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Research data keyboard_double_arrow_right Dataset 2022Embargo end date: 18 Jan 2022 FrancePublisher:Harvard Dataverse Authors:Bonilla-Findji, Osana;
Bonilla-Findji, Osana
Bonilla-Findji, Osana in OpenAIREEitzinger, Anton;
Eitzinger, Anton
Eitzinger, Anton in OpenAIREAndrieu, Nadine;
Andrieu, Nadine
Andrieu, Nadine in OpenAIRELäderach, Peter;
+3 AuthorsLäderach, Peter
Läderach, Peter in OpenAIREBonilla-Findji, Osana;
Bonilla-Findji, Osana
Bonilla-Findji, Osana in OpenAIREEitzinger, Anton;
Eitzinger, Anton
Eitzinger, Anton in OpenAIREAndrieu, Nadine;
Andrieu, Nadine
Andrieu, Nadine in OpenAIRELäderach, Peter;
Läderach, Peter
Läderach, Peter in OpenAIRERecha, John;
Recha, John
Recha, John in OpenAIREAmbaw, Gebermedihin;
Ambaw, Gebermedihin
Ambaw, Gebermedihin in OpenAIREKakeeto, Ronald;
Kakeeto, Ronald
Kakeeto, Ronald in OpenAIREdoi: 10.7910/dvn/ellgkb
handle: 10568/118437
<p align="justify"> This 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 2021. </br> <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> <li> their related impacts at household level and farm level </li> </ul> The CSA framework allows to address three key research questions: </br> <ol> <li value="1"> Who within each CSV community adopts which CSA technologies and practices and which are their motivations, enabling factors? To which extent farmers access and use climate information services? </li value="1"> </br> <li value="2"> Which is the gender-disaggregated perceived effects of CSA options on farmers’ livelihood, agricultural, food security 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 value="2"> </br> <li value="3"> Which are the CSA performance, synergies and trade-offs found at farm level? </li value="3"> <br> The CSA framework proposes a small set of standard Core Indicators linked to the research questions, and Extended indicators covering aspects related to the enabling environment. At household level (17 Core indicators): </br> <br> <ul> <li type="circle"> 7 Core Uptake indicators (they track CSA Implementation and adoption drivers; CSA dis-adoption and drivers; Access to climate information services and agro-advisories, Capacity to use them and constraining factors). </li type="circle"> <li type="circle"> 10 Core Outcome indicators (they track farmers perceptions on the effects of CSA practices on their Livelihoods, Food Security and Adaptive Capacity and on Gender dimensions). </li type="circle"> </br> </ul> Those include namely: CSA effect on yield/production, on Income, on Improved Food Access and Food Diversity, on Vulnerability to weather related shocks and on Changes in agricultural activities induced by access to climate information. Four are Gender related Outcome indicators (Decision-making on CSA implementation or dis-adoption, Participation in CSA implementation, CSA effect on labour, Decision making and control on CSA generated income). </ul> </br> <br> <ul> <li type="circle"> An additional set of complementary Extended indicators allows to determine and track changes in enabling conditions and farmers characteristics such as: Livelihood security, Financial enablers, Food security, Frequency of climate events, Coping strategies, Risk Mitigation Actions, Access to financial services and Training, CSA Knowledge and Learning. </li type="circle"> </ul> </br> <br> At farm level, 7 CORE indicators </br> <br> <ul> <li type="circle"> 7 Core indicators are used to determine the CSA performance of the farms as well as synergies and trade-offs among the three pillars (productivity, adaptation and mitigation, via farm model analysis). </li type="circle"> </ul> </br> </ol> This integrated framework (Bonilla-Findji et al 2021).is associated with a cost-effective data collection App (Geofarmer) that allowed capturing information in almost real time </br> <br> The survey questionnaire is structured around different thematic modules (M1A Demographic, M1B Farming system, M1C Financial services, M2 Climate events, M3, Climate Information Services, M4 Food Security, M5 CSA practices; Farm Calculator, Crop calculator and Animal Calculator) whose questions allow assessing standard CSA metrics and the specific. /<br>
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.7910/dvn/ellgkb&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.7910/dvn/ellgkb&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Embargo end date: 18 Jan 2022 FrancePublisher:Harvard Dataverse Authors:Bonilla-Findji, Osana;
Bonilla-Findji, Osana
Bonilla-Findji, Osana in OpenAIREEitzinger, Anton;
Eitzinger, Anton
Eitzinger, Anton in OpenAIREAndrieu, Nadine;
Andrieu, Nadine
Andrieu, Nadine in OpenAIRELäderach, Peter;
+3 AuthorsLäderach, Peter
Läderach, Peter in OpenAIREBonilla-Findji, Osana;
Bonilla-Findji, Osana
Bonilla-Findji, Osana in OpenAIREEitzinger, Anton;
Eitzinger, Anton
Eitzinger, Anton in OpenAIREAndrieu, Nadine;
Andrieu, Nadine
Andrieu, Nadine in OpenAIRELäderach, Peter;
Läderach, Peter
Läderach, Peter in OpenAIRERecha, John;
Recha, John
Recha, John in OpenAIREAmbaw, Gebermedihin;
Ambaw, Gebermedihin
Ambaw, Gebermedihin in OpenAIREKakeeto, Ronald;
Kakeeto, Ronald
Kakeeto, Ronald in OpenAIREdoi: 10.7910/dvn/ellgkb
handle: 10568/118437
<p align="justify"> This 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 2021. </br> <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> <li> their related impacts at household level and farm level </li> </ul> The CSA framework allows to address three key research questions: </br> <ol> <li value="1"> Who within each CSV community adopts which CSA technologies and practices and which are their motivations, enabling factors? To which extent farmers access and use climate information services? </li value="1"> </br> <li value="2"> Which is the gender-disaggregated perceived effects of CSA options on farmers’ livelihood, agricultural, food security 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 value="2"> </br> <li value="3"> Which are the CSA performance, synergies and trade-offs found at farm level? </li value="3"> <br> The CSA framework proposes a small set of standard Core Indicators linked to the research questions, and Extended indicators covering aspects related to the enabling environment. At household level (17 Core indicators): </br> <br> <ul> <li type="circle"> 7 Core Uptake indicators (they track CSA Implementation and adoption drivers; CSA dis-adoption and drivers; Access to climate information services and agro-advisories, Capacity to use them and constraining factors). </li type="circle"> <li type="circle"> 10 Core Outcome indicators (they track farmers perceptions on the effects of CSA practices on their Livelihoods, Food Security and Adaptive Capacity and on Gender dimensions). </li type="circle"> </br> </ul> Those include namely: CSA effect on yield/production, on Income, on Improved Food Access and Food Diversity, on Vulnerability to weather related shocks and on Changes in agricultural activities induced by access to climate information. Four are Gender related Outcome indicators (Decision-making on CSA implementation or dis-adoption, Participation in CSA implementation, CSA effect on labour, Decision making and control on CSA generated income). </ul> </br> <br> <ul> <li type="circle"> An additional set of complementary Extended indicators allows to determine and track changes in enabling conditions and farmers characteristics such as: Livelihood security, Financial enablers, Food security, Frequency of climate events, Coping strategies, Risk Mitigation Actions, Access to financial services and Training, CSA Knowledge and Learning. </li type="circle"> </ul> </br> <br> At farm level, 7 CORE indicators </br> <br> <ul> <li type="circle"> 7 Core indicators are used to determine the CSA performance of the farms as well as synergies and trade-offs among the three pillars (productivity, adaptation and mitigation, via farm model analysis). </li type="circle"> </ul> </br> </ol> This integrated framework (Bonilla-Findji et al 2021).is associated with a cost-effective data collection App (Geofarmer) that allowed capturing information in almost real time </br> <br> The survey questionnaire is structured around different thematic modules (M1A Demographic, M1B Farming system, M1C Financial services, M2 Climate events, M3, Climate Information Services, M4 Food Security, M5 CSA practices; Farm Calculator, Crop calculator and Animal Calculator) whose questions allow assessing standard CSA metrics and the specific. /<br>
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.7910/dvn/ellgkb&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.7910/dvn/ellgkb&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2019Embargo end date: 20 Dec 2019 FrancePublisher:Harvard Dataverse Authors:Bonilla-Findji, Osana;
Bonilla-Findji, Osana
Bonilla-Findji, Osana in OpenAIREEitzinger, Anton;
Eitzinger, Anton
Eitzinger, Anton in OpenAIREAndrieu, Nadine;
Andrieu, Nadine
Andrieu, Nadine in OpenAIREJarvis, Andy;
+3 AuthorsJarvis, Andy
Jarvis, Andy in OpenAIREBonilla-Findji, Osana;
Bonilla-Findji, Osana
Bonilla-Findji, Osana in OpenAIREEitzinger, Anton;
Eitzinger, Anton
Eitzinger, Anton in OpenAIREAndrieu, Nadine;
Andrieu, Nadine
Andrieu, Nadine in OpenAIREJarvis, Andy;
Jarvis, Andy
Jarvis, Andy in OpenAIRERecha, John;
Recha, John
Recha, John in OpenAIREAmbaw, Gebermedihin;
Ambaw, Gebermedihin
Ambaw, Gebermedihin in OpenAIREKakeeto, Ronald;
Kakeeto, Ronald
Kakeeto, Ronald in OpenAIREdoi: 10.7910/dvn/rjgsdf
handle: 10568/106309
This 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> Universe: 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"). Implementation was carried out by locally trained enumerators using the Geofarmer Smart Monitoring App for data collection. 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. 9 individuals had unrecorded birth years.
CGIAR CGSpace (Consu... arrow_drop_down CGIAR CGSpace (Consultative Group on International Agricultural Research)Dataset . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.7910/dvn/rjgsdf&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert CGIAR CGSpace (Consu... arrow_drop_down CGIAR CGSpace (Consultative Group on International Agricultural Research)Dataset . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.7910/dvn/rjgsdf&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2019Embargo end date: 20 Dec 2019 FrancePublisher:Harvard Dataverse Authors:Bonilla-Findji, Osana;
Bonilla-Findji, Osana
Bonilla-Findji, Osana in OpenAIREEitzinger, Anton;
Eitzinger, Anton
Eitzinger, Anton in OpenAIREAndrieu, Nadine;
Andrieu, Nadine
Andrieu, Nadine in OpenAIREJarvis, Andy;
+3 AuthorsJarvis, Andy
Jarvis, Andy in OpenAIREBonilla-Findji, Osana;
Bonilla-Findji, Osana
Bonilla-Findji, Osana in OpenAIREEitzinger, Anton;
Eitzinger, Anton
Eitzinger, Anton in OpenAIREAndrieu, Nadine;
Andrieu, Nadine
Andrieu, Nadine in OpenAIREJarvis, Andy;
Jarvis, Andy
Jarvis, Andy in OpenAIRERecha, John;
Recha, John
Recha, John in OpenAIREAmbaw, Gebermedihin;
Ambaw, Gebermedihin
Ambaw, Gebermedihin in OpenAIREKakeeto, Ronald;
Kakeeto, Ronald
Kakeeto, Ronald in OpenAIREdoi: 10.7910/dvn/rjgsdf
handle: 10568/106309
This 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> Universe: 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"). Implementation was carried out by locally trained enumerators using the Geofarmer Smart Monitoring App for data collection. 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. 9 individuals had unrecorded birth years.
CGIAR CGSpace (Consu... arrow_drop_down CGIAR CGSpace (Consultative Group on International Agricultural Research)Dataset . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.7910/dvn/rjgsdf&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert CGIAR CGSpace (Consu... arrow_drop_down CGIAR CGSpace (Consultative Group on International Agricultural Research)Dataset . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.7910/dvn/rjgsdf&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu