Agricultural data for Ethiopia, gathered from research institutes and government sources into one searchable catalogue.
1,091–1,100 of 1,112 datasets
CIMMYT Ethiopia
Processed dataset from a household-level survey describing the main farm characteristics, production, and resource allocation in two municipalities. The survey covers 300 farms across three districts (Kham, Moke, and Nonghet) in Xieng Khouang Province, collected between December 2023 and May 2024.
CIMMYT Ethiopia
Wheat head blast index (%) data for four CIMMYT nurseries (15HLBSN, 15HZAN, 41SAWSN, and 56IBWSN) is presented. Field trials took place in Quirusillas and Okinawa (Bolivia) and Jashore (Bangladesh) during the 2024 cycles. Two sowings were made in each location/cycle except for Quirusillas where only one sowing was made.
CIMMYT Ethiopia
This study examines the challenges and strategies of women postgraduate students in science, technology, engineering, and mathematics (STEM) in African institutions. Using a mixed-methods approach, it combines data from an online survey conducted in 2020 with 163 women STEM doctoral alumni from 40 universities in sub-Saharan Africa, with qualitative data from seven focus groups conducted in 2019/2020 with 39 master's and doctoral students in STEM at four universities in East and West Africa (Kenya, Rwanda, and Senegal). The study reveals common challenges such as financial stress, gender stereotypes, and harassment, and highlights the resilience and success strategies of these women, contributing to identifying best practices for promoting inclusion in STEM.
CIMMYT Ethiopia
This dataset investigates factors influencing the performance of doctoral students in Science, Technology, Engineering, and Mathematics (STEM) at African universities within sub-Saharan Africa, with a particular focus on gender-based differences. The data were collected from March to May 2020 using an online survey administered via SurveyMonkey. This survey was part of a larger research initiative undertaken by the Regional Scholarship and Innovation Fund (RSIF) to inform the development of a gender strategy for the program. RSIF, a flagship program of the Partnership for Skills in Applied Sciences, Engineering and Technology (PASET), aims to strengthen applied science, engineering, and technology (ASET) capabilities in Africa for socio-economic transformation. The survey, available in both English and French, was completed by 227 alumni (163 women and 64 men) who had pursued a STEM PhD at a university in sub-Saharan Africa within the last 20 years. Due to the absence of a comprehensive sample frame of recent PhD students in STEM at SSA universities, probability sampling was not feasible. Participants were recruited through multiple channels, including postings on the RSIF website, outreach to African university professors, collaborations with organizations promoting women in STEM (e.g., Mawazo Institute and Portia), and networks of former PhD students from the 11 RSIF African host universities (AHUs). The survey collected data on a wide range of variables, including: demographics, socioeconomic status, PhD funding sources, motivation for pursuing a doctorate, psychosocial wellbeing during PhD training, perceptions of gender stereotypes and discrimination, university resources (e.g., scientific writing courses, gender and diversity offices), PhD performance, PhD completion status, and persistence in STEM fields. Before participating in the survey, respondents were presented with a standard informed consent form outlining the study's voluntary nature, data confidentiality, potential risks and benefits, expected duration, and the types of information requested. Of the initial 262 individuals who completed the survey, the final sample comprised 227 respondents after removing those from universities outside of SSA.
CIMMYT Ethiopia
Selected soil properties were predicted from 375 topsoil samples subjected to spectral analysis (MIRS). A subset of samples were also subjected to wet chemistry analysis, and results were used to calibrate a machine-learning algorithm developed by the International Centre for Research in Agroforestry (ICRAF) in Kenya. Coordinates were truncated to protect farmer's privacy. Unless specified, all properties were predicted. This dataset can be linked with yield data (coming soon) and previous soil analysis data ( https://hdl.handle.net/11529/10549139 ) through the unique farm identifer "fid". A link is provided to match terms used in the "terminag" GitHub ( https://github.com/reagro/terminag/ ) as of June 2025.
CIMMYT Ethiopia
Selected soil properties were predicted from 524 topsoil samples subjected to spectral analysis (MIRS). A subset of samples were also subjected to wet chemistry analysis, and results were used to calibrate a machine-learning algorithm developed by the International Centre for Research in Agroforestry (ICRAF) in Kenya. Coordinates were truncated to protect farmer's privacy. Unless specified, all properties were predicted. This dataset can be linked with yield data (coming soon) and previous soil analysis data ( https://hdl.handle.net/11529/10549138 ) through the unique farm identifer "fid". A link is provided to match terms used in the "terminag" GitHub ( https://github.com/reagro/terminag/ ) as of June 2025
CIMMYT Ethiopia
Selected soil properties were predicted from 232 topsoil samples subjected to spectral analysis (MIRS). A subset of samples were also subjected to wet chemistry analysis, and results were used to calibrate a machine-learning algorithm developed by the International Centre for Research in Agroforestry (ICRAF) in Kenya. Coordinates were truncated to protect farmer's privacy. Unless specified, all properties were predicted. This dataset can be linked with yield data (coming soon) and previous soil analysis data ( https://hdl.handle.net/11529/10549136 ) through the unique farm identifer "fid". A link is provided to match terms used in the "terminag" GitHub ( https://github.com/reagro/terminag/ ) as of June 2025.
CIMMYT Ethiopia
El conjunto de datos describe un monitoreo integral de la fenología en la región Mixteca de Oaxaca, México, una zona predominantemente agrícola de temporal en la que los pequeños agricultores cultivan milpa (maíz, frijol y calabaza) de manera tradicional. Para evaluar el impacto del cambio climático en los ciclos agrícolas, se emplearon datos satelitales de MODIS y datos meteorológicos en cuadrícula provenientes de plataformas como Daymet y AgERA5, con una resolución de 1 km, que abarcan desde 1980 hasta 2024. Estos datos permiten determinar con precisión el inicio, pico y final de la temporada de crecimiento de los cultivos en la región, así como analizar tendencias climáticas y fenoclimáticas a lo largo de más de 40 años. Los análisis revelan que, en promedio, la temporada de cultivo comienza alrededor del 3 de junio, con el pico vegetal entre el 12 de agosto y el 9 de septiembre, y la finalización en finales de noviembre. Además, se detectó un aumento en la frecuencia de eventos de sequía y una disminución significativa en las precipitaciones a partir de 2013, evidenciando un cambio en las condiciones climáticas que afecta directamente la producción agrícola. Los mapas geoespaciales generados con TIMESAT permiten visualizar estos cambios a diferentes escalas, facilitando la toma de decisiones para la gestión agrícola y la adaptación al cambio climático. El conjunto de datos incluye archivos en diversos formatos (PDF, ZIP, NetCDF, CSV), con información sobre precipitaciones, temperaturas, radiación solar y estadísticas fenológicas, abarcando desde 1980 hasta 2023. Estos datos serán utilizados para desarrollar soluciones personalizadas y específicas para cada subregión, con el fin de mitigar el impacto del cambio climático en los medios de vida de los pequeños agricultores en la región Mixteca. The dataset describes a comprehensive monitoring of phenology in the Mixteca region of Oaxaca, Mexico, an area predominantly engaged in rainfed agriculture where small farmers traditionally cultivate milpa (maize, beans, and squash). To evaluate the impact of climate change on agricultural cycles, satellite-based MODIS data and gridded meteorological data from platforms such as Daymet and AgERA5 were used, with a resolution of 1 km, covering the period from 1980 to 2024. These data allow for precise determination of the start, peak, and end of the crops’ growing season, as well as analysis of climatic and phenoclimatic trends over more than 40 years. The analyses reveal that, on average, the planting season begins around June 3rd, with the vegetation peak between August 12th and September 9th, and the harvest ending in late November. Additionally, an increase in the frequency of drought events and a significant decrease in rainfall since 2013 have been observed, indicating a change in climatic conditions that directly affects agricultural production. The geospatial maps generated with TIMESAT enable visualization of these changes across different scales, facilitating decision-making for agricultural management and climate adaptation. The dataset includes files in various formats (PDF, ZIP, NetCDF, CSV) containing information on precipitation, temperatures, solar radiation, and phenological statistics, covering the period from 1980 to 2023. These data will be used to develop tailored, subregion-specific solutions aimed at mitigating the impacts of climate change on the livelihoods of small farmers in the Mixteca region.
CIMMYT Ethiopia
The study uses a dataset containing information from 351,297 records and 154 variables related to socioeconomic, demographic, labor, and well-being aspects in the United States, extracted from a publicly accessible source via IPUMS (https://cps.ipums.org/cps/). Data were collected between 2013 and 2020. The Current Population Survey (CPS), conducted monthly by the U.S. Census Bureau, employs a combination of in-person and telephone interviews to obtain representative data from the population. IPUMS CPS consolidates and facilitates access to data from the CPS since 1962 for research purposes. These data include household characteristics, employment information, income, health, access to social programs, family structure, educational attainment, racial and Hispanic origin, citizenship, housing, and participation in social assistance programs.
CIMMYT Ethiopia
The collected data originate from a study conducted in the Eastern and Northern provinces of Zambia during the low season (December 2019 to February 2020). The sample includes 528 households selected through random sampling in various agricultural camps, with information on agricultural production, marketing decisions, seasonal food prices, food consumption, nutritional content of crops, and anthropometric measures of children under five years old. The households are located in the districts of Katete in the Eastern Province and Mbala in the Northern Province. Additionally, sociodemographic data and information on access to services were collected, along with variables related to crop diversification and household resources. The data were used to analyze the relationship between nutrient deficiencies from own production (NDOP), food sale and consumption decisions, and child growth outcomes, employing econometric methods to address potential endogeneity biases.