CIMMYT Ethiopia
NG 2016 NOTs soil leaf yield database
Nutrient Ommission Trials (NOTs) from three states in Nigeria. Six treatments, yield, soil and ear leaf nutrient contents.
1 resource
Published 15 Aug 2019
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Agricultural data for Ethiopia, gathered from research institutes and government sources into one searchable catalogue.
191–200 of 275 datasets
CIMMYT Ethiopia
Nutrient Ommission Trials (NOTs) from three states in Nigeria. Six treatments, yield, soil and ear leaf nutrient contents.
CIMMYT Ethiopia
TAMASA Agronomy Panel Survey 2016/17 Season. This file contains the maize grain yield from approximately 578 maize fields in the Southern Highlands, Northern and Eastern Zones of Tanzania in collected May-August 2017. Maize grain yield data can be linked to associated maize yield and soil by the common HHID.
CIMMYT Ethiopia
Experiments at multiple locations in Tanzania (800 to 1500m) to calibrate development or phenology of maize varieties. Locations are Igeri, Minja, Mitalula, Mlaaleni, Monduli, Selian, Seatondale, Suluti, Uyole, W. Kilimanjaro. Observations include dates of emergence, tassel, silking and maturity; Daily min & max temperatures are in a separate file
CIMMYT Ethiopia
Survey at multiple locations in Tanzania (680 to 2100 m) to establish baseline biomass yields at farm level.
CIMMYT Ethiopia
CIMMYT and IITA. 2017. TAMASA Tanzania Agronomic Panel Survey for 2016. Version 1. Taking Maize Agronomy to Scale Project, CIMMYT and IITA. Tabular dataset
CIMMYT Ethiopia
This dataset provides supplementary files for the trials and sites described in the 2011 paper in https://www.nature.com/nclimate/ available at https://dx.doi.org/10.1038/NCLIMATE1043
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.
CIMMYT Ethiopia
This file provides supporting material for the manuscript entitled ' Estimation of Physiological Genomic Estimated Breeding Values (PGEBV) Combining full Hyperspectral and Marker Data Across Environments for Grain Yield Under Combined Heat and Drought Stress in Tropical Maize (Zea mays L.)'. The file includes spreadsheets containing information on the experimental structure ('Experimental structure' spreadsheet), agronomic data ('Agronomic data' spreadsheet), hyperspectral data ('Hyperspectral data' spreadsheet) and molecular marker information ('markerdata' spreadsheet). A separate self explanatory summary of the information contained in the file can be found the the 'Read me first' spreadhsheet.
CIMMYT Ethiopia
Large tracts of land in the upland plateau ecology of Odisha are not suitable for rice production and are largely left fallow. In Mayurbhanj district of the plateau area, nearly half of the total cultivable area (43%) falls into the upland category that is not very suitable for growing rice. The tribal farming population either leave this land fallow or grow short duration rice during rainy season with very low productivity. Some farmers also grow traditional maize varieties with low yields and mostly for household consumption. Since 2013, CSISA has been engaged in efforts to sustainably intensify maize production in Mayurbhanj district, working towards enhancing farmers’ access to modern inputs (especially hybrid seed and machinery services for line-sowing), providing training on best agronomic practices, and linking them with output markets, especially poultry feed mills located in the coastal area of Odisha. While there is evidence that the number of farmers engaged in commercial maize production is rapidly growing, there has been no systematic and representative assessment of farmers’ maize growing practices and the impacts that commercial maize production has had on farmers’ livelihoods. The objectives of this study are: (1) current maize growing practices, (2) marketing practices and profitability of maize production, and (3) livelihood impacts of commercial maize production in a remote tribal area of Odisha.
CIMMYT Ethiopia
Drought stress, heat stress, and combination of drought stress and heat stress have been recognized as the major abiotic constraints to maize yields in the main production regions. The phenotypic data used in the current study had been published by Jill E. Cairns et al in 2013 in the journal of Crop Science 53 :1335–1346 ( https://dx.doi.org/10.2135/cropsci2012.09.0545 ). In this study, the association mapping and genomic prediction analyses were conducted in a collection of 300 tropical and subtropical maize inbred lines to reveal the genetic architecture of grain yield and flowering time under well-watered, drought stress, heat stress, and combined drought and heat stress conditions. The genetic architecture information of the grain yield and flowering time revealed in this study, and the genomic regions identified for the different trait-environment combinations are helpful accelerating the efforts on rapid development of the stress-tolerant maize germplasm through marker-assisted selection or genomic selection.