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Women in agriculture and climate risks: Hotspots for development
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Presentation providing insights from the recently published paper, "Women in agriculture and climate risks: Hotspots for development," co-authored by Nitya Chanana-Nag and Pramod K. Aggarwal. Paper Abstract: "There is rising interest among research and development practitioners to arrive at impact driven solutions in the field of gender and climate change adaptation. Climate change adaptation interventions can be better targeted by being linked with type of climatic risks experienced by women farmers, their social profile and their needs based on the role they play in agriculture. This study presents a methodology to identify hotspots where climate change adaptation and gender based interventions could be prioritized. The methodology is illustrated for India. The results suggest 36 hotspots across 10 states in India, where large number of women farmers are impacted by high levels of drought probability, excess rainfall and heat wave. The target population in these hotspots comprise 14.4% of the total women farmers in the country. A socioeconomic characterization of the hotspot population highlights barriers, such as labor, credit and market access for female cultivators and lower wage rates for female laborers in these hotspots. Based on the constraints as well as the climatic risks faced by these women in the hotspots, the potential of climate-smart agriculture technologies and practices are emphasized. Additionally, a comparison of current research being done in the field with the results of the study highlights the potential to learn from current efforts for efficient scalability of gender and climate change adaptation interventions."
CCAFS | CGIAR Research Program on Climate Change, Agriculture and Food Security
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Women in agriculture and climate risks: Hotspots for development
- 1 Women in agriculture and climate risks: Hotspots for development Nitya Chanana and Pramod K. Aggarwal CGIAR Program on Climate Change, Agriculture and Food Security (CCAFS) South Asia Borlaug Institute for South Asia, International Maize and Wheat Improvement Center, CIMMYT, New Delhi Reference: Chanana-Nag, N., Aggarwal, P.K. Woman in agriculture, and climate risks: hotspots for development. Climatic Change 158, 13–27 (2020). https://doi.org/10.1007/s10584-018-2233-z
- Background: Climate risks and gender in agriculture CLIMATE CHANGE DOES NOT AFFECT EVERYONE EQUALLY Women comprise 60% of the total female working population in agriculture in South Asia but… …the gender gap in agriculture makes women disproportionately vulnerable to climate risks Emerging literature highlights the need to focus on women farmers for better gender integration Role in agriculture, socio-cultural and economic factors play a major role in understanding impacts and respective adaptation strategies NEED FOR GENDERED ADAPTATION INTERVENTIONS
- Rationale for Hotspot methodology Better targeting of climate change adaptation interventions Linking with type of climatic risks, social profile and needs based on role in agriculture Prioritization of regions as a first step for climate change and gender based interventions First level of targeting broad geographical regions for implementing climate change adaptation measures for female farmers Integrating gender in large scale adaptation projects: Enabling agencies to strategize on how best to maximize the impact of women focussed interventions
- Research Objectives 1 • Methodology for identification of hotspots for climate risks and women in agriculture and illustrate it for India • Regions where there is high concentration of women farmers impacted by high degree of climatic risks 2 • Highlight the socio-economic profile of the hotspots for suggesting adaptation options (focussing on CSA) 3 • Compare the findings with those of other studies outside the hotspot areas
- Women in agriculture Methodology: Objective 1 Hotspots for climate risks and women in agriculture Climate risks District level statistics Source: India Population Census 2011 Female Laborer Female cultivator Number of females in agriculture Drought probability Extreme rainfall Heat waves Gridded data aggregated at district level Source: Indian Meteorological Department Very Low Low Medium High Very high Jenk’s Natural breaks classification- based on the natural groupings within the data Using a geographic information system (GIS) software
- Methodology: Objective 2 Socio-economic profiling 1. Geographical division of hotspots into zones 2. Constraints and suggestions based on socio-economic characteristics Poverty (District HCR) Population category (General, SC/ST) Role in agriculture (Cultivator/Laborer)
- Methodology: Objective 3 Learnings from other studies Topic covered Search terms Climate Change TI=(climat* OR variabili* OR resilien* OR weather* OR s hock* OR adapt* OR flood* OR drought* OR warm* OR heat OR rain) Gender AND TI=(gender OR women OR woman OR female OR men OR man OR male OR young OR youth) India AND TO=(India) Literature review • Search results from database (Web of Science)Identification of articles • title (TI) and topic (TO) search: period (2007 - 2017) Screening • Only primary studies (journal articles, working paper, reports) in gender, agriculture, and climate change in India Eligibility
- Women in agriculture Climate risks Overlay (High and Very High) Results: Objective 1 Hotspots for climate risks and women in agriculture
- • 36 hotspot districts across 10 states spread across the northern and central parts of the country • Hotspots comprise of a total of 13.6 million women farmers, 57.4% of whom work as labourers. • Represents 14.4% of the total women involved in agriculture in the country Results: Objective 1 Hotspots for climate risks and women in agriculture
- Poverty (HCR) a 27% Population category b General Scheduled groups % female cultivators 59% 16% Cultivator constraints • Lower literacy levels • Limited decision making (socio-cultural norms) • Limited access to Credit, Information, and Markets • Increased labour hours due to male outmigration • Smaller land holding and low income Suggested Climate- Smart solutions ICT-based agro-advisories, Contingent Crop Planning Livestock Management Labour saving practices such as Direct Seeded Rice and System of Rice Intensification % female labourers 15% 10% Labourer constraints • Low and variable wage income • Minimal asset endowments Suggested Climate- Smart solutions • ICT for awareness and capacity building, Livestock management, improved seeds, (a) Poverty is represented by the district Head Count Ratio (Urban and rural combined) which has been taken from the study by Mohanty et al. (2016); (b) The classification is as per the Census of India (Census of India 2011). Scheduled groups comprise of Scheduled Caste and Scheduled Tribe Zone 1 West: Affected by heat waves Results: Objective 2 Socio-economic profiling
- Only 2 out of 23 studies conducted in hotspots in the last 10 years Bihar • Off-farm opportunities • Access to schooling & exposure to training, and improving • Access to irrigation North East: • Community-based water initiatives • Site-specific training programs • Stress tolerant seed varieties (for flood) Western states: • Women’s self-help groups (SHGs) to provide access to inputs, credit, community participation, and market, especially for poor cultivators • Agroforestry for women labourers in tribal areas Southern states: • Women’s self-help groups (SHGs) for livestock management, small-scale technologies related to soil moisture conservation, and capacity building for improved decision-making within household • Strengthening of local social networks Results: Objective 3 Learnings from other studies
- Conclusions Combining a top-down methodology with a bottom-up assessment for a comprehensive climate change adaptation planning for vulnerable female farmers • A new methodology for future course of action Policy provisions and implementation CSR programs • The adoption and effectiveness of these adaptation options will depend on several socioeconomic factors and local conditions that need to be researched upon further in these hotspots.
- Photo: Neil Palmer/IWMI THANK YOU !! Reference: Chanana-Nag, N., Aggarwal, P.K. Woman in agriculture, and climate risks: hotspots for development. Climatic Change 158, 13–27 (2020). https://doi.org/10.1007/s10584-018-2233-z
Editor's Notes
- Introducing the hotspot concept Not done in the field of gender/women
- Climate change impacts agriculture by way of crop loss and damage and is a major threat to future food security are likely to be more vulnerable to climate risk compared to men due to differences in access to resources (credit, market, technology, information), labor contribution, and decision making power There still exists a gap in projects to address gender needs and issue. So how is it that we can streamline efforts and target specific areas to address
- Literature review: Title and Topic search in Web of Science (10 years) & Scholar Only primary studies Root form for ‘Climate change’, ‘Gender’ and India
- The values of drought were derived using the standardized precipitation index (SPI) data from 1981 to 2011 for the months of June to September, and indicate the probability of occurrence of moderate and severe drought. Extreme rainfall is defined as the average number of days when daily rainfall exceeded 50 mm during the months of June, July, August, September, and October through 1981–2011. Similarly, heat wave has been defined as the annual average number of days when maximum temperature of a station exceeded the normal maximum temperature of a station by more than or equal to 4–7° (depending on the region) from March to May during 1981–2011. The grid size of precipitation data used is 0.25° (~ 25 km) and that of temperature is 1° (~ 110 km).
- The values of drought were derived using the standardized precipitation index (SPI) data from 1981 to 2011 for the months of June to September, and indicate the probability of occurrence of moderate and severe drought. Extreme rainfall is defined as the average number of days when daily rainfall exceeded 50 mm during the months of June, July, August, September, and October through 1981–2011. Similarly, heat wave has been defined as the annual average number of days when maximum temperature of a station exceeded the normal maximum temperature of a station by more than or equal to 4–7° (depending on the region) from March to May during 1981–2011. The grid size of precipitation data used is 0.25° (~ 25 km) and that of temperature is 1° (~ 110 km).
- The values of drought were derived using the standardized precipitation index (SPI) data from 1981 to 2011 for the months of June to September, and indicate the probability of occurrence of moderate and severe drought. Extreme rainfall is defined as the average number of days when daily rainfall exceeded 50 mm during the months of June, July, August, September, and October through 1981–2011. Similarly, heat wave has been defined as the annual average number of days when maximum temperature of a station exceeded the normal maximum temperature of a station by more than or equal to 4–7° (depending on the region) from March to May during 1981–2011. The grid size of precipitation data used is 0.25° (~ 25 km) and that of temperature is 1° (~ 110 km).
- There are 94.2 million females involved in agriculture in India, across 641 districts with different levels of participation. Female farmers in districts with “High” and “Very high” levels of participation comprise 40.3% of the total female population in agriculture in the country.
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