Potential of climate-smart agriculture in reducing women farmers’ drudgery in high climatic risk areas
Abstract
Climate-smart agriculture (CSA) has a significant role to play in reducing the gender gap in labor burden for women in agriculture. A targeted approach to address this gap can be useful in developing a women-responsive climatic risk management plan focused on reducing their labor burden in agriculture, especially in areas with high climate risks. The paper therefore presents a top–down approach to identify potential labor-saving CSA technologies for women farmers in areas facing high climate risks. It involves mapping women in agriculture, climate risks, and poverty hotspots and entails understanding the role of women in agricultural activities to identify the suitable CSA options for reducing the levels of labor drudgery. The study is illustrated for Nepal where feminization of agriculture is rapidly increasing, a high level of climatic risks persists, and adaptive capacity to climate change is very low, especially among women in agriculture. Results are presented for two hotspot districts, Rupandehi and Chitwan. Household socioeconomic characteristics were found to play a major role in women’s labor contribution in different crop production activities. Discussions with farmers provided a list of more than 15 CSA interventions with labor reduction as well as yield-improving potential. Accordingly, considering the local crop, agro-climate, and social conditions, and women’s participation in different agricultural activities, CSA technologies and practices such as direct seeded rice (zero tillage and low tillage using machine), green manuring (GM), laser land leveling (LLL), and system of rice intensification (SRI) were found to potentially reduce women’s drudgery in agriculture along with improvement in productivity and farm income.
1 Introduction
Women’s involvement in agriculture and their contributions to food security has been widely recognized in the developing countries. Women play a key role in improving agricultural productivity and food security in the farming communities (Agarwal 2013; Aly and Shields 2010). In the last few decades, women’s involvement, access to productive resources, and decision-making roles in agriculture and allied sectors have been the focus areas of research and development in the global south. Many studies claim that differences in men and women’s responsibilities, priorities, and access to resources and services at the community and household levels are responsible for the gender gap in agriculture in many developing countries (Quisumbing et al. 2014; FAO 2011). A clear linkage has been shown among social, economic, and gender dimensions in agriculture (Peterman et al. 2014; FAO 2010a).
Agriculture is one of the most vulnerable sectors to climate change impacts. Within agriculture, however, several studies highlight that women are likely to be affected more than men by climate impacts, especially in the developing countries where their involvement in agriculture is high (Goldsmith et al. 2013; MacGregor 2010; UNDP 2013; Goh 2012; Nellemann et al. 2011). Most of this gender gap analysis in agriculture in the context of climate change have been limited to access to resources and decision making (Kristjanson et al. 2017). However, the labor-intensive roles that women play in agriculture from sowing, weeding, to harvesting also determine the nature and severity of climate change impacts they face. Climate change impacts such as decreasing supply of crop residues and biomass for energy and livestock feed, increasing severity of weeds, crop re-sowing/transplanting requirement, and loss of crop yields are likely to affect women more, given their involvement in related activities (Bradshaw and Linneker 2017; Nelson and Huyer 2016). Thus, there is a need to understand climate change impacts on women based not just on their social, cultural, and economic characteristics but also their role and responsibilities in specific agricultural activities (Nightingale 2011; Morton 2007).
The climate-smart agriculture (CSA) approach is emerging as a new paradigm for adapting agriculture to the changing climate. This approach seeks solutions that improve agricultural productivity, build resilient food production systems, and reduce greenhouse gas emissions (FAO 2010b; Steenwerth et al. 2014). CSA includes a range of technologies, practices, and services to minimize the impact of climate change in agriculture. These CSA options range from a simple adjustment in crop management practices to the transformation of agricultural production systems to adjust to new climatic conditions in a particular location (Khatri-Chhetri et al. 2017; Vermeulen et al. 2012; Howden et al. 2007).
However, the efficacy of CSA options in terms of its benefits to both men and women stands to lose out if the gender gap in agriculture is not taken into account. (Nelson and Huyer 2016). For instance, the level of women’s involvement in agriculture, such as their labor contribution in different agricultural activities (from land preparation to crop harvesting), can have significant implications for the adoption and sustainability of the CSA approach. There are plenty of suggestions for gender-differentiated climate change adaptation strategies in agriculture. Improving women’s access to productive resources, finance and knowledge, promoting off-farm employment, and capacity building on adaptation options can empower them to adapt to a changing climate (Huyer et al. 2015; Edmunds et al. 2013; Chaudhury et al. 2012; Huyer 2016). While the role of CSA in agriculture has been widely discussed, its potential to help women in reducing their labor burdens in particular is not clear.
This paper therefore presents a systematic approach to assess the labor-reducing potential of selected CSA technologies and practices. The objective is to highlight hotspots of women in agriculture, climate risks, and poverty; assess CSA interventions relevant for women in two of these hotspots, based on their role in agriculture and factors affecting the role; and evaluate some of the CSA technologies on their labor-reducing potential for women in agriculture. The study was conducted in Nepal, a country characterized by low-ranked gender-related development indicators and increasing feminization of agriculture due to male out-migration (UNDP 2016; CBS 2013a; Gartaula et al. 2010; Tamang et al. 2014).
2 Methods and data
2.1 Identification of hotspots
Three indicators were used to map women in agriculture–poverty–climate risks hotspots across Nepal. The first indicator was women’s involvement in agriculture which was calculated as the percentage of women in agriculture in the district multiplied by a weight. The weight was the proportion of total number of women involved in agriculture in the district compared to that of the country. Data for this indicator was collected from the national population and housing census survey in 2011. Participation in agriculture also included forestry and fishing industries. The second indicator was climate risk exposure at the district level developed by the Ministry of Environment, Government of Nepal. This exposure indicator included annual temperature and rainfall trends, droughts, floods and landslide risks, and population dependency in natural resources and other risks factors (GoN 2010). The third indicator was the district level poverty (% of population under poverty line). The poverty levels were taken from the census data, which was conducted by the Government of Nepal in 2013 (CBS 2013b).
The hotspots were identified by overlaying women’s participation in agriculture with climate risk exposure and poverty levels using a GIS (geographic information system) tool. Since the study focused on women’s participation in agriculture, 50% weight was given to this indicator while a weight of 25% was given to each of the other two indicators. All three indicators—women’s participation in agriculture, climatic risks and poverty level—were combined and normalized between 0 and 1 values. Using the “Jenks Natural break” method in GIS, the district values (combining women’s participation in agriculture, climate exposure, and poverty) were categorized as low (below 0.4), medium (0.4–0.65), and high (more than 0.65). Districts under the “high” category were defined as the hotspots, where both the women’s participation and the climate risks and poverty were categorized as high.
2.2 Assessment of women’s role in agriculture
Two women–agriculture–climate change vulnerability hotspot districts (Rupandehi and Chitwan) were randomly selected for a more detailed assessment. Gender disaggregated data on labor contribution was collected from a cross-section survey of randomly selected 215 agricultural households in 2013/2014. Quantitative data was collected using a questionnaire on men and women’s role in different agricultural activities such as land preparation, crop sowing, tillage, weeding, harvesting, and threshing in major crops. Participation has been measured in terms of labor days contributed to particular agricultural activity.
To compare men and women’s participation in agricultural activities, we compared the average labor contribution to each agricultural activity from land preparation to crop harvesting using mean comparison test (t test). Additionally, to understand variables affecting women’s participating in agriculture, a multiple regression model was run using total labor contribution by women in different agricultural activities (dependent variable) and independent variables such as landholding size, family size, main occupation of household head, gender of household head, and non-agricultural income.
2.3 Potential benefits of CSA options for women
Technology adoption for three major crops in the study areas
District | Rice | Maize | Wheat | |
---|---|---|---|---|
Chitwan | ➣ Direct seeded rice using machine ➣ Zero-till DSR using machine ➣ Organic rice production | ➣ Laser land leveling ➣ Agro advisory services | ➣ Zero-tillage maize | ➣ Zero-tillage wheat ➣ Power tiller seed drill ➣ Surface seeding |
Rupandehi | ➣ Direct seeded rice using machine ➣ Direct seeded rice hand broadcasted ➣ Direct seeded rice zero-till plot ➣ Green manure rice ➣ SRI | ➣ Crop insurance ➣ Laser land leveling ➣ Submergence tolerance Variety (Swarna Sub-1) ➣ Agro advisory services | ➣ Zero-tillage wheat ➣ Power tiller seed Drill ➣ Surface seeding |
Main indicators and sub-indicators used for CSA technology evaluation
Indicator | Proxy indicators |
---|---|
Efficiency | • Productivity • Unit cost of production • Gross margin • Benefit-cost ratio • Profits |
Equity | • Employment generation • Additional calorie produced |
Gender | • Women participation |
Sustainability of natural resources | • Nitrogen use efficiency • Water use efficiency • Energy use efficiency |
Environmental | • Greenhouse gas emission • Carbon sequestration |
3 Results and discussion
3.1 Women in agriculture–poverty–climate risk hotspots
Women–agriculture–climate risk hotspots in Nepal
3.2 Climatic risks and impacts in study areas
Historical trend of climatic extremes events and impacts (source: Household Survey 2013/2014)
Trend of extreme weather events in Nepal (source: National Emergency Operation Center)
3.3 Labor gap assessment in agriculture
Total labor days contribution by men and women
Men and women participation in different agricultural activities (days/ha)
Activity | Paddy | Wheat | Maize | ||||||
---|---|---|---|---|---|---|---|---|---|
Male labor | Female labor | Mean t test | Male labor | Female labor | Mean t test | Male labor | Female labor | Mean t test | |
Nursery preparation | 5.57 | 2.95 | 7.02*** | – | – | – | – | – | – |
Land preparation | 8.56 | 2.51 | 18.00*** | 7.42 | 2.14 | 8.91*** | 5.91 | 3.39 | 3.91*** |
Transplanting | 0 | 22.95 | 19.91*** | – | – | – | – | – | – |
Seeding | – | – | – | 4.58 | 1.23 | 9.43*** | 4.69 | 3.35 | 2.01* |
Weeding | 0.27 | 4.78 | 32.56*** | 1.00 | 1.21 | 1.9 | 11.71 | 16.07 | 3.01** |
Irrigation application | 0.73 | 3.10 | 9.44*** | 1.17 | 0.12 | 11.38*** | 0.37 | 0.19 | 1.3 |
Fertilizer application | 18.25 | 6.08 | 17.80*** | 1.75 | 0.31 | 12.04*** | 1.23 | 0.47 | 4.11*** |
Pesticide application | 12.44 | 12.85 | 0.54 | 0.5 | 0.01 | 3.98*** | 0.11 | 0.08 | 0.37 |
Harvesting + threshing | 23.64 | 12.23 | 8.20*** | 25.01 | 29.10 | 3.36*** | 20.62 | 23.02 | 1.56* |
3.4 Determinants of women’s participation in agriculture
Household characteristics and women’s involvement in agriculture
Dependent variable: Total labor contribution by women in different agricultural activities | Coefficient (SE) | t value |
---|---|---|
Landholding size (hectare) | − 6.47 (2.94) | − 2.20** |
Family size (number) | 1.89 (0.86) | 2.19** |
Main occupation of household head (1, agriculture; 0, otherwise) | 8.79 (3.81) | 2.31** |
Gender of household head (1, women; 0, otherwise) | 1.05 (5.84) | 0.18 |
Non-agriculture income (%) | − 0.18 (0.08) | − 2.36*** |
Constant | 62.73 (4.77) | 13.12** |
Number of observation | 215 | |
R 2 | 0.10 | |
F (5, 209) | 4.2 | |
Prob > F | 0.001 |
3.5 Women and CSA technologies
List of women-led agricultural activities and CSA interventions
Key activities | Key climate-smart interventions | Expected impact on labor/yield/income |
---|---|---|
Weeding | • Weed management activities | • Reduction in labor hours |
Collection of fodder or fuelwood | • Agroforestry | • Reduction in fuelwood collection time |
Collection of water for domestic or irrigation purpose | • Management of water harvesting structures • Management of irrigation through solar pumps | • Reduction in water collection time |
Horticultural activities (vegetable cultivation and high-value fruit) | • Water-smart technologies such as drip irrigation, especially for drought-prone areas • Improved home gardens | • Reduction in time and labor for irrigation, additional source of income (leading to improved food security) |
Sowing | • Improved high-yielding variety of seeds • Direct seeded rice, zero-tillage wheat | • Improved yield and income |
Livestock management (fodder collection and milking) | • Fodder cultivation and management (fodder bank, improved varieties, silage/hey preparation) • Weather-friendly housing for livestock • Connect with local dairy • Livestock manure management | • Improve milk production during weather stress conditions • Better livestock management leading to secured income especially in cases of crop loss, reduced labor for livestock-related activities • Increase nutrient supply for crop cultivation |
Weather information, agro-advisory, and market information | • Agro-advisory and market information customized for women | • Access to information for better management of activities, especially useful for females responsible for all agricultural operations including marketing of produce |
Post-harvest | • Improved post-harvesting practices such as improved storage and processing methods | • Reduces labor as well as food/crop losses during post-harvest operations |
Value addition | • Capacity building on value addition in agricultural products before marketing | • Increase value of agricultural produce |
Domestic energy | • Biogas | • Meeting energy requirements at lower costs |
Capacity building | • Capacity building on application and implementation of weather resilient technologies and services | • Better and timely use of weather-resilient technologies • Socioeconomic empowerment of women farmers by strengthening their knowledge and skills |
3.6 Impact of technologies on CSA indicators
Level of women’s labor contribution reduction under different CSA technologies compared to baseline (i.e., FP)
Impact of CSA technologies on five indicators
In zero-till DSR and GM, no women’s participation was observed whereas a higher proportion of participation of women was observed in farmers’ conventional practice (without CSA technology interventions). Sustainability index was found to be higher in zero-till DSR, indicating higher sustainability than other farming technologies. The environmental index was found to be higher for GM applied in rice fields indicating a higher potential of greenhouse gas emission compared to other technologies. Lowest emission values were calculated for DSR using machine and DSR hand broadcasting, which is much lower than the GHG emission index of GM rice and farmers’ practice (FP).
4 Conclusions
The study adopts a top–down approach of examining the potential of CSA to reduce women’s labor burden in agriculture in the wake of climate change. A targeted approach using the hotspot methodology can prove to be useful in cases where prioritization of resources is a criterion. This method of hotspot identification and targeting of CSA options is seldom being used by most of the current development-related programs on women and agriculture. National and sub-national government and development organizations also focus on implementation of outcome-oriented strategies which tend to cover a large proportion of the target population, without much streamlining. This study promotes a more focused approach to identify more vulnerable regions (hotspots) related to women in agriculture–poverty–climate risk and promote technologies for reducing labor burdens for women in those regions.
Based on the activities majorly conducted by women, this study shows that CSA technologies and practices such as direct seeded rice, zero tillage machines, laser land leveling, and green manuring can reduce women’s labor burden in agriculture. Other CSA technologies such as crop harvesters, weeder, solar pump irrigation, and post-harvest management practices can also substantially reduce women’s labor burden. However, apart from reduction in labor hours, CSA also has an instrumental role in improving women’s access to agricultural resources and decision-making process as well as provide linkages to new market opportunities. Further studies, therefore, are needed to consider holistic approaches that would evaluate the implications of different CSA interventions on men and women in different socioeconomic settings and roles.
Notes
Acknowledgments
This work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. For details, please visit https://ccafs.cgiar.org/donors.
Disclaimer
The views expressed in this document cannot be taken to reflect the official opinions of these organizations.
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