CIMMYT Research Data & Software Repository Network
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1114 research outputs found
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T10SLRE: A novel ensemble learning approach for rapid and non-destructive prediction of bread loaf volume in wheat using NIR spectroscopy
No full textNear infrared spectroscopy and quality data generated to develop prediction models for bread loaf volume
Replication Data for: Consumer acceptance of foods derived from blended wheat flour in Nairobi, Kenya
No full textThis dataset was collected in 2023 as part of a consumer research study conducted in Nairobi, Kenya, aimed at assessing the acceptance of wheat flour blended with underutilized crops—sorghum, millet, and cassava. A total of 1,871 consumers participated in structured sensory evaluations of chapati and bread prepared using wheat flour blended at varying ratios (up to 20%). The study employed both blind and informed tasting approaches to assess product preferences and willingness to pay. After the sensory tests, participants were given a one-kilogram package of blended flour for home use, followed by a phone-based survey capturing their real-life cooking experiences and post-use perceptions.
The dataset provides comprehensive details on participants' demographics and socioeconomic profiles, along with individual preferences for each food product, including sensory evaluation scores and rankings. It also includes data on participants' willingness to pay, based on different information treatments. In addition, the dataset captures responses from a follow-up survey, offering insights into participants' experiences, satisfaction, and acceptance of the blended flour products after using them at home
Soil properties predicted from mid-infrared spectral (MIRS) analysis of soil samples collected in 2023 (second year) before and/or after establishing on-farm trials on yield response to lime rates in Tanzania
No full text 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.</p
Replication Data for: Making it to the PhD: Gender and student performance in sub-Saharan Africa
No full textThis 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.</p
Grain yield, biomass, and nutritional values of various cropping systems tested on-station with/without fertilizer application on two soils in Zimbabwe between 2020 and 2023
No full textThis database contains data on grain, biomass, protein, and calorie yields from on-station trials in Zimbabwe, conducted at the Domboshava Training Centre (DTC) (17.62°S, 31.17°E) and the University of Zimbabwe farm (UZ) (17.73°S, 31.020°E), characterized by different soil types.
The trials tested maize monocropping, maize-legume rotations, and intercropping with various layouts (traditional intercropping and double-row strip cropping), with and without fertilizer application. Two contrasting legumes, cowpea and pigeon pea, were included in the cropping systems. The experiment was established during the 2019/2020 growing season, but data were collected from the 2020/21 to 2022/23 seasons to allow the rotations to develop. It was carried out under Conservation Agriculture and rainfed conditions.
The dataset is divided into three parts:
1) Field measurement, including treatments, grain yields, and crop biomass;
2) Calculations at the cropping system level (total biomass, calories, and proteins);
3) Processed data used to evaluate the stability of each cropping system, as shown in a radar plot presented in the related paper.</p
2016 CIMMYT Product Announcement and Results of the Maize Regional Trials for for Southern Africa for 2016
No full textSummarized results from the Regional Trials for CIMMYT Maize Hybrids in Southern Africa for 2016.
These trials included:
1. EHYB16 – Early/extra-early maturing elite pre-released and released hybrids regional trial;
2. IHYB16 – Intermediate maturing elite pre-released and released hybrids regional trial;
3. LHYB16 – Late maturing elite pre-released and released hybrids regional trial;
4. WEHYB16 – Early maturing elite pre-released and released WEMA project hybrids regional trial;
5. WLHYB16 – Medium/Late maturing elite pre-released and released WEMA project hybrids regional trial;
6. ADVQPM16 – Advance elite pre-released and released quality protein maize (QPM) hybrids regional tria
2014 Update: CIMMYT maize inbred lines and pre-commercial hybrids with potential resistance to maize lethal necrosis (MLN)
No full textSince 2011 MLN has become a disease of serious concern in the east African countries of Kenya, Tanzania, Uganda and possibly Rwanda. CIMMYT has been working in close collaboration with the Kenya Agricultural Research Institute (KARI), private sector partners and virology experts from the USA to combat the disease through host-controlled resistance. A CIMMYT-KARI MLN Screening Facility was established at Naivasha in September 2013, and a large array of maize germplasm is presently being evaluated against the disease under artificial inoculation. Subsequent to the development of effective protocols, CIMMYT and KARI have been conducting MLN screening trials in Kenya since 2012, to identify promising inbred lines and pre-commercial maize hybrids with resistance to MLN. This is the second update of the information on potential MLN-resistant or moderately resistant inbred lines and pre-commercial hybrids (in CIMMYT genetic backgrounds), following the first update that was shared with public and private sector partners in May 2013
Analysis of Household-Level Survey Data: Farm Characteristics and Resource Allocation in Laos PDR (2024)
No full textProcessed 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.</p
Weed diversity, richness, evenness, and their relationship to various cropping systems' biomass, tested on-station with/without fertilizer application on two soils in Zimbabwe between 2022 and 2023
No full textThis database contains weed density, weed biomass, crop biomass, richness, evenness, and diversity data from on-station trials in Zimbabwe conducted at Domboshava Training Centre (DTC; 17.62°S,31.17° E) and the University of Zimbabwe farm (UZ: 17.73°S, 31.020 E), characterised by different soil types.
It tested maize monocropping, maize-legume rotations, and intercropping with various layouts (traditional intercropping and double row strip cropping), with and without fertilizer application. Two contrasting legumes, cowpea and pigeon pea, were tested in the cropping systems.
The experiment was established during the 2019/2020 growing season, but data was collected from the 2021/22 to 2022/23 season to allow the rotations to develop.
The experiment was carried out under Conservation Agriculture and rainfed conditions.
The dataset is divided into three parts: (1) field measurement data, including treatments, weed biomass, and crop biomass; (2) calculations at the cropping system level (density, richness, Pielou, and Shannon diversity), and (3) weed species abundance (all the weeds identified and collected including those that were difficult to identify and termed "Other".)<p/
Replication Data for: Understanding the interactions of genotype with environment and management (G×E×M) to maize productivity in conservation agriculture systems of Malawi
No full textThe database consists of data collected over seven seasons to evaluate maize productivity among smallholder farmers in Malawi, focusing on the performance of various maize genotypes under distinct management practices. The key objectives of the study included assessing the interactions between genotype (G), environment (E), and management (M) to optimize maize production amid climatic variability and soil fertility challenges