This database comprises grain and nutritional yield data from on-farm trials implemented in the Dedza and Mzimba districts of Malawi over three growing seasons (2017/18 to 2019/20). The study was designed to evaluate yields resulting from treatments involving different cereals and legumes within intercropping systems. The complete dataset supported the findings published in: 'Small grains for small farms? Food and nutrition security from maize, sorghum, and millet cropping systems in Southern Africa.
This dataset was collected using two questionnaires developed by CIMMYT: the "Household Survey Questionnaire" and the "Lead Farmer Survey Questionnaire". The dataset includes comprehensive household-level, farm-level, plot-level, and village-level information related to carbon farming in India. The household-level data (from 841 households) covers demographics, landholding, crop production, input use, irrigation, sustainable agricultural practices, awareness and participation in carbon credit programs, payment arrangements, training, and social networks. It enables analysis of factors influencing farmers’ participation in carbon farming, adoption of sustainable practices, and the economic, environmental, and social impacts of such participation. The village-level data, provided by lead farmers—either carbon farmers or other knowledgeable farmers—includes details on cropping patterns, adoption of resource-conserving technologies, residue management practices, irrigation water availability and quality, distances to agricultural and community facilities, and specific carbon farming activities within the village. This information supports analysis of the dissemination of sustainable practices, local infrastructure and resource conditions, and the role of lead farmers in promoting carbon farming and encouraging other farmers to participate.
Research on the effect of split nitrogen application on wheat grain yield and quality under conservation agriculture. The study was conducted during the 2016-2017 and 2017-2018 fall-winter cycles in the Bajío region of Guanajuato. The effects of study locations, wheat varieties, nitrogen fractionation, and their interactions were evaluated.
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.
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.
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.
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.
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.
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
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.
