasm adapted to Mega-environment 1 (ME1) which represents high temperature areas.
CIMMYT Ethiopia
CIMMYT annually distributes improved germplasm developed by its researchers and partners in international nurseries trials and experiments. The High Temperature Wheat Yield Trial (HTWYT) is a replicated yield trial that contains spring bread wheat (Triticum aestivum) germplasm adapted to Mega-environment 1 (ME1) which represents high temperature areas.
CIMMYT Ethiopia
CIMMYT annually distributes improved germplasm developed by its researchers and partners in international nurseries trials and experiments. The High Temperature Wheat Yield Trial (HTWYT) is a replicated yield trial that contains spring bread wheat (Triticum aestivum) germplasm adapted to Mega-environment 1 (ME1) which represents high temperature areas.
CIMMYT Ethiopia
Gene-based marker data from international screening nurseries for marker-assisted selection.
CIMMYT Ethiopia
The genotypes from the International Bread Wheat Screening Nursery (IBWSN) multiplication trial, which is designed to rapidly assess a large number of advanced generation (F3-F7) lines of spring bread wheat under Mega-environment 1 (ME1) that represents diversity for a wide range of latitudes, climates, day lengths, fertility conditions, water management, and (most importantly) disease conditions, were used for this study.
CIMMYT Ethiopia
The genotypes from the International Bread Wheat Screening Nursery (IBWSN) multiplication trial, which is designed to rapidly assess a large number of advanced generation (F3-F7) lines of spring bread wheat under Mega-environment 1 (ME1) that represents diversity for a wide range of latitudes, climates, day lengths, fertility conditions, water management, and (most importantly) disease conditions, were used for this study.
CIMMYT Ethiopia
In genomic prediction deep learning artificial neural network are part of machine learning methods that incorporate parametric, non-parametric and semi-parametric statistical models. Kernel methods are seeing more flexible, and easier to interpret than neural networks. Kernel methods used in genomic predictions comprise the linear genomic best linear unbiased predictor (GBLUP) kernel (GB) and the Gaussian kernel (GK). These kernels have being used with two statistical models, single environment and genomic × environment (GE) models. Recently near infrared spectroscopy (NIR) has being used as phenotype method for prediction of unobserved line performance in plant breeding trials. In this study, we used a non-linear Arc-cosine kernel (AK) that emulates deep learning artificial neural network. We compared AK prediction accuracy with GB and GK kernel methods in four genomic data sets one of them including also pedigree (ABLUP) and NIR (NBLUP) information. Results show that for all four data sets AK and GK kernels gave higher prediction accuracy than the linear GB kernel for single environment as well as GE multi-environment models. In addition, AK gave similar or slightly higher prediction accuracy than the GK kernel.