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Shin, S., M. Ichihara, K. Sokourenko, and C. Liao. 2024. Everyday climate adaptation practices in agriculture contribute to food security in Sub-Saharan Africa. Ecology and Society 29(4):32.ABSTRACT
Sub-Saharan Africa (SSA) faces considerable threats to its food security because of the adverse effects of climate change. Agriculture, which both influences and is influenced by climate change, requires a thorough understanding of how it impacts and is impacted by these changes. Such understanding is essential for guiding everyday adaptation strategies that uphold sustainable practices and food security. This study explores the impact of various climate adaptation strategies, demographic, and economic factors on dietary diversity across SSA by using Household Dietary Diversity Score (HDDS) as an objective and standardized measure. The research integrates everyday adaptation practices such as tree management, home gardening, crop diversity, intercropping, and composting, alongside demographic factors to assess their influence on food security. The findings reveal tree management and home gardening consistently show a positive influence on HDDS, regardless of seasonal variability. Crop diversity and intercropping also positively impact HDDS, although their effectiveness varies across seasons. Meanwhile, irrigation emerges as a critical factor in maintaining dietary diversity during challenging seasons. Female control within households emerges as a significant demographic factor positively associated with HDDS. Moreover, dietary diversity is generally lower in West Africa, particularly during adverse seasons, because of less stable and extreme agricultural conditions. Despite these adaptation practices, the study identifies a significant policy gap, as existing agricultural policies in the region do not fully support the integration of these everyday practices or address gender-specific needs. Therefore, there is a critical need for sustainable, gender-responsive, and region-specific agricultural policies that effectively incorporate these everyday climate adaptation practices to enhance resilience and food security in SSA.
INTRODUCTION
Anthropogenic climate change is exerting profound effects on the global environment and human well-being (IPCC 2019). Negative impacts span across reduced agricultural yield, increased food insecurity, environmental degradation, and the displacement of populations, with these effects particularly acute in the developing world (IPCC 2019). Conditions are expected to worsen because of the continuing influence of both past and ongoing greenhouse gas emissions (IPCC 2014). Extreme weather events, such as droughts, floods, and heatwaves disrupt agricultural predictability and timelines, exacerbate soil erosion, and accelerate the degradation of natural resources, all of which contribute to greater food insecurity (Leal Filho et al. 2021). Moreover, climate change is projected to adversely affect up to 22% of the land area used for key global crops by 2050, and in Sub-Saharan Africa (SSA), up to 56% of agricultural land may see negative impacts (Campbell et al. 2011).
Meanwhile, the African continent is grappling with significant malnutrition and food insecurity challenges. Recent estimates show that 78% of the African population cannot afford or access a healthy diet, nearly two-thirds (60.9%) report experiencing moderate to severe levels of food insecurity across the continent, and close to one in five Africans (19.7%) are struggling with hunger (FAO 2023). The situation worsens along the urban-rural continuum for African citizens, with rural dwellers experiencing a greater degree of food insecurity than those living in peri-urban or urban contexts.
This situation is attributed to various factors including climate-related disturbances, conflicts, land use change, agricultural practices, income disparities, and economic downturns exacerbated by the COVID-19 pandemic (World Health Organization 2021). An estimated 426 million Africans lack consistent access to adequate and nutritious food (African Union Commission 2021). Concurrently, Africa is experiencing one of the fastest-growing populations in the world, with projections estimating to reach approximately 2.5 billion by 2050, a significant increase from the current 1.3 billion (United Nations 2022). The Sahel Region already exhibits a scenario where population growth is surpassing food availability, leading to increased reliance on food imports (Davis et al. 2014). The ongoing climate instability is expected to continue affecting crop yields adversely, with particularly severe consequences in SSA (African Union Commission 2021).
Responding to these challenges, the concept of “everyday adaptation” to climate change has become crucial. It encompasses a myriad of local and informal behaviors at the individual, household, and community levels (Brugger and Crimmins 2013, Cuni-Sanchez et al. 2022). These practices of daily life are rooted in practical knowledge, personal experiences, and creative improvisations that serve as an “every day form of resistance” (Rendall 1984, Scott 1985:36) against climate change risks and interrelated socioeconomic vulnerabilities (Assefa and Gebrehiwot 2023). In the rural context, specific everyday adaptation strategies include adjusting planting dates, crop diversification (IPCC 2019), water conservation (Leroux et al. 2022), and seasonal migration (Wang et al. 2022).
Existing research in SSA shows that household dietary diversity and food security are influenced by socioeconomic factors. The Household Dietary Diversity Score (HDDS) functions as a population-level indicator, providing a proxy measure for food accessibility (an important layer of food security) and diet diversity. Typically, HDDS is derived from self-reported data on the consumption of various food groups within a given reference period and can be used to understand the economic ability of households to access (i.e., produce, purchase, or otherwise secure) a variety of foods. Although HDDS has not been validated to predict nutrient adequacy, it has been shown to proxy for energy sufficiency and is highly correlated with other dietary diversity indicators like the Food Consumption Score (Maxwell et al. 2014, Vaitla et al. 2017). A high dietary diversity has also been linked to improved anthropometric outcomes, such as child growth (Ruel 2003). To date, several studies have documented the ways that HDDS is affected by household demographics and resources, including head of household gender (Abafita and Kim 2014, Jima et al. 2022, Khoza et al. 2022), family size (Jima et al. 2022), household income (Fikire and Zegeye 2022), and land size (Ngema et al. 2018).
Studies have also highlighted the range of everyday climate adaptation strategies in agriculture. First, tree management not only acts as a natural carbon sink (Griscom et al. 2017) but also enhances dietary diversity through increased availability of fruits and nuts (Powell et al. 2013), supports animal husbandry (Franzel et al. 2014), and provides essential fuel for 2.4 billion people (Wan et al. 2011), contributing to household income in tropical regions (Angelsen et al. 2014). Moreover, trees enhance food system resilience to climate change, especially in arid regions and during lean seasons in Africa (Koffi et al. 2020). Home gardens improve microclimates and soil fertility, impacting socioeconomic and nutritional outcomes significantly, though their broader effects on food security and dietary diversity require further investigation (Marambe et al. 2012, Rammohan et al. 2019). Crop diversification is recognized for enhancing food security and dietary diversity (Schlenker and Lobell 2010, Rowhani et al. 2011). Soil management through intercropping is essential for biodiversity and crop nutrition (Hameed et al. 2023), requiring balanced fertilizer use to sustain productivity and adapt to climate variability (Kerr et al. 2019, Kihara et al. 2022). Additionally, irrigation is vital for maintaining food and livelihood stability, increasing yields and incomes, especially for smallholder farmers facing climate challenges (Xie et al. 2014, Azzarri et al. 2016).
Although considerable research has been conducted on how demographic factors and climate adaptation practices influence food security, our understanding remains incomplete, especially across the diverse contexts of SSA. A significant gap in the existing literature is the limited scope of studies that analyze the impacts of climate adaptation across multiple countries within SSA. Most research focuses on localized scenarios or specific national contexts (Ngema et al. 2018, Sultan and Gaetani 2016), which do not adequately reflect the diverse ecological, cultural, and socioeconomic realities across the continent. The broader approach allows us to identify patterns and outliers in adaptation efficacy, providing a richer, more comprehensive understanding of regional vulnerabilities and strengths. This type of analysis is crucial for developing effective, scalable, and sustainable food security interventions that are sensitive to regional differences (Maxwell et al. 2014, Vaitla et al. 2017). Moreover, most research tends to focus on localized scenarios or specific national contexts such as Ethiopia (Abafita and Kim 2014, Maxwell et al. 2014), Malawi (Kerr et al. 2019), Tanzania (Blakstad et al. 2019, Kihara et al. 2022), Ghana (Kotu et al. 2022), Senegal (Leroux et al. 2022), South Africa (Megbowon and Mushunje 2018), or specific regions like East Africa (Franzel et al. 2014, Oladele et al. 2019), or Central Africa (Blomme et al. 2018). These studies often fail to capture the diverse ecological, cultural, and socioeconomic realities across the continent. In addition, existing studies often isolate single adaptation strategies, such as tree management (Wan et al. 2011, Franzel et al. 2014, Koffi et al. 2020), home gardening (Blakstad et al. 2019, Rammohan et al. 2019), crop diversification (Schlenker and Lobell 2010, Rowhani et al. 2011), soil management (Kerr et al. 2019, Kihara et al. 2022, Hameed et al. 2023), without considering the potential synergistic effects of integrating multiple adaptation practices.
Acknowledging these challenges, this research addresses the central question: How do everyday climate adaptation practices influence household dietary diversity and food security in Sub-Saharan Africa amidst climate variability? By examining a comprehensive range of climate adaptation variables—including tree management, home gardening, soil management, and crop diversity—this study seeks to uncover their effects on food security across different sub-regions of SSA, such as East, West, and Central Africa. Because identifying localized determinants and constraints of climate adaptation is crucial (Carletto et al. 2016), this research simultaneously defines universal, standardized attributes that facilitate meaningful comparisons across varied agricultural contexts (Frelat et al. 2016). By integrating a broad spectrum of everyday climate adaptation practices and demographic factors, this research seeks to offer a comprehensive perspective on the factors of household food security amid climate change. This approach offers a more detailed perspective of how diverse adaptation strategies can collectively contribute to enhancing food security. Ultimately, this research offers perspectives for decision makers and interested parties, contributing to the broader literature by addressing the multifaceted and interconnected nature of climate adaptation practices in SSA.
CONCEPTUAL FRAMEWORK
The conceptual framework in Figure 1 outlines the pathway of influence from household characteristics and geographical factors to food security outcomes. It starts with “DRIVERS,” encompassing household demographics and geographics as control variables, which affect food production decisions. The “FOOD SYSTEM” involves “climate adaptation strategies” like tree management and crop diversity that are predictor variables applied to food production. These strategies are hypothesized to positively influence the “OUTCOMES” for food security and diets. The modified HDDS indicator helps to capture variations in dietary diversity and food security status within households, with the use of locally appropriate examples for food groups. The model acknowledges seasonal variations with “Good Season” (best month) and “Bad Season” (worst month) scenarios, self-reported by research participants to account for seasonal variations in food consumption (Appendix 1).
METHODS
Data
The dataset utilized in the research is from the Rural Household Multiple Indicator Survey (RHoMIS), a comprehensive tool created to efficiently gather a wide array of indicators from farm households (Appendix 1). The RHoMIS tool collects information across 758 variables, encompassing household demographics, farm areas, agricultural production, and socioeconomic indicators, among others. The scope of the RHoMIS dataset is extensive, covering 21 countries across Central America, SSA, and Asia (13,310 households). The primary respondents were usually the household heads or other senior members, selected because of their comprehensive knowledge of both household affairs and broader community and environmental interactions. Each survey session lasted approximately one hour, predominantly relying on the respondent’s recall over the previous 12 months (van Wijk et al. 2020).
From the extensive RHoMIS dataset, we employed a selective approach to data extraction to ensure that our analysis was focused and relevant to our research objectives. Using a digital extraction tool designed for RHoMIS, we filtered the dataset to retrieve data specific to Sub-Saharan Africa (11 countries) because the original RHoMIS dataset was collected exclusively in these specific nations, including countries in East Africa (Burundi, Ethiopia, Kenya, Malawi, Tanzania, Uganda; N = 5279), West Africa (Burkina Faso, Ghana, Mali; N = 4270), and Central Africa (Democratic Republic of the Congo, Zambia; N = 1461; Fig. 2). Regarding variables, this study particularly focused on demographic and climate adaptation variables for their direct relevance to understanding the impacts of climate adaptation strategies (Appendix 1). This customization facilitated the examination of specific aspects of agricultural practices, household demographics, and socioeconomic indicators, ensuring that the data extracted were not only relevant but also conducive to a robust comparative analysis across different settings.
In this study, HDDS serves as the dependent variable, using a 4-week recall period to measure consumption of 10 food groups, providing insights into dietary diversity across good and bad agricultural seasons. Demographic (Table 1) and climate adaptation variables (Table 2) are analyzed to understand their impact on HDDS during both good and bad agricultural seasons. In demographic variables (Table 1), the research included variables such as the gender of the household head, control of activities by females, family size, education level of the head, total household income, land size, and greenhouse gas emissions. These variables are crucial for understanding the socioeconomic context and environmental impact of agricultural practices. Regarding climate adaptation variables, our study focuses on variables that capture the effectiveness of adaptation strategies, including the Household Dietary Diversity Score (HDDS) during the good/bad agricultural season, tree management, presence of home gardens, crop diversity, intercropping practices, composting, irrigation, and fertilizer use (Table 2). These indicators are pivotal for assessing how adaptation practices influence agricultural productivity and dietary diversity. The dataset underwent thorough data cleaning, including removing inaccurate or incomplete observations, recoding variables for clarity, standardizing data, and excluding outliers. This process yielded a robust and reliable dataset comprising 8829 samples for analysis (Appendix 2).
Data analysis
The initial stage of our analysis involved an in-depth examination of individual variables with the distribution, mean, and standard deviation for exploring general patterns and potential anomalies. For the bivariate analysis, we applied a T-test to compare means of HDDS across different sub-groups of key climate variables, and performed correlation analysis to quantify the strength and direction of relationships between pairs of variables. The multivariate regression analysis is designed to delve into the complex relationships between a set of independent variables and the HDDS as the dependent variable, across two distinct timeframes: good and bad seasons. This dual-model approach allows us to compare and contrast the effects of various factors on HDDS during these different seasonal conditions. The regression model employed was as follows:
(1) |
In this model, HDDS represents the dependent variable, β0 is the intercept, while β1, β2, β3, ... , β14 correspond to the coefficients of the independent variables, and ε signifies the error term.
In the regression analysis, the sample size was reduced to N = 1223 from the cleaned sample set of N = 8829 because of missing data across various explanatory variables. As detailed in Table 3, although the dataset initially contains observations for 8829 households, not all variables essential for the regression model were collected for each household. For instance, the use of fertilizer was recorded for only 3534 households, and irrigation data was available for 4708 households. To ensure the reliability and comparability of our results across different analytical methods, we maintained a consistent sample size of 1223 observations throughout the univariate, bivariate, and multivariate analyses, by using STATA 16 software (StataCorp 2019). The regression model demonstrated robustness and reliability, confirmed by the normal distribution of residuals, minimal multicollinearity, and homoscedastic variance across its predictions (Appendix 3).
RESULTS
Characteristics of the households
Demographic and climate adaptation characteristics and seasonal comparison of HDDS reveals substantial seasonal fluctuations (Fig. 3). A full set of datasets, suitable for comparative analysis, has been provided in Appendix 4.
During the good season, the average HDDS was 6.23, compared to the bad season, where the mean HDDS dropped to 3.95 (Table 3). The distribution of HDDS in the good season showed a slight left-skew, with scores of 6 (N = 207) and 7 (N = 199) being the most frequently reported, reflecting improved dietary diversity during periods of higher agricultural productivity. In contrast, the HDDS distribution during the bad season was right-skewed, with the most common scores being 2 (N = 183) and 3 (N = 151), indicating limited dietary options and constrained food access. This disparity highlights the significant impact of seasonal variations on agricultural output and, consequently, the variety of food available to households.
Among surveyed households, 48% were in East Africa, followed by 38% in West Africa, and 15% in Central Africa. Regarding gender, we found evidence of disparity in household leadership and control over agricultural activities. Eighteen percent of households were led by women (Household head) and 20% reported female control over farm activities (Female control). Sixty-four percent of household heads received some formal education. A considerable portion of the households (32%) were categorized as smallholders, operating on less than 1 hectare of land, and on average, households owned 4.39 hectares. However, the large standard deviation suggests a wide range of landholdings.
In terms of the everyday adaptation strategies, 69% of households engaged in tree management, and 37% of households maintained home gardens. Households reported an average of 4.01 different crop species, with a standard deviation of 2.31. Intercropping was practiced by 47% of households, and over half of the households used composting. Only 11% of households utilized irrigation, pointing to potential gaps in water management infrastructure and technology. There was a wide range in the quantity of fertilizer used based on the high standard deviation (64.49) with an average use of 71.33 kg per year.
Household dietary diversity by seasons and agricultural practices
The correlation matrix delineates the relationships between various sociodemographic and climate adaptation variables with HDDS between good and bad agricultural seasons (Table 4 and Appendix 5). In the context of regional analysis, East Africa’s correlation with HDDS was marginal in the good season (r = 0.06) but in the bad season exhibited a moderate positive correlation (r = 0.21). Central Africa displayed a moderate positive correlation in the good season (r = 0.25) but diminished substantially in the adverse season (r = 0.09). West Africa consistently exhibited a negative correlation with HDDS, regardless of the season (r = -0.22 and r = -0.27).
Demographic determinants such as the household head gender, educational attainment, and total income each possessed a minimal positive correlation with HDDS in both the good and bad season. Female control of agricultural decision making demonstrated a more substantial positive correlation with HDDS across both seasons (r = 0.22 and r = 0.26). Household size inversely correlated with HDDS in both seasons, though the correlation remained modest (r = -0.10 and r = -0.11, p < 0.001). Although land size did not manifest a significant correlation, being a smallholder was positively correlated with HDDS during both seasons (r = 0.21). Greenhouse gas emissions were also positively correlated with HDDS in both seasons (r = 0.14) and r = 0.17, p < 0.001).
Regarding climate adaptation practices, all of them were positively correlated with HDDS regardless of the season. In the case of tree management and crop diversity, the correlation with HHDS was much stronger in the good season (r = 0.22 and r = 0.27) compared to the bad season (r = 0.10 and r = 0.14). The data suggested that regional characteristics, the extent of female control within households, and selected climate-adaptive agricultural practices served as significant determinants of dietary diversity within SSA populations.
The t-test analysis discerned the impact of various climate adaptation agricultural practices on HDDS across different agricultural seasons (Table 5). In the good season, the T-test results indicated that tree management and irrigation were associated with the highest mean HDDS, 6.46 and 7.03, respectively, and had substantial T-values of 7.73 and 3.99. Home gardening and intercropping also showed strong positive associations with HDDS, with a mean HDDS of 6.72 and 6.66 and a high T-value of 6.3 and 6.69, respectively. Composting and crop diversity were also associated with relatively high HDDS, 6.3 and 6.25, respectively; however, the T-values of 2.94 (composting) and 2.23 (intercropping) suggested a less pronounced impact on HDDS compared to other practices.
In the bad season, irrigation was associated with the highest mean HDDS (5.03) and a T-value of 4.85. Tree management, home gardening, and intercropping also continued to show strong positive effects on HDDS with their respective mean HDDS as 4.07, 4.45, and 4.26, and also had fairly high T-values of 3.56, 5.75, and 4.34. Composting, with a mean HDDS of 4.02 and a T-value of 1.23, showed a low T-value suggesting that its impact is less significant under challenging seasonal conditions. Similarly, crop diversity appeared to have the least pronounced effect in the bad season, with the lowest mean HDDS of 3.97 and a T-value of 2.12.
In summary, although all the examined agricultural practices correlated positively with HDDS, tree management, irrigation, and home gardening emerged as the important practices for improving dietary diversity in both good and bad seasons. Intercropping and composting also contributed positively, especially in the good season. Crop diversity, although beneficial, appeared to have a less significant impact on HDDS than the other practices.
Linear regression analysis
Our regression analysis revealed the effect of various factors on HDDS in both good and bad agricultural seasons in SSA (Fig. 4, Appendix 6). Model 1 (good season) offered a reasonable level of explanatory capability, capturing about 26.68% of the variation in HDDS, and the F-statistic of 25.8 and a Prob > F near zero suggested the model’s predictors collectively were statistically significant. In the bad agricultural season (Model 2), the model’s explanatory power decreased, with an R-squared of 0.1687 (16.87% of the variation in HDDS) and the lower F-statistic of 14.38, while still indicating statistical significance.
Figure 4 visualizes regression coefficients that quantify the influence of various factors on HDDS across both good and bad agricultural seasons in Sub-Saharan Africa. The figure displays each variable as a pair of horizontal bars, with coefficients for the good season in blue and for the bad season in red, enabling direct comparisons. Positioned along the y-axis, variables are easily comparable, with their coefficients extending across the x-axis to illustrate magnitude and direction. Error bars accompany each bar, signifying standard errors to depict the statistical precision of these estimates.
During the good season (Model 1), households in Central Africa had 2.05 higher HHDS, suggesting that, on average, they consumed 2.05 more food groups than those in East Africa. The results for West Africa were not statistically significant. Female control within the household suggested that an increase from no female control to full female control (0 to 1 on the scale) corresponded to an increase of 0.71 HDDS during the good and 1.03 during the bad. As the number of household members increased, HDDS marginally decreased. However, the other demographic factors of sex of head, education level, total income, and land size had statistically no significant impact on HDDS in the good season. Smallholder households (less than 1 hectare) showed a positive effect, increasing HDDS by 0.46 during the good season, highlighting the role of smallholders in enhancing food diversity and supporting the narrative of their contribution to food security. Households practicing tree management had 1.01 HDDS more than others. The other climate-adaptive practices, home gardens (coefficient 0.48), crop diversity (coefficient 0.13), intercropping (coefficient 0.29), and composting (coefficient 0.24) showed a similarly positive but relatively lower impact on HDDS. And irrigation was not statistically significant in the good season.
In bad agricultural seasons (Model 2), the coefficient for Central Africa dropped to 0.60 but remained positive, indicating a persistent but attenuated advantage in HDDS compared to East Africa. West Africa, with a negative coefficient of -0.43, suggested that dietary diversity was adversely affected in this region during bad seasons. A 1% increase in female control increased HDDS by 1.03 like Model 1, emphasizing the potential significance of female control in sustaining dietary diversity during challenging periods. Family size maintained its negative impact on HDDS with a coefficient of -0.04. GHG emissions had a consistent positive coefficient though very small (0.01). Sex of head, education level, total income, and land size remained non-significant. Among the climate-adaptive practices, tree management and home gardening retained positive importance for dietary diversity during less favorable agricultural conditions. However, crop diversity, intercropping, and composting did not show statistical significance. Irrigation showed a significantly positive coefficient of 0.49, highlighting its crucial role during challenging seasons. The effect size of fertilizer use was indiscernible; however, it appeared to be positively significant in both models.
DISCUSSION
This research provides a valuable contribution to the field by elucidating the intricate dynamics among everyday adaptation strategies, demographic variables, and food security in SSA, providing insights that advance our understanding of how everyday adaptation can enhance dietary diversity and food security in the context of climate change. Our correlation analysis (Table 4), t-test results (Table 5), and the regression findings (Fig. 4, Appendix 6) consistently demonstrate that tree management exerts a substantial positive effect on HDDS across both good and bad seasons. By providing a perennial supply of nutrient-rich foods (such as fruits and nuts) and vital ecosystem services (improved soil fertility, animal shelter, and pollinator habitat), trees can play a crucial role in sustaining diets in challenging periods and building environmental resilience (Powell et al. 2013, Koffi et al. 2020).The positive association between tree management and dietary diversity emphasizes its importance in agricultural practices and policy making in SSA, both for climate resilience and sustainable food security.
Home gardens, which have been known to provide access to diverse and nutritious foods (especially in contexts with high reliance on staple foods), also appear to be correlated with improved HDDS regardless of season (Tables 4, 5, and 6). Although home gardens hold promise for dietary diversity and food security in SSA, their sustainability is contingent on overcoming challenges, like limited water availability, high labor costs, and demands on women’s time (Blakstad et al. 2019). Effective home gardening interventions also require sustained support and community involvement (Nielsen et al. 2018).
Our study also reveals a positive correlation between crop diversification and HDDS during good seasons, albeit with a smaller impact compared to other variables (Fig. 4, Appendix 6). Although crop diversity had a relatively small effect size during bad seasons, it remains a critical strategy for maintaining agricultural resilience. The reduced impact in bad seasons may be due to environmental stressors such as drought or pests, which disproportionately affect diverse cropping systems (Schlenker and Lobell 2010). Such findings echo prior research indicating that cultivating a variety of crops can enhance access to diverse food groups and improve nutritional outcomes in climate-stressed contexts (Schlenker and Lobell 2010, Rowhani et al. 2011). However, the link weakens in adverse agricultural seasons, which may be due to stressors like heat impairing crop yield (Schlenker and Lobell 2010). Moreover, the relationship between household dietary diversity and crop diversity is complex, influenced by gender dynamics (Khoza et al. 2022), household structure (Nixon et al. 2023), market access, and household income (Islam et al. 2018). Certain crops may be sold at higher prices during adverse weather seasons, which can reduce household dietary diversity but provide an important source of income. Although positive correlations exist, some meta-analyses report no significant association, highlighting the multi-faceted nature of this relationship (Sibhatu and Qaim 2018). Diversification also carries challenges, including increased costs and labor demands, alongside the risk of crop failures (Burlingame and Dernini 2019). This underscores the need for policies and interventions that support sustainable, localized agricultural practices, focusing on biodiversity and resource conservation.
Although some climate adaptation practices demonstrated smaller effect sizes (Fig 4), their practical significance should not be overlooked. For instance, composting had a relatively modest impact on HDDS during the bad season (T-value of 1.23). However, the practice remains essential because soil management practices, and intercropping and use of compost and manure as organic fertilizers—strategies previously identified in studies as enhancing biodiversity, soil health, crop resilience, nutrient density, and food security—exhibit a positive effect on HDDS during good seasons. However, the effect was insignificant for bad seasons (Fig. 4, Appendix 6), which may be related to increased challenges of pest and crop disease management during periods of volatile or extreme weather (Hameed et al. 2023, Prihadyanti and Aziz 2023). In many contexts throughout SSA, households facing resource constraints often prioritize immediate survival necessities, potentially foregoing long-term agricultural resilience practices such as intercropping and composting, especially in periods of heightened water stress or crop damage (Kotu et al. 2022). Equally crucial are factors related to water availability and management, which our results indicate have a statistically significant positive effect on HDDS during bad seasons. In SSA, agriculture is predominantly rainfed, making it highly susceptible to climate variability, droughts, and floods (Xie et al. 2014, Azzarri et al. 2016). During difficult seasons, characterized by inadequate or erratic rainfall, irrigation provides a critical buffer against climatic uncertainties, ensuring water availability for crops (Benson 2015). This highlights the importance of implementing policies and allocating resources to support the development and access of sustainable irrigation systems, tailored to the needs and conditions of diverse agroecological zones across SSA.
The observation that female control of households positively correlates with HDDS stands out as a significant outcome of this study. Although female control is not classified as a “climate adaptation practice” per se, it is a critical socioeconomic factor that can influence the effectiveness of such practices. The Women’s Empowerment in Agriculture Index (WEAI) highlights the significant and transformative contributions of women to agricultural growth, particularly in developing countries, by measuring empowerment across five domains: community leadership roles, choices regarding farming practices, management of income, availability of resources for production, and time allocation (IFPRI 2012). Women’ empowerment in agriculture provides a framework for understanding how female empowerment, particularly in household decision making, can directly affect dietary and food security outcomes (Johnson and Diego-Rosell 2015). In households where women have greater control, decisions related to agricultural practices, such as crop diversification, use of home gardens, or tree management, tend to focus on enhancing nutritional outcomes and ensuring food security. For example, in regions where women are responsible for managing home gardens, the diversity of crops grown can contribute directly to dietary diversity. Furthermore, women’s involvement in agricultural decision making is often linked to improved conservation and sustainability practices, as evidenced by research highlighting the role of women in forest management and their contribution to both conservation and community health objectives (Wan et al. 2011).
The study reveals regional differences in household dietary diversity scores across East, West, and Central Africa, influenced by various agricultural practices, adaptation strategies, and regional specificities. For example, East Africa’s agroecology is highly variable, ranging from arid regions to tropical humid highlands (FAO 2000), and supports the cultivation of diverse crop and livestock species, which appears to improve HDDS in favorable growing seasons (FAO - CMCC 2016). Notably, countries like Kenya and Ethiopia are increasingly adopting climate-smart agricultural practices to counter these climate-related challenges (Oladele et al. 2019), which may explain better HDDS performance during difficult seasons (Table 4). West African countries, on the other hand, primarily feature tropical climates, but with arid conditions in Sahelian contexts, which make countries like Mali and Burkina Faso (FAO 2000) more vulnerable to droughts, which can adversely affect crop yields and may explain lower HDDS in challenging seasons (see Tables 4 and Fig. 4). The findings reveal that key climate adaptation practices, such as irrigation and intercropping, are less prevalent in West Africa compared to other regions. For instance, only 3% of households in West Africa practice land irrigation, compared to 14% in non-West African regions. Similarly, the adoption of intercropping is lower in West Africa (37%) than in other regions (53%). These lower rates of adoption could explain the negative correlation between irrigation (r = -0.1973) and HDDS in West Africa, suggesting that inadequate or inefficient implementation of these practices may exacerbate food insecurity rather than alleviate it (Appendix 6). Rain-dependent agriculture exacerbates this vulnerability, particularly for countries in the Sahel, placing countries like Burkina Faso at greater risk of food insecurity than coastal neighbors like Ghana (Sultan and Gaetani 2016). However, tree management in West Africa still shows a positive correlation with HDDS (r = 0.1286; Appendix 6), indicating that practices promoting the use of trees might mitigate some of the negative impacts on dietary diversity. Central African countries, especially the Democratic Republic of the Congo, benefit from humid tropical conditions with consistent rainfall and dense rainforest zones (FAO 2000), which support a diversity of crops year-round and may be responsible for the relatively higher HDDS seen in the regression model (Blomme et al. 2018). Additionally, biodiversity in the Congo Basin supports more varied diets, and more stable climates and growing conditions in countries like Zambia also enhance dietary diversity through more reliable food availability (FAO - CMCC 2016). Understanding region- and country-specific agricultural conditions is key to designing targeted food security interventions amidst climate change. This regional analysis emphasizes the importance of locally appropriate adaptation strategies that not only consider the unique environmental conditions, but also take into account socioeconomic nuances. These findings underscore the complexity of food security, which is influenced by a myriad of factors including climate, economic stability, gender dynamics, and agricultural practices.
CONCLUSION
This study provides valuable insights into the role of everyday climate adaptation strategies in improving food security among agricultural farmers in Sub-Saharan Africa. Our findings demonstrate that practices such as tree management and home gardening are strongly associated with greater dietary diversity, while the effectiveness of crop diversity and intercropping is particularly pronounced during favorable agricultural seasons. Irrigation, however, emerges as a critical factor in maintaining dietary diversity during bad seasons. Moreover, the research delineates significant regional disparities in HDDS across Central, East, and West Africa. West Africa, characterized by less stable and extreme agricultural conditions, exhibits lower relative HDDS. These distinctions in dietary diversity effects highlight the importance of researching context-specific everyday adaptations, which can also inform more structural and large-scale agricultural policies and interventions. Additionally, the study emphasizes the influential role of gender dynamics with female control within households, pointing toward the critical need for gender-responsive agricultural policies.
This study highlights the impact of everyday climate adaptation practices on food security in Sub-Saharan Africa, but we also acknowledge several limitations. First, the data used are limited to a few countries, which may not fully represent the diverse conditions across East, West, and Central Africa, thus warranting caution in drawing broad conclusions about these regions. Further research is necessary to validate the results across a broader range of countries within Sub-Saharan Africa. For example, additional country-level data could enable valuable cross-country comparisons, which may potentially reveal intraregional differences of everyday adaptations. Additionally, the binary nature of most climate adaptation variables simplifies the complexity and may mask the complexity and varying degrees of implementation of these strategies. Future research should incorporate more detailed and gradated questions, such as the extent of crop diversity (types of intercropping systems), the scale of home gardens, or the specific types of composting techniques used. Additionally, HDDS, although insightful, has limitations. The variability in food groupings and reference periods across different studies, coupled with the lack of universally agreed-upon cutoff scores, can challenge the interpretation and comparison of HDDS data (Megbowon and Mushunje 2018). Incorporating multiple food security indexes alongside HDDS is crucial for a comprehensive assessment. More research requires careful adaptation and integration with other indicators for a comprehensive understanding of food security dynamics. Last, several unmeasured factors, such as household access to markets, availability of agricultural support services, and other socioeconomic conditions, may influence both the adoption of climate adaptation practices and the associated dietary outcomes. To establish a causal relationship between these practices and dietary diversity, methods, such as randomized controlled trials or longitudinal studies, would be necessary. Although our findings offer valuable insights into the potential associations between climate adaptation practices and dietary diversity, they should be interpreted with caution, as the current evidence is correlational rather than causal.
Policies should focus on promoting tree management, home gardening, and crop diversity through subsidies for seedlings, training programs in sustainable farming techniques, and community-led agriculture initiatives. For example, a program similar to Ethiopia’s Green Legacy Initiative (UN DESA 2019), which focuses on large-scale tree planting, could be promoted in other parts of SSA. Given the pivotal role of irrigation, development interventions can invest in scalable irrigation solutions, such as micro-irrigation systems, which are cost-effective and suitable for small-scale farmers. Furthermore, implementing gender-sensitive agricultural policies is essential to ensure equitable access to resources and opportunities for women farmers. This includes securing land rights for women, providing access to microfinance, and offering specialized training in climate-resilient farming practices (IFPRI 2012). Emphasizing the sustainable use of fertilizers is essential for preserving soil health over the long term and agricultural productivity (Kihara et al. 2022). Additionally, developing region-specific strategies that represent the distinct economic, social, cultural, and climatic characteristics is crucial. For instance, in arid areas like the Sahel, investments in drought-resistant crop varieties and water conservation techniques would be more effective, whereas in more humid regions, strategies might focus on managing excess water and promoting agroforestry.
This study’s findings illuminate the potential of targeted climate adaptation strategies to significantly improve food security in the everyday agricultural production activities in SSA. Although the current research provides a foundational understanding, further detailed investigations are necessary to capture the full spectrum of these strategies’ impacts. Policy makers and stakeholders must consider these findings to formulate comprehensive, resilient, and sustainable food security policies. The path forward requires a nuanced approach that integrates agricultural practices with gender considerations, setting the stage for a food-secure future for SSA amidst the challenges of climate change.
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ACKNOWLEDGMENTS
We thank John Sipple and Kristie LeBeau for their guidance and support throughout the research process. We acknowledge funding from Cornell University Library for covering the publication cost.
Use of Artificial Intelligence (AI) and AI-assisted Tools
We did not use AI and AI-assisted tools in the process of writing our paper.
DATA AVAILABILITY
The data used in this research is publicly available.
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Table 1
Table 1. Demographic variables and hypotheses.
Variables | Definition | Measurement | Hypothesis | ||||||
Sex of Head | Gender of the household head | Dummy: female (1) and male (0) | +/- | ||||||
Female Control | Proportion of total value of activities (TVA) controlled by a female | Continuous: 0–1 scale | + | ||||||
Family Size | Number of household members | Continuous | - | ||||||
Education Level | The education level of the head of the household | Dummy: Any Education (1) and No education (0) | + | ||||||
Total Income | Total income for the household, calculated by adding the incomes from crops, livestock and off-farm | Continuous: USD per Household, Purchasing power parities, and year | +/- | ||||||
Land Size | Land owned by the household, both agricultural and non-agricultural | Continuous as measured in hectares | + | ||||||
GHG Emission | Greenhouse Gas (GHG) emissions calculated using the IPCC Tier 1 approach | Ton CO2-equivalent per farm per year | + | ||||||
Table 2
Table 2. Climate adaptation variables and hypotheses.
Variables | Definition | Measurement | Hypothesis | ||||||
HDDS in Good/Bad Season (Dependent Variable) | Households’ Dietary Diversity Score/The good season is defined by the respondent. | Ordinal: Scale from 0 to 10 | +/- | ||||||
Tree Management | Whether household makes use of any trees on land | Dummy: Yes (1) and No (0) | + | ||||||
Home Garden | Whether household has a garden or other place where you grow vegetables and fruits for home consumption | Dummy: Yes (1) and No (0) | + | ||||||
Crop Diversity | Number of cultivated crop species | Continuous | + | ||||||
Intercropping | Whether household grows crops intercropped with other plants | Dummy: Yes (1) and No (0) | + | ||||||
Composting | Whether household uses manures or compost during the last 12 months | Dummy: Yes (1) and No (0) | + | ||||||
Irrigation | Whether a household has an irrigation system for their farm | Dummy: Yes (1) and No (0) | + | ||||||
Fertilizer Use | The amount of fertilizer in total used during the last 12 months | Continuous: kg per year | +/- | ||||||
Table 3
Table 3. Characteristics of the households (N = 1223).
Category | Variable | Obs† | Mean | Std.Dev.‡ | Min | Max | |||
Dependent | HDDS in Good Season | 1223 | 6.23 | 2.23 | 0 | 10 | |||
HDDS in Bad Season | 1223 | 3.95 | 2.52 | 0 | 10 | ||||
Region | East Africa | 1223 | 0.43 | 0.50 | 0 | 1 | |||
Central Africa | 1223 | 0.12 | 0.32 | 0 | 1 | ||||
West Africa | 1223 | 0.45 | 0.50 | 0 | 1 | ||||
Demographic | Sex of Head | 1223 | 0.16 | 0.37 | 0 | 1 | |||
Female Control | 1223 | 0.19 | 0.31 | 0 | 1 | ||||
Family Size | 1223 | 7.31 | 3.30 | 0 | 16 | ||||
Education Level | 1223 | 0.60 | 0.49 | 0 | 1 | ||||
Total Income | 1223 | 705 | 829 | 0 | 3331 | ||||
Land Size | 1223 | 6.62 | 100.07 | 0 | 3500 | ||||
Smallholder (< 1 ha) | 1223 | 0.29 | 0.45 | 0 | 1 | ||||
Climate Adaptation | Tree Management | 1223 | 0.82 | 0.39 | 0 | 1 | |||
Home Garden | 1223 | 0.40 | 0.49 | 0 | 1 | ||||
Crop Diversity | 1223 | 4.78 | 2.33 | 1 | 16 | ||||
Intercropping | 1223 | 0.49 | 0.50 | 0 | 1 | ||||
Composting | 1223 | 0.54 | 0.50 | 0 | 1 | ||||
Irrigation | 1223 | 0.09 | 0.29 | 0 | 1 | ||||
Fertilizer Use | 1223 | 75.16 | 63.68 | 0.17 | 333 | ||||
GHG Emission | 1223 | 24.07 | 34.07 | 0.00 | 413 | ||||
† Obs = The number of objects. ‡ Std.Dev. = standard deviation. |
Table 4
Table 4. Correlation table of variables with Household Dietary Diversity Score (HDDS) in good and bad seasons (N = 1223).
Category | Variable | HDDS in Good Season | HDDS in Bad Season | ||||||
Correlation | Sig. Level† | Correlation | Sig. Level† | ||||||
Region | East Africa | 0.06 | ** | 0.02 | 0.21 | *** | 0.00 | ||
Central Africa | 0.25 | *** | 0.00 | 0.09 | *** | 0.00 | |||
West Africa | -0.22 | *** | 0.00 | -0.27 | *** | 0.00 | |||
Demographic | Sex of Head | 0.06 | ** | 0.04 | 0.11 | *** | 0.00 | ||
Female Control | 0.22 | *** | 0.00 | 0.26 | *** | 0.00 | |||
Family Size | -0.10 | *** | 0.00 | -0.11 | *** | 0.00 | |||
Education Level | 0.19 | *** | 0.00 | 0.14 | *** | 0.00 | |||
Total Income | 0.10 | *** | 0.00 | 0.11 | *** | 0.00 | |||
Land Size | -0.02 | 0.54 | 0.01 | 0.71 | |||||
Smallholder (< 1 ha) | 0.21 | *** | 0.00 | 0.21 | *** | 0.00 | |||
GHG Emission | 0.14 | *** | 0.00 | 0.17 | *** | 0.00 | |||
Climate Adaptation | Tree Management | 0.22 | *** | 0.00 | 0.10 | *** | 0.00 | ||
Home Garden | 0.18 | *** | 0.00 | 0.16 | *** | 0.00 | |||
Crop Diversity | 0.27 | *** | 0.00 | 0.14 | *** | 0.00 | |||
Intercropping | 0.19 | *** | 0.00 | 0.12 | *** | 0.00 | |||
Composting | 0.08 | *** | 0.00 | 0.04 | 0.22 | ||||
Irrigation | 0.11 | *** | 0.00 | 0.14 | *** | 0.00 | |||
Fertilizer Use |
0.21 | *** | 0.00 | 0.23 | *** | 0.00 | |||
† Significant level. * p < 0.1, ** p < 0.05, *** p < 0.01. |
Table 5
Table 5. T-test for the climate adaptation variables on Household Dietary Diversity Score (HDDS) in good and bad seasons (N = 1223).
Variable | Answer | Obs† | HDDS in Good Season | HDDS in Bad Season | |||||
Mean | T-value | Mean | T-value | ||||||
Tree Management | Yes | 999 | 6.46 | 7.73 | 4.07 | 3.56 | |||
No | 224 | 5.21 | 3.41 | ||||||
Home Garden | Yes | 487 | 6.72 | 6.30 | 4.45 | 5.75 | |||
No | 736 | 5.91 | 3.61 | ||||||
Crop Diversity | More than one | 1201 | 6.25 | 2.23 | 3.97 | 2.12 | |||
One crop | 22 | 5.18 | 2.82 | ||||||
Intercropping | Yes | 599 | 6.66 | 6.69 | 4.26 | 4.34 | |||
No | 624 | 5.82 | 3.64 | ||||||
Composting | Yes | 664 | 6.40 | 2.94 | 4.02 | 1.23 | |||
No | 559 | 6.03 | 3.85 | ||||||
Irrigation | Yes | 112 | 7.03 | 3.99 | 5.03 | 4.85 | |||
No | 1111 | 6.15 | 3.84 | ||||||
† Obs = The number of objects. |