The picture can't be displayed. ` Sex-disaggregated Data in Climate, Agriculture and Food Security An assessment of current trends and gaps Working Paper No. 12 [xxx] Working paper Samuel Partey| Hanna North | Sophia Huyer December • 2023 To cite this working paper Partey, S., North, H., Huyer, S. 2023. AICCRA Working Paper: Sex-disaggregated Data in Climate, Agriculture and Food Security; An assessment of current trends and gaps. Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) and Women in Global Science and Technology (WISAT). Acknowledgements Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) is a project that helps deliver a climate-smart African future driven by science and innovation in agriculture. It is led by the Alliance of Bioversity International and CIAT and supported by a grant from the International Development Association (IDA) of the World Bank. About AICCRA Working Papers Titles in this series aim to disseminate interim research on the scaling of climate services and climate- smart agriculture in Africa, in order to stimulate feedback from the scientific community. Photos © CCAFS/S. Samuel; @AICCRA/S. Huyer Disclaimer This working paper has not been peer reviewed. Any opinions stated herein are those of the author(s) and do not necessarily reflect the policies or opinions of AICCRA, donors, or partners. Licensed under a Creative Commons Attribution – Non-commercial 4.0 International License. © 2023 Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Partner About AICCRA Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) is a project that helps deliver a climate-smart African future driven by science and innovation in agriculture. It is led by the Alliance of Bioversity International and CIAT and supported by a grant from the International Development Association (IDA) of the World Bank. Explore our work at aiccra.cgiar.org aiccra.cgiar.org aiccra@cgiar.org CGIARAfrica AICCRA WORKING PAPER NO. XX | SEX-DISAGGREGATED DATA IN CLIMATE, AGRICULTURE AND FOOD SECURITY CONTENTS CONTENTS .......................................................................................... 1 ABBREVIATIONS .................................................................................. 3 1.1 Background ...................................................................................... 4 01. INTRODUCTION .............................................................................. 4 2. METHODS ........................................................................................ 5 2.1 Data collection ................................................................................... 5 2.2 Data analysis ..................................................................................... 7 1 1 AICCRA WORKING PAPER NO. XX | SEX-DISAGGREGATED DATA IN CLIMATE, AGRICULTURE AND FOOD SECURITY 3. RESULTS .......................................................................................... 8 3.1 Overview of available data on gender and climate adaptation and mitigation in the agriculture context .................................................................................. 8 3.2 Gender-differentiated perceptions, vulnerabilities and impacts of climate change11 3.3 Gender-rated trends: CCAFS baseline and midline data analysis .................... 21 3.4 Gender and Climate-Smart Agriculture (CSA) ............................................ 15 CONCLUSION ..................................................................................... 32 APPENDIX A: LIST OF SOURCES CONSULTED .............................................. 3 APPENDIX B: LIST OF PRACTICAL RESOURCES ........................................... 10 REFERENCES ........................................................................................ I 2 2 AICCRA WORKING PAPER NO. XX | SEX-DISAGGREGATED DATA IN CLIMATE, AGRICULTURE AND FOOD SECURITY ` ABBREVIATIONS AfDB African Development Bank AFOLU Agriculture, forestry and other land use (AFOLU) AICCRA Accelerating Impacts of CGIAR Climate Research in Africa CCAFS Climate Change Agriculture and Food Security CGIAR Consultative Group of International Agricultural Research Centres CSA climate-smart agriculture FAO Food and Agriculture Organization of the United Nations GIS Geo-information software IFPRI International Food and Policy Research Institute ILO International Labour Organisation ILRI International Livestock Research Institute IPCC International Panel on Climate Change LSMS+ World Bank Living Standards Measurement Study-Plus LMICs Low- and middle-income countries SDGs Sustainable Development Goals WHO World Health Organisation 3 3 01. INTRODUCTION This report reviews trends and availability of sex-disaggregated data on various aspects of gender and CSA, including agricultural innovation, decision-making, time use and access to resources. It uses data collected by the CGIAR and other organizations to analyse gender trends at global, regional and national levels. 1.1 Background Climate change is a global challenge. The IPCC projects events such as warming temperatures and dryness, sea level rise, coastal erosion and ocean and lake acidification, resulting in coral bleaching and an increasing frequency and severity of droughts in some regions, with a concomitant decrease in water supply, which impact agricultural production, traditional fishing, food security and human health. In the agricultural sector, efforts towards building resilience comprise both climate change adaptation and reduction in greenhouse gas emissions. According to the IPCC, emissions from agriculture, forestry and other land use (AFOLU), constituted 15% (8.7 GtCO2-eq) of total emissions in 2019 (IPCC, 2022). For this reason, the concept of climate-smart agriculture (CSA) is oriented towards climate-responsive options for the agricultural sector. CSA is defined as agriculture that (a) sustainably increases agricultural productivity and incomes, (b) adapts and builds resilience to climate change, and (c) reduces and/or removes GHG emissions where possible (FAO 2013). The relative importance of outcomes for food security, adaptation and mitigation varies across locations and situations, as do potential synergies and trade-offs between objectives (Lipper et al. 2014), providing a challenge for prioritizing investments. The potential for CSA to catalyse mitigation and adaptation co-benefits and trade-offs is gendered. In Oceania, South Asia and sub-Saharan Africa, agriculture employs about 60% women, making it an important enterprise for women’s empowerment and a way out of poverty. However, compared to men, women farmers are more vulnerable to climate change due to their lack of substantial access to agricultural inputs, financial credits, labour and land, which are critical for their activities and livelihoods (Doss et al., 2018; Ali et al., 2020). To use the example of West Africa, a search on Scopus and Web of Science produced 352 scientific articles, book chapters and reviews (Figure 3), which identified five main constraints for women’s participation in agriculture and effective climate change responses in West Africa. 4 Lack of land Lack of credit/finance 75% 88% Increased workload 73% 82% Limited control/decision making on/of household 77% resources Limited education and access to information Figure 1 Top five factors mentioned in the literature as barriers to women’s participation in agriculture and effective climate response in West Africa. N = 352 scientific publications In sub-Saharan Africa, customary laws, cultural values and norms, restrict women’s access to agricultural inputs and assets (Sheahan and Barrett, 2017; Huyer, 2016). In Mali and Senegal, it was found that although more than 70% of women are engaged in agriculture, just 5% and 13% of them have access to land (Dankelman et al., 2008; UNDP, 2012). In Bangladesh, women were found to be less likely to buy micro-insurance than their male counterparts because of financial or resource constraints and less access to information and extension (Kumar and Clarke, 2015). Addressing gender inequalities in the agricultural sector is a global challenge that require evidence-based policy decisions towards equality. Global policy milestones and development agenda such as the UN Agenda 2030 Sustainable Development Goals (SDGs) place emphasis on the collection of gender indicators to monitor the achievement of gender equality in all sectors including agriculture. According to Doss et al. (2018), Goal 2 of the SDGs explicitly mentions the need to address the challenges and constraints faced by women farmers calling for the collection of sex disaggregated data on CSA. This is crucial in the quest to identifying research gaps and building the necessary evidence for scaling gender-transformative CSA technologies and practices that can transform and reorient agricultural systems1. 2. METHODS 2.1 Data collection Data were collected from multiple sources including databases, websites and summarized from project reports (see Table 1). The databases provide information on the structure of research on gender and climate adaptation and mitigation in the agriculture context. Additional data were collected from internet searches and consultation with the scientific literature dealing with climate change and agricultural data. 1 See Huyer, 2023b. 5 Table 1 List of databases consulted and the resulting resources by search term2 Database Name Search Terms/ Criteria Outputs African Development Agricultural holders Datasheet: Distribution of Bank Gender Data agricultural holders by gender Portal Asian Development Gender Sex-Disaggregated Data on Bank Data Portal Asset Ownership (agricultural land) Agriculture Agriculture, Value Added (% of GDP) in Asia and the Pacific CCAFS Sex- By region: quantitative data only Links to available CSA datasets Disaggregated Data by region and by country on Climate-Smart Agriculture in CCAFS Publications CCAFS CSV data By region: quantitative data only Links to available CSA datasets by region and by country Data 2x Databases: Agriculture Link to Women’s Empowerment in Agriculture Index Equal Measures 2030 SDG Gender Index Index ranking scores of the state of gender equality aligned with 14 of the 17 SGDs FAO AQUASTAT Water withdrawal by sector Agricultural water withdrawal (10^9m3/year) by country Area under agricultural water management Area equipped for irrigation (1000 ha) Access to improved drinking water source Rural population with access to safe drinking water (%) FAO Gender and Distribution of agricultural holders by sex Datasheet by country Land Rights Database Incidence of female agricultural landowners Datasheet by country Distribution of agricultural land area owned Datasheet by country by sex Distribution of agricultural land value owned Datasheet by country by sex FAO Stat Population and employment: employment Datasheet: employment in indicators agriculture (female/male %) Production: Value of agricultural production Datasheet: Gross production value by country IFPRI Institutional Gender CCAFS-IMPACT Lite Survey Repository IFPRI-CCAFS Gender and Climate Change Survey Bangladesh Integrated Household Survey Bangladesh Agricultural Value Chain (AVC) Impact Evaluation (Baseline and Midline) Bangladesh Climate Change Adaptation Survey Bangladesh Integrated Household Survey Pakistan Rural Household Survey 2 Refer to Appendices A and B for a comprehensive list of the data sources referenced in this report. 6 ILO Stat Employment in agriculture Datasheet: Percentage of employed population employed in agriculture (%) by sex Gender wage gap by occupation Datasheet: % difference in average earnings between men and women Child employment in agriculture Datasheet: % of children aged 5- 17 who are employed in agriculture UN Statistics: Time Time use statistics Datasheet download for women Use Data Portal and men UN Statistics: Gender Quantitative Indicators: Average number of Datasheet Portal hours spent on domestic chores and care work by age, sex and location Quantitative Indicators: Average number of Datasheet hours spent on unpaid domestic chores and care work by age, sex and location Quantitative Indicators: Percentage Datasheet distribution of employed population in agriculture by sex Quantitative Indicators: Gender gaps in Datasheet wages, by occupation, age and persons with disabilities UN Water Data Portal Country and regional snapshots of SDG progress UN Women Same results shared from UN Statistics Gender Data Portal WHO Agriculture Link to Global database on the Implementation of Nutrition Action (GINA) World Bank Gender Gender Modelled estimates from ILO on Data Portal labour force participation World Bank Living National surveys in 6 countries in Africa on Reports; database Standards men’s and women’s ownership of and rights Measurement Study- to physical and financial assets; labor market Plus (LSMS+) outcomes; health; education; food security 2.2 Data analysis Sex disaggregated data collected at the household level is prone to bias, not least because of men’s and women’s differential understanding of survey questions, but also due to their different roles, responsibilities and influence over on- and off-farm activities. Men and women will have an inherent difference in understanding of the effects of climate change and mitigation simply due to their interactions with different tasks. Various resources presenting considerations for mitigating discrepancies between men’s and women’s survey responses exist, such as those from the UN Statistics Division (2016) as follows: • Consider the ownership status of agricultural parcels as women are more likely than men to have holdings that are not registered • Lower holding size limits exclude parcels owned by women disproportionately to men, and excludes the economic activities carried out by women on small parcels that are likely to be gendered in nature 7 • Consider intrahousehold surveys to understand labour divisions as opposed to female vs male-headed households as this better represents the experience of women within the household • Employ female survey enumerators to survey women and separate men and women during the survey • Consider all steps in value chain production to account for the different responsibilities of men and women in production. Most available gender data in agriculture is disaggregated by the sex of the household head, making it difficult to assess gender dynamics in households and other aspects of community production. The CCAFS Household Surveys record the sex of respondents and it is possible to disaggregate responses using these data; a useful tool, given the small sample sizes of female headed households in the climate-smart villages. Tables 14 and 15 list the sample sizes for these datasets and while in some cases there is a large sample size for female headed households (e.g., Nicaragua: FHH=70, MHH=350), there is still a statistical disparity. Intrahousehold analyses, such as those conducted in the CCAFS Household Survey Monitoring datasets, allow for more similar sampling sizes. The Monitoring set was conducted using the GeoFarmer app, which collects data directly from farmers and allows for better sex disaggregation. 3. RESULTS 3.1 Overview of available data on gender and climate adaptation and mitigation in the agriculture context Many sources on gender and climate adaptation and mitigation in agriculture (refer to Appendices A and B) provide sex-disaggregated data, which help to build a cohesive picture of women’s constraints to agricultural innovation, decision-making, time use and access to resources. In addition, several databases provide information on the gender-climate change nexus in the agriculture sector. For instance, the African Development Bank (AfDB) Gender Data Portal currently provides data on gender indicators for all countries in Africa. Data on 79 gender indicators from national surveys, statistical estimates and other robust sources are available. The AfDB is leading the production of sex-disaggregated data to assess gender gaps and develop responses. The Data are used by policy makers, development institutions, civil society, and private sector as an important tool to engage in evidence-based policy dialogue and action on the ground. Another database was compiled by Gender and Social Inclusion Unit of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), a list of CCAFS publication containing sex-disaggregated data on climate-smart agriculture. Over 100 publications are categorized by region of data collection. The database covers global data on agricultural issues, access to climate services, climate adaptation/CSA, gender patterns in mobility and agricultural production, including workloads, motivations to change, negative perceptions/give up on adaptation strategies/CSA etc. A review of practical resources also identified several guidance documents and practical tools/methods in the field of gender, agriculture, and climate resilience. There are more guidance resources available in the form of Info-notes and working papers than practical 8 resources including toolkits and methods. Most of the resources adopt quantitative methods, with some mixed methods approaches, and fewer qualitative and participatory approaches. More resources are focused on thematic areas3 including mapping vulnerability and resilience, evaluating specific agricultural practices, and highlighting opportunities/barriers for innovation. For instance, Table 2 lists the resources for mapping causes and patterns of gendered vulnerability and resilience to climate shocks and stressors, in relation to agriculture. Table 2 Practical resources for mapping causes and patterns of gendered vulnerability and resilience to climate shocks and stressors, in relation to agriculture Resource Resource No. 1 Climate-Smart Agriculture Rapid Appraisal (CSA-RA) (subtool of CSA guide) (IFAD, CIAT, IITA, CCAFS, Sokoine University) (Mwongera et al., 2015) 2 Enhancing Women’s Assets to Manage Risk under Climate Change. Potential for group- based approaches (IFPRI) (Ringler et al., 2021) 3 Training manual on gender and climate change resilience (Arrow, UN women) (Dharmistha, 2021) 4 Gender Household Survey, CCAFS Dataverse (harvard.edu) (CCAFS, IFPRI, ILRI, 2013) 5 Climate Change & Food Security Vulnerability Assessment Toolkit (Bioversity and IDS) (Ulrichs et al., 2015) 6 Mainstreaming gender and social differentiation into CCAFS research activities in West Africa: lessons learned and perspectives (CCAFS) (Ouédraogo et al., 2018) 7 Aflotoxins in food and feed (GCAN, IFPRI) (Brown, 2018) 8 Using natural areas and empowering women to buffer food security and nutrition from climate shocks: Evidence from Ghana, Zambia, and Bangladesh (GCAN, IFPRI) (Cooper, 2018) 9 Policy note on the interlinkages of Climate Change, Gender and Nutrition in Nigeria (GCAN, IFPRI) (Thomas et al., 2018) 10 A user guide to the CCAFS Gender and Climate Change Survey data (CCAFS) (Bryan et al., 2018) 11 Can Women’s Empowerment Increase Animal Source Food Consumption in Flood Prone Areas of Bangladesh? (IFPRI, University of Southern California) (Theys, 2018) 12 Agriculture and Youth in Nigeria: Aspirations, Challenges, Constraints, and Resilience (IFPRI) (El Didi et al., 2020) 13 Gender-differences in Agro-Climate Information Services (Findings from ACIS baseline survey in Ha Tinh and Dien Bien provinces, Vietnam) (CCAFS) (Duong et al., 2017) 14 Achieving Dryland Women’s Empowerment: Environmental resilience and social (Natural Resources Institute, UNDP, UNCCD) (Nelson et al., 2015) 15 Integration of gender considerations in Climate-Smart Agriculture R4D in South Asia and SSA – useful research questions (GENNOVATE) (Farnworth et al., 2017) 16 The Vulnerability and Risk Assessment Methodology (Oxfam) (Kelsey and Morchain, 2018) 17 Insurance for Rural Resilience and economic development (IFAD) (IFAD, 2020) On the other hand, few resources exist on analysing the enabling environment, understanding agricultural research and extension systems, or assessing gendered outcomes (Table 3). For instance Gender and Inclusion Toolbox for Participatory Research in Climate Change and Agriculture (CCAFS, ICRAF, CARE, FAO) (Jost et al, 2014) is a resource at district and landscape level which can support the analysis of the gender dimensions of climate change and agriculture research and extension. This tool is also a useful resource for analysing opportunities, barriers, 3 Theme wise list of resources is presented in Appendix A. 9 preferences, and decision-making regarding climate change adaptation innovation and interventions at the household, community, and landscape level. Table 3 Practical resources for assessing gendered CRA outcomes at different scales Resource.no. Resource 1 Enhancing Women’s Assets to Manage Risk under Climate Change. Potential for group- based approaches (IFPRI) (Ringler et al., 2021) 2 Mainstreaming gender and social differentiation into CCAFS research activities in West Africa: lessons learned and perspectives (CCAFS) (Ouedraogo et a., 2018) 3 Training manual on gender and climate change resilience (Arrow, UN women) (Dharmistha, 2021) 4 A user guide to the CCAFS Gender and Climate Change Survey data (CCAFS) (Bryan et al., 2018) 5 Gender Equality, M&E and evaluation of climate services (Gumucio et al., 2018) 6 Using natural areas and empowering women to buffer food security and nutrition from climate shocks: Evidence from Ghana, Zambia, and Bangladesh (GCAN, IFPRI) (Cooper, 2018) 7 Women’s involvement in coffee agroforestry value-chains: Financial training, Village Savings and Loans Associations, and Decision power in Northwest Vietnam (CCAFS) (Simelton et al., 2021) In addition to the above, tools, methodologies and frameworks have also been identified across journal articles. The gender and climate hotspot mapping methodology, for instance, identifies hotspot areas for gender and climate risks. It can be utilised for identification of gender and climate risk hotspot at the sub-national (district) level to enable prioritisation for CSA or climate change and gender focussed interventions. Other usages include identification of subnational hotspots of gender, climate risks and poverty in Nepal. The latest usage of the methodology highlights hotspots of areas where machine-transplanted rice can potentially reduce women’s labor in India. The hotspot methodology is a practical tool that can be used by all stakeholders as a first step for prioritising interventions geographically. However, usability of the methodology may be limited for organisations without the required technical skill set (GIS software and experts familiar with GIS) (Chanana-Nag and Aggarwal, 2018, Khatri-Chhetri et al., 2019, Gartaula et al., 2020). Another tool that can be useful to assess the gender impacts of CSA is the “Gender Empowerment Index” (Huyer, 2023a; Tesfaye et al, 2021). The index uses both quantitative and qualitative data to measure empowerment levels for men and women farmers at inter- household and intra-household levels. It is based on sub-parameters covering four major measurable indicators—political, economic, agricultural, and social. Another index, ‘the decision- making index’ has been used as a tool by (Van Aelst & Holvoet, 2018) to highlight how adaptation decisions in Tanzania are affected by women’s participation in intra-household decision making. A number of frameworks have been presented that can be useful in assessing and analyzing different outcomes of gender and CSA. For instance, based on literature review and field experiences from Zambia and Mexico, (Beuchelt & Badstue, 2013) present a conceptual framework highlighting pathways for enhancing gender and social equity in nutrition- and CSA projects. Similarly, (Wong, 2016) provides a gender lens to the ‘contextual–procedural– distributive’ equity framework, to assess the effectiveness of the implementation process and outcomes of key climate-finance targeted intervention in CSA from a gender equity perspective. The framework can enable stakeholders, especially researchers and policymakers to adopt an 10 inclusive approach by understanding various context-specific challenges of gender equity for implementing CSA. Khalil et al (2020) propose a framework for ‘informed autonomous adaptation’. It uses local context understanding and adaptation action to support a range of outcomes through female contributions to the mobilization and acquisition of local knowledge, social capital and network building with the help of outside aid actors, such as NGOs, for grassroots innovation. 3.2 Gender-differentiated perceptions, vulnerabilities and impacts of climate change 3.2.1 Gender and climate change perception There is no database platform or data repository on the association between gender and climate change perception. However, climate change perceptions and their individual-level determinants have been extensively studied, with sex-disaggregated data. Perceptions about climate change are mostly determined based on responses to changes in rainfall and temperature patterns witnessed over several years (often beyond 20 years). Generally, evidence from the literature reveals increased awareness of climate change among both men and women as well as its implications for agriculture productivity, food security and livelihoods (Partey et al., 2020; Lawson et al., 2020; Assan et al., 2020). Climate change is often perceived as increased frequency of droughts, changes in rainfall patterns (late onset and unpredictability of rains); increased flash floods, increased strong winds; increased evapotranspiration and spontaneous bushfires (Diarra et al., 2021; Partey et al., 2020; Sanogo et al., 2017; Nyantakyi-Frimpong and Bezner-Kerr, 2015). The literature also attests to the fact that perceptions of men and women about climate change are normally based on experiential knowledge accumulated over 20 years (Partey et al., 2020). Generally, gender differences in climate change knowledge and perception are very context specific. Some studies have shown that women may be typically more likely than men to believe that climate change is happening (e.g., Hornsey et al., 2016; McCright, Dunlap, & Xiao, 2013); worry about its effects (e.g., McCright, 2010; McCright and Sundström, 2013); perceive more climate change risks (e.g., Brody et al., 2008; Hamilton, 2011; van der Linden, 2015); express more knowledge about climate change (e.g., McCright, 2010); and perceive global warming as posing a threat within their lifetime (Hamilton, 2011). Moreover, women are less likely than men to endorse denialist beliefs about climate change (e.g., Feygina et al., 2010; McCright and Dunlap, 2011) and express skepticism about its existence on social media (Holmberg & Hellsten, 2015). In Africa, climate change perception was found to be ungendered (Sraku-Lartey et al., 2020; Assan et al., 2020); except in a few cases such as Sanogo et al. (2017) who found that in the Koutiala, and Yanfolila districts of Southern Mali, men were more likely to perceive climate change better as they are the main actors in rainfed agriculture. In Eastern Uganda Kisauzi et al. (2012) reported that male and female farmers’ perceptions of climate change did not differ significantly on all the parameters investigated except on frequency of droughts, with women more likely to perceive increased drought frequency compared to men (Table 4). In the same study, it was found that a high percentage of men (63%) and women (53%) expected climate change effects to become more severe, which is consistent with the IPCC predictions. 11 Table 4 Farmers’ perceptions of climate changes over the past 30 years (Source: Kisauzi et al. 2012) In the Pra River Basin of Ghana, Bessah et al. (2021) found that farmers’ observed trends of climatic events in the previous 20 years were similar for men and women. In addition, both sexes had similar sources of weather information (Figure 2). Similarly, Assan et al. (2020) found similarities in climate change perceptions between men and women, and rising temperatures, shortened cropping season, and increasing erratic rainfall as the main climatic stressors. Lack of money and inadequate access to labour among women, and inadequate access to extension and old age or poor health among men were the major constraints to mitigating climate change impacts. In Nigeria, women had less access to information and training (Nnadi et al, 2022). Meanwhile, a systematic review by Haque et al. (2023) revealed female farmers tend to be more concerned about climate change. This necessitates the need to understand climate change from a cultural standpoint to assess the level of informed decision-making for adaptation. 12 Figure 2 Gender differences in climate information sources (Source: Bessah et al., 2021) Figure 3 Gender differences in perception of climate induced changes and information sources (Source: Bessah et al., 2021) While climate change perceptions are comparable between men and women in Africa, men’s greater engagement in agricultural activities can make them perceive more long-term changes in climate than women. In the 2012 CCAFS/IFPRI/ILRI Gender Survey conducted in Senegal, most respondents stated that they had observed a change in weather patterns over the course of their lifetimes, with changes in drought and rainfall being the most highly reported change in all for both sexes (Table 5). Heat, fire and cold spells were least reported. From the survey, there are clear indications that in predominantly, climate change perceptions do not differ significantly between men and women, except in some cases of perceptions of long-term changes. However, adaption measures may be different. 13 Table 5 Percentage of people reporting climate shocks and long-term weather patterns, disaggregated by sex of respondent and by project site (Source: Twyman et al, 2014) In Asia, studies revealed no significant differences between men and women on climate perception in India (Palanisami et al., 2015), Thailand and Vietnam (Waibel et al., 2018). In Vietnam, male respondents reported that climate variability is due to human and non-human activities at 33% and 44%, respectively (McKinley et al., 2016). Females’ responses were similar but with more emphasis on humankind activities. Female respondents reported that climate variability is due to human and non-human activities at 41% and 43%, respectively. There appears to be consensus among the respondents that temperatures are increasing and becoming more variable, precipitation is decreasing, and sea-level rise is not presently a concern in their respective regions (McKinley et al., 2016) (Figure 4). 3.2.2 Gender and climate change vulnerabilities and impacts With increased awareness of climate change and its impacts on agriculture and livelihoods, one would expect men and women to show the same or similar level of response to minimizing climate-related risks. However, available research reveals gender disparities in the vulnerability 14 and impacts of climate change. The state of vulnerability and adaptive capacity are influenced by factors such as the ownership of/access to land, access to financial credit, level of education, Figure 4 Perceived changes in precipitation, sea level and drought in the last ten years, Vietnam (McKinley et al., 2016) access to employment, wages/income, and decision-making rights. The literature reveals women are normally disadvantaged in relation to these areas, constraining them from adopting agricultural innovations that improve farm productivity and diminishing their adaptive capacity to climate change (Diouf et al., 2019; Huyer et al., 2021). In developing countries, women make up 45% of the agricultural labour force, ranging from 20% in Latin America to up to 60% in parts of Africa and Asia (FAO, 2016) but they often lack substantial access to agricultural inputs, financial credits, labour and land, which are critical for their activities and livelihoods (Doss et al., 2018; Ali et al., 2020). This situation poses as a major threat to food security due to the substantial contributions of women to food production at multiple scales. As in many parts of sub-Saharan Africa and Asia, customary laws, cultural values and norms, and the role of women in the household are often cited as the major contributors to increasing gender inequalities in access to agricultural inputs and assets (Sheahan and Barrett, 2014; Huyer, 2016; World Bank, 2012). In Mali, the productivity gap between male and female agricultural plot managers in Mali is 20.18% (Singbo et al, 2021); in one site in Tanzania, females made up 25 percent of the sample, had 6 percent lower productivity, provided 64.70 percent on- farm labour and had 0.32 hectares less land compared to males (Nchangi et al, 2021). In respect to access to land, it is reported that traditional laws on inheritance (such as the patrilineal system in most of West Africa) and intra-household dynamics restrict women’s access to lands (Agana, 2012; Glazenbrook 2011; Whitehead and Tsikata 2003). Studies also indicate that lands apportioned to women can be lower in quality and lack water sources for irrigation (Agana, 2012). With high aridity and sporadic rainfall patterns, women farmers especially in the Sahel of West Africa become more vulnerable to droughts and suffer high risks of productivity failure due to lack of irrigation facilities. In Table 6, Olaniyan (2017) presents reasons reported by female-led farming households in The Gambia as to why they produce less than their male counterparts. 15 Table 6 Factors responsible for low production in some female-led households in The Gambia (Olaniyan, 2017) Increased gender disparities also exist in employment and wages in developing countries (Heintz and Pickbourne, 2012). In Northern Ghana, Whitehead (2009) reported that women earn about one-third to half of men’s wage. In the Fonseca Gulf of Honduras, it was reported that even though women make up to 93% of the labour force of cashew processing companies, they earn on average 68% less than men (Muriel et al. 2020). With comparatively higher wages and access to properties, men are able to meet the collateral requirements of financial institutions to access loans. In addition, the higher income of men mean they can invest in alternative livelihoods that can serve as important safety nets to alleviate risks posed by climate change. In addition to their limited access to assets and agricultural resources, increased workloads as a result of climate impacts increase women’s vulnerability to climate change (Jost et al., 2016). For example, women in West Africa invest a great deal of their time in taking care of children, collecting fuelwood, cooking, fetching water, shopping from local markets and indulging in daily running of other household-related responsibilities. In nomadic communities of West Africa where men often migrate with livestock in search of grazing pasture, women must manage activities traditionally handled by men. Limited access to extra labour, means such workloads overburden women and affect the time available for their agricultural activities (Djoudi et al., 2013). Studies show such roles coupled with limited access to agricultural inputs and financial resources also limit women’s ability to participate in farm decision-making and adopt innovative technologies for improved adaptive capacity and increased productivity (Jost et al., 2016; Huyer et al., 2021; Murray et al, 2015). Ayilu et al. (2016) reported that despite the dominance of women in fish trading in West African countries such as Ghana, Togo, Benin and Nigeria, gender inequalities limit their participation in strategic decision-making pertaining to fisheries management, fish processing and cross-border trade. In contrast to conventional knowledge, Andersen et al. (2017) found that female headed households in Brazil, Mexico and Peru were slightly less vulnerable and more resilient than male headed households even though the former usually have lower education levels. Vulnerability 16 and resilience indicators were measured by a combination of the level of household incomes per capita and the degree of diversification of these incomes. Households which simultaneously had incomes below the national poverty line and were poorly diversified (Diversification Index below 0.5) were classified as highly vulnerable, whereas households with highly diversified incomes above the poverty line were classified as highly resilient. As an example, Table 7 shows the probability of being highly vulnerable, by household type (%), in Peru. Table 7 Probability of being highly vulnerable, by household type (%), Peru 2008 (Source: Andersen et al., 2017) Sex disaggregated data are extremely limited on climate impacts. A review by Goh (2012) provides some evidence on how men and women are impacted by climate change in relation to their agricultural activities, water and energy resources, climate-related disasters etc. (Tables 8 to 13). Generally, the literature reveals that men and women feel the impacts of climate change, but the impacts are local specific. It takes adaptive capacity to minimize climate-related risks and impacts. Where women have adequate farm resources, decision making powers, access to land etc., impacts are minimal than when they do not. 17 Table 8 Climate change impact on water and energy resources (Source: Goh, 2012) 18 Table 9 Climate change impact in agricultural production (Source: Goh, 2012) Table 10 Climate change impact on food security (Source: Goh, 2012) 19 Table 11 Climate change impact on migration and conflict (Source: Goh, 2012) Table 12 Impacts from climate-related natural disasters (Source: Goh, 2012) 20 Table 13 Climate change impact on migration and conflict (Source: Goh, 2012) 3.3 Gender-rated trends: CCAFS baseline and midline data analysis Between the CCAFS baseline and midline household surveys, it is evident that changes to climate have increased as a driver to agricultural crop or land management in male and female-headed households, as seen in Figure 5. Overall, male-headed households reported higher responses to climate drivers in all cases except for markets. Male-headed households reported higher drivers from pests and diseases in the midline survey, however, the percentage of female-headed households reporting this driver did not change much between surveys but was still higher than male-headed households in both surveys. Female-headed households reported much lower incidence of driving change in response to interventions or external projects between the two surveys while male-headed households reported a slightly higher response to this driver. The most common adaptation changes cited by both men and women in the CCAFS/IFPRI/ILRI Gender Surveys were related to crop production, but responses also included soil and water conservation, changes in crop varieties or types, changes in planting dates, and tree planting, as described in Table 14. Table 15 shows common reasons to not implement changes, the most common being that respondents didn’t know what to do or didn’t have the money to implement changes. 21 100 90 80 70 60 Baselines FHH 50 Baselines MHH 40 Midlines FHH Midlines MHH 30 20 10 0 MARKETS CLIMATE LAND LABOUR PESTS & PROJECTS DISEASE Figure 5 Percentage of households reporting drivers to changes in agricultural crop or land management between baseline and midline datasets. Source: CCAFS Baseline and Midline Household Survey (2010-2012, 2019-2020) 22 % of Households Table 14 Top five most common changes made by men and women to adapt to climate changes (percent of those who reported making an agricultural, livestock, or livelihood change in response to climate change) in four locations (Source: Twyman et al, 2014) 23 Table 15 Top five most common reasons given by men and women for why changes were not made (percent of those who reported not making an agricultural, livestock, or livelihood change in response to climate change) in four locations (Source: Twyman et al, 2014) 3.3.1 Changes to crop production Generally, the CCAFS Household Surveys reported changes to crop patterns as indicated by responses at the household level. Table 16 shows the types of changes adopted by female and male-headed households in the 15 countries included in the study. The changes that were made the most were introduction of new varieties, planting drought tolerant varieties or planting higher yielding varieties. A major caveat to this dataset is that there were very few female- headed households included in the survey. In only seven of the countries (Costa Rica, Ethiopia, Kenya, Mozambique, Nicaragua, Tanzania, Uganda) did female-headed households reach 10% of the number of male-headed households surveyed. In these cases, female-headed households adopted changes at a rate of 10% higher than men only in the case of planting drought-tolerant varieties in Uganda. Otherwise, the number of households making changes to crop patterns was fairly even, or male-headed households adopted more changes more female-headed 24 households. In some contexts, such as Costa Rica and Kenya, female-headed households did not adopt several changes made by male-headed households. In Figure 6 the analysis in Table 16 is presented in graphical form to highlight regional trends. Unlike in West Africa, male-headed households appeared to have made more changes to crop patterns than females in East Africa, Latin America and Asia (Figure 6). In West Africa, female- headed households responded to climate shocks by introducing new varieties of crops, planting higher yielding varieties, high quality varieties, improved seeds, disease resistant varieties and short cycle varieties of crops. The regional analysis reveals that adoption of adaptation measures in male-headed compared to female-headed households may be context specific. Figure 6 suggests that female-headed households are making fewer changes to their cropping patterns than male-headed households, with the exception of introduction or testing of new crops in West Africa. This exception should, however, be treated with caution as there were only 27 female-headed households included in the study compared with 664 male-headed households; the biggest discrepancy of the four regions listed. Figure 7 suggests that families are focusing their energy on fewer changes on the farm because they will likely have found specific practices which they like and are invested in, rather than trying a lot of new things (Ouedraogo et al., 2019). 70 63 60 58 60 57 50 50 50 47 43 42 40 34 29 30 30 23 26 26 28 22 4 22 22 23 2 22 19 20 15 11 12 10 11 12 10 3 5 7 0 Introduced any new Are you testing any new Stopped growing a crop Stopped growing a crop crop?(over some time) crop (still not sure about) (totally) (in one season) Asia FHH Asia MHH Latin America FHH Latin America MHH East Africa FHH East Africa MHH West Africa FHH West Africa MHH Figure 6 Changes to crop patterns practiced by households that have been farming in the area in question for over 10 years. [Asia (Bangladesh, India and Nepal); Latin America (Costa Rica and Nicaragua); East Africa (Ethiopia, Kenya, Mozambique, Tanzania and Uganda); West Africa (Burkina Faso, Ghana, Mali, Niger and Senegal)]. Source: CCAFS, Baseline Household Surveys (2010-2012) 25 Percentage of HHs Table 16 Percentage of households changing crop patterns in the last 10 years by type of change, disaggregated by sex of household head and country n Introduce Planting Planting Planting Planting Planting Planting Planting Planting Planting Planting Planting Testing a Stopped Other d new higher better pre- shorter longer drought flood salinity- toxicity- disease- pest- new using a HHs variety of yielding quality treated/ cycle cycle tolerant tolerant tolerant tolerant resistant resistant variety variety crops variety variety improved variety variety variety variety variety variety variety variety seed Bangladesh MHH 946 79% 48% 44% 8% 32% 5% 5% 13% 5% 0% 11% 7% 6% 53% 0% FHH 27 48% 26% 26% 4% 19% 4% 4% 7% 11% 0% 11% 4% 7% 19% 0% Burkina Faso MHH 131 73% 44% 33% 56% 66% 19% 21% 5% 0% 0% 5% 2% 37% 29% 0% FHH 7 57% 14% 14% 43% 43% 14% 14% 0% 0% 0% 14% 0% 14% 29% 0% Costa Rica MHH 116 62% 51% 38% 21% 10% 6% 11% 2% 0% 0% 12% 14% 9% 50% 12% FHH 22 27% 9% 14% 18% 5% 0% 0% 0% 0% 0% 0% 0% 0% 23% 0% Ethiopia MHH 101 26% 12% 30% 1% 22% 6% 20% 3% 0% 0% 1% 2% 1% 4% 1% FHH 39 23% 18% 31% 3% 15% 0% 10% 5% 0% 0% 3% 8% 5% 5% 0% Ghana MHH 129 95% 81% 74% 50% 83% 40% 18% 2% 0% 0% 18% 19% 7% 50% 0% FHH 9 78% 78% 67% 44% 89% 44% 22% 0% 0% 0% 33% 33% 0% 0% 0% India MHH 1364 74% 75% 44% 65% 34% 14% 7% 3% 1% 1% 29% 14% 13% 54% 0% FHH 33 58% 55% 33% 39% 21% 21% 18% 3% 0% 3% 18% 12% 6% 48% 3% Kenya MHH 180 83% 83% 78% 64% 79% 42% 83% 11% 0% 0% 42% 47% 41% 66% 0% FHH 96 69% 75% 65% 54% 82% 48% 92% 18% 0% 0% 31% 49% 28% 58% 0% Mali MHH 139 54% 28% 11% 3% 42% 0% 12% 0% 0% 0% 0% 0% 37% 23% 0% FHH 2 50% 50% 0% 0% 50% 0% 0% 0% 0% 0% 0% 0% 0% 50% 0% Mozambique MHH 172 36% 24% 22% 33% 17% 30% 40% 8% 4% 0% 7% 7% 14% 11% 1% FHH 106 34% 26% 23% 30% 20% 34% 44% 6% 2% 0% 13% 15% 17% 17% 0% Nepal MHH 663 79% 68% 37% 20% 14% 2% 1% 0% 0% 0% 0% 0% 3% 71% 0% FHH 26 92% 81% 31% 23% 4% 0% 0% 0% 0% 0% 0% 0% 0% 81% 0% Nicaragua MHH 350 63% 66% 52% 22% 45% 21% 33% 19% 0% 0% 45% 42% 9% 25% 1% FHH 70 67% 67% 59% 30% 43% 21% 33% 19% 0% 0% 41% 37% 4% 21% 0% Niger MHH 135 62% 49% 29% 4% 51% 8% 8% 4% 0% 1% 3% 1% 18% 27% 1% FHH 5 100% 100% 80% 80% 100% 20% 0% 0% 0% 0% 0% 0% 20% 0% 0% 0 0 Senegal MHH 134 83% 78% 58% 11% 75% 31% 2% 2% 0% 0% 0% 4% 9% 45% 0% FHH 4 100% 100% 25% 0% 100% 0% 0% 0% 0% 0% 0% 0% 25% 100% 0% Tanzania MHH 109 83% 78% 81% 70% 80% 60% 82% 4% 2% 1% 77% 69% 16% 55% 0% FHH 31 71% 68% 68% 45% 68% 58% 71% 3% 3% 3% 61% 61% 10% 45% 0% Uganda MHH 221 82% 79% 65% 29% 53% 20% 46% 1% 9% 3% 45% 41% 13% 47% 1% FHH 58 76% 71% 57% 7% 45% 7% 59% 2% 9% 3% 41% 34% 14% 55% 2% n(FHH) > 10% of n(MHH) <10% difference between FHH and MHH adopting this change Women more than men reported making this change Men more than women reported making this change 1 100 90 80 70 TOTAL MIDLINES FHHs 60 TOTAL MIDLINES MHHs 50 TOTAL Baselines FHHs 40 TOTAL Baselines MHHs 30 TOTAL Midlines 20 TOTAL Baselines 10 0 0 to 1 2 to 10 11+ Figure 7 Adaptation index difference for households from Baseline and Midline datasets. Countries include Ghana, Kenya, India and Nepal. Source: CCAFS, Baseline and Midline Household Surveys (2010-2012) In Figure 8, countries are grouped by region and the same analysis as Table 17 is presented in graphical form. This is an attempt to increase the sample size of female-headed households, however there is still a major discrepancy between female and male-headed household sizes in this case [West Africa n(MHH)=668, n(FHH)=27; East Africa n(MHH)=783, n(FHH)=330; Latin America n(MHH)=466, n(FHH)=92; Asia n(MHH)=2310, n(FHH)=60]. Observing trends from East Africa and Latin America, which have the closest sample sizes, the general trend is that changes to crop patterns occur at a similar rate in both regions, but there is a tendency for male-headed households to adopt changes more than female-headed households to a higher degree than the inverse. In both regions the most adopted changes are planting new varieties and higher yielding varieties, while East Africa also has a high rate of adopting drought-tolerant varieties. Table 17 presents the climate-related reasons that influence households’ decisions to change their crop patterns. It is evident that nearly all households reported changing their crops because of more erratic rainfall. Differences were evident between male-headed and female- headed households. For most female-headed households in Costa Rica, Ethiopia, Kenya, Nicaragua and Uganda, less overall rainfall was the key driver for changes in crop patterns. Meanwhile, male-headed households in Mozambique reported higher reasons for changes for all changes except erratic rainfall (which was 100% of all households in the survey). All other countries which reported higher sex-disaggregated rates of climate-related reasons for changing crop patterns should be investigated individually to take context into account. Consistent with Table 17, Figure 9 shows that most decisions to change cropping in the last 10 years were rainfall related. Erratic rainfall appears to be the greatest factor influencing crop changes in the last 10 years. Decisions influenced by erratic rainfall were comparable for male- and female-headed households in Latin America and East Africa. In Asia and West Africa, however, decisions to change crops were predominant in male-headed households. 3 3 % of Households 90 80 70 60 50 40 30 20 10 0 ew va rie ty of c rops uce d n od Intr Plan tin g h igh er y ielding v ari ety Plan tin g b ette r q uali ty va rie ty Plan tin g p re-tr eate d/im prove d se ed Plan tin g s horte r c ycl e va rie ty Plan tin g l onge r c ycl e va rie ty Plan tin g d rough t t oleran t v ari ety Plan tin g f lood to leran t v ari ety Plan tin g s ali nity -to ler an t v ari ety Plan tin g t oxic ity -to leran t v ari ety Plan tin g d ise ase -re sis tan t v ari ety Plan tin g p est- res ist an t v ari ety Te sti ng a new va rie ty Sto pped usin g a va rie ty Other Type of Crop Change Asia MHH Asia FHH Latin America MHH Latin America FHH West Africa MHH West Africa FHH East Africa MHH East Africa FHH Figure 8 Changes in crops planted in the last 10 years by region and sex of household head. (Asia is Bangladesh, India and Nepal; Latin America is Costa Rica and Nicaragua; East Africa is Ethiopia, Kenya, Mozambique, Tanzania and Uganda; West Africa is Burkina Faso, Ghana, Mali, Niger and Senegal). Source: CCAFS, Baseline Household Survey (2010-2012). 3 3 % of households by sex of household head Table 17 Weather/climate related reasons for households changing crops in the last 10 years by % of households by sex of household head and by country n HHs More Less More More More Strong Later Earlier More More Highe Higher higher lower erratic overall overall frequen frequen winds start start cold frequent r tides temper ground rainfall rainfall rainfall t t floods of of spells cyclones salinit (sea atures water drought rains rains or y level table s foggy has days risen) Bangladesh MHH 946 100% 20% 14% 21% 17% 8% 11% 12% 15% 18% 19% 7% 8% 6% FHH 27 100% 33% 7% 33% 15% 4% 4% 11% 30% 4% 0% 0% 22% 26% Burkina Faso MHH 131 80% 26% 23% 34% 21% 21% 26% 26% 21% 20% 22% 19% 21% 21% FHH 7 86% 43% 0% 43% 14% 29% 29% 14% 14% 0% 0% 0% 29% 14% Costa Rica MHH 116 100% 23% 1% 12% 0% 3% 2% 0% 4% 0% 0% 0% 6% 14% FHH 22 100% 45% 9% 32% 9% 9% 5% 5% 23% 0% 0% 0% 32% 18% Ethiopia MHH 101 100% 18% 0% 7% 3% 0% 10% 0% 0% 0% 0% 0% 0% 0% FHH 39 100% 59% 3% 38% 3% 3% 46% 13% 0% 0% 0% 0% 0% 0% Ghana MHH 129 98% 16% 5% 3% 0% 3% 12% 0% 1% 0% 1% 0% 1% 1% FHH 9 100% 11% 0% 22% 0% 11% 22% 11% 0% 0% 0% 0% 0% 0% India MHH 1364 100% 21% 3% 7% 2% 4% 6% 4% 6% 2% 2% 2% 3% 7% FHH 33 100% 55% 3% 36% 0% 3% 6% 9% 21% 0% 0% 0% 27% 18% Kenya MHH 180 100% 13% 2% 2% 0% 0% 3% 1% 0% 1% 0% 0% 0% 0% FHH 96 100% 58% 3% 44% 2% 16% 61% 24% 0% 0% 0% 0% 3% 0% Mali MHH 139 27% 71% 40% 63% 41% 29% 57% 63% 28% 28% 29% 28% 32% 29% FHH 2 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Mozambique MHH 172 100% 74% 69% 77% 68% 68% 70% 69% 68% 68% 69% 69% 69% 68% FHH 106 100% 40% 0% 4% 0% 1% 0% 2% 25% 0% 0% 0% 12% 33% Nepal MHH 663 4% 23% 9% 27% 21% 17% 19% 11% 6% 10% 17% 5% 10% 10% FHH 26 8% 23% 4% 8% 8% 12% 15% 15% 0% 0% 4% 0% 15% 12% Nicaragua MHH 350 100% 18% 25% 32% 9% 18% 12% 12% 1% 1% 0% 0% 4% 0% FHH 70 100% 49% 4% 9% 7% 0% 3% 6% 34% 0% 0% 0% 3% 36% Niger MHH 135 100% 64% 30% 53% 4% 1% 46% 56% 1% 0% 1% 0% 5% 1% FHH 5 100% 100% 0% 60% 0% 20% 100% 20% 0% 0% 0% 0% 0% 0% 4 Senegal MHH 134 100% 47% 46% 41% 26% 33% 40% 37% 3% 1% 8% 1% 1% 19% FHH 4 100% 75% 0% 100% 0% 25% 75% 0% 0% 0% 0% 0% 0% 0% Tanzania MHH 109 100% 37% 36% 36% 30% 32% 35% 36% 6% 5% 6% 3% 3% 12% FHH 31 100% 42% 3% 35% 0% 13% 35% 6% 0% 3% 0% 0% 3% 0% Uganda MHH 221 100% 29% 11% 21% 10% 10% 16% 13% 17% 7% 12% 3% 5% 4% FHH 58 100% 41% 5% 52% 2% 7% 47% 17% 3% 2% 2% 3% 17% 3% n(FHH) > 10% of n(MHH) No significant difference (10+% difference) Women more than men reported this reason Men more than women reported this reason 5 100 90 80 70 60 50 40 30 20 10 0 rat ic r ain fal l e er Mo Le ss ove ral l ra infal l More ove ral l ra infal l More fre quen t d rough ts More fre quen t fl oods Str ong w inds La ter s tar t o f ra ins Ea rlie r s tar t o f ra ins More co ld sp ells or fo gg y d ay s More fre quen t c ycl ones High er s ali nity High er t ides ( sea l eve l h as ris en ) high er tempera tures lower g roundwate r t ab le r Reason for change of crops in the last 10 years Asia MHH Asia FHH Latin America MHH Latin America FHH East Africa MHH East Africa FHH West Africa MHH West Africa FHH Figure 9 Weather/ climate related reasons for changing crops in the last 10 years by region and sex of household head. (Asia is Bangladesh, India and Nepal; Latin America is Costa Rica and Nicaragua; East Africa is Ethiopia, Kenya, Mozambique, Tanzania and Uganda; West Africa is Burkina Faso, Ghana, Mali, Niger and Senegal). Source: CCAFS, Baseline Household Survey (2010-2012). 6 % of Households by Sex of HHH 3.3.2 Changes to livestock production Figure 10 shows factors influencing changes in livestock production, disaggregated by sex and by region. The sample sizes of female-headed households are very small in Asia and West Africa so that results are not reliable. In East Africa, factors reported are high in all areas and comparable between male and female headed households, but male-headed households reported factors relating to projects and policy at notable higher levels. In Latin America, similar trends are seen between male and female-headed households, but market and productivity factors are reported at greater rates by male-headed households. 60 50 40 30 20 10 0 ce pri tte r Be New opportu nity to se ll More producti ve More fre quen t d rough ts More fre quen t fl oods High er t ides Fre quent c ycl ones More sa lin iza tio n Insu ffic ient la bour Able to hire la bour More re sis tan t t o dise ase s New dise ase s a re occu rri ng Gove rnmen t/ project told us t o Gove rnmen t/ project sh owed us h ow Policy ch an ge s Asia FHH Asia MHH East Africa FHH East Africa MHH Latin America FHH Latin America MHH West Africa FHH West Africa MHH Figure 10 Percentage of households reporting factors in changes in livestock production by region. Source: CCAFS Baseline Household Survey (2010-2012) 3.3.3 Changes to agricultural practices Table 18 shows changes to agricultural practices made by female- and male-headed households in the 10 years prior to the household surveys undertaken by the CGIAR. The sample sizes of female-headed households are low in Asia and West Africa but more comparable in Latin America (20%) and East Africa (42%). In the latter two regions, the rate of reporting reasons for 3 3 % of Households changing crops between FHHs and MHHs were generally similar with some exceptions. In Latin America, men reported changing crops due to pests and diseases more than women, and in East Africa, having sufficient labour to make changes as well as government/project intervention and resistance to pests and diseases were reported more by MHHs than FHHs. Table 18 Reasons for changing agricultural practices in the previous 10 years, disaggregated by sex of household head and by region Asia East Africa Latin America West Africa FHH MHH FHH MHH FHH MHH FHH MHH TOTAL 86 2973 330 783 92 466 27 668 Reason for changing agricultural practices in the past 10 years: Insufficient labour when 14% 21% 32% 32% 7% 10% 33% 21% needed Sufficient labour 10% 16% 34% 40% 30% 34% 48% 27% Able to hire labour 6% 20% 35% 36% 20% 20% 26% 21% Unable to hire labour (too 6% 13% 32% 32% 9% 8% 33% 14% expensive) Unable to hire labour (not 2% 8% 24% 25% 1% 4% 0% 4% available) More resistant to 10% 17% 45% 51% 24% 28% 19% 9% pests/diseases New pests/diseases have come 15% 22% 42% 42% 10% 17% 41% 17% Government/ project told us to 3% 6% 30% 37% 0% 2% 15% 15% Government/ project showed 3% 6% 31% 35% 4% 8% 11% 14% us how Policy changes 5% 6% 23% 25% 8% 5% 7% 4% No significant difference (<5% difference) Women more than men reported this reason Men more than women reported this reason 3.3.4 Changes to agricultural practices Figure 11 shows some income sources of households in the CCAFS Household Surveys (baselines), disaggregated by region. In all regions except for West Africa, female-headed households received significantly more remittances than male-headed households. None of the households in these regions received significant payments for environmental services. Women receive fewer loans from formal sources in all regions except Latin America, but in the case of informal sources, women consistently accessed fewer loans. Between the baseline and midline household surveys, the households surveyed by the CGIAR reported changes to their off-farm income (Figure 12). Male-headed households obtained paid employment more than female-headed households, but in both cases the percentage of households reporting this income source decreased between the baselines and midlines. The inverse of this is true for business other than farm products. Female-headed households reported receiving remittances or gifts to a much higher degree than men in both surveys, but male-headed households received payment from projects/government significantly more than female-headed households. Additionally, male-headed households received loans significantly more than female-headed households. 4 70 60 50 40 30 20 10 0 -10 … … … se’s g.… … s … gif t tal … or… al rm rm e el nt (e . n fa rm en cts / nk r fa on me ha or es nm proje a b a info n la nd me an yo u r o w ploy er t nc vir o om m out ou n so (oth itt a r e n t fr fro m ut y nt o d em Rem en it f ro dit tin g o me pai iness nts fo ym cre d y Bus me r p a n/ /cr e Ren tin g plo ther Pay he Lo a an Ren Lo Em O Ot Income Source Asia FHH Asia MHH East Africa FHH East Africa MHH Latin America FHH Latin America MHH West Africa FHH West Africa MHH Figure 11 Percent of households accessing income sources, disaggregated by sex of household head and by region. Source: CCAFS, Baseline Household Survey (2010-2012) Renting out your own land Renting out your farm machinery or animals for traction Loan/credit from an informal source Loan/credit from a bank or other formal institution Other payment from projects/ government including… Payments for environmental services Remittances or gifts Business (other than farm products) Other paid employment Employment on someone else’s farm 0 10 20 30 40 50 60 70 % of Households Midline MHH Midline FHH Baselines MHH Baselines FHH Figure 12 Percentage of respondents receiving sources of off-farm income reported from 2011 (baseline) to 2018 (mid-term) across households in Ghana, Kenya, India and Nepal. Source: CCAFS Household Surveys (Baseline and Midline) Education and household size play an important role in the number of income sources a family has. Table 19 shows the results of a multiple regression analysis to determine how important these influences are. The regression also included the sex of the household head, though this was not found to be a significant factor in any case. The highest level of education in a household was the most commonly significant factor. 5 % of HHs by Sex of Head of Household Table 19 Household socio-economic variables affecting the number of income sources they have access to, disaggregated by sex of household head and by region Asia East Africa Latin America West Africa FHH MHH FHH MHH FHH MHH FHH MHH Total HHs 86 2973 330 783 92 466 27 668 Average HH Size 5 6 5 6 5 5 6 7 Average HH Secondar Secondar Primary Primary Secondar Secondar Primary Primary Education y y y y Average Number of 2 2 2 2 2 2 2 2 Income Sources per HH Significant variables HH Size (p<0.001, +ve) HH Education HH Education HH Size (p<0.05, +ve) affecting number of HH Education (p<0.05, (p<0.001, +ve) (p<0.001, +ve) income sources -ve) EDUCATION LEVEL HHs w no formal 14 6 17 10 5 5 19 25 education (% of HHs) HHs w Primary 27 26 51 52 41 49 67 52 Education (% of HHs) HHs w Secondary 38 38 28 29 42 34 11 22 Education (% of HHs) HHs w Post- 21 29 5 9 11 13 4 1 Secondary Education (% of HHs) 3.3.5 Changes to receiving assistance Receiving assistance in response to a loss from a climate-related shock helps families get back on their feet. Table 20 shows intersections between marital status and decision-making power of households and their support networks. Divorced, single or widowed male-headed households have poor support networks in this dataset in that they are not accessing sources of assistance. Table 21 shows the group activities that enabled households to recover from shocks; however rates of group membership were low. Collective savings and credit were the most helpful group activities. Data show that female-headed households receive more information from friends and family (Figure 13) than male-headed households, but they have less access to extension information. 6 Table 20 Sources of assistance to households who experienced a climate-related shock in past 5 years across all HHs in dataset, disaggregated by type of household (marital status) and sex of household head HH Type: Male Male Female Female Female Child Other MHH (all) FHH (all) headed, headed, headed, headed, headed, headed with a wife divorced divorced husband husband (age 16 or wives , single , single away, away, or or or husband wife under)/O widowed widowed makes makes rphan most most household househo /agricultur ld/agricu al ltural decisions decision s n % n % n % n % n % n % n % n % n % TOTAL 3681 234 124 13 18 1 4 3704 155 Received Help 1043 28 234 100 124 100 13 100 18 100 1 100 4 100 1277 34 155 100 Did Not 2638 72 0 0 0 0 0 0 0 0 0 0 0 0 2638 71 0 0 Receive Help Sources of Help Received Friends, 37% 2% 39% 23% 28% 0% 0% 31% 36% relatives, neighbours Government 67% 9% 68% 46% 78% 100% 100% 56% 67% agencies Politicians, e.g. 3% 0% 15% 0% 17% 0% 25% 2% 14% MPs NGOs/CBOs 23% 4% 36% 38% 28% 0% 75% 20% 35% Religious 2% 0% 3% 0% 0% 0% 0% 2% 3% organisations A local 7% 0% 2% 0% 11% 0% 0% 6% 3% community group that you are a member of Other 1% 0% 1% 0% 6% 0% 0% 1% 1% Table 21 Group activities that helped solve problems related to climate shocks experienced in the previous 5 years, disaggregated by household type and by sex of household head for households who are members of a group Male Male Female Female Female Other MHH FHH TOTAL headed, headed, headed, headed, headed, with a wife divorced, divorced, husband husband or wives single or single or away, away, wife widowed widowed husband makes makes most most household/ household/ agricultura agricultura l decisions l decisions n % n % n % n % n % n % n % n % n % Total HHs 54 86 9 1 56 9 6 1 13 2 1 0 55 88 75 12 62 10 1 0 8 0 Which group activities helped the problem: Tree nursery 2% 0% 2% 0% 0% 0% 2% 1% 2% Fish ponds 0% 0% 0% 0% 0% 0% 0% 0% 0% Fishing 1% 0% 0% 0% 0% 0% 1% 0% 0% Forest product 0% 0% 4% 0% 0% 0% 0% 3% 0% collection Water 0% 0% 4% 0% 0% 0% 0% 3% 0% catchment management 7 Soil 1% 0% 5% 0% 0% 0% 1% 4% 1% improvement activities Crop 3% 0% 5% 0% 0% 0% 3% 4% 3% introduction/ substitution Irrigation 5% 11% 0% 0% 0% 0% 5% 0% 5% Savings and/or 14% 0% 9% 17% 15% 0% 13% 11% 13% credit Marketing 5% 0% 0% 0% 0% 0% 5% 0% 4% agricultural products (i.e. livestock, crops, trees or fish) Productivity 2% 0% 7% 0% 0% 0% 2% 5% 3% enhancement (i.e. livestock, crops, trees or fish) Seed 1% 11% 2% 0% 0% 0% 2% 1% 2% production Vegetable 2% 0% 2% 0% 0% 0% 2% 1% 2% production Others related 5% 0% 0% 0% 8% 0% 5% 1% 4% to soil, land or water management 90 79 80 77 70 Latin America FHH 60 5254 Latin America MHH 50 45 37 39 40 East Africa FHH 25 26 28 30 East Africa MHH 20 2121 20 18 14 16 West Africa FHH 8 10 10 5 0 2 4 0 1 West Africa MHH 0 Extension services Neighbor or family Self-learning Other (No CCAFS) member Figure 13 Sources of information where households learned to implement CSA options. Source: CCAFS, Household Surveys, Monitoring Dataset (2021) 3.3.6 Changes to receiving climate information Figure 14 clearly shows that more female-headed households receive no climate forecasts than male-headed households, especially in West Africa. Though Figure 15 shows more information being relayed to both men and women, especially with respect to rain forecasts, men are still much more likely to be the sole recipients of forecasts. 8 % of HHs by Sex of HHH 90 82 7 79 80 71 5 74 70 66 64 60 56 50 44 40 34 36 29 30 25 26 18 21 20 10 0 Asia East Africa Latin America West Africa Male-headed household received no forecast Male-headed household received some forecast Female-headed household received no forecast Female-headed household received some forecast Figure 14 Households who received or didn’t receive climate forecasts (%) disaggregated by sex of household head and region. Source: CCAFS, Baseline Household Surveys (2010-2012) 2500 2000 1500 1000 500 0 Forecast of the Forecast of Forecast of pest or Forecast of the Forecast of the weather for today, drought, flood, disease outbreak start of the rains weather for the 24 hours and/or frost, cyclone, tidal following 2-3 next 2-3 days surge or other months extreme event Figure 15 Participants’ response to who received forecasting information by sex of recipient across all households in survey. Source: CCAFS, Baseline Household Surveys (2010-2012) Figure 16 shows changes in recipients of weather-related information in Ghana, with both women and men receiving more information except for the 2–3-month weather forecast. In all cases, however, women are more frequently the sole recipients of forecasts, even if only marginally. 9 Number of respondents % of household Men Women Both Men Women Both Men Women Both Men Women Both Men Women Both Figure 16 Change in percentage of household members receiving weather-related information from 2011 (baseline) to 2018 (mid-term) in Ghana. Source: Ouedraogo, et al. (2019) – CCAFS Midline Household Survey Dataset At the regional level, Figure 17 still shows trends in receiving weather/climate forecasts being more favourable for male-headed households or being of a near equal degree to female-headed households. Female-headed households do not receive more forecasts in any case. 80 66 69 70 62 57 60 55 53 52 50 4546 43 40 40 42 40 34 37 37 28 28 2929 3338 37 30 3029 30 34 2631 30 1618 21 22 20 8 13 5 9 1311 10 0 Forecast of drought, Forecast of pest or Forecast of the start Forecast of the Forecast of the flood, frost, cyclone, disease outbreak of the rains weather for the weather for today, tidal surge or other following 2-3 months 24 hours and/or next extreme event 2-3 days Type of Forecast Received Asia FHH Asia MHH East Africa FHH East Africa MHH Latin America FHH Latin America MHH West Africa FHH West Africa MHH Figure 17 Percentage of households who received climate forecasts to any family member by type of forecast, disaggregated by sex of head of household and by region. Source: CCAFS, Baseline Household Survey (2010-2012) Female headed-households report receiving more forecasting information in the midline CCAFS household surveys than in the baseline survey (Figure 18) across the Ghana, Kenya, India and Nepal sites. In fact, more female-headed households reported receiving this information than male-headed households. 10 % of Households by sex of HHH 80 70 60 50 40 30 20 10 0 Forecast of drought, Forecast of pest or Forecast of the start Forecast of the Forecast of the flood, frost, cyclone, disease outbreak of the rains weather for the weather for today, tidal surge or other following 2-3 months 24 hours and/or next extreme event 2-3 days Baseline FHH Baseline MHH Midline FHH Midline MHH Figure 18 Change in percentage of household members receiving weather-related information from 2011 (baseline) to 2018 (mid-term) across all sites in Ghana, Kenya, India and Nepal. Source: CCAFS Baseline and Midline Household Surveys (2010-2012, 2019-2020) In the CCAFS/IFPRI/ILRI datasets, in Kenya women made the most use of weather forecasts, but men received more information in Uganda. In Senegal, the ratio of access to weather forecasts and acting on them is quite evenly distributed between men and women (Table 22). In Table 23, men were receiving the most information from different sources in Nyando and Rakai. Women accessed a comparable amount of information from different sources in Wote, but in Kaffrine there was no particular pattern. 11 % of Households Table 22 Percent of men and women who have access to and make use of different types of weather and agricultural information (Source: Twyman et al, 2014) 12 Table 23 Percent of men and women reporting access to different information sources (Source: Twyman et al, 2014) The percentage of female-headed households who accessed information on crops was higher than for men (Figure 19) in the CCAFS monitoring dataset, especially in West Africa. This is likely an outlier due to the small number of female-headed households there. A higher percentage of households didn’t access any information, or otherwise accessed it but didn’t use the information. Figure 20 explores reasons why users who accessed agricultural management advisories didn’t use the information. The most notable pattern is that female-headed households consistently did not implement the CSA option more than male-headed households because they did not understand the information provided by the advisories. This pattern is also true for households responding that they didn’t know what decisions to make, with the exception of surveyed households in West Africa. Figure 21 shows the same information as Figure 20 with results disaggregated by the sex of the respondent. In this case, the main patterns were that women reported not understanding the information more than male respondents, and male respondents reported not trusting the information as a reason they were unable to use climate forecasting in all regions. More men reported not having the resources to use forecasting 13 information and more women reported not knowing what decisions to make in response to forecasts in Latin America and West Africa. The inverse was true in East Africa. 60 56 50 45 42 40 38 31 32 30 2825 2727 2220 Latin America FHH 20 15 16 1516 Latin America MHH 11 8 11 1011 12 9 10 9 East Africa FHH 10 4 6 6 6 7 6 4 3 1 23 33 00 01 East Africa MHH 0 West Africa FHH ce sse d in form ati on on an im als Acce sse d in form ati on on cr ops Acce sse d in form ait on on pest s Acce sse d in form ati on on w ate r Acce sse d in form ati on on others Didn't a cce ss inform ati on A No ch an ge s b ut a cce sse d in form ati on West Africa MHH c Figure 19 Types of management advisory received or not received by sex of household head and by region. Source: CCAFS, Household Surveys, Monitoring Dataset (2021) 50 45 46 45 42 40 40 37 37 34 35 30 30 Latin America FHH 25 26 23 22 24 25 Latin America MHH 20 20 21 20 20 16 East Africa FHH 14 15 12 13 13 12 East Africa MHH 10 6 West Africa FHH 5 West Africa MHH 0 Didn't know what Didn't have the Didn't trust the Didn't understand decisions to make resource to information / was the information implement changes not accurate enough Figure 20 Reasons users were unable to use daily/weekly forecasts to take agricultural decisions by sex of household head and by region. Source: CCAFS, Household Surveys, Monitoring Dataset (2021) 14 % of HHs by Sex of HHH % of HHs by Sex of HHH 60 60 51 50 40 40 35 35 32 31 31 28 29 30 25 26 24 21 20 18 18 16 15 15 12 13 7 8 10 10 0 Didn't know what Didn't have the Didn't trust the Didn't understand the decisions to make resource to information / was not information implement changes accurate enough East Africa Female East Africa Male Latin America Female Latin America Male West Africa Female West Africa Male Figure 21 Reasons users were unable to use daily/weekly forecasts to take agricultural decisions by sex of respondent and by region. Source: CCAFS, Household Surveys, Monitoring Dataset (2021) 3.4 Gender and Climate-Smart Agriculture (CSA) 3.4.1 CSA practices and technologies With climate projections pointing to economic damages in agriculture, forestry, fishery, energy, and tourism (high confidence), and decreased water availability and other eco-system effects (IPCC, 2022), addressing climate impacts will require considerable changes in agricultural practices to improve adaptive capacity and sustain food security. Varying results of CSA preference and adoption among men and women are reported in the literature under different contexts and geographies. As CSA adoption is influenced by needs and interplay of factors, it may be difficult to develop a unified set of gender-responsive CSA practices and technologies across and within regions. Differences in the adoption of CSA practices and technologies between men and women may be related to factors such as state of climate vulnerability; agricultural production system (e.g. livestock, crop, fisheries); profit (return on CSA implementation); awareness of CSA options; access to labour, land, irrigation, social capital; essential inputs (e.g. improved seeds, fertilizer); and access to credit (Partey et al., 2018). As with any innovative agricultural technology, men and women may be confronted with different needs, opportunities and challenges in implementing CSA (Kristjanson et al., 2017; Jost et al., 2016). It is these needs as well as expected benefits of CSA options and ease of implementation (based on available resources) that are paramount in determining CSA adoption. The available scholarship shows that women and men may need similar CSA options based on crop type, livestock, challenges (e.g. drought, low soil fertility, erosion, limited feed etc.) and expected outcome (e.g. increased income, improved yields, animal growth etc.). However, what normally creates the difference is the financial resources needed to acquire inputs and access 15 % of responses land. In The Gambia for instance, Olaniyan (2017) found that both men and women viewed vaccination of animals, restricted grazing, and domestication of fast-growing small ruminants as climate-smart options for improving the resilience of their livestock systems. However, women were constrained from implementing these CSA options because of limited financial resources. In Northern Benin, Yegbemey et al. (2013) reported that even though CSA options such as crop diversification strategies, revised farming practices and farming calendar adjustment are the most common adjustment of both men and women, women usually do not have the resources for implementation. In Togo, Ali et al. (2020) reported that women’s access to credit, membership in farmer association, access to extension and training increased their likelihood of adopting technologies that improve the production of soybean and adaptation to climate change and variability. It was evident from the study that women could lose about 0.3% of soybean revenue for non-adaptation to climate change (Ali et al., 2020). In Southern Africa, women combined CSA adoption with increased manual labour to make up for lower levels of resources (Mutenje et al, 2019). In the Upper East Region of Ghana Kumasi et al. (2019) found that most CSA practices were common to men and women. Some such as changing herd composition and fertilizer/pesticide application were male dominated, while water harvesting was female dominated (Table 24). Table 24 Gender-responsive adaptation strategies and CSA options in the Upper East Region of Ghana (Kumasi et al., 2019) Strategy type Strategy adopted Gender adoption of Strategy (%) (%) Yes No Men Women Men and Not Women applicable 1. Agricultural Techniques • Crop selection 95 5 3 1 91 5 • Adapt planting dates and 93 7 4 0 89 8 densities • Adapt Fertilizer/Pesticides 42 58 5 0 37 58 application • Adapt tillage practices 80 20 7 1 73 20 • Change the pastoral system 50 50 10 0 41 50 • Change the herd composition 36 64 6 0 30 64 • Apply different feed techniques 3 0 7 0 1 4 0 1 5 7 1 2. Water management techniques • Use Water harvesting techniques 100 0 4 21 75 0 • Improve, construct or rehabilitate 33 67 1 0 32 67 terraces • Use irrigation 47 53 2 0 45 53 • Improve water sites in pastoral 51 49 8 4 39 49 areas 3. Communal pooling • Restore and preserve homestead 44 56 8 0 36 57 or mountain forest to reduce erosion • Rangeland preservation and 47 53 2 0 46 53 grazing restrictions • Soil erosion prevention programs 63 37 9 1 54 37 • Communal water harvesting 11 89 1 0 9 89 • Communal irrigation 31 69 5 5 23 67 16 Ouedraogo et al. (2018) reported gender differences in the prioritization of CSA options in the Lawra and Jirapa districts of Ghana. Among the top ten ranked CSA options, they found four - (a) use of drought-tolerant/short cycle variety, (b) use of improved seed, (c) use of composting and (d) weather information) were common to both men and women. Meanwhile, farmer-managed natural regeneration of trees (FMNR) was ranked by men only while earth-bund was ranked by women only. These differences were attributed to the different needs of men and women. Similarly, because of their access to degraded lands, women farmers in Kampa-Zarma, Niger adopted zaï techniques for the production of cassio tora and cowpea. This practice markedly improved land productivity and empowered rural women to participate in community resources management (Ouedraogo et al., 2018). As clearly demonstrated in the literature, those women who are able to access land, are often allocated marginal lands (Amigun et al., 2011; Patel et al., 2014), closer to the homestead. This may push them to adopt soil and water conservation practices. In Nigeria, Oyawole et al. (2020) found the probability of women adopting soil restoring CSA practices such as green manure and agroforestry was higher than for men. It was however also found that due to their access to land, male plot managers were more inclined to adopt crop rotation practices. Figure 22 Awareness of CSA practices among men and women in Senegal (Kristjanson et al. 2015). Gender trends in awareness of CSA also affect adoption of climate smart practices. A household survey conducted in Senegal, by Kristjanson et al. (2015) found that awareness of men and women differed repecting some CSA practices (Table 25). Men were more aware of CSA practices such as crop residue mulching, improved high-yielding varieties, composting, water harvesting, improved feed management etc. In the CCAFS/IFPRI/ILRI Gender Surveys from 2012, men were more aware of CSA practices than women, especially in Kaffrine, Senegal. Table 26 shows this, with the exception of Nyando, where men and women indicated similar awareness of CSA. In the cases that women are more aware, they are more likely to adopt CSA practices as shown in Table 27. This could be because their access to information is much less than men’s overall, and so their benefits from one source could be greater (Abdur Rashid Sarker et al., 2013; Africa Enterprise Challenge Fund and University of Reading, 2014). 17 Table 25 Awareness of CSA practices among men and women in Senegal (Kristjanson et al. 2015). Table 26 Percent of men and women aware of various CSA practices in each site (Twyman et al, 2014) 18 Table 27 Percent of men and women adopting CSA practices in each site (of those who are aware) (Twyman et al, 2014) In the monitoring dataset for the CCAFS Household Surveys, the most common motivation for implementing CSA options was listed as being ‘because of learning or training’ (Figure 23). Climate-related drivers are the next most motivating factors, with few gender differences. Figure 24 shows the main reasons households stopped implementing CSA options, with labour inputs constituting a major factor, especially in West Africa. 19 80 7269 70 60 53 50 50 Latin America FHH 43 43 Latin America MHH 40 37 East Africa FHH 30 25 2525 East Africa MHH 20 17 West Africa FHH 15 119 11 910 11 8 9 11 10 West Africa MHH 10 6 8 9 0 2 0 0 1 0 Adapt to As a response Because of Because of Other future climate to a climate learning or new market shocks event training opportunities Figure 23 Main motivation for households to implement CSA options. Source: CCAFS, Household Surveys, Monitoring Dataset (2021) 60 51 50 4445 40 3333 34 Latin America FHH 29 30 27 29 Latin America MHH 25 24 24 East Africa FHH 20 20 1818 1817 17 18 East Africa MHH 14 West Africa FHH 10 10 10 8 5 6 West Africa MHH 5 5 6 5 0 0 Did not Did not help to Expensive to Lots of work Other generate adapt to implement required economic climate/\n benefits weather events Figure 24 Main reason households stopped implementing CSA options. Source: CCAFS, Household Surveys, Monitoring Dataset (2021) Climate change is affecting farming practices and driving farmers to adopt Climate-Smart Agricultural practices to mitigate the effects. Table 28 shows the options adopted by male and female respondents from a CGIAR monitoring survey using the GeoFarmer app. Most practices are site-specific, accommodating the needs of the communities, but agroforestry, improved varieties, water and soil conservation and water harvesting were practiced in all regions. Few practices were characterised by significant gender differences. 20 % of HHs by Sex of HHH % of HHs by Sex of HHH Table 28 CSA options adopted in response to climate change, disaggregated by sex of respondent and region. Highlighted cells show cases where the difference in adoption between female and male respondents is more than 5% East Africa Latin America West Africa Changes Changes Changes Changes Changes Changes made to adapt made as a made to adapt made as a made to adapt made as a to Future response to a to Future response to a to Future response to a Climate climate event Climate climate event Climate climate event Shocks Shocks Shocks Femal Mal Femal Mal Femal Mal Femal Mal Femal Mal Femal Mal e e e e e e e e e e e e n 590 682 814 1016 146 236 197 185 231 431 223 320 Agro- ecological pest/disease 5% 8% 2.5% 2% management Agroforestry 3% 4% 2.5% 3% 16% 17% 14% 4% 3% 6% 2% 2% Boundary planting 7% 4% 10% 11% Cookstove 0.5 1% 3% 1.5% % Crop residue 0.4 incorporation 12% 11% 6% 6% % Crop rotation 2% 2% 4% 5% 12% 4% 5% 5% Enclosures 2% 2% 3% 3% Farmer managed natural 20% 22% 22% 25% regeneration Green manure 1% 2% 2% 2% Home garden diversification 7% 5% 7% 5% Home gardens diversification + Water 0.7% 2% 1% 3% harvest Improved 0.3 1.4 breeds 0.3% 0.6% % % Improved varieties 27% 25% 17% 17% 14% 13% 12% 16% 22% 11% 25% 22% Integrated nutrient 0.5 3 0.9% 0. management % % Intercropping 2.1 1.6 2.1% 0.5% 6.5% 11% 7% 7% % % Micro-dose inorganic 9% 6% 9% 6% fertilizer Mixed organic and inorganic 1% 4% 2% 6% fertilizer Organic 0.4 fertilizer 12% 17% 25% 18% % Pasture 0.4 management 28% 24% 21% 15% 2.1% % Reduce tillage 7.5% 5% 7% 7% 14% 15% 2% 7% Water and soil conservation 25% 30% 44% 47% 14% 14% 17% 16% 11% 4% 4% 3% Water 0.1 0.6 harvesting 0.5% 0.1% 9% 14% 19% 19% 2% 4% 1% 3% % % Water harvesting + 5% 7% 5% 10% Aquaculture 21 3.4.2 Influential factors to women participation in CSA The factors affecting women’s use of CSA are illustrated through a set of sex-disaggregated databases that help to build a cohesive picture of constraints for women to agricultural innovation, decision-making, and access to resources, as well as constraints related to their daily activities and time use. a) On-farm decision-making Reliable decision-making data are very difficult to obtain due to perceptions from respondents influencing their answers. Some studies have tried to quantify this. Table 29 lists some examples where these data are present. Table 29 Datasets with sex-disaggregated data on decision making SOURCE NAME YEAR SCALE DISAGGREGATION DESCRIPTION/VARIABLES IFPRI Pakistan Rural 2012 Household, Sex, education, Decision-making power in Household agricultural marital status, household spending Panel Survey position in family AFRICAN Economic 1990- Country, Sex, country Distribution of agricultural DEVELOPMENT Empowerment 2012 agricultural holders disaggregated by sex BANK – Ownership of Land and House: Agricultural Holders Some country/site specific projects exist to quantify the decision-making power of men and women in the agricultural sector. Figure 25 shows one example of this from the Madhya Pradesh Climate-Smart Village in India, where men are reported to have more autonomous decision- making power than women over all crop and livestock-related decisions. However, households reported sharing decision-making responsibilities in the majority of cases. As discussed in the methods section of this report, it is very difficult to obtain reliable and unbiased data on decision making at the household level. In the literature, men are thought to have more powers in household decision making due to their control over financial resources and readily access to land and farm resources (Kristjanson et al., 2017; Jost et al., 2016) while women tend to report “joint” decision-making in the household more frequently (Liaqat et al., 2021). Figure 25 Decision-making by activity of women and men farmers in a) agriculture and b) livestock rearing in Climate-Smart Village Madhya Pradesh, India. (Source: Chanana et al. 2018) 22 One notable inclusion in availability of decision-making data is from the African Development Bank. Figure 26 shows the sex-disaggregation of agricultural holders in 24 African countries with the year of latest data availability. The FAO defines the term ‘agricultural holder’ as a person or group who exercise management control and makes major decisions over an agricultural holding (FAO, 2015). In the case of the data from the African Development Bank, there are significantly fewer female than male agricultural holders in all countries listed except for Cabo Verde. 100 96 92 95 94 97 96 90 91 92 90 91 94 90 85 81 81 84 81 77 80 80 65 67 69 70 68 60 5150 50 40 35 33 31 32 30 23 19 16 20 19 20 15 19 8 10 9 10 4 5 8 6 9 10 3 4 6 0 01) ria (2 0 Alge Botsw an a ( 2004) Burki na F aso (1 993) Cab o Verde ( 2004) Comoros ( 2004) Côte d'Iv oire (2 001) Democra tic Republic of th e C ongo … Eg yp t (1 999) Eth iopia (2012) Gam bia (2002) Guinea (2 001) Le so tho (2 000) Mad ag asc ar (2005) Mala wi (1 993) Mali (2 005) Morocco (1 996) Mozam bique (2 000) Nige ria (2 007) Se neg al (1998) Se ych elle s ( 2011) Tu nisia (2 005) Uga nda ( 1992) Unite d Republic of T an zan ia (2002) Za mbia (2000) Country Name (Year of Most Recent Data Available) Female Male Figure 26 Agricultural holders disaggregated by sex in 24 African countries and most recent year of data availability. Source: African Development Bank (1990-2012) b) Access to resources Access to and ownership of resources is a major constraint to women’s ability to adopt agricultural innovation. Household-level data exists to attempt to quantify this, some examples of which are in Table 30. In most developing countries, women have limited access to land, financial credits, labour, improved seeds etc. (Jost et al., 2016). For example, in West Africa patrilineal systems of inheritance restricts allocation of lands to women (Agana, 2012). Moreover, lands normally available to women may be characterized by low fertility and may be located far from water sources (Quan et al., 2004). 23 Distribution of Agricultural Holders (%) Table 30 Household-level data sources for sex-disaggregated data on asset ownership SOURCE DATASET YEAR SCALE DISAGGREGATION DESCRIPTION/VARIABLES NAME CCAFS IMPACT-Lite 2014 Household, Sex, age, Plot ownership and characteristics, Surveys agricultural on/off-farm product consumption, plot renting; livestock ownership; IFPRI Pakistan Rural 2012 Household, Sex, education, marital Assets owned, age and value ‘if sold Household agricultural status, position in today’; livestock; farming equipment Panel Survey family IFPRI Bangladesh 2018 Household, Sex, age, ethnicity, Agricultural plot value ‘if sold today’ Agricultural agricultural marital status, and characteristics of plot (e.g. size, Value Chain religion, migration irrigation); livestock; machinery; (AVC) Impact status, position in technology; land Evaluation family IFPRI Bangladesh 2012 Household, Sex, age, education, Ownership of productive capital, Integrated agricultural marital status, who owns it following a change in Household literacy, situation (e.g. divorce, death); roster Survey (BIHS) of agricultural land and water bodies owned, their value ASIAN Sex- 2017 Country, Sex, country Ownership of agricultural land DEVELOPMENT Disaggregated agricultural BANK Data on Asset Ownership: Georgia, Mongolia, and the Philippines UN WOMEN Progress on 2021 Continent, Continent Women’s and community the unspecified participation in integrated water Sustainable management Development Goals: The Gender Snapshot 2021 FAO Water and 2005, Continent, Sex, age Area equipped for irrigation AQUASTAT Gender: 2008, managed by sex, agricultural National Data 2013 holdings equipped for irrigation by sex, age structure of water managers The Asian Development Bank published data on agricultural land ownership in 2017. Figure 27 demonstrates men own more land than women in the three countries listed (Georgia, Mongolia and Philippines). This is also characteristic of the trend in Africa where farmlands is predominantly male owned. 24 60.0 50.0 47.7 40.0 34.1 30.6 30.0 20.0 12.6 10.0 6.3 8.0 4.1 4 1.4 2.0 2.6 3.2 .8 – Georgia Mongolia Cavite, Philippines Documented Ownership Women Documented Ownership Men Reported Ownership Women Reported Ownership Men Figure 27 Sex disaggregated data on agricultural land ownership for three locations in Asia. Source: Asian Development Bank, 2017 The World Bank Living Standards Measurement Study-Plus (LSMS+) presents individual- disaggregated survey data collected in low and middle-income countries, including sex- disaggregated data on landowners without rights to sell or bequeath. Data in four countries in Africa shows that overwhelmingly when women are landowners, they tend not to have the ability to sell or bequeath rights to the land (Figure 28). Figure 28 Share of landowners that do not have sell or bequeath rights. Source: Hasanbasri et al, 2021 c) Water and irrigation Table 31 shows a summary of women’s participation in integrated water resource management by region, compared with community participation. In 5 of the 7 regions listed, women’s participation is lower than user/community participation. Considering their role in household 25 Incidence of Ownership of Agricultural Land (%) health and sanitation, as well as irrigation of household crops, this can be an important gap (OECD, 2019). Table 31 Participation of women in integrated water resource management (2018-2019) by percentage of countries in each region Levels of user/community Levels of women’s participation SDG Region participation Oceania, excluding Australia and New Zealand 0 20 Central and Southern Asia 9 27 Latin America and the Caribbean 9 26 Eastern and South-Eastern Asia 13 13 Sub-Saharan Africa 27 46 Northern Africa and Western Asia* 33 33 Europe and Northern America* 50 80 Australia and New Zealand 100 100 World 22 37 *Regions marked with a star have a lower country/population coverage than UN Women’s criteria of 50 per cent of countries and/or 66 percent of population coverage in the region. Source: UN Women, Progress on the Sustainable Development Goals: The Gender Snapshot 2021, adapted from UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water Survey, 2018-2019. In terms of land management and irrigation, little data exists on the total area managed by women and men, though some studies (such some of those listed in Table 30) list irrigation as a household resource that is included in household-level studies. d) Access to Information Across low- and middle-income countries (LMICs), 81% of women now own a mobile phone compared to 87% of men (GSMA, 2023). Even though 60 million additional women owned one in 2022, 440 million still do not (compared to 290 million men). Growth in mobile ownership for both women and men has remained relatively flat and the gender gap has seen little change as a result. Women are currently 7% less likely than men to own a mobile phone, which translates into 130 million fewer women than men owning one. This gender gap in mobile ownership varies significantly by region (Figure 29). For example, the gender gap is 2% in East Asia and Pacific, but 13% i 26 Figure 29 Gender gap in mobile ownership and internet connectivity For example, the gender gap is 2% in East Asia and Pacific, but 13% in Sub-Saharan Africa. South Asia still has the widest gender gap in mobile ownership at 15%, but this has narrowed significantly over the past five years, down from 28% in 2018. Of all the countries surveyed by GSMA in 2022, Pakistan recorded the widest gap in mobile ownership at 35%, followed by Ethiopia at 27% (Figure 30). Figure 30 Male and female mobile ownership and mobile internet adoption, by country. Source: GSMA, 2023 According to the GSMA Mobile Gender Gap Report 2023, more women in low- and middle- income countries (LMICs) are using mobile internet than ever before, but their rate of adoption has slowed for the second year in a row. While 61% of women across these countries now use mobile internet, only 60 million women started using mobile internet in 2022 compared to 75 million in 2021. Men’s rate of adoption also slowed in 2022, highlighting that progress on digital inclusion for all has stalled across LMICs. This gender gap has seen little change since 2017 and the 440 million women who still do not own a mobile phone are proving difficult to reach (Figure 31). As revealed by the report, gender gaps in mobile internet use are wider than gender gaps in mobile ownership in all markets. Even in countries with relatively small gender gaps in mobile ownership, such as Ghana, Kenya, Nigeria and India, the gender gap in mobile internet use can be substantial. For example, in Ghana, women are 7% less likely than men to own a mobile phone, but 26% less likely to use mobile internet (Figure 30). 27 The GSMA survey cited affordability; literacy and digital skills literacy; and relevance as the top barriers to mobile ownership for men and women. In rural Africa, the cultural settings and women’s limited control of household financial resources are cited as potential barriers (Partey et al., 2018). In rural Kenya, Krell et al. (2020) such barriers including limited technical literacy as constraints to women’s patronage of mobile services for agricultural activities. Women are therefore likely to have limited access to extension services and agricultural information delivered through mobile phone platforms and hence stand higher chance of facing climate- related risks. The cultural setting of rural Africa and women’s limited control of household financial resources are cited as potential barriers (Partey et al., 2018). In rural Kenya, Krell et al. (2020) such barriers including limited technical literacy as constraints to women’s patronage of mobile services for agricultural activities. Women are therefore likely to have limited access to extension services and agricultural information delivered through mobile phone platforms and hence stand higher chance of facing climate-related risks. ITU figures are somewhat older but provide useful information on access to internet through different digital technologies. Internet access is gendered where men are more likely to use the internet than women. In Figure 31, this is the case for most of the countries listed, with a significance of p<0.05. In the case of broader regionality, such as those presented in Figure 32, the internet is used more by men in most contexts, but the comparison is very small in the Americas, Small-Island Developing States and in Developed countries. The gap is highest in developing countries. 100 90 80 70 60 50 40 30 20 10 0 ria nia Alge e Ar Austr ali a Azer baij an Braz il Cab o Verde Côte d'Iv oire Cuba m Denmark Dominica n Rep. El Sa lva dor Est onia Fra nce Isr ae l Jap an Mozam bique Pale sti ne Sw itz erla nd Male Female Figure 31 Individuals using the internet by sex, Rural (%), p<0.05. Source: ITU (2017-2020) 28 % of Individuals using the internet 100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 rld d Wo e elo p elo ping Dev Dev Le ast Deve loped Countri es (LD Cs) La nd Lo cke d Deve loping C ountri es ( LLD Cs) Sm all Isl an d Deve loping S tat es (SI DS) Afric a Arab St ate s Asia & Pac ific CIS Eu rope Th e Ameri ca s Total Female Male Figure 32 Percentage of individuals using the Internet, by sex, 2019 e) Gaps in income, credit and finances Table 32 shows the sex wage gap data available from 2009 to 2020 for workers in skilled agriculture, forestry and fishery. These data indicate that the wage gap is positive, meaning that women make less than men, in all countries listed except for Argentina, Belarus, Belize, Brunei, Dominican Republic, Israel and Russia (7/22 countries). With comparatively higher wages and access to properties, men are more able to meet the collateral requirements of financial institutions to access loans in many countries. In addition, the higher income of men mean they can invest in alternative livelihoods that can serve as important safety nets to alleviate risks posed by climate change. Table 32 Sex wage gap by occupation: Skilled agricultural, forestry and fishery workers. Data are expressed as a percentage of men’s average wages 2 009 2 010 2 011 2 012 2 013 2 014 2 015 2 016 2 017 2 018 2 019 2020 Afghanistan 2 9.3 Argentina - 8.8 Belarus - 9.0 Belize -16.7 1 8.5 - 13.0 Brunei -45.9 Darussalam Czechia 1 0.5 9 .9 1 0.4 9.7 1 4.0 1 1.5 1 1.1 1 0.8 1 0.8 Dominican -4.3 Republic Finland 5 .9 2 .1 2 .2 3.7 5 .8 2 .2 Israel -17.2 2.8 -0.7 29 % of Individuals Korea, Republic 36.2 38.3 27.4 27.3 31.4 18.3 30.7 19.4 27.8 16.7 19.8 16.6 of Malaysia 1 2.9 2 8.6 2 1.4 4 1.3 3 5.8 27.4 3 2.8 3 1.2 Mexico 37.2 Pakistan 53.3 61.5 61.4 Portugal 1 4.9 - 1.9 8 .6 7 .6 1 8.2 1 6.0 6.8 3 .4 17.9 5 .6 Russian -8.3 -9.5 Federation Slovakia 1 6.5 2 4.0 1 5.3 10.0 South Africa 1 0.0 2 5.0 Spain 3 .3 Sweden 1 1.1 7.1 1 1.5 10.8 8 .4 Thailand 12.2 1 4.4 1 4.4 Ukraine - 3.0 8.3 United Kingdom 1.4 -2.8 1.8 2.1 2.9 3.4 Source: ILOSTAT. Recently-published (2021) data from UN Women show that, in all countries listed, female- headed, small-scale producers earn less income than their male counterparts. None of the exceptional countries from Table 34 were listed for comparison with these results which are shown in Figure 33. Financial capacity is explored in the Global Findex Database, where Figure 31 shows income sources such as (a) saving money, (b) borrowing money, and (c) receipt of payment for agricultural products disaggregated by sex and by country income level. In this dataset, women save less money, borrow less money are receive less money from agricultural products than their male counterparts at all levels of country income. The data on borrowing are mirrored in the results of Table 34 which shows female-headed households using less credit than their male counterparts in Kenya. 6000 3 5000 2.5 4000 2 3000 1.5 2000 1 1000 0.5 0 0 Male-headed small-scale producers Female-headed small-scale producers Figure 33 Small-scale producers’ annual income, disaggregated by sex and juxtaposed against the male-female income ratio. Source: UN Women, Progress on the Sustainable Development Goals: The Gender Snapshot 2021, from Global SDG Indicator Database, 2021 30 Annual income (constant 2011 USD) Burkina Faso Bangladesh Mali Nicaragua Nigeria Malawi Ghana Niger Ethiopia Ecuador Peru Kyrgyzstan Pakistan Senegal Nepal Mongolia India Guatemala Mexico Armenia Viet Nam Georgia Iraq Rwanda Republic of… Cameroon Uganda Sierra Leone Male-to-female income ratio a) Saved to start, b) Borrowed to c) Received payments operate, or expand start, operate, or for agricultural a farm or business expand a farm or products in the past 40% 40% 40% business year 30% 30% 30% 20% 20% 20% 10% 10% 10% 0% 0% 0% me in co me High er m iddle in co me Middle inco me Lo wer m iddle in co me Lo w in co me me in co in co High High er m iddle in co me Middle inco me Lo wer m iddle in co me Lo w in co me er m iddle in co me Middle inco me Lo wer m iddle in co me Lo w in co me % age 15+ Upp Upp Upp Male (% age 15+) Female (% age 15+) Figure 34 Financial inputs for investing in agricultural endeavours and payments received for agricultural products, disaggregated by sex and by country income status. Source: Global Findex Database (2017) Table 33 Use of credit from household survey data in Kenya Female Headed Male Headed Households Households Used Credit Yes 40% 57% No 60% 43% Guarantee Title Deed 37.5% 29% Own Farm Production 37.5% 58% Farmer's Coop 25% 13% Source: CCAFS, Climate-Smart Soil Practices in Western Kenya (2017). Another source of potential income rather than selling agricultural products is in renting assets. In Table 35, the percentage of female and male headed households renting assets shows that male-headed households are renting their assets more. These data, however, do not include information on the amount received for renting which would be a valuable descriptor. Table 34 Percentage of households renting out assets disaggregated by sex of household head across all households surveyed in the CCAFS Gender Baseline Study Female Headed Male Headed Households Households Renting out your farm machinery (e.g., tractor, thresher, pump, 2.2% 7.9% etc.) or animals for traction Renting out your own land 5.0% 7.4% Source: CCAFS, Gender Baseline Survey (2013). 31 % of population Control over income is another highly gendered issue in agricultural households. Figure 35 shows the breakdown of income control based on votes from household-level surveys in Bangladesh. The only avenue in which women have comparable control over income is in income in livestock in this case, but women otherwise have significantly less control over income in all other areas in this context. 80 70 60 50 40 30 20 10 0 r r tri es tock e in jo b rk rs y l ab o us y l ab o nc ed d w o othe da in d liv es da itta ure om loye rem sal ari ult tag e e fr ltu re mp ric co t ric u ag om sel f e inc luding n-ag nc no ss i sin e bu Both Female Male Figure 35 Income control description from household-level surveys, p<0.01 between female and male famers in Bangladesh. Source: CCAFS, IMPACT-Lite Surveys (2014) CONCLUSION Reliable sex-disaggregated data are critical for understanding the community and intra- household dynamics of how individuals respond to climate change. The ability for small-scale farmers to adapt to climate change is contingent upon several variables which are highly gendered including access to resources, decision-making power and prevailing gender divisions of labour and responsibilities. This review has synthesized available data and presented the current state of available data on the nexus between gender, agriculture, food security and climate change. In order to advance the gender agenda, further research needs to be conducted to fill data gaps and ensure the representation of all people in all contexts and at all socio-economic intersections. Gender intersects with other factors such as age, marital status, intrahousehold dynamics and education. It is an important aspect for understanding women’s access to environmental services and participation in climate change mitigation. Acosta et al. (2021), for example, describe the relationship between education level and use of climate forecasts as conditional on age. 32 Votes Even though intrahousehold studies are more relevant to exploring gender dynamics, most available gender data in agriculture is disaggregated by the sex of the household head. The CCAFS Household Surveys do record the sex of respondents and it is possible to disaggregate responses using these data; a useful tool given the small sample sizes of female headed households in the Climate-Smart Villages. Intrahousehold analyses, such as those conducted in the CCAFS Household Survey Monitoring datasets, allow for more similar sampling sizes. The Digital data collection, such as through the GeoFarmer app can collect data directly from farmers and can allow for better sex disaggregation. Interconnected constraints to climate change adaptation are not well represented in the current data. Data2x (2021a) identifies gender gaps by six development areas which intersect with agriculture and climate change and present the following gaps which remain unchanged or newly identified since their 2014 data mapping exercise: Economic opportunities: Access to Childcare Employment Mobility Entrepreneurship Migrant Worker Conditions Pay Gaps & Opportunity Costs Education: Excluded Girls Learning Outcomes Environment: Consumption & Production Disaster Risk Management Disaster-related Mortality/Illness Environment & Health Inclusion in Decision-making Land & Resource Rights Health: Adolescent Health Mental Health Utilization of Health Services Violence against Women Ageing Population Cause of Death Disability Sexual & Reproductive Health & Rights Human Security: Conflict & Gender Violence War-related Mortality/Illness Human Trafficking Safety in Public Spaces Violence against Children Public Participation: Local Representation Professional Leadership Voter Registration & Turnout Violence against Women in Politics 33 Data2x (2021b) recommends addressing gender data gaps by intentionally and deliberately incorporating sex-disaggregation in all agricultural data collection and relating it to climate change. In other climate-related sectors, quantitative indicators can provide robust evidence of gender disparities such as disaster recovery time or sex-disaggregation of time spent on activities which are disaster-related, but not many examples of this exist. Simelton et al. (2021) conducted a multiple regression analysis to determine the relationship between income, sex and plot-level data on disaster recovery time, and this work could be taken further. Not many disaster datasets exist, and they are most often highly generalized at the country level and not sex-disaggregated. The EM-DAT disaster database tracks natural, technological and complex (health) disasters from 1900 to the present across the world, however there is no associated understanding of the effect of these disasters on agricultural productivity. Some platforms are available which track sex-disaggregated data (Table 30) but there are still gaps that need to be addressed to fill gaps in agriculture and climate change mitigation. By observing surveying techniques that allow respondents to feel comfortable and accurately represented, as well as standardizing survey collection that focuses on the individual rather than household representation, more robust and reliable sex-disaggregated data can fill in the research gaps and lead to better representation of gender dynamics in climate change mitigation. Time-use information that is quantitative and comparative within and between households remains an important gap (e.g. time spent on tasks performed by female vs male household members; time and labour distributions throughout the day; focus on time-poverty and improving time-saving practices). Time-use could be used to better understand gendered resource ownership, access and gender norms. Quantifying income from same tasks instead of listing tasks is another approach to highlighting gender differences in agricultural production. 34 APPENDIX A: LIST OF SOURCES CONSULTED Organisation Year Name Brief description Sex- Sector Climate change focus CSA Scale Geog Type Link / Author Disaggre- practices- focus gated Risk Adapt Mit’n technologies Impact African 1990- Gender Data Portal: The Gender Data Portal Y agricultur country Africa Dataset https://genderdata. Development 2012 Agricultural Holdings currently provides data on e org/portal/#/dataVi Bank gender indicators for all sualization countries in Africa. Data on 79 gender indicators from national surveys, statistical estimates and other robust sources are available African 1990- Gender Data Portal: Poverty Y gap country Africa Dataset https://genderdata. Development 2013 Rate by Sex of the Household org/portal/#/dataVi Bank Head (from UNSD) sualization Asian 2014- Sex-Disaggregated Data on Sex-Disaggregated Data on Y agricultur country Asia Dataset https://data.adb.org Development 2015 Asset Ownership: Georgia, Asset Ownership e /dataset/sex- Bank Mongolia, and the disaggregated-data- Philippines asset-ownership CCAFS 2018- CSA Monitoring Survey This dataset contains the files Y agricultur Y Y Y Y househol Africa, Dataset https://dataverse.h 2021 Results produced in the e d Asia, arvard.edu/datavers implementation of the Latin e/CCAFSbaseline?q= “Integrated Monitoring Americ &types=dataverses Framework for Climate-Smart a %3Adatasets&sort= Agriculture” in CCAFS’ Climate- dateSort&order=des Smart Village sites. This c&page=1 monitoring framework developed by CCAFS was 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 3 3 CCAFS 2020 Midline Household Survey Midline information on Y agricultur househol Report https://ccafs.cgiar.o Results: Bagerhat, changes in household e d rg/resources/public Bangladesh characteristics, climate and ations/midline- farming practices following the household-survey- baseline results-bagerhat- bangladesh CCAFS 2020 Midline Household Survey Y agricultur househol Report https://ccafs.cgiar.o Results: Rupandehi, Nepal e d rg/resources/public ations/midline- household-survey- results-rupandehi- nepal CCAFS 2019 Midline Household Survey Y agricultur househol Report https://ccafs.cgiar.o Results: Vaishali, Bihar State, e d rg/resources/public India ations/midline- household-survey- results-vaishali- b ihar-state-india CCAFS 2019 Midline Household Survey Y agricultur househol Dataset Data (Ghana and South Asia) e d CCAFS 2010- Baseline Household Survey Baseline information on Y agricultur househol Dataset https://ccafs.cgiar.o 2012 Data household characteristics, e d rg/resources/tools/c climate and farming practices cafs-baseline- survey-data-and- materials CCAFS 2016 Gender Differences in Y agricultur househol Report https://cgspace.cgia Climate Change Perception e d r.org/bitstream/han and Adaptation Strategies: dle/10568/70965/V The Case of Three Provinces NM_gender_report in Vietnam’s Mekong River _final.pdf Delta CCAFS Database on sex- Y agricultur Database https://ccafs.cgiar.o disaggregated data in CCAFS e rg/index.php/resour research ces/tools/sex- disaggregated-data- climate-smart- agriculture-ccafs- publications CGIAR 2017 Household Survey Data on Y agricultur househol Dataset https://dataverse.h Cost Benefit Analysis of e d arvard.edu/dataset. Climate-Smart Soil Practices xhtml?persistentId= in Western Kenya doi:10.7910/DVN/K 6JQXC 4 CGIAR 2014 Climate shocks and choice of Y agricultur househol Working https://repository.ci adaptation strategy for e d Paper mmyt.org/xmlui/bit Kenyan maize-legume stream/handle/108 farmers; Insights from 83/4147/99541.pdf poverty, food security and gender perspectives CGIAR 2016 Gender Differences in Y agricultur househol Report https://cgspace.cgia Climate Change Perception e d r.org/handle/10568 and Adaptation Strategies: /70965 The Case of Three Provinces in Vietnam’s Mekong River Delta CRED 1900- EM-DAT: the international N Database https://public.emda 2021 disaster database t.be/ Equal Various Gender Advocates Data Hub Y rural country Database https://data.em203 Measures 0.org/data-gaps/ 2030 FAO 2005, AQUASTAT - FAO's Global Y agricultur country Database http://www.fao.org 2010, Information System on e /aquastat/en/overvi 2 013 Water and Agriculture ew/ FAO FAO Gender and Land Rights Y agricultur country Database http://www.fao.org Database e /gender-landrights- database/data- map/statistics/en/ FAOStat 1947- Employment Indicators: Y agricultur country Database http://www.fao.org 2020 Employment in agriculture e /faostat/en/#data (%) FAOStat 1961- Agricultural Production N agricultur country Database http://www.fao.org 2021 e /faostat/en/?#data/ QCL ICRAF 2014 The Role of Grassroots Y agricultur househol Thesis https://data.worlda Institutions in Enhancing e d groforestry.org/data Adaptation to Climate set.xhtml?persistent Variability in Smallholder Id=doi:10.34725/DV Farming Systems N/23640 IFPRI 2016 Gender Dimensions on Y agricultur househol Report https://www.ifpri.or Farmers’ Preferences for e d g/publication/gende Direct-Seeded Rice with r-dimensions- Drum Seeder in India farmers%E2%80%99 -preferences-direct- seeded-rice-drum- seeder-india 5 IFPRI 2012 Pakistan Rural Household Y agricultur househol Dataset https://dataverse.h Panel Survey (PRHPS) e d arvard.edu/dataset. xhtml?persistentId= doi:10.7910/DVN/T 9GGYA IFPRI 2014 Bangladesh Climate Change Y agricultur househol Dataset https://dataverse.h Adaptation Survey (BCCAS) e d arvard.edu/dataset. xhtml?persistentId= doi:10.7910/DVN/2 7883 IFPRI 2018 IFPRI-CCAFS Gender and Y agricultur househol Dataset https://dataverse.h Climate Change Survey Data: e d arvard.edu/dataset. Rakai, Uganda xhtml?persistentId= doi:10.7910/DVN/C BVLK5 IFPRI 2018 IFPRI-CCAFS Gender and Y agricultur househol Dataset https://dataverse.h Climate Change Survey Data: e d arvard.edu/dataset. Wote, Kenya xhtml?persistentId= doi:10.7910/DVN/R BW801 IFPRI 2018 IFPRI-CCAFS Gender and Y agricultur househol Dataset https://dataverse.h Climate Change Survey Data: e d arvard.edu/dataset. Nyando, Kenya xhtml?persistentId= doi:10.7910/DVN/J U7QP6 IFPRI 2018 IFPRI-CCAFS Gender and Y agricultur househol Dataset https://dataverse.h Climate Change Survey Data: e d arvard.edu/dataset. Kaffrine, Senegal xhtml?persistentId= doi:10.7910/DVN/V A2MER IFPRI 2018 IFPRI-CCAFS Gender and Y agricultur househol Dataset https://www.ifpri.or Climate Change Survey Data: e d g/publication/ifpri- Bangladesh ccafs-gender-and- climate-change- survey-data- bangladesh IFPRI 2018 Bangladesh Agricultural Y agricultur househol Dataset https://dataverse.h Value Chain (AVC) Impact e d arvard.edu/dataset. Evaluation: Baseline Survey xhtml?persistentId= doi:10.7910/DVN/X NAHHB IFPRI 2015 Bangladesh Integrated HH Y agricultur househol Dataset https://dataverse.h Survey (2015) e d arvard.edu/dataset. xhtml?persistentId= 6 doi:10.7910/DVN/B XSYEL IFPRI 2018- Bangladesh Integrated HH Y agricultur househol Dataset https://www.ifpri.or 2019 Survey (2018-2019) e d g/publication/bangl adesh-integrated- household-survey- bihs-2018-2019 IFPRI 2011- Bangladesh Integrated Y agricultur househol Dataset https://www.ifpri.or 2012 Household Survey (BIHS) e d g/publication/bangl 2011-2012 adesh-integrated- household-survey- bihs-2011-2012 ILOStat 1990- Labour Statistics on Women Y agricultur country Dataset https://ilostat.ilo.or 2020 e g/topics/women/ Indian 2011 The Gender Asset and Y agricultur country Report https://www.resear Institute of Wealth Gaps: Evidence from e chgate.net/publicati Management Ecuador, Ghana, and on/264859166_The Bangalore Karnataka, India _Gender_Asset_and (IIMB) _Wealth_Gaps_Evid ence_from_Ecuador _Ghana_and_Karnat aka_India IPCC 2021 Climate Change 2021: The N Report https://www.ipcc.ch Physical Science Basis /report/ar6/wg1/do wnloads/report/IPC C_AR6_WGI_SPM.p df ITU 2016- Individuals Using Internet By Y rural country Dataset https://www.itu.int/ 2020 Gender ITU- D/ict/statistics/Gen der/index.html ITU 2020 Key ICT Indicator Aggregates Y rural country Dataset https://www.itu.int/ ITU- D/ict/statistics/Gen der/index.html Our World in Agricultural Production N agricultur country Database https://ourworldind Data e ata.org/agricultural- production#cereals UN Statistics Various Time use data portal Y rural country Database https://unstats.un.o Division rg/unsd/gender/tim euse/ UN Water Various UN Water Data Portal N rural country Database https://www.sdg6d ata.org/ 7 UN Water 2021 Summary Progress Update N rural project Report https://www.unwat 2021: SDG 6 — water and er.org/publications/ sanitation for all summary-progress- update-2021-sdg-6- water-and- sanitation-for-all/ UN Water 2019 UN World Water Y rural country Report https://www.unwat Development Report 2019 er.org/publications/ world-water- development- report-2019/ UN Women 2021 Progress on the Sustainable Y rural country Dataset https://www.unwo Development Goals: The men.org/en/digital- Gender Snapshot library/publications/ 2021/09/progress- on-the-sustainable- development-goals- the-gender- snapshot-2021 UN Women Various UN Women Count Y rural country Database https://data.unwo men.org/data- portal/ UNESCO 2019 UNESCO gender and water Toolkit https://en.unesco.o data initiative rg/wwap/water- gender United Data 2x Y Resource https://data2x.org/ Nations Base Foundation USAID 2019 Ethnographic research on Y agricultur househol Dataset https://gardian.bigd the food security of rice e d ata.cgiar.org/datase farmers in Vietnam t.php?id=5d9c6549b f0671243446a34a#! / WHO 2019 Global database on the N rural country Database https://extranet.wh Implementation of Nutrition o.int/nutrition/gina/ Action (GINA) en/mechanisms/su mmary World Bank 1980- Living Standards Y agricultur househol Dataset https://microdata.w 2020 Measurement Study (LSMS) e d orldbank.org/index. php/catalog/lsms World Bank Various Gender Data Portal Y rural country Database https://www.world bank.org/en/data/d atatopics/gender/ab out 8 World Bank Various Global Findex Database Y agricultur country Database https://globalfindex. e worldbank.org/ 9 APPENDIX B: LIST OF PRACTICAL RESOURCES Thematic area Resource Climate-Smart Agriculture Rapid Appraisal (CSA-RA) (sub-tool of CSA guide) (IFAD, CIAT, IITA, CCAFS, Sokoine University) (Mwongera et al., 2015) Guide to Participatory Scenario Planning (PSP): Experiences from the Agro-Climate Information Services for women and Practical resources for ethnic minority farmers in South-East Asia (ACIS) project in Ha Tinh and Dien Bien province, Vietnam (CARE, ICRAF) understanding/changing the Enhancing Women’s Assets to Manage Risk under Climate Change. Potential for group- based approaches (IFPRI) (Ringler et enabling environment al., 2021) Land rights knowledge and conservation in rural Ethiopia: Mind the gender gap (IFPRI) (Quisumbing and Kumar, 2014) Training manual on gender and climate change resilience (Arrow, UN women) (Dharmistha, 2021) Guide to UNFCCC Negotiations with agriculture – Toolkit (CCAFS, CTA, Farming first) Climate-Smart Agriculture Rapid Appraisal (CSA-RA) (subtool of CSA guide) (IFAD, CIAT, IITA, CCAFS, Sokoine University) (Mwongera et al., 2015) Enhancing Women’s Assets to Manage Risk under Climate Change. Potential for group- based approaches (IFPRI) (Ringler et al., 2021) Training manual on gender and climate change resilience (Arrow, UN women) (Dharmistha, 2021) Gender Household Survey, CCAFS Dataverse (harvard.edu) (CCAFS, IFPRI, ILRI, 2013) Climate Change & Food Security Vulnerability Assessment Toolkit (Bioversity and IDS) (Ulrichs et al., 2015) Practical resources for mapping Mainstreaming gender and social differentiation into CCAFS research activities in West Africa: lessons learned and causes and patterns of gendered perspectives (CCAFS) (Ouédraogo et al., 2018) vulnerability and resilience to Aflotoxins in food and feed (GCAN, IFPRI) (Brown, 2018) climate shocks and stressors, in Using natural areas and empowering women to buffer food security and nutrition from climate shocks: Evidence from relation to agriculture Ghana, Zambia, and Bangladesh (GCAN, IFPRI) (Cooper, 2018) Policy note on the interlinkages of Climate Change, Gender and Nutrition in Nigeria (GCAN, IFPRI) (Thomas et al., 2018) A user guide to the CCAFS Gender and Climate Change Survey data (CCAFS) (Bryan et al., 2018) Women’s Empowerment and Crop Diversification in Bangladesh a Possible Pathway to Climate Change Adaptation and Better Nutrition (IFPRI) (De Pinto et al., 2019) Can Women’s Empowerment Increase Animal Source Food Consumption in Flood Prone Areas of Bangladesh? (IFPRI, University of Southern California) (Theys, 2018) Agriculture and Youth in Nigeria: Aspirations, Challenges, Constraints, and Resilience (IFPRI) (El Didi et al., 2020) 10 Gender-differences in Agro-Climate Information Services (Findings from ACIS baseline survey in Ha Tinh and Dien Bien provinces, Vietnam) (CCAFS) (Duong et al., 2017) Achieving Dryland Women’s Empowerment: Environmental resilience and social (Natural Resources Institute, UNDP, UNCCD) (Nelson et al., 2015) Integration of gender considerations in Climate-Smart Agriculture R4D in South Asia and SSA – useful research questions (GENNOVATE) (Farnworth et al., 2017) Enhancing Women’s Assets to Manage Risk under Climate Change. Potential for group- based approaches (IFPRI) (Ringler et al., 2021) Mainstreaming gender and social differentiation into CCAFS research activities in West Africa: lessons learned and perspectives (CCAFS) (Ouédraogo et al., 2018) Practical resources for analysing the Gender-differences in Agro-Climate Information Services (Findings from ACIS baseline survey in Ha Tinh and Dien Bien gender dimensions of CRA provinces, Vietnam) (CCAFS) (Duong et al., 2017) agricultural research and extension institutional systems Insurance for Rural Resilience and economic development (IFAD) (IFAD, 2020) Gender and Inclusion Toolbox-Participatory Research in Climate Change and Agriculture (CARE, ICRAF) (Jost et al., 2014) Checklist: Gender-inclusive actionable agro-advisories (ICRAF) (Simelton and Le 2020) Gendered targeting of agricultural extension and weather variability in Africa south of the Sahara (IFPRI, FAO) (Azzarri and Nico, 2021) Enhancing Women’s Assets to Manage Risk under Climate Change. Potential for group- based approaches (IFPRI) (Ringler et al., 2021) Mainstreaming gender and social differentiation into CCAFS research activities in West Africa: lessons learned and perspectives (CCAFS) (Ouédraogo et al., 2018) Practical resources for analysing Gendered targeting of agricultural extension and weather variability in Africa south of the Sahara (IFPRI, FAO) (Azzarri and opportunities, barriers, preferences, Nico, 2021) and decision making regarding CRA Using natural areas and empowering women to buffer food security and nutrition from climate shocks: Evidence from innovation and interventions at the Ghana, Zambia, and Bangladesh (GCAN, IFPRI) (Cooper, 2018) household, community, and Land rights knowledge and conservation in rural Ethiopia: Mind the gender gap (Quisumbing and Kumar, 2014) landscape level A Gender-responsive Approach to Climate-Smart Agriculture: Evidence and guidance for practitioners (Nelson and Huyer, 2016) Gender profile of climate-smart agriculture in Ghana (CCAFS, ICRISAT, ILRI, CSIR) (CCAFS, 2021). Women’s involvement in coffee agroforestry value-chains: Financial training, Village Savings and Loans Associations, and Decision power in Northwest Vietnam (CCAFS) (Simelton et al., 2021) 11 Gender differences in access to information and adoption of climate-smart agriculture practices in Uganda: The role of women's empowerment (IFPRI, University of Hohenheim) (Khan et al., 2021) Gender and Institutional Aspects of Climate-Smart Agricultural Practices: Evidence from Kenya (CCAFS) (Bernier et al., 2015) Gender and Inclusion Toolbox-Participatory Research in Climate Change and Agriculture (CCAFS, ICRAF, CARE, FAO) (Jost et al., 2014) Enhancing Women’s Assets to Manage Risk under Climate Change. Potential for group- based approaches (IFPRI) (Ringler et al., 2021) Mainstreaming gender and social differentiation into CCAFS research activities in West Africa: lessons learned and perspectives (CCAFS) (Ouedraogo et a., 2018) Land rights knowledge and conservation in rural Ethiopia: Mind the gender gap (Quisumbing and Kumar, 2014) A Gender-responsive Approach to Climate-Smart Agriculture: Evidence and guidance for practitioners (Nelson and Huyer, 2016) Gender profile of climate-smart agriculture in Ghana (CCAFS, ICRISAT, ILRI, CSIR) (CCAFS, 2021). Practical resources for appraising Training manual on gender and climate change resilience (Arrow, UN women) (Dharmistha, 2021) specific CRA practices Women’s Empowerment and Crop Diversification in Bangladesh a Possible Pathway to Climate Change Adaptation and Better Nutrition (IFPRI) (De Pinto et al., 2019) Integration of gender considerations in Climate-Smart Agriculture R4D in South Asia and SSA – useful research questions (GENNOVATE) (Farnworth et al., 2017) Gender Household Survey, CCAFS Dataverse (CCAFS) (harvard.edu) (CCAFS, IFPRI, ILRI, 2013) A user guide to the CCAFS Gender and Climate Change Survey data (CCAFS) (Bryan et al., 2018) Gender Equality, M&E and evaluation of climate services (CCAFS) (Gumucio et al., 2018) Integrating Gender into the CSV Approach of Scaling of Adaptation Options in Agriculture (CCAFS and Future Earth) (Chanana et al., 2018) Enhancing Women’s Assets to Manage Risk under Climate Change. Potential for group- based approaches (IFPRI) (Ringler et al., 2021) Mainstreaming gender and social differentiation into CCAFS research activities in West Africa: lessons learned and Practical resources for assessing perspectives (CCAFS) (Ouedraogo et a., 2018) gendered CRA outcomes at different Training manual on gender and climate change resilience (Arrow, UN women) (Dharmistha, 2021) scales A user guide to the CCAFS Gender and Climate Change Survey data (CCAFS) (Bryan et al., 2018) Gender Equality, M&E and evaluation of climate services (Gumucio et al., 2018) Using natural areas and empowering women to buffer food security and nutrition from climate shocks: Evidence from Ghana, Zambia, and Bangladesh (GCAN, IFPRI) (Cooper, 2018) 12 Women’s involvement in coffee agroforestry value-chains: Financial training, Village Savings and Loans Associations, and Decision power in Northwest Vietnam (CCAFS) (Simelton et al., 2021) 13 REFERENCES Abdur Rashid Sarker, Md., Alam, K., & Gow, J. (2013). Assessing the determinants of rice farmers’ adaptation strategies to climate change in Bangladesh. International Journal of Climate Change Strategies and Management, 5(4), 382–403. https://doi.org/10.1108/IJCCSM-06-2012- 0033 Acosta, M., Riley, S., Bonilla-Findji, O., Martínez-Barón, D., Howland, F., Huyer, S., ... & Chanana, N. (2021). Exploring Women’s Differentiated Access to Climate-Smart Agricultural Interventions in Selected Climate-Smart Villages of Latin America. Sustainability, 13(19), 10951. https://doi.org/10.3390/su131910951 Africa Enterprise Challenge Fund, & University of Reading. (2014). Assessing the Impacts of Shamba Shape Up (Issue October). https://cgspace.cgiar.org/rest/bitstreams/64998/retrieve Agana, C. (2012) Women’s land rights and access to credit in a predominantly patrilineal system of inheritance: a case study of the Frafra traditional area, Upper East region. M.Phil thesis, Kwame Nkrumah University of Science and Technology, Ghana. Ali, E., Awade, N.E. and Abdoulaye, T., 2020. Gender and impact of climate change adaptation on soybean farmers’ revenue in rural Togo, West Africa. Cogent Food & Agriculture, 6(1), p.1743625. Azadi, H., Moghaddam, S.M., Burkart, S., Mahmoudi, H., Van Passel, S., Kurban, A. and Lopez-Carr, D., 2021. Rethinking resilient agriculture: From climate-smart agriculture to vulnerable-smart agriculture. Journal of Cleaner Production, 319, p.128602. Barasa, P.M., Botai, C.M., Botai, J.O. and Mabhaudhi, T., 2021. A Review of Climate-Smart Agriculture Research and Applications in Africa. Agronomy, 11(6), p.1255. Ben Ayed, R., & Hanana, M. (2021). Artificial intelligence to improve the food and agriculture sector. Journal of Food Quality, 2021. Bessah, E., Raji, A. O., Taiwo, O. J., Agodzo, S. K., Ololade, O. O., Strapasson, A., & Donkor, E. (2021). Gender-based variations in the perception of climate change impact, vulnerability and adaptation strategies in the Pra River Basin of Ghana. International Journal of Climate Change Strategies and Management, 13(4/5), 435-462. Beuchelt, T. D., & Badstue, L. (2013). Gender, nutrition- and climate-smart food production: Opportunities and trade-offs. Food Security, 5(5), 709–721. https://doi.org/10.1007/s12571- 013-0290-8 Brugere, C. and M. Williams. 2017. Women in aquaculture profile. https://genderaquafish.org/portfolio/women-in-aquaculture/ by the Rural Poor: Gender perspectives from selected Asian countries. Bangkok. Available at: https://www.fao.org/3/i5477e/i5477e.pdf. Chanana, N., Khartri-Chhetri, A., Pande, K. & Joshi, R. 2018. Integrating Gender into the Climate Smart Village Approach of Scaling Out Adaptation Options in Agriculture. CCAFS Info Note. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). Copenhagen, Denmark. Available online at: www.ccafs.cgiar.org. Clarke, D., & Kumar, N. (2016). Microinsurance Decisions: Gendered Evidence from Rural Bangladesh. Gender, Technology and Development, 20(2). Dankelman, I., Alam, K., Khurshid, A., Ahmed, W. B., Gueye, Y. D., Fatema, N., & Kutin, R. M. (2008). Gender, climate change and human security: Lessons from Bangladesh, Ghana and Senegal. Report prepared for the Hellenic Foundation for European and Foreign Policy (eliamep). Prepared by the Women’s Environment and Development Organization (wedo) with abantu for Development in Ghana, Action Aid Bangladesh, and Environment and Development Action i i in the Third World (enda) in Senegal. ABANTU for Development in Ghana, Action Aid Bangladesh, and ENDA in Senegal. Data2x. 2021a. Where are the gender data gaps? [Online]. Available at https://data2x.org/where- are-the-gaps/. Data2x. 2021a. Where are the gender data gaps? [Online]. Available at https://data2x.org/where- are-the-gaps/. Data2x. 2021b. Where are the women? Filling the gap in sex-disaggregated data in agriculture. [Online]. Available at https://data2x.org/where-are-the-women-filling-the-gap-in-sex- disaggregated-data-in-agriculture/. Data2x. 2021b. Where are the women? Filling the gap in sex-disaggregated data in agriculture. [Online]. Available at https://data2x.org/where-are-the-women-filling-the-gap-in-sex- disaggregated-data-in-agriculture/. Doss, C., & Kieran, C. (2014). Standards for Collecting Sex-Disaggregated Data for Gender Analysis; a Guide for CGIAR Researchers. Doss, C., Meinzen-Dick, R., Quisumbing, A., & Theis, S. (2018). Women in agriculture: Four myths. Global Food Policy, 16, 69–74. https://doi.org/10.1016/j.gfs. 2017.10.001 Fairchild, E., & Petrzelka, P. (2022). Landownership and power: reorienting land tenure theory. Agriculture and Human Values, 1-10. FAO. 2015. World Programme for the Census of Agriculture 2020. Volume 1: Programme, concepts and definitions. Roma. FAO. 2016b. Sex-Disaggregated Data and Gender Indicators in Agriculture: A Review of Data Gaps and Good Practices. Roma FAO. 2020. The State of World Fisheries and Aquaculture 2020. Sustainability in action. Rome. Gartaula, H., Sapkota, T., Khatri-Chhetri, A., Prasad, G., & Badstue, L. (2020). Gendered impacts of greenhouse gas mitigation options for rice cultivation in India. Climatic Change, 163(2), 1045– 1063. https://doi.org/10.1007/s10584-020-02941-w Gharehgozli, O., & Atal, V. (2020). Revisiting the gender wage gap in the United States. Economic Analysis and Policy, 66, 207-216. Gillwald A, Milek A, Stork C (2010) Towards evidence-based ICT policy and regulation: gender assessment of ICT access and usage in Africa. Volume One. 2010 Policy Paper 5. Cape Town: Research ICT Africa Retrieved from www.ictworks.org/sites/default/files/uploaded_pics/2009/Gender_Paper_Sept_2010.pdf GSMA (2019). The Mobile Gender Gap Report 2019. Available at https://www.gsma.com/mobilefordevelopment/wp-content/uploads/2019/03/GSMA- Connected-Women-The-Mobile-Gender-Gap-Report-2019.pdf. Accessed on 14th May 2022. Gumucio, T., Huyer, S., Hansen, J., Simelton, E., Partey, S. T., & Schwager, S. (2018). Inclusion of gender equality in monitoring and evaluation of climate services. CCAFS Working Paper. Hamilton, L. C. (2011). Education, politics and opinions about climate change evidence for interaction effects. Climatic Change, 104(2), 231-242. Hariharan, V. K., Mittal, S., Rai, M., Agarwal, T., Kalvaniya, K. C., Stirling, C. M., & Jat, M. L. (2020). Does climate-smart village approach influence gender equality in farming households? A case of two contrasting ecologies in India. Climatic Change, 158(1), 77–90. https://doi.org/10.1007/s10584-018-2321-0 Hasanbasri, A., Kilic, T., Koolwal, G., & Moylan, H. (2021, June 12). Advancing gender equality through intra-household survey data collection on asset ownership and labor. World Bank Data Blog. https://www.worldbank.org/en/programs/lsms/initiatives/lsms-plus Huyer, S. (2023a). From resilience to empowerment: The Gender and Climate Empowerment Index for climate-resilient agriculture. Accelerating the Impact of CGIAR Climate Research. Huy er, S. (2023b). Gender transformation through scaling in climate resilient agriculture: The experience of AICCRA. Huyer, S., & Partey, S. (2020). Weathering the storm or storming the norms? Moving gender equality forward in climate-resilient agriculture. Climatic Change, 158(1), 1-12. Huyer, S., Gumucio, T., Tavenner, K., Acosta, M., Chanana, N., Khatri-Chhetri, A., Mungai, C., Ouedraogo, M., Otieno, G., Radeny, M. and Recha, J., 2021. From vulnerability to agency in climate adaptation and mitigation. Advancing gender equality through agricultural and environmental research: Past, present, and future, p.261. IPCC. (2022). Climate Change 2022: Impacts, Adaptation, and Vulnerability, Contribution of WG II to the 6th Assessment Report of the IPCC (H.-O. Pörtner, D. C. Roberts, M. Tignor, E. S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, & B. Rama, Eds.). Cambridge University Press. Jost, C., Ferdous, N. & Spicer, T.D. 2014. Gender and Inclusion Toolbox: Participatory Research in Climate Change and Agriculture. Copenhagen, Denmark: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS); CARE International and the World Agroforestry Centre (ICRAF). Jost, C., Kyazze, F., Naab, J., Neelormi, S., Kinyangi, J., Zougmore, R., Aggarwal, P., Bhatta, G., Chaudhury, M., Tapio-Bistrom, M.L. and Nelson, S., 2016. Understanding gender dimensions of agriculture and climate change in smallholder farming communities. Climate and Development, 8(2), pp.133-144. Khalil, M. B., Jacobs, B. C., McKenna, K., & Kuruppu, N. (2020). Female contribution to grassroots innovation for climate change adaptation in Bangladesh. Climate and Development, 12(7), 664–676. https://doi.org/10.1080/17565529.2019.1676188 Khatri-Chhetri, A., Regmi, P. P., Chanana, N., & Aggarwal, P. K. (2019). Potential of climate-smart agriculture in reducing women farmers’ drudgery in high climatic risk areas. Climatic Change, 1–14. https://doi.org/10.1007/s10584-018-2350-8 Krell, N. T., Giroux, S. A., Guido, Z., Hannah, C., Lopus, S. E., Caylor, K. K., & Evans, T. P. (2021). Smallholder farmers' use of mobile phone services in central Kenya. Climate and Development, 13(3), 215-227. Kristjanson, P., Bryan, E., Bernier, Q., Twyman, J., Meinzen-Dick, R., Kieran, C., ... & Doss, C. (2017). Addressing gender in agricultural research for development in the face of a changing climate: where are we and where should we be going?. International Journal of Agricultural Sustainability, 15(5), 482-500. Krkoška Lorencová, E., Loučková, B., & Vačkářů, D. (2019). Perception of climate change risk and adaptation in the Czech Republic. Climate, 7(5), 61. Liaqat, S., Donald, A. A., Jarvis, F. B., Perova, E., & Johnson, H. C. (2021). Lost in Interpretation : Why Spouses Disagree on Who Makes Decisions. https://doi.org/10.1596/1813-9450-9883 McKinley J, Adaro C, Pede VO, Rutsaert P, Setiyono T, Cong Thang T, Lien Huong D, Trung Kien N, Balangue Z, Bandyopadhyay S, Sheinkman M, and Wassman R. 2016. Gender Differences in Climate Change Perception and Adaptation Strategies: The Case of Three Provinces in Vietnam’s Mekong River Delta, CCAFS Report. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). Copenhagen, Denmark. Available online at www.ccafs.cgiar.org. McOmber C, Panikowski A, McKune S, Bartels W, Russo S (2013) Investigating climate information services through a gendered lens. CCAFS Working Paper no. 42. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). Copenhagen, Denmark. Available online at: www.ccafs.cgiar.org Muriel, Juliana, Manuel Francisco Moreno, Mónica Juliana Chavarro, Jennifer Twyman, Jenny Wiegel, and Judith María Castro. 2020. “Estrategia de Género e Inclusión Social Para La Cadena de Valor de Marañón En La Región Golfo de Fonseca, Honduras.” Working Paper 181. Cali, Colombia: Alianza de Bioversity, el Centro Internacional de Agricultura Tropical (CIAT) y Swisscontact. Murray, U., Gebremedhin, Z., Brychkova, G., & Spillane, C. (2016). Smallholder farmer and climate smart agriculture: Technology and labour-productivity constraints among women smallholders in Malawi. Gender, Technology and Development, 20(2), 117–148. https://doi.org/https://doi.org/10.1177/0971852416640639 Mutenje, M. J., Farnworth, C. R., Stirling, C., Thierfelder, C., Mupangwa, W., & Nyagumbo, I. (2019). A cost-benefit analysis of climate-smart agriculture options in Southern Africa: Balancing gender and technology. Ecological Economics, 163(May), 126–137. https://doi.org/10.1016/j.ecolecon.2019.05.013 Mwongera, C., Shikuku, K. M., Winowiecki, L. A., Twyman, J., Läderach, P., Ampaire, E. L., ... & Twomlow, S. (2015). Climate-smart agriculture rapid appraisal (CSA-RA): A prioritization tool for outscaling CSA-Step-by-step guidelines. Nchanji, E. B., Collins, O. A., Katungi, E., Nduguru, A., Kabungo, C., Njuguna, E. M., & Ojiewo, C. O. (2021). What does gender yield gap tell us about smallholder farming in developing countries? Sustainability (Switzerland), 13(1), 1–20. https://doi.org/10.3390/su13010077 Ogunlana, E. A., Salokhe, V., & Lund, R. (2006). Alley farming: A sustainable technology for crops and livestock production. Journal of Sustainable Agriculture, 29(1), 131-144. Olaniyan, O.F., 2017. Adapting Gambian women livestock farmers’ roles in food production to climate change. In: Future of Food: Journal on Food, Agriculture and Society. Witzenhausen : University of Kassel, Department of Organic Food Quality and Food Culture. - Vol. 5, No. 2 (2017), S. 56-66 Ouedraogo M, Houessionon P, Partey S, Cramer L, Zougmore R, Thornton P, Jasaw GS, Buah S, Riba A, Barahona C. 2019. CCAFS midline synthesis report - Ghana (GH0108). Wageningen, Netherlands: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). Palanisami, K., Haileslassie, A., Kakumanu, K. R., Ranganathan, C. R., Wani, S. P., Craufurd, P., & Shalander, K. (2015). Climate Change, Gender and Adaptation Strategies in Dryland Systems of South Asia: A Household Level Analysis in Andhra Pradesh, Karnataka and Rajasthan States of India; Research Report No. 65. Partey, S.T., Dakorah, A.D., Zougmoré, R.B., Ouédraogo, M., Nyasimi, M., Nikoi, G.K. and Huyer, S., 2020. Gender and climate risk management: evidence of climate information use in Ghana. Climatic Change, 158(1), pp.61-75. Partey, S.T., Zougmoré, R.B., Ouédraogo, M. and Campbell, B.M., 2018. Developing climate-smart agriculture to face climate variability in West Africa: challenges and lessons learnt. Journal of cleaner Production, 187, pp.285-295. Pearson, A. R., Ballew, M. T., Naiman, S., & Schuldt, J. P. (2017). Race, class, gender and climate change communication. In Oxford research encyclopedia of climate science. Poortinga, W., Whitmarsh, L., Steg, L., Böhm, G., & Fisher, S. (2019). Climate change perceptions and their individual-level determinants: A cross-European analysis. Global environmental change, 55, 25-35. Quan, J., Tan, S., & Toulmin, C. (2004). Land in Africa: market asset or secure livelihood?. Available at https://agris.fao.org/agris-search/search.do?recordID=GB2013203012 Ringler, C., Quisumbing, A. R., Bryan, E., & Meinzen-Dick, R. S. (2014). Enhancing women’s assets to manage risk under climate change: Potential for group-based approaches. Sheahan, M., & Barrett, C. B. (2017). Ten striking facts about agricultural input use in Sub-Saharan Africa. Food Policy, 67, 12-25. Simelton, E., Tuan, M.D. & Houzer, E. 2021. When the “strong arms” leave the farms—migration, gender roles and risk reduction in Vietnam. Sustainability. 2021(13), pp. 4081. Singbo, A., Njuguna-Mungai, E., Yila, J. O., Sissoko, K., & Tabo, R. (2021). Examining the Gender Productivity Gap among Farm Households in Mali. Journal of African Economies, 30(3), 251–284. https://doi.org/10.1093/jae/ejaa008 Tesf aye, A., Radeny, M., Ogada, M. J., Recha, J. W., Ambaw, G., Chanana, N., Huyer, S., Demeke, G., & Solomon, D. (2022). Gender empowerment and parity in East Africa: evidence from climate-smart agriculture in Ethiopia and Kenya. Climate and Development, 1–11. https://doi.org/10.1080/17565529.2022.2154124 Thornton, P. K., Kruska, R. L., Henninger, N., Kristjanson, P. M., Reid, R. S., & Robinson, T. P. (2003). Locating poor livestock keepers at the global level for research and development targeting. Land Use Policy, 20(4), 311-322. Twyman, J., Green, M., Bernier, Q., Kristjanson, P., Russo, S., Tall, A., Ampaire, E., Nyasimi, M., Mango, J., McKune, S., Mwongera, C. & Ndourba, Y. 2014 Gender and Climate Change Perceptions, Adaptation Strategies, and Information Needs Preliminary Results from four sites in Africa. CCAFS Working Paper no. 83. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). Copenhagen, Denmark. Available online at: www.ccafs.cgiar.org UN Statistics Division. 2016. Integrating a Gender Perspective into Statistics. United Nations: NY. United Nations Development Programme (UNDP). (2012). Gender, agriculture and food security. New York: UNDP. Van Aelst, K., & Holvoet, N. (2018). Climate change adaptation in the Morogoro Region of Tanzania: women’s decision-making participation in small-scale farm households. Climate and Development, 10(6), 495–508. https://doi.org/10.1080/17565529.2017.1318745 Waibel, H., Pahlisch, T. H., & Völker, M. (2018). Farmers’ perceptions of and adaptations to climate change in Southeast Asia: the case study from Thailand and Vietnam (pp. 137-160). Springer International Publishing. Williams TO, Mul M, Cofie O, Kinyangi J, ZougmoreR, Wamukoya G, Nyasimi M, Mapfumo P, Speranza CI, Amwata D, Frid-Nielsen S, Partey S, Girvetz E, Rosenstock T, Campbell BM. 2015. Climate Smart Agriculture in the African Context. Background Paper. Feeding Africa Conference 21-23 October 2015. World Bank, FAO, ILRI, AU-IBAR. 2011. What does sex disaggregated data say about livestock and gender in Niger? [brief]. Available at: https://cgspace.cgiar.org/bitstream/handle/10568/ 16569/SexDisaggrigated.pdf?sequence=2&isAllowed=y Yiridomoh, G. Y., Appiah, D. O., Owusu, V., & Bonye, S. Z. (2021). Women smallholder farmers off-farm adaptation strategies to climate variability in rural savannah, Ghana. GeoJournal, 86(5), 2367- 2385. aiccra.cgiar.org info@cgiar.org CGIARAfrica