CIMMYT Research Data & Software Repository Network
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1114 research outputs found
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TAFSSA On-Farm Datasets Haryana-India
No full textTAFSSA On-Farm participatory research trials on diversified cropping systems in three villages of Karnal districts to provide nutritional and resilient systems. The on-farm data completed three crop seasons and one cropping cycle. The data sets cover crop yields, water, input use, economics, nutritional productivity and efficiencies of diverse cropping systems
CIMMYT Eastern Africa Maize Regional On-Station (Stage 4) and On-Farm (Stage 5) Trials: Results of the 2022 to 2023 Seasons and Product Announcement
No full textNew and improved maize hybrids, developed by the CIMMYT Global Maize Program, are available for uptake by public and private sector partners, especially those interested in marketing or disseminating hybrid maize seed across Eastern Africa and similar agro-ecological zones. Following a rigorous trialing and a stage-gate advancement process culminating in the 2023 Stage 5 trials, CIMMYT advanced a total of eight new elite maize hybrids in Eastern Africa in 2024. Phenotypic data collected in Stage 4 and Stage 5 trials for the selected hybrids as well as information about the trial sites are provided in this dataset. These trials were conducted through a network of partners, including NARES and private seed companies, in Eastern Africa under various management and environmental conditions
Replication Data for: Effect of processing on the retention of grain zinc, iron and phytic acid in wheat
No full textData generated to understand the retention and bioavailability of iron and zinc during chapati and leavened bread productio
Soil properties predicted from mid-infrared spectral (MIRS) analysis of soil samples collected in 2022 before establishing on-farm trials on yield response to lime rates in Tanzania
No full textSelected soil properties were predicted from 71 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. When calculated from other predicted properties, the variable name contained the string: "Estimated".
A link is provided to match terms used in the "terminag" GitHub (https://github.com/reagro/terminag/).</p
Key informant interview data to identify soil characteristics of the study villages, Punjab
No full textThis survey was done in 122 villages of Punjab, as part of the SPIA-funded project on the impact of Conservation Agriculture (CA), particularly the impact of Happy Seeder technology, on yield and residue burning. During the exploratory surveys, we found that the adoption of CA depends on the soil structure. To identify the soil characteristics, our team of enumerators interviewed 2-3 knowledgeable people per sample village. The participants were asked to draw the map of village and mark the regions with clayey / loamy / sandy soils and with saline / sodic soils within the village boundaries using an A3-size graph sheet. From this graph sheet the percentage of cultivated area under different soil types and salinity / sodicity were calculated
Landscape diagnostic survey data of wheat production practices and yield of 2018, 2019, 2020, 2021 from Nepal's Terai
No full textThe objective of the Landscape Diagnostic Survey (LDS) for wheat is to bridge the existing data-gap around current production practices of wheat, and also to help in evidence-based planning. The LDS is designed in a way that data is collected from randomly selected farmers spread uniformly within the Feed the Future (FtF) districs. The data has been collected from farmers largest wheat plot for 2018, 2019, 2020 and 2021. The surveys for the year 2020, and 2021 were done via phone surveys due to COVID and can be used as panel data with the hhid. Survey questionnaire captures all production practices applied by farmers from land preparation to harvesting, including detailed sections on fertilizer use, weed control and irrigation application. Data is captured through electronically enabled Open Data Kit (ODK) tool on mobile phone or tablet. The sample size for this survey in 2018 is 1577 households, in 2019 is 1207 households, in 2020 is 1028 households, and 953 households for 2021. The inputs use were asked for largest rice grown plots as farms may have multiple plots and inputs use might be different in different plots
Replication Data for: Exploiting genomic tools for genetic dissection and improving the resistance to Fusarium stalk rot in tropical maize
No full textFusarium stalk rot (FSR) poses a threat to maize cultivation around the world. Phenotypic selection to improve FSR resistance cannot meet this challenge alone. Genome-wide association studies (GWAS) and genomic prediction (GP) can also help to uncover genetic determinants of FSR resistance that could be used in breeding.
This study provides genotypic and phenotypic data from 562 tropical maize inbred lines that were used to perform GWAS and GP analyses for two populations. The FSR severity is presented in the phenotypic data files. The measurement details and results of the analysis are presented in the accompanying journal article
Household Survey, “Wheat Seed Information Networks and Seed Acquisition Practices in Bihar”
No full textThis dataset is built from a survey among wheat farming households in Bihar state. The survey was conducted by the International Maize and Wheat Improvement Center (CIMMYT) between August and September 2021. In total, the questionnaire was administered to 1,008 wheat-growing households. The survey elicited information on various topics, for example, socio-demographic characteristics, wheat cultivation, varietal use, access to seed sources, and asset ownership. We collected gender-disaggregated data asking men and women in the same household a predefined set of questions, such as influence in intra-household decision-making
Using a Video-based Product Ranking Tool (VPRT) as a basis for market segmentation in Tanzania (Groundnut)
No full textThe groundnut data was collected from small-scale groundnut farmers in the Tanzanian regions of Dodoma, Shinyanga, Mtwara, Lindi, and Songwe in May and November 2023. The sample size consists of 2,200 small-scale farmers. The phase one data responses are from farmers in Dodoma and Shinyanga, collected in May 2023, while the phase two data is from farmers in Mtwara, Lindi, and Songwe, collected in November 2023. The farmer profiling questions covered the following topics: socio-demographic information, with variables such as gender, age, family size, and education; farm information variables, including farm size, crops grown, animals kept, input use, and key income sources; information on groundnut farming variables, such as seed purchase, cropping system, seed volume and plot size, harvest volume and usage; and food security and hunger questions. The data also includes the identification and selection of preferred groundnut seed variety concepts presented in video format. For the video data collection, eight product descriptions were conceptualized into videos, with presentations made by both a male and a female seed multiplier. Each farmer saw only three of the eight videos at a given time (incomplete block design). The videos viewed by each farmer were selected at random, resulting in a total of 6,600 videos presented to 2,200 small-scale farmers overall
Plot-level datasets for groundtruthing and satellite detection of tillage operations, Punjab (India)
No full textThe intensive, irrigated rice-wheat systems of the northwestern Indo-Gangetic Plains (NW IGP) are associated with the widespread burning of excess rice residue that cannot be otherwise disposed-off within the limited turn-around time. The second-generation direct seeders (SGDS) for wheat sowing, such as Happy Seeder, facilitate sowing under heavy stubble conditions, and thereby avoid the need for residue burning. The objective of the study is to test the causal relationship between Happy Seeder diffusion and reduction in residue burning and, ultimately, reduction in air pollution in the NW IGP. We draw on data from remote sensing, a systematic review of literature, existing primary datasets, and new surveys among farm-households, service providers, and village elders. The estimated reduction in air pollution due to the technology diffusion will then be converted to savings in human health costs. As part of this study, we aim to study the village characteristics and do groundtruthing for remote sensing of plots where SGDS has adopted. The plot level datasets were constructed twice (for 2021 and 2022 Rabi seasons) for identifying tillage types and residue burning through remote sensing. These datasets were not from the same set of farmers, and the plot selection was not random