2018 - CSA Monitoring: Tuma La Dalia Climate-Smart Village (Nicaragua)

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Bonilla-Findji, Osana; Eitzinger, Anton; Andrieu, Nadine; Jarvis, Andy; Martínez, Deissy; Aguilar, Amilcar, 2019, "2018 - CSA Monitoring: Tuma La Dalia Climate-Smart Village (Nicaragua)", https://doi.org/10.7910/DVN/IAWC28, Harvard Dataverse, V1, UNF:6:J9+geIW89q8voEdVrAMTgw== [fileUNF]
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Description
This dataset contains the files produced in the implementation of the “Integrated Monitoring Framework for Climate-Smart Agriculture” in the Tuma-La-Dalia Climate Smart Village (Nicaragua) in April 2018. 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:
  • Adoption of CSA practices and technologies, as well as access to climate information services and
  • Their related impacts at household level (and farm level, in selected sites). This framework proposes standard Descriptive Indicators to track changes in:
    • 5 enabling dimensions that might affect adoption patterns,
    • 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
    • 4 CORE indicators on Gender aspects (Participation in decision-making, Participation in implementation, Access/control over Resources and work time).
    • At farm level, 7 CORE indicators are suggested to determine farms CSA performance, as well as synergies and trade-offs among the three pillars.
This integrated framework is associated with a cost-effective data collection App (Geofarmer) that allowed capturing information in almost real-time.
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.
The framework responds to three main research questions:
  1. Within each CSV community, who adopts which CSA technologies and practices and what are their motivations, enabling/constraining factors?
  2. 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 labor)?
How does CSA perform at farm level, and what synergies and trade-offs exist (whole farm model analysis)? (2019-12-10)
Subject Earth and Environmental Sciences; Agricultural Sciences
Keyword Monitoring, Climate Smart Agriculture, Households, Livelihoods, Adaptation, Food Security, Climate Shocks, Latin America and the Caribbean, Decision and Policy Analysis - DAPA
Related Publication Kristjanson 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.orghandle: 10568/16426
Notes Universe: At the time of data collection, all survey participants resided within 7 communities in Olopa Village, Guatemala ("El Consuelo ", " Las Veguitas", " Hilipo", " Aguas Amarillas", " Wasaka abajo", “Guapotalito", " San Benito", or “La Primavera”). Implementation was carried out by locally trained enumerators using the Geofarmer Smart Monitoring App for data collection. The initial sample target was : 140 households, including the ones covered in the initial CCAFS Baseline (HBS). A total of 262 individuals were sampled: 105 adult females, 96 adult males (age of 35 or above), 35 young females and 22 young males (under age of 35). Two adults were surveyed from each household. 4 individuals had unrecorded birth years.
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M0_LAM_NIC_TMD_2018_cleaned_anonymized.tab
NIC-TMD Cleaned anonymized responses M0-M5/Tabular Data - 93.0 KB
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22 Variables, 265 Observations UNF:6:8QvEZusb2RFs6t/QDDo7kQ==
M1_LAM_NIC_TMD_2018_cleaned_anonymized.tab
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58 Variables, 266 Observations UNF:6:SWtb2TB0ZMQiBM44aIAn+g==
M2_LAM_NIC_TMD_2018_cleaned_anonymized.tab
NIC-TMD Cleaned anonymized responses M0-M5/Tabular Data - 76.7 KB
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36 Variables, 266 Observations UNF:6:u+vfBPqynnclSlN6gwmncA==
M3_LAM_NIC_TMD_2018_cleaned_anonymized.tab
NIC-TMD Cleaned anonymized responses M0-M5/Tabular Data - 93.9 KB
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36 Variables, 265 Observations UNF:6:QQ2iZz1DnNWnlG3xFJYZ0Q==
M4_LAM_NIC_TMD_2018_cleaned_anonymized.tab
NIC-TMD Cleaned anonymized responses M0-M5/Tabular Data - 71.4 KB
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26 Variables, 265 Observations UNF:6:sM6MGkyx9yuNElg8sQQeYQ==
M5_LAM_NIC_TMD_2018_cleaned_anonymized.tab
NIC-TMD Cleaned anonymized responses M0-M5/Tabular Data - 329.3 KB
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150 Variables, 266 Observations UNF:6:E3FL37BHYfBGNi4rnJ+Okg==
1. 2018_LAM NIC TMD_ Questionnaire Codified.xls
NIC-TMD Additional information/MS Excel Spreadsheet - 95.0 KB
Published Dec 11, 2019
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MD5: edae248b1d68be946d67008d980c8b29
2. 2018 LAM NIC TUMA LA DALIA_CCAFS_Arbol_de_Preguntas_Cuestionario.pdf
NIC-TMD Additional information/Adobe PDF - 1.6 MB
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3. 2018_LAM NIC TMD_ Glosario practicas ASAC.pdf
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4. 2018_LAM NIC TMD_ Informed consent.docx
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