Agricultural data for Ethiopia, gathered from research institutes and government sources into one searchable catalogue.
401–410 of 417 datasets
CIMMYT Ethiopia
Wheat head blast index (%) data for four CIMMYT nurseries (16HLBSN, 16HZAN, 42SAWSN, and 57IBWSN) is presented. Field trials took place in Okinawa (Bolivia) and Jashore (Bangladesh) during the 2025 cycles. Two sowings were made in Jashore while only one was made in Okinawa.
CIMMYT Ethiopia
This research focuses on the development and evaluation of remote sensing approaches for the assessment of wheat grain quality and productivity. Grain yield, grain nitrogen content, and grain protein content are key traits in wheat breeding and crop management; however, their measurement is often labor-intensive, time-consuming, and costly. Advances in precision agriculture and unmanned aerial vehicle (UAV) technologies provide new opportunities to estimate these traits rapidly and non-destructively through spectral information collected during the growing season. Field trials were conducted over four consecutive growing seasons in Northwest Mexico under contrasting agronomic management conditions, including different tillage systems, irrigation regimes, and nitrogen fertilization levels. Hyperspectral and multispectral imagery acquired at multiple growth stages was used to derive spectral and thermal variables associated with crop performance and grain quality attributes. Several modeling strategies combining vegetation indices, spectral bands, and thermal information were investigated using different statistical and machine learning techniques. Their predictive ability was assessed across environments and growth stages to identify robust approaches for trait estimation. The outcomes of this work contribute to the advancement of high-throughput phenotyping methodologies and support more efficient wheat breeding and nitrogen management practices through the integration of UAV-based remote sensing technologies.
CIMMYT Ethiopia
The dataset comprises information from 5,689 wheat fields, capturing crop management from sowing to harvesting, including detailed field-level inputs and production practices. Collected over six years (2016–17 to 2021–22) across Bihar and the eastern part of Uttar Pradesh, it includes yield measurements obtained through physical crop-cutting experiments. Complementing this, field-specific weather data such as temperature and solar radiation were accessed from secondary source (global gridded meteorological product AgERA5 v2.0) to align with the wheat-growing season, providing a comprehensive view of yield performance under varying management and weather conditions.
CIMMYT Ethiopia
Data from experiments evaluating Biological Nitrification Inhibition (BNI) wheat. A pair of Roelfs wheat variety (with and without the BNI trait) was planted in June 2024 in Batan and December 2024 in Obregon in a split plot randomized design. Main plot was nitrogen fertilizer rate (0, 75, 150, 225 kg/ha) in the form of ammonium sulphate while split plots were variety. Additionally, synthetic nitrification inhibition (SNI) was applied to non BNI variety for N rates of 75, 150, 225 kg/ha to act as an external control. Nitrous oxide emissions (N2O) were measured throughout the season with static chambers using laser gas analyzers (Aeris Ultra Mira). Leaching was measured by burying 7 g of ionic resin in plastic tubes (5 cm diameter) at 60 cm below the wheat. Mineral N was measured at the beginning, mid, and end of each season.
CIMMYT Ethiopia
This dataset was generated as part of the Guiding Acid Soil Management Investments in Africa (GAIA) project, which aims to support evidence-based investments in acid soil management across sub-Saharan Africa. Within this project, researcher-managed on-farm trials were established to quantify crop yield responses to increasing lime application rates. The experimental design followed a randomized complete block design (RCBD) in which each participating farm represented one replication (block). A total of 80 farms were included, and four lime application rates were tested within each farm. Lime was applied at the onset of the trial (Year 1). Crop yields measured during the first year capture the direct response to lime application, while yields measured in the second year allow evaluation of the residual effects of the initial lime treatment. This dataset contains plot-level yield measurements for maize, wheat, faba bean, and soybean collected during the first and second years after trial establishment in Ethiopia. Predicted soil properties associated with the experimental sites are available from external datasets and can be linked through the soil sample unique identifier ( SSN ). These soil datasets are available at: Predicted Soil Properties Dataset: hdl.handle.net/11529/10549136 Predicted Soil Properties Dataset: doi:10.71682/10549328 To facilitate interoperability and harmonization of agronomic terminology, a lookup table is provided that maps variable names and terms used in this dataset to the terminology defined in the terminag GitHub repository .
CIMMYT Ethiopia
Forty-one years of data from the Elite Spring Wheat Yield Trials (ESWYT), including average yield and phenological information at the site-year-OCC (occurrence) level.
CIMMYT Ethiopia
The data in this dataset are used to explore the Quadratic Genomic Selection Index (QGSI), a genomic extension of the quadratic phenotypic selection index (QPSI) that integrates genomic estimated breeding values (GEBVs) within a unified quadratic framework. Classical linear phenotypic (LPSI) and genomic (LGSI) selection indices assume additivity and linearity. This can limit their ability to exploit nonlinear trait relationships. QGSI combines additive, squared, and cross-product terms of GEBVs. It enables phenotype-free, rapid-cycle multi-trait selection while capturing genome-wide nonlinear relationships. Two genomic prediction strategies were used to evaluate QGSI: (i) a maximum-likelihood additive genomic model and (ii) a nonlinear multi-trait Gaussian kernel (reproducing kernel Hilbert space, RKHS) model that accommodates epistatic signals. Analyses were performed using multiple real wheat datasets. The results of the comparisons are reported in the accompanying article.
CIMMYT Ethiopia
Yellow rust (YR) is a major threat to both bread and durum wheat production. In this study, we evaluated alternatives to visual scoring. We tested the predictability (PA) using genomic and phenomic data for YR severity under multiple prediction scenarios in one biparental bread wheat and one biparental durum wheat population. YR scoring was conducted on two dates, with YR severity visually assessed while a multispectral camera on an unmanned aerial vehicle (UAV) was used to conduct high-throughput phenotyping (HTP). All lines were also genotyped. We tested a diverse set of models, including parametric, machine learning, and deep learning approaches. The results of the analysis are reported in the accompanying article.
CIMMYT Ethiopia
Gene-based marker data from international screening nurseries for marker-assisted selection .
CIMMYT Ethiopia
Gene-based marker data from international screening nurseries for marker-assisted selection .