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Methodology: To carry out the calculation of these agroclimatic indicators, daily data of the following climatic variables were used at a resolution of 5 km: Maximum and minimum temperatures (source: CHIRTS). The indicators were calculated for each month during a period of 33 years (1983 - 2016). With the above, the indicators were calculated per month during 1983 -2016 and finally, in order to summarize the calculated indicators, an aggregation of data was carried out, calculating the average in the following time periods: 1983 - 2016, 1990 - 2016, 1995 - 2016, 2000 – 2016, 2005 – 2016, 2010 – 2016. The purpose for which these crop-specific indicators were created is to group or characterize the different accessions available in the Genesys database, considering the climatic data from where they were collected. For this, it is necessary to carry out a characterization of zones based on these specific ones per crop, which are part of evaluating when crops are exposed to heat stress.

The Kenya climate risk profile data contains climate, biophysical, socio economic and demographic characteristics, crops production, stakeholders, characterization of selected value chains and risks and adaptation components. All the dataset, except climate records, were collected in three phases between 2016 and 2021. The risk profiles covered the 45 rural counties of Kenya (excluding the 2 urban counties of Nairobi and Mombasa) and were developed in partnership with the Kenya Ministry of Agriculture, Livestock, Fisheries and Cooperatives (MoALFC). Methodology: The methodology combined literature review (peer-reviewed journals, grey literature), data collection from key statistical resources (national census, county development plan, etc.), climate modelling and qualitative data collection tools such as key informant interviews, participatory workshops, and focus group discussions. For each profile, a prioritization process took place in the county with the key relevant stakeholders. The process included a presentation of the ten main value chains (VCs) of the county and a selection of the four main value chains by assessing them against a set of criteria: contribution to food security, productivity, importance to the economy; resilience to current and future climate change; population engaged in the value chain; and engagement of poor and marginalized groups.

Methodology: To carry out the calculation of these agroclimatic indicators, daily data of the following climatic variables were used at a resolution of 5 km: Maximum and minimum temperatures (source: CHIRTS), precipitation (source: CHIRPS), solar radiation (AGMERRA), data of soil (SoilGrids). The indicators were calculated for each month during a period of 33 years (1983 - 2016). With the above, the indicators were calculated per month during 1983 -2016 and finally, in order to summarize the calculated indicators, an aggregation of data was carried out, calculating the average in the following time periods: 1983 - 2016, 1990 - 2016, 1995 - 2016, 2000 – 2016, 2005 – 2016, 2010 – 2016 The purpose for which these indicators were created is to group or characterize the different accessions available in the Genesys database, considering climatic data from where they were collected. For the above, it is necessary to carry out a characterization of zones based on agroclimatic indicators, which are framed in evaluating the following stresses: Heat, Drought and Flooding, in addition to including indicators that capture the behavior of the photoperiod.

This document contains a review of global digital resources relevant to climate-informed agroecological transitions. The purpose of the review was to catalog relevant digital resources and assess their role in inclusive knowledge development, with special attention to farmers’ co-creation of knowledge for on-the-ground practices. To this end, we identified existing digital tools relevant to technical advisory and performance assessment and reviewed their functions (i.e., the purpose of using a tool) against indicators for exemplary features (i.e., the channels in which a user can engage with the tool) that could support socially inclusive, climate-informed agroecological transitions.<br><br> Metodology:To evaluate exemplary features of agricultural digital tools – defined here as an app, online resource (not platforms), or other software available on a digital device (e.g., phone, smartphone, computer, etc.), including those that are language, audio, and visually based – we developed 87 indicators (see Indicators Explained) relating to seven categories: (i) performance assessment, (ii) technological specifications, (iii) social inclusion and co-creation, (iv) scaling, (v) climate change adaptation, mitigation and whether the tool calculates greenhouse gas emissions, and (vi) agroecological principles Exemplariness was defined in this review as best fitting the requirements of the target users and best addressing agroecological and climate change mitigation and/or adaption outcomes. All indicators for categories i-v were developed and validated via several rounds of internal reviews and expert consultations. The 12 agroecological indicators (i.e., principles) were adopted and refined from the FAO 10 Elements of Agroecology (FAO, 2018), HLPE (HLPE, 2019) and TAPE (FAO, 2019) reports. Sixty (60) tools were selected for a full review against the 87 indicators based on their applicability to provide technical advisory and/or performance assessment on climate-informed agroecological transitions. Tools were classified as technical advisory resources if they delivered any recommendations regarding farming practices and as performance assessment resources if they included review of farm status or operations. Tools were mostly identified via Google searches, expert interviews, and platforms such as the CGIAR Evidence Clearing House and Digital Agri Hub. Each tool was reviewed by one analyst by accessing the tool, when available, or by reviewing materials online. A second analyst validated individual indicator responses when subjectivity in responses was an issue. Indicators were marked as ‘unknown’ when subjectivity in responses persisted or when information was not available.

<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>

The date sets compiled and analyzed the gender and social aspects of development and related policies in Vietnam’s Mekong River Delta.

Field evaluation of breeding lines developed for drought tolerance for adaptation, yield, response to field diseases and agronomic quality

The decision matrix is a meta-analysis of climate services literature in the form of an Excel spreadsheet.