2018- CSA Monitoring: Hoima Climate-Smart Village (Uganda)doi:10.7910/DVN/RJGSDFHarvard Dataverse2019-12-203Bonilla-Findji, Osana; Eitzinger, Anton; Andrieu, Nadine; Jarvis, Andy; Recha, John; Ambaw, Gebermedihin; Kakeeto, Ronald, 2019, "2018- CSA Monitoring: Hoima Climate-Smart Village (Uganda)", https://doi.org/10.7910/DVN/RJGSDF, Harvard Dataverse, V3, UNF:6:coXhMAAzBe0u6RpHTfz1bA== [fileUNF]2018- CSA Monitoring: Hoima Climate-Smart Village (Uganda)doi:10.7910/DVN/RJGSDFBonilla-Findji, OsanaEitzinger, AntonAndrieu, NadineJarvis, AndyRecha, JohnAmbaw, GebermedihinKakeeto, RonaldInternational Center for Tropical Agriculture (CIAT) / CGIAR Research Program on Climate Change, Agriculture and Food Security (Flagship 2)2019-12-20G135Harvard DataverseRecha, JohnBonilla-Findji, OsanaOrtega, Angelly2019-12-202019-12-20Agricultural SciencesEarth and Environmental SciencesMonitoringClimate Smart AgricultureHouseholdsLivelihoodsAdaptationFood SecurityClimate ShocksFarmEast AfricaDecision and Policy Analysis - DAPAThis dataset contains the files produced in the implementation of the “Integrated Monitoring Framework for Climate-Smart Agriculture” in the Hoima Climate Smart Village (Uganda) in October 2018. <br> This monitoring framework developed by CCAFS is meant to be deployed annually across the global network of Climate-Smart Villages to gather field-based evidence by tracking the progress on: <br> <ul> <li> Adoption of CSA practices and technologies, as well as access to climate information services and <li> their related impacts at household level and farm level This framework proposes standard Descriptive Indicators to track changes in: <ul> <li type="circle"> 5 enabling dimensions that might affect adoption patterns,</li> <li type="circle"> a set of 5 CORE indicators at Household level to assess perceived effects of CSA practices on Food Security, Productivity, Income and Climate vulnerability and </li> <li type="circle">4 CORE indicators on Gender aspects (Participation in decision making, Participation in implementation, Access/control over Resources and work time).</li> <li type="circle">At farm level, 7 CORE indicators are suggested to determine farms CSA performance, as well as synergies and trade-offs among the three pillars.</li> </ul> </ul> <br> This integrated framework is associated with a cost-effective data collection App (Geofarmer) that allowed capturing information in almost real time. <br> The survey questionnaire is structured around different thematic modules (Demographic, Livelihoods, Food Security, Climate events, Climate Services, CSA practices, Financial Services) connected to standard CSA metrics and the specific indicators. <br> The framework responds to three main research questions: <ol> <li value="1">Within each CSV community, who adopts which CSA technologies and practices and what are their motivations, enabling/constraining factors?</li> <li> What are the gender-disaggregated perceived effects of CSA options on farmers’ livelihood (agricultural production, income, food security, food diversity and adaptive capacity) and on key gender dimensions (participation in decision-making, participation in CSA implementation and dis-adoption, control and access over resources and labour)?</li> <li>How does CSA perform at farm level, and what synergies and trade-offs exist (whole farm model analysis)? </li> </ol>2017-10-012018-10-012018-10-012018-10-24Survey dataSocio-economic DataGeographic DataEnvironmental DataCapacity BuildingUniverse: At the time of data collection, all survey participants resided within 7 communities in Hoima Climate Smart Villages, Uganda ("Kibaire", "Kiranga", "Kyamongi", "Kasinina", "Mparangasi", "Nyakakonge", or "Katikara"). <br> Implementation was carried out by locally trained enumerators using the Geofarmer Smart Monitoring App for data collection. <br> A total of 453 farmers were interviewed: 115 adult females, 145 adult males (age 35 or over), 108 young females and 76 young males (under age 35). Where possible, two adults and one “young” person were surveyed from each household. <br> 9 individuals had unrecorded birth years.to download, please contact: Bonilla-Findji Osana\ email: o.bonilla@cgiar.org<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a><ul> <li>EA_UGA-HMA Cleaned anonymized responses M0-M5 <li>EA_UGA-HMA_Anonymized responses_Calculator <li>EA_UGA-HMA_Additional information <ol> <li value="1">2018_ EA_UGA-HMA Questionnaire Codified</li> <li>2018_EA_UGA-HMA_Questions Tree Short</li> <li>2018_EA_UGA-HMA_Glossary_Main_CSA_practices</li> <li>2018_EA_UGA-HMA_Inform Consent</li> <li>Comments on Hoima data cleaning anonimized</li> <li>2018_EA_UGA-HMA_PROTOCOL OF CLEANING AND PREPARATION OF DATA FOR ANALYSIS AND PUBLICATION IN REPOSITORY</li> <li>Minimum Risk Review Application_ME for IDRC_20180628 </li> <li>IRB Approval Letter_CSA_perf - extension 2019 4 </li> <li>Final_Enumerators Recordings anonymized_ Hoima_2018</li> </ol> </ul>Kristjanson, P., Neufeldt, H., Gassner, A. et al. 2012. Are food insecure smallholder households making changes in their farming practices? Evidence from East Africa. Food Sec. 4, 381–397 doi:10.1007/s12571-012-0194-zKristjanson, P., Neufeldt, H., Gassner, A. et al. 2012. Are food insecure smallholder households making changes in their farming practices? Evidence from East Africa. Food Sec. 4, 381–397 doi:10.1007/s12571-012-0194-zKristjanson P, Garlick C, Cramer L, Förch W, Thornton PK Ngungu A. 2014. Global Summary of Baseline Household Survey Results. Version 2. CCAFS Working Paper no. 56. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). Copenhagen, Denmark. Available online at: www.ccafs.cgiar.org10568/16426Kristjanson P, Garlick C, Cramer L, Förch W, Thornton PK Ngungu A. 2014. Global Summary of Baseline Household Survey Results. Version 2. CCAFS Working Paper no. 56. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). Copenhagen, Denmark. Available online at: www.ccafs.cgiar.orgRecha J, Radeny M, Kimeli P, Hafashimana D, Masanyu J, Ssekiwoko F, Odongo W. 2016. Progress in achieving household food security in climate-smart villages in the Albertine Rift, western Uganda. CCAFS Info Note. Copenhagen, Denmark: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS).10568/79933Recha J, Radeny M, Kimeli P, Hafashimana D, Masanyu J, Ssekiwoko F, Odongo W. 2016. Progress in achieving household food security in climate-smart villages in the Albertine Rift, western Uganda. CCAFS Info Note. Copenhagen, Denmark: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS).Recha J, Kimeli P, Atakos V, Radeny M, Mungai C. 2017. Stories of Success: Climate-Smart Villages in East Africa. Wageningen, Netherlands: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS).10568/81030Recha J, Kimeli P, Atakos V, Radeny M, Mungai C. 2017. Stories of Success: Climate-Smart Villages in East Africa. Wageningen, Netherlands: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS).Mubiru, D.N., Radeny, M., Kyazze, F.B., Zziwa, A., Lwasa, J., Kinyangi, J. and Mungai, C. 2018. Climate trends, risks and coping strategies in smallholder farming systems in Uganda. Climate Risk Management 22: 4-21 https://doi.org/10.1016/j.crm.2018.08.00410568/98910Mubiru, D.N., Radeny, M., Kyazze, F.B., Zziwa, A., Lwasa, J., Kinyangi, J. and Mungai, C. 2018. Climate trends, risks and coping strategies in smallholder farming systems in Uganda. Climate Risk Management 22: 4-21 https://doi.org/10.1016/j.crm.2018.08.004Radeny M, Desalegn A, Mubiru D, Kyazze F, Mahoo H, Recha J, Kimeli P, Solomon D. 2019. Indigenous knowledge for seasonal weather and climate forecasting across East Africa. Climatic Change 156(4):509-526. https://doi.org/10.1007/s10584-019-02476-910568/103231Radeny M, Desalegn A, Mubiru D, Kyazze F, Mahoo H, Recha J, Kimeli P, Solomon D. 2019. Indigenous knowledge for seasonal weather and climate forecasting across East Africa. Climatic Change 156(4):509-526. https://doi.org/10.1007/s10584-019-02476-9https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/WQALYLhttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IUJQZVhttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/Q7PKLN1. UGA_HMA_FC_96_data_2018-1.tab2930text/tab-separated-valuesUNF:6:4SsrGEUaJXTfsVfYj5Hbpg==2. UGA_HMA_CC_105_Cassava_2018-1.tab2039text/tab-separated-valuesUNF:6:lpVbeG4Clzbr2qHtdWPMXQ==2. UGA_HMA_CC_106_Beans_2018-1.tab2350text/tab-separated-valuesUNF:6:MoAUPNyDCDVgM7wmlmwMWA==2. UGA_HMA_CC_107_Maize_2018-1.tab1746text/tab-separated-valuesUNF:6:DH1vzN9LduPFvQbznHeuXQ==2. 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