• Energy Research

The main purpose of this work was analyzing how an innovation platform can foster and provide a basis for multi-actor collaboration in order to enable climate-smart agriculture (CSA) implementation at the local level. Using a mix of social (interactions between stakeholders, knowledge changes, adoption of practices) and technical indicators (income, fulfillment of caloric requirements of the household, farm resource use, planned biodiversity or greenhouse gas emission changes), we monitored the collaboration between an NGO, local civil authorities, associations, and farmers that aimed to achieve a common goal linked to the participatory and contextualized development of CSA in Colombia. We found that multiple stakeholder engagements led to improved interactions between members of the platform and their local environment, a proactive participation in the platform meetings and a significant increase in farmer knowledge levels on the challenges posed by climate change and the resultant extreme events. The platform also facilitated the adoption of best-bet practices that contribute towards CSA when farmers both diversify their production and decrease the use of mineral fertilizers. Our findings suggest that innovation platforms can facilitate the collective understanding and use of CSA options corresponding to local conditions and priorities.

The main purpose of this work was analyzing how an innovation platform can foster and provide a basis for multi-actor collaboration in order to enable climate-smart agriculture (CSA) implementation at the local level. Using a mix of social (interactions between stakeholders, knowledge changes, adoption of practices) and technical indicators (income, fulfillment of caloric requirements of the household, farm resource use, planned biodiversity or greenhouse gas emission changes), we monitored the collaboration between an NGO, local civil authorities, associations, and farmers that aimed to achieve a common goal linked to the participatory and contextualized development of CSA in Colombia. We found that multiple stakeholder engagements led to improved interactions between members of the platform and their local environment, a proactive participation in the platform meetings and a significant increase in farmer knowledge levels on the challenges posed by climate change and the resultant extreme events. The platform also facilitated the adoption of best-bet practices that contribute towards CSA when farmers both diversify their production and decrease the use of mineral fertilizers. Our findings suggest that innovation platforms can facilitate the collective understanding and use of CSA options corresponding to local conditions and priorities.

<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>

<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>

Much of the literature examining the role of gender in processes of climate change adaptation in the agricultural sector has focused primarily on differences between male and female farmers, implicitly treating men and women as homogenous groups. Where heterogeneity exists within these groups which impacts climate change adaptation efforts and outcomes, an understanding of such intersectionalities is vital to the design of effective and equitable policy. The objective of this study is to investigate whether interaction effects among socio-economic factors are meaningful drivers of observed differences among female farmers in their adoption of climate-smart agricultural (CSA) practices, as well as their use of climate information and financial services. This study employs data from farmer surveys in three Climate-Smart Villages in Latin America, analyzed using ordinal logistic regression and canonical correspondence analysis. The results indicate that important interaction effects are present: the relationship between higher educational attainment and increased adoption of CSA practices, for example, is conditional on the degree of livelihood diversification. The relationship between greater educational attainment and increased use of climate forecasts is likewise conditional on age. These results suggest the need for researchers and policymakers to anticipate potential intersectionalities when designing research efforts and development interventions.

Much of the literature examining the role of gender in processes of climate change adaptation in the agricultural sector has focused primarily on differences between male and female farmers, implicitly treating men and women as homogenous groups. Where heterogeneity exists within these groups which impacts climate change adaptation efforts and outcomes, an understanding of such intersectionalities is vital to the design of effective and equitable policy. The objective of this study is to investigate whether interaction effects among socio-economic factors are meaningful drivers of observed differences among female farmers in their adoption of climate-smart agricultural (CSA) practices, as well as their use of climate information and financial services. This study employs data from farmer surveys in three Climate-Smart Villages in Latin America, analyzed using ordinal logistic regression and canonical correspondence analysis. The results indicate that important interaction effects are present: the relationship between higher educational attainment and increased adoption of CSA practices, for example, is conditional on the degree of livelihood diversification. The relationship between greater educational attainment and increased use of climate forecasts is likewise conditional on age. These results suggest the need for researchers and policymakers to anticipate potential intersectionalities when designing research efforts and development interventions.