CIMMYT Research Data & Software Repository Network
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CIMMYT Eastern Africa Maize Regional On-Station (Stage 4) and On-Farm (Stage 5) Trials: Results of the 2021 to 2022 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 rigorous trialing and a stage-gate advancement process and culminating in the 2022 Eastern Africa Regional On-Farm Trials, CIMMYT has advanced a total of 6 new elite maize hybrids, each of which met the stringent performance criteria for CIMMYT’s eastern Africa early (EAPP1B), intermediate (EAPP1A) or late (EAPP2) maize breeding pipelines. 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
30th High Rainfall Wheat Yield Trial
No full textCIMMYT annually distributes improved germplasm developed by its researchers and partners in international nurseries trials and experiments. The High Rainfall Wheat Yield Trial (HRWYT) contains very top-yielding advance lines of spring bread wheat (Triticum aestivum) germplasm adapted to high rainfall, Wheat Mega-environment 2 (ME2HR)
21st High Temperature Wheat Yield Trial
No full textCIMMYT annually distributes improved germplasm developed by its researchers and partners in international nurseries trials and experiments. The High Temperature Wheat Yield Trial (HTWYT) is a replicated yield trial that contains spring bread wheat (Triticum aestivum) germplasm adapted to Mega-environment 1 (ME1) which represents high temperature areas
13th Stem Rust Resistance Screening Nursery
No full textThe Stem Rust Resistance Screening Nursery (STEMRRSN) is a single replicate nursery that contains diverse spring bread wheat (Triticum aestivum) germplasm adapted to all mega-environments with total 50-100 entries and white/red grain color
Agricultural terms from e-Agrology and their mapping with terms from OLS, AGROVOC and RHoMIS
No full texte-Agrology is a system that leverages data generated in the agricultural production cycle to optimize resources and improve the decision-making process in the field. Furthermore, linking e-agrology terms with tools such as OLS, AGROVOC and RHoMIS would help increase the reach of this tool.
e-Agrology es un sistema que aprovecha los datos generados en el ciclo productivo agrícola para optimizar los recursos y mejorar la toma de decisiones en el campo. Además, relacionar los términos de e-agrology con herramientas como OLS, AGROVOC y RHoMIS ayudaría a aumentar el alcance de esta herramienta
Replication Data for: Multimodal Deep Learning Methods Enhance Genomic Prediction of Wheat Breeding
No full textIn plant breeding research, several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes. To increase the GP accuracy of unobserved phenotypes while simultaneously accounting for the complexity of genotype × environment interaction (GE), deep learning (DL) neural networks have been developed.These analyses can potentially include phenomics data obtained through imaging.
The two datasets included in this study contain phenomic, phenotypic, and genotypic data for a set of wheat materials. They have been used to compare a novel DL method with conventional GP models.The results of these analyses are reported in the accompanying journal article
Harvestplus household survey, Zambia 2011, section on storage and climate
No full textIn this data base, representative georeferenced farmer survey data from Zambia from 2011 are combined with climate data to estimate storage losses, analyze their relationship with climate, and estimate the effect of climate change on storage losses. The storage loss data include importance of different pests (maize weevils and larger grain borer), and farmers’ estimates of storage loss due to both pests, in grain and cobs. The climate data include temperature (from WorldClim) and relative humidity (from CHIRTS) over the storage season in 2011
Data from survey about intention to adopt an agricultural mobile app and a choice experiment to make it more attractive, form Guanajuato, Mexico
No full textIn 2019, we conducted a survey through face-to-face interviews with farmers in the El Bajío region of Guanajuato using the GeoODK mobile phone app, an open-source tool. A database (2014–2019) of presumably active farmers in the innovation hub containing their corresponding municipalities was used to select the respondents in two stages. First, respondents that were connected to the hub were randomly selected from the mentioned database. In some cases, we learned that the farmer passed away or could not be reached after several attempts. Then, another farmer who connected to the innovation hub in the same municipality was surveyed. Two municipalities were removed from the sampling frame due to security issues resulting from increased drug-cartel activity in the region. The second stage of the sampling comprised farmers not connected to the innovation hub, in the same municipalities, approached at meeting points (while they were waiting in a queue) or before events in the region (e.g., association, presentation of agricultural products, etc.). Around one out of two non-connected farmers who were approached agreed to take the survey. Therefore, a similar number of non-connected farmers were approached and interviewed in the same municipalities. A total of 394 responses were obtained (205 from MasAgro-connected farmers and 189 from non-connected farmers), with no missing values. We obtained prior informed verbal consent from all respondents, and no personal data were gathered. Farmers were surveyed using standardized questions based on literature with sections covering general information and demographic characteristics, their history of use of mobile phones to access agronomic data and recommendations, and questions of the model used. Each construct was based on three to five items, as recommended. A total of 30 measurement items adapted from prior studies were carefully rephrased in the context of an agriculture-related mobile-phone app, with response selections on a seven-point Likert scale ranging from “Totally disagree” (1) to "Totally agree" (7).
To investigate incentives for the farmers to use an agricultural advisory app in which they share their data, a Discrete Choice Experiment (DCE) was designed, presenting respondents with a choice between two or more alternatives described by pre-established attributes. that simulate different configurations which the AgroTutor app could take when launched. Six attributes were identified (data input requirements, data-usage cost, access to trainings, access to shared data, replacing extension service visits). An efficient design with 24 alternatives was chosen and arranged into 12 choice sets with a comparison of 2 alternatives. These were then assigned into two blocks of six choice, respondents randomly allocated to the two blocks
Fertility Maps of Tlalquiltenango, Morelos. 2020
No full textSoil sampling with 1x1 km grid in the agricultural area of the Tlalquiltenango Municipality at at 0 to 30 cm depth
Large-scale data of crop production practices applied by farmers on their largest rice plot during 2018 in eight Indian states
No full textLandscape Diagnostic Survey (LDS) data contains current rice production practices of rice applied by 8,355 farmers in eight states of India. The objective of collecting this data is to bridge the existing data-gap and to generate data-based evidence that can help in evidence-based planning. The LDS is designed in a way that data is collected from randomly selected farmers spread uniformly within a KVK (government extension system) domain/district. Survey questionnaire captures all production practices applied by farmers from land preparation to harvesting, including detailed sections on rice establishment, fertilizer use, weed control and irrigation application. Data is captured through electronically enabled Open Data Kit (ODK) tool on mobile phone or tablet