• Energy Research

In our multidisciplinary methodology, we translated the exposure to CC into direct impact on crops and assessed sensitivity and adaptive capacity using the sustainable rural livelihoods framework

<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>

Les haricots secs (Phaseolus vulgaris L.) sont une culture de subsistance importante en Amérique centrale. Le changement climatique futur pourrait menacer la production de haricots secs et mettre en péril la sécurité alimentaire des petits agriculteurs. Nous avons estimé les changements de rendement des haricots secs dus aux changements climatiques dans ces pays à l'aide de données à échelle réduite provenant de modèles de circulation mondiale (GCM) au Salvador, au Guatemala, au Honduras et au Nicaragua. Nous avons généré des données météorologiques quotidiennes, que nous avons utilisées dans le sous-modèle du système d'aide à la décision pour le transfert de technologie agricole (DSSAT). Nous avons comparé différents cultivars, sols et options d'engrais au cours de trois saisons de plantation. Nous avons analysé les rendements simulés pour classer spatialement les points de changement climatique à fort impact dans les quatre pays. Les résultats montrent un corridor de rendements réduits du lac Nicaragua au centre du Honduras (diminution de 10 à 38 %). Les rendements ont augmenté dans les hauts plateaux guatémaltèques, vers la côte atlantique et dans le sud du Nicaragua (augmentation de 10 à 41 %). Certains agriculteurs pourront s'adapter au changement climatique, mais d'autres devront changer de culture, ce qui nécessitera un soutien extérieur. Les instituts de recherche devront concevoir des technologies permettant aux agriculteurs de s'adapter et de fournir aux décideurs des stratégies réalisables pour les mettre en œuvre. El frijol seco (Phaseolus vulgaris L.) es un cultivo de subsistencia importante en Centroamérica. El cambio climático futuro puede amenazar la producción de frijol seco y poner en peligro la seguridad alimentaria de los pequeños agricultores. Estimamos los cambios en el rendimiento de los frijoles secos debido al cambio climático en estos países utilizando datos reducidos de los modelos de circulación global (GCM) en El Salvador, Guatemala, Honduras y Nicaragua. Generamos datos meteorológicos diarios, que utilizamos en el submodelo de frijol seco del Sistema de Apoyo a la Decisión para la Transferencia de Agrotecnología (DSSAT). Comparamos diferentes cultivares, suelos y opciones de fertilizantes en tres temporadas de siembra. Analizamos los rendimientos simulados para clasificar espacialmente los puntos de alto impacto del cambio climático en los cuatro países. Los resultados muestran un corredor de rendimientos reducidos desde el lago Nicaragua hasta el centro de Honduras (disminución del 10-38 %). Los rendimientos aumentaron en el altiplano guatemalteco, hacia la costa atlántica y en el sur de Nicaragua (aumento del 10-41 %). Algunos agricultores podrán adaptarse al cambio climático, pero otros tendrán que cambiar los cultivos, lo que requerirá apoyo externo. Las instituciones de investigación deberán idear tecnologías que permitan a los agricultores adaptarse y proporcionar a los responsables políticos estrategias viables para implementarlas. Drybeans (Phaseolus vulgaris L.) are an important subsistence crop in Central America. Future climate change may threaten drybean production and jeopardize smallholder farmers' food security. We estimated yield changes in drybeans due to changing climate in these countries using downscaled data from global circulation models (GCMs) in El Salvador, Guatemala, Honduras, and Nicaragua. We generated daily weather data, which we used in the Decision Support System for Agrotechnology Transfer (DSSAT) drybean submodel. We compared different cultivars, soils, and fertilizer options in three planting seasons. We analyzed the simulated yields to spatially classify high-impact spots of climate change across the four countries. The results show a corridor of reduced yields from Lake Nicaragua to central Honduras (10-38 % decrease). Yields increased in the Guatemalan highlands, towards the Atlantic coast, and in southern Nicaragua (10-41 % increase). Some farmers will be able to adapt to climate change, but others will have to change crops, which will require external support. Research institutions will need to devise technologies that allow farmers to adapt and provide policy makers with feasible strategies to implement them. الفاصوليا الجافة (Phaseolus vulgaris L.) هي محصول كفاف مهم في أمريكا الوسطى. قد يهدد تغير المناخ في المستقبل إنتاج الفول الجاف ويعرض الأمن الغذائي لصغار المزارعين للخطر. قدرنا تغيرات الغلة في الفاصوليا الجافة بسبب تغير المناخ في هذه البلدان باستخدام بيانات مصغرة من نماذج الدوران العالمية (GCMs) في السلفادور وغواتيمالا وهندوراس ونيكاراغوا. أنشأنا بيانات الطقس اليومية، والتي استخدمناها في النموذج الفرعي للفول الجاف لنظام دعم اتخاذ القرار لنقل التكنولوجيا الزراعية (DSSAT). قارنا الأصناف المختلفة والتربة وخيارات الأسمدة في ثلاثة مواسم للزراعة. قمنا بتحليل الغلة المحاكية لتصنيف البقع عالية التأثير لتغير المناخ مكانيًا عبر البلدان الأربعة. تظهر النتائج ممرًا من انخفاض الغلة من بحيرة نيكاراغوا إلى وسط هندوراس (انخفاض بنسبة 10-38 ٪). وزادت الغلة في المرتفعات الغواتيمالية، باتجاه ساحل المحيط الأطلسي، وفي جنوب نيكاراغوا (زيادة بنسبة 10-41 ٪). سيتمكن بعض المزارعين من التكيف مع تغير المناخ، لكن سيتعين على الآخرين تغيير المحاصيل، الأمر الذي سيتطلب دعمًا خارجيًا. ستحتاج المؤسسات البحثية إلى ابتكار تقنيات تسمح للمزارعين بالتكيف وتزويد صانعي السياسات باستراتيجيات مجدية لتنفيذها.

Adapting to climate risks is central to the goal of increasing food security and enhancing resilience of farming systems in East Africa. We examined farmers’ attitudes and assessed determinants of adaptation using data from a random sample of 500 households in Borana, Ethiopia; Nyando, Kenya; Hoima, Uganda; and Lushoto, Tanzania. Adaptation was measured using a livelihood-based index that assigned weights to different individual strategies based on their marginal contributions to a household's livelihood. Results showed that farmers’ attitudes across the four sites strongly favored introduction of new crops, changes in crop varieties, and changes in planting times. Farmers disfavored soil, land, and water management practices. At lower levels of adaptation (25% quantile), adaptation index correlated positively with membership to farmers’ groups, household size, sex of the household head, and number of months of food shortage. Farmer group membership enhanced adaptation at intermediate (50% quantile) level whereas access to credit increased adaptation at high (75% quantile) level. Food insecurity, however, correlated negatively with the likelihood to choose individual adaptation strategies suggesting that although households adapted to improve food security status of their households, hunger was a barrier to adaptation. Our findings suggest that providing climate information to inform timely planting, promoting crop diversification, and encouraging adoption of adapted varieties of crops might be successful to enhancing resilience of farming systems in the short-term. In the long-term, increased investment in reducing hunger, encouraging groups formation, and easing liquidity constraints will be required to promote adaptation through implementation of soil, water, and land management strategies.

This dataset contains the files produced in the pilot implementation of the “Integrated Monitoring Framework for Climate-Smart Agriculture” in the Lawra-Jirapa Climate Smart Village (Ghana) in October 2017. <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 <\ul> <br> <ol> The CSA framework allows to address three key research questions: <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="2"> Which are 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> <li value="3"> Which are the CSA performance, synergies and trade-offs found at farm level?</li> </ol> <br> This 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. <br> <ul> At household level (17 Core indicators): <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> <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> <br> 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 labor, Decision making and control on CSA generated income). <br> <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, Frecuency of climate events, Coping strategies, Risk Mitigation Actions, Access to financial services and Training, CSA Knowledge and Learning.</li> <br> At farm level, 7 CORE indicators: <br> <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> <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. Universe: At the time of data collection, all survey participants resided within 7 communities in Lawra Jirapa Village, Ghana (Baazu, Bompari, Doggoh, Jeffiri, Kulkarni, Oribili or Tuori). Implementation was carried out by locally trained enumerators using the Geofarmer Smart Monitoring App for data collection. A total of 357 farmers were interviewed, consisting of 103 adult females and 137 adult males (age 35 or above), 65 young females and 52 young males (under 35). Where possible, two and one young person were surveyed from each household.

This dataset contains the files produced in the implementation of the “Integrated Monitoring Framework for Climate-Smart Agriculture” in the Olopa Climate Smart Village (Guatemala) in April 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: <ul> <li type= disc> Adoption of CSA practices and technologies, as well as access to climate information services and <li type= disc> Their related impacts at household level (and farm level, in selected sites). <ul> This framework proposes standard Descriptive Indicators to track changes in: <li> 5 enabling dimensions that might affect adoption patterns, <li> 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> 4 CORE indicators on Gender aspects (Participation in decision-making, Participation in implementation, Access/control over Resources and work time). <li> At farm level, 7 CORE indicators are suggested to determine farms CSA performance, as well as synergies and trade-offs among the three pillars. </ul> </ul> 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 labor)?</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 Olopa Village, Guatemala ("Tituque", " Valle nuevo", " El Guayabo Tercer Caserio", " Tuticopote Abajo Caserio El Bendito", " Tuticopote Abajo Caserio El Bendito", " Nochan", or " La Prensa"). 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 279 individuals were sampled: 93 adult females, 75 adult males (age 35 or above) 59 young females and 47 young males (under age of 35). Two adults were surveyed from each household. 5 individuals had unrecorded birth years.