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

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

Using a Video-based Product Ranking Tool (VPRT) as a basis for maize market segmentation in Kenya and Uganda in 2022

The maize data was collected in 2022 from small-scale farmers in the districts of Mubende and Mityana in Uganda, and the counties of Kakamega and Bungoma in Kenya. The sample size consists of 2,422 small-scale farmers. Farmer profiling questions cover various topics, including: Socio-demographic Information: Variables such as gender, age, family size, and education. Farm Information: Details about farm size, crops grown, animals kept, input usage, and key income sources. Maize Farming Variables: Aspects such as seed purchase, cropping system, seed volume, plot size, harvest volume, and usage. The data also includes the process of identifying and selecting preferred maize seed varieties, presented in video format. For the video data collection, product descriptions were developed into videos featuring presentations by both a male and a female seed multiplicator. Each farmer was randomly shown three of the eight videos available, utilizing an incomplete block design. In total, 7,266 video presentations were made to the 2,422 farmers

Data on performance of machine-led direct seeded rice (DSR) techniques compared to conventional rice transplanting in eastern India

Direct Seeded Rice (DSR) using seed-cum-fertilizer drill is an emerging rice establishment technique in India. How the two versions of DSR (zero-till & dust mulch) are comparable with the most common conventionally transplanted rice is less known. To fill the knowledge gap, CSISA in collaboration with Krishi Vigyan Kendra (KVK) conducted on-farm agronomic trial in eastern India covering different agro-climatic zones. The trial was conducted with 10 KVKs continuously for five years starting from 2017. Across the years and geographies, it was done on 1,315 farmers' fields. The dataset formed the foundational evidence to inform initial policy handles and invites researchers to plan further research

Replication Data for: Making it to the PhD: Gender and student performance in sub-Saharan Africa

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.</p

Mid-density genotypic data (DArTAGTM DNA fingerprints) of BNI short arm (T3BL.3NsbS) translocation lines and their recurrent parents

A number of BNI short arm (T3BL.3NsbS) translocation lines and their recurrent parents were fingerprinted with the DArTAGTM bread wheat SNP panel which is included in the CGIAR mid-density genotyping services (https://excellenceinbreeding.org/toolbox/services/mid-density-genotyping-service). This data server as a pedigree verification data set for the translocation lines

Replication Data for: Understanding the interactions of genotype with environment and management (G×E×M) to maize productivity in conservation agriculture systems of Malawi

The 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

Evaluating sustainable cropping systems: A comparative study of agricultural practices in Central Malawi

This database contains grain, biomasss and nutritional yield data from on-farm trials in Kasungu, Mchinji, and Lilongwe districts of central Malawi, conducted over three cropping seasons (2014–15 to 2016-17). The trials tested sustainable and resilient cropping systems, including Conservation Agriculture (CA) with minimum tillage, glyphosate herbicide, and maize-legume rotations, compared to conventional ridge-and-furrow sole maize (True farmer practice). The experiments were conducted on 24 farms, with each farm serving as one replicate. Data collected included grain yield, biomass, protein, and energy yields, providing insights into system performance and sustainability

National-scale wheat area dataset of Ethiopia for the 2020/21 rainfed cropping seasons

The national-scale wheat area dataset of Ethiopia for the 2020/21 rainfed cropping seasons shows the spatial distribution and density of rainfed wheat at 10 m spatial resolution for the 2020 Belg and 2020/21 Meher seasons at the national scale. The binary dataset depicts whether a pixel is classified as wheat or non-wheat (labeled as "1" and "0", respectively) during the period ranging from April to December 2020. For datatset generation, state-of-the-art, efficient satellite data pre-processing, in-depth in situ data cleaning, and Random Forest classification methods have been applied to a multispectral, gap-filled Sentinel-2 satellite time series from April to December 2020 and the multi-source Ethiopian Crop Type 2020 (EthCT2020) ground reference dataset (Blasch et al., 2024). This dataset with a classification overall accuracy of 80% was developed with the goal to complement the Ethiopian Wheat Rust Early Warning and Advisory System’s baseline data for the disease dispersal and environmentally suitability forecast models as well as building the basis for new information layers regarding the host susceptibility. Overall, the dataset can serve as a baseline input parameter for crop models, climate models, crop disease and pest surveillance and forecasting, and agricultural monitoring in the smallholder cropping systems of Ethiopia.</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

This 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

Replication Data for: Consumer acceptance of foods derived from blended wheat flour in Nairobi, Kenya

This 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