Alliance Bioversity CIAT Datasets

Permanent URI for this collectionhttps://hdl.handle.net/10568/108279

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Now showing 1 - 20 of 311

Planteome/CO_326-coconut-traits: CO_326-coconut-traits
(Dataset, 2024-06) Garavito-Guyot, Andrea; Bocs Sidibe, Stephanie; Hamelin, Chantal; Laporte, Marie-angelique; Baudouin, Luc
Coconut Ontology v1.0 - Coconut trait dictionary started by CIRAD, based on the "Guidelines for collecting coconut germplasm characterisation data during prospecting missions", COGENT (to be published); the "Descriptors for Coconut, (Cocos nucifera L.)", IPGRI (1995); and the "Manual on standardized research techniques in coconut breeding", IPGRI (1996).
Planteome/CO_340-cowpea-traits: CO_340-cowpea-traits ontology
(Dataset, 2024-06) Agbaje, Tunde; Ofodile, Sam; Valette, Leo; Laporte, Marie-Angelique; Hazekamp, Thomas; Silamana, Barry; Compaore, Eveline; Tengey, Theophilus; Lawan Umar, Muhammad; Diallo, Sory; De Souza, Kaue; Manners, Rhys Evan Joseph; Arnaud, Elisabeth; Van Etten, Jacob; Ehlers, Jeff D; Ousmane, Boukar
Planteome/CO_370-apple-traits: Apple Trait Ontology v1.0
(Dataset, 2024-06) Chuprikova, Ekaterina; Guerra, Walter; Laporte, Marie-angelique; Arnaud, Elisabeth; Humann, Jodi
Planteome/CO_359-sunflower-traits: CO_359-sunflower-traits Ontology
(Dataset, 2024-06) Stanton, Evan; Laporte, Marie-angelique
Planteome/CO_338-chickpea-traits: CO_338-chickpea-traits ontology
(Dataset, 2024-06) Rani Das, Roma; Laporte, Marie-angelique; Valette, Leo; Hazekamp, Thomas; Arnaud, Elisabeth; Rathore, Abishe
Planteome/CO_345-brachiaria-traits: CO_345-brachiaria-traits ontology
(Dataset, 2024-06) Hernandez Mahecha, Luis Miguel; Castiblanco Vargas, Eveline Valheria; Worthington, Margaret Leigh; Valette, Leo; Laporte, Marie-angelique
Exploring Research Gaps in Climate Security Pathways for Somalia (Web of Science Dataset)
(Dataset, 2024-01-01) Silveira-Carneiro, Bia; Sax, Niklas Maximilian; Pacillo, Grazia
The dataset displays the Web of Science results that was used to explore the research gaps of exisiting acedemic literature on climate security in Somalia. Results were generated after one general climate security query and a second round the included more context specific keywords for the case of Somalia.
Global climate hazard indices: heat, drought, flood and compound
(Dataset, 2024-06) CGIAR, focus Climate Security
This dataset contains raster files of climate hazard indices at a 0.7° spatial resolution, globally. Climate hazards consist of (1) heat hazard, (2) flood hazard, (3) drought hazard, (4) compound hazard which takes into account the co-occurring effects of heat, flood and drought. The climate hazards are given for a baseline period (spanning 1981 to 2010), and a near-term future period (2020 to 2040).
Merged conflict dataset
(Dataset, 2024-06) CGIAR FOCUS Climate Security
This dataset consists of merged conflict events that were sourced from ACLED (the Armed Conflict Location and Event Data Project) and UCDP-GED (Uppsala Conflict Data Program’s Georeferenced Event Dataset). Conflict events were merged daily at 3km spatial resolution. Period covered: 1981 to 2023. Countries covered: Mozambique, Togo, Haiti, Libya, Papua New Guinea, Guinea, Ghana, Ivory Coast, Benin.
Sampling Strategies for Genotyping Common Bean (P. vulgaris) Genebank Accessions with DArTseq: A Comparison of Single Plants, Multiple Plants, and DNA Pools
(Dataset, 2024-01) Correa Abondano, Miguel Angel; Ospina Colorado, Jessica Alejandra; Carvajal Yepes, Monica; Wenzl, Peter
Genotyping large-scale gene bank collections requires an appropriate sampling strategy to represent the diversity within and between accessions. A panel of 44 common bean (Phaseolus vulgaris L.) landraces from the Alliance Bioversity and CIAT gene bank, was genotyped with DArTseq using three sampling strategies: a single plant per accession (random individual), 25 individual plants per accession jointly analyzed after genotyping (in silico-pool), and by pooling tissue from 25 individual plants per accession (seq-pool). Sampling strategies were compared to assess technical aspects of the samples, marker information content and genetic composition of the panel. The seq-pool strategy resulted in more consistent DNA libraries for quality and call rate, although with fewer polymorphic markers (6,142 SNPs) than the in silico-pool (14,074) or the random individual sets (6,555). Estimates of allele frequencies by seq-pool and in silico-pool genotyping were consistent, but results suggest that the difference between pools depends on population heterogeneity. Principal Coordinate Analysis (PCoA), Hierarchical Clustering, and the estimation of admixture coefficients derived from random individuals, in silico and seq-pools successfully identified the well-known structure of Andean and Mesoamerican genepools of P. vulgaris across all datasets. In conclusion, seq-pool proved to be a viable approach for characterizing common bean germplasm compared to genotyping individual plants separately by balancing genotyping effort and costs. This study provides insights and serves as a valuable guide for gene bank researchers embarking on genotyping initiatives to characterize their collections. It aids curators in effectively managing the collections and facilitates marker-trait association studies, enabling the identification of candidate markers for key traits. Methodology:The data was collected from 44 accesions of Phaseolus vulgaris. Briefly, 25 plants were sown in the greenhouse and DNA was collected using two sampling methods. DNA was extracted from each individual plant and leaf tissue from all 25 plants was pooled together and a single extraction was made. The DNA was sent to DArT P/L for genotyping with DArTseq. Using the generated SNP data, sampling methods were compared by the estimation of allele frequencies, genetic distances, Principal Coordinate Analysis (PCoA), and the estimation of admixture coeficients with snmf. (2023-12)
Cross-border displacement
(Dataset, 2024-06) CGIAR FOCUS Climate Security
This dataset consists of the total number of displaced populations across countries during the period 2012 to 2022. Data were sourced from UNHCR data finder, then aggregated for this time period.
Food Vendors Mapping in Three Sublocations of Vihiga County, Kenya: A Food Environment Study (July–August 2023)
(Dataset, 2025) Maina, Evalyn Waruguru; Aluso, Lillian Olimba; Wanyama, Rosina Nanjala; Termote, Celine; Akingbemisilu, Tosin Harold
This study aims to understand the food environment by examining both formal and informal food vendors in selected villages and markets of Vihiga County. The Primary data was collected through a food vendor mapping and census by use of a semi-structured questionnaire deployed using ODK.This study aims to understand the food environment by examining both formal and informal food vendors in selected villages and markets of Vihiga County. The Primary data was collected through a food vendor mapping and census by use of a semi-structured questionnaire deployed using ODK. Metodology:The survey employed a multi-stage sampling technique, enabling sequential sampling across multiple hierarchical levels. In the first stage, Vihiga County was purposively selected, encompassing all food vendors as the study population. In the second stage, three sub-locations—Ebungangwe, Emanda, and Mwitubwi—situated in Emuhaya, Vihiga, and Luanda Sub-Counties, respectively, were purposively chosen. Vendors in villages within these sub-locations and nearby markets were comprehensively mapped. The study relied on primary data collected directly from food vendors in Vihiga County.The survey employed a multi-stage sampling technique, enabling sequential sampling across multiple hierarchical levels. In the first stage, Vihiga County was purposively selected, encompassing all food vendors as the study population. In the second stage, three sub-locations—Ebungangwe, Emanda, and Mwitubwi—situated in Emuhaya, Vihiga, and Luanda Sub-Counties, respectively, were purposively chosen. Vendors in villages within these sub-locations and nearby markets were comprehensively mapped. The study relied on primary data collected directly from food vendors in Vihiga County.
Global database of factors influencing the adoption of diversified farming systems
(Dataset, 2024-07) Sanchez Bogado, Andrea Cecilia
We present a global dataset documenting the effect of factors on the adoption of diversified farming systems compiled through a systematic review of primary studies. The dataset includes 1,979 effect sizes from 153 primary peer-reviewed studies It contains evidence from the effect of the impact of 63 factors across eight key categories—biophysical context, farmers’ attitudes, political and institutional context (access to knowledge, land tenure, financial risk management), and five forms of capital (financial, human, natural, physical, and social)—on the adoption of ten diversified practices. Data collection and reporting follows the best standards for systematic reviews and is specifically designed for use in meta-analysis.
ERA long-term experiment data and analytics (EiA2030/ERA_ltes R-code github)
(Dataset, 2025-01-10) Steward, Peter Richard; Muller, Lolita; Rosenstock, Todd Stuart; Joshi, Namita; Youngberg, Brayden
This repository, developed under the CGIAR Excellence in Agronomy (EiA) Initiative, focuses on the analysis of long-term agronomic experiments (LTEs) to better understand the relationships between agricultural practices, climatic factors, and experimental outcomes. The dataset comprises 34,815 individual observations from 181 LTEs, derived from 211 publications across 260 sites in 28 countries (as of 2025-10-01 v0.0.1_alpha). This analysis explores a wide array of agronomic management practices, including crop rotation, fertilization, and intercropping, along with their impacts on productivity, economic performance, and sustainability. The accompanying R Markdown vignette facilitates systematic mapping, geospatial visualization, and climate impact analyses using robust statistical methods. Users can integrate NASA POWER and CHIRPS climate data to examine the role of precipitation, temperature, and other climatic variables in influencing agronomic outcomes. The dataset and tools are open-access and reusable for researchers and practitioners aiming to advance agricultural adaptation and mitigation strategies under climate change. This work has been made possible through funding from the CGIAR Climate Action Lever and represents a collaboration of the Alliance of Bioversity International and CIAT.
Holistic Localized Performance Assessment for Agroecology (HOLPA) survey
(Dataset, 2024) Sanchez Bogado, Andrea Cecilia; Jones, Sarah; Lamanna, Christine
The farm-household level HOLPA involves an interview, and fieldwork surveys, ideally conducted jointly with the household head. The interview survey collects data on context, adherence to agroecology principles, and agronomic, environmental, economic, and social performance. The fieldwork survey provides an on-site assessment of respondent and farming system context, while gathering observational data related to four key performance themes: biodiversity, climate mitigation, crop and pasture health, and soil health.
Baseline Survey Data on Crop Insurance Uptake and Agricultural Practices Among Smallholder Farming Households in Kenya.
(Dataset, 2024) Wanjau, Agnes Njambi; Ageyo, Collins Odhiambo; Otieno, Felix Owino; Mwungu, Chris Miyinzi; Ghosh, Aniruddha
This household survey was conducted among smallholder farmers in five Kenyan counties in May 2023. The project aimed to assess current crop insurance practices and willingness to adopt crop insurance solutions, such as satellite-based Soil Moisture Index and Picture-Based Insurance (PBI). Other topics covered included household and farm characteristics, poverty, agricultural and livestock production, and food security. The five counties covered by the project included Bungoma, Busia, Makueni, Embu, and Uasin Gishu. As part of the anonymization process, household identity variables such as phone numbers, GPS coordinates, individual names, and village names were removed and stored securely to ensure participants' confidentiality.
Household and dietary assessment of women within the reproductive age of 18 to 45 years and adolescents in Vihiga County, Kenya (July 2023)
(Dataset, 2024) Maina, Evalyn Waruguru; Aluso, Lillian Olimba; Wanyama, Rosina Nanjala; Termote, Celine; Akingbemisilu, Tosin Harold
The primary objective of this dataset is to evaluate the socio-economic characteristics, behaviour and dietary intake patterns of women within the reproductive age of 18 to 45 years and adolescents in Vihiga County, Kenya. This data serves as a foundation for understanding nutritional behaviors and informing targeted interventions to improve dietary outcomes in the study population. Methodology: The survey used a multi-stage sampling technique to facilitate sequential sampling across two or more hierarchical levels. In the first stage, Vihiga County was purposively selected and all households constituted in the study population. The second stage three sub-locations that is Ebunangwe, Emanda and Mwitubwi sub-locations in Emuhaya, Vihiga and Luanda Sub-Counties respectively were purposively selected. A listing of the households with woman within the reproductive age of 18 to 45 years was obtained from local authorities with the assistance of community health workers. In households with available adolescents, consent was obtained from the mothers to interview the adolescents. Households from thirty-two (32) randomly selected villages within each of three selected sub-locations were interviewed attaining a sample size of 96 women and 19 adolescents. The survey tool encompassed socio-economic and household characteristics, as well as a single 24-hour qualitative dietary intake questionnaire for both women and adolescents. Data collection was carried out using the KOBO Toolbox platform. Prior to deployment, all tools were pre-tested and refined as necessary. Local enumerators were recruited and trained to administer the surveys effectively. The collected data was exported in Excel format for subsequent cleaning and analysis. The primary tools utilized for data cleaning and analysis were Python and R.
Accuracy in agricultural citizen science: farmers’ assessment of harvest output in Ghana
(Dataset, 2024) Ulzen, Jacob; Van Etten Etten, Jacob; Dorado Betancourt, Hugo Andres; Osei Bonsu, Nana Oduro; Bush, George
This study aims to assess methods for farmer-led yield estimation in decentralized on-farm trials, addressing whether farmers can accurately collect data. Key research questions involve evaluating yield estimation techniques and farmer accuracy without extensive training. Methodology: We tested volumetric and weighing methods for maize and cocoa yield estimation with 40 farmers in different locations in Ghana.
Methods used in Quantifying Green House Gas Emissions from the Food Systems
(Dataset, 2024) Ngaiwi, Mary Eyeniyeh; Vanegas Cubillos, Martha Cristina; Sylvester, Janelle Marie; Verchot, Louis Vincent; Castro Nunez, Augusto Carlos
This document encompasses the a collection of studies from literature that have used different methods to estimate greenhouse gas emissions from the different stages of the food system. Methodology: We conducted a comprehensive review of 124 methods used in estimating greenhouse gas emissions from the food system. Through extensive debates and rounds of discussions, we categorized these methods into Inventory, Life cycle analysis, process based-models, input-output models, direct measurements, and remote sensing. The life cycle analysis (LCA) category focuses solely on evaluating the environmental impacts and services throughout the life cycle of a food system, estimating GHG emissions based on energy and material inputs/outputs. Inventories encompass methods such as IPCC tiers 1, 2, and 3, as well as bottom-up emissions estimation approaches. Process-based models consist of various components, including diet models, biophysical models for land use related to crops and livestock, IGES GHG calculation method, and the LandGEM model. Direct measurements involve techniques such as static chambers, Drager-tube techniques, and eddy covariance. The input-output (IO) method estimates GHG emissions by tracing inputs and outputs across the economy, capturing interconnections between sectors and providing insights into product emissions. Remote sensing is the utilization of satellite or aerial imagery and remote sensing technologies to monitor emissions across large geographic areas.
Immediate impacts of COVID-19 pandemic on bean value chain in selected countries in sub-Saharan Africa
(Dataset, 2024) Nchanji, Eileen Bogweh; Lutomia, Cosmas Kweyu
Immediate impacts of COVID-19 pandemic on bean value chain in selected countries in sub-Saharan Africa. Methodology:The study aimed to examine the immediate effects of the COVID-19 pandemic on the bean value chain in nine sub-Saharan African countries. Data were collected from various stakeholders, including farmers, coordinators, aggregators, processors, and consumers using quantitative tools that were prepared by gender and social inclusion expert at the International Center for Tropical Agriculture (CIAT). The tools collected data on immediate effects of coronavirus pandemic on production, distribution, and consumption of beans. The data collection tool focused disruptions in input supply, labour availability, transportation, and market access during the pandemic. The study also explored changes in food security before and during the pandemic to emphasize the potential role of pre-existing challenges in vulnerability to COVID-19. Growers of common beans and consumers of bean grain and other bean products were targeted by the study. Farmers were drawn from rural areas while targeted consumers were from urban and peri-urban areas. Due to strict coronavirus containment measure, the tool was designed in Alchemer and links to the survey questions shared with study participants via multiple digital platforms.

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