Agricultural data for Ethiopia, gathered from research institutes and government sources into one searchable catalogue.
191–200 of 1,115 datasets
CIMMYT Ethiopia
Summary results and individual trial results from the International Late White Hybrid - ILWH, (Elite Tropical Late White Normal and QPM Hybrid Trial - CHTTW) conducted in 2006.
CIMMYT Ethiopia
Summary results and individual trial results from the International Early White Variety - IEWV, (Tropical Early White Variety Trial - EVT14B) conducted in 2006.
CIMMYT Ethiopia
Summary results and individual trial results from the International Late Yellow Variety - ILYV, (Tropical Late Yellow Normal and QPM Synthetic Variety Trial - EVT13S) conducted in 2006.
CIMMYT Ethiopia
Several introgression lines carrying the BNI translocation, together with their respective recurrent spring bread wheat parents, were multiplied for distribution at CIMMYT’s Mexicali station. To confirm the presence of the target translocations and to verify line purity and genetic relationships, all materials were fingerprinted using the mid-density 3.9K TaDArTAG v2 genotyping panel in combination with KASP molecular markers.
CIMMYT Ethiopia
Preliminary yield and agronomic trial data collected from 6 different trials carried out in the 2024/2025 season at CENEB Obregon field station. Trial 1,2 and 3 comprise of CIMMYT spring wheat introgression lines carrying the I chromosome from Leymus mollis while Trial 4, 5 and 6 comprise of lines carrying Leymus racemosus T3BL.3NS with BNI activity, H chromosomes also from L. racemosus, and some lines with the I chromosome from L. mollis.
CIMMYT Ethiopia
Yield trial data of CIMMYT Elite Spring Wheat lines with the BNI short arm translocation in South Asia, 2024/25.
CIMMYT Ethiopia
Grain Yield and Grain Protein data of CIMMYT Elite Spring Wheat lines with the BNI short arm translocation (T3BL.3NsbS) from Leymus racemosus.
CIMMYT Ethiopia
The observation of crop types is paramount for a myriad of agricultural and environmental remote sensing applications, including land use mapping, crop growth monitoring, and yield forecasting. Accurate georeferenced and quality-controlled crop type data is integral to the calibration of machine learning algorithms. However, publicly available field data remains scarce, particularly within the dynamic smallholder farming systems of sub-Saharan Africa. To address this gap, the EthCT2020 dataset was established for the 2020/21 cropping season in Ethiopia. This dataset comprises 2,793 harmonized and quality-controlled in-situ samples of annual crops, representing seven crop groups and 22 classes across the diverse agricultural landscape of Ethiopia, with a specific emphasis on wheat-based farming systems. The data collection process involved a nationwide campaign implemented through a stratified sampling approach grounded in the wheat crop calendar, resulting in the acquisition of 1,263 samples from this campaign (GDCC; Source 1). Additionally, the dataset was enhanced by incorporating 1,530 wheat samples sourced from specialized databases dedicated to wheat disease surveillance, specifically the Wheat Rust Toolbox (WRTB; Source 2), which contributed 734 samples, and from an internal farm household survey database (FHSD; Source 3), which added 796 samples. The field data were meticulously labeled in accordance with the guidelines established by the Joint Experiment for Crop Assessment and Monitoring (JECAM) and the FAO Indicative Crop Classification for the definition and collection of agricultural land data. Extensive processing of the EthCT2020 dataset was conducted to ensure its quality and accuracy, which included data harmonization and mixed pixel assessment through visual interpretation of satellite imagery, coupled with quality control measures based on Sentinel-2 NDVI homogeneity analysis. The EthCT2020 dataset is distinctive in its diversity and spatial precision, and it represents a valuable resource for further development of innovative remote sensing technologies and predictive models within the Ethiopian agricultural context. Additionally, it serves as a foundational input for climate models and forecasting systems for crop diseases and pests.
CIMMYT Ethiopia
The dataset used in this study was collected as part of the evaluation of the Maize Commercialization Model (MCM) in Nepal, implemented under the Nepal Seed and Fertilizer (NSAF) project led by CIMMYT and funded by USAID. The dataset comprises survey responses from 493 smallholder households across five districts—Banke, Bardiya, Kailali (MCM intervention areas), and Dang, Kanchanpur (non-MCM comparison areas). The survey utilized a multistage random sampling approach, ensuring adequate representation of both male- and female-headed households (MHH and FHH). Key variables include demographic characteristics, agricultural practices, access to resources, maize productivity, and market participation. The dataset enables gender-disaggregated analysis of the impact of bundled agricultural interventions on commercialization outcomes. Data collection was conducted through structured household interviews, ensuring consistency and reliability. The dataset is anonymized to protect respondent confidentiality and is available in CSV and STATA formats for further analysis.
CIMMYT Ethiopia
Maize lethal necrosis (MLN) is a disease caused by two viruses that has threatened food security for smallholder farmers in sub-Saharan Africa. Strong resistance to MLN was conferred by a recessive QTL on chromosome 6. This QTL was identified in the KS23 germplasm from Thailand. The genotypic data provided in this study were used to elucidate the enzyme potentially underlying the MLN resistance. The identification of the gene and the impact of knocking it out are described in the accompanying publication.