Measuring adaptive capacity: An index-based approach for farmers in Niger

Measuring adaptive capacity: An index-based approach for farmers in Niger | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Measuring adaptive capacity: An index-based approach for farmers in Niger Sanoussi Ibrahim Oumarou, Roman Hinz, Ibrahima Thione Diop, Rüdiger Schaldach This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5866839/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract In Niger, a Sahelian country characterized by resource scarcity, even minor climatic variations can severely impact agricultural households. This study develops an adaptive capacity index using a robust framework that integrates an in-depth literature review with site-specific parameters. Primary data from 343 households in highly vulnerable agricultural regions were complemented by qualitative insights from expert interviews and workshops. The analysis revealed significant disparities in household asset portfolios, with many households lacking critical elements, particularly education and skills for accessing information, technologies, and off-farm income opportunities. Households with moderate adaptive capacity were more likely to engage in diversified off-farm activities, which significantly enhanced their ability to adapt to climate change. Insights from this analysis, combined with comparative analysis of factors that can serve as policy levers, provide a foundation for evidence-based interventions. The proposed index-based approach offers valuable insights for addressing climate vulnerabilities and strengthening resilience in Niger's agricultural sector. Earth and environmental sciences/Environmental social sciences/Climate-change adaptation Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts/Governance Scientific community and society/Social sciences/Decision making Scientific community and society/Social sciences/Economics Earth and environmental sciences/Natural hazards Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Climate change is a pervasive threat that exacerbates poverty and inequality 1–5 , posing far-reaching consequences for global efforts to improve population well-being. Livelihoods in agriculture-dependent economies are particularly vulnerable to climate risks, as numerous studies have shown 6–10 . However, the resources underpinning these livelihoods – such as labor and crop diversification within economic portfolios – can play a critical role in addressing current and future climate challenges 11,12 . Consequently, assessing the adaptive capacity of agricultural producers, especially in vulnerable countries, has become increasingly urgent 2–5 . Reliable information is essential for developing and implementing effective adaptation policies, highlighting the need for robust assessments to guide decision-making. Adaptive capacity is a critical attribute as it reflects a system’s ability to mobilize resources to respond, recover and/or maintain its functions in the face of stresses and shocks 13 . It emphasizes the strengths of a system that can reduce the biophysical and socio-economic vulnerabilities associated with climate change 14 . It identifies key strategic factors that can act as levers for developing and implementing effective adaptation measures, particularly in contexts of resource scarcity. In Sahelian countries like Niger, even minor changes in climatic parameters can have significant negative impacts on producers, particularly those with limited adaptive capacity. From the first vulnerability assessments 7 up to now (see ND-GAIN Index, 2022), Niger has consistently been ranked among the most vulnerable and least prepared countries globally to adapt to climate change. With two-thirds of the country located in the Sahara zone, the area suitable for agricultural production is limited 8,15 . Agriculture in Niger is predominantly rain-fed and heavily dependent on climatic conditions. Approximately 80% of the population is employed in the agricultural sector, with nearly 83% of these workers living in rural areas. Agriculture contributes around 40% to the country's GDP, making poverty reduction and community well-being heavily reliant on the sector’s performance 16 . However, agricultural productivity remains strikingly low and is regularly threatened by factors such as rainfall deficits, inter- and intra-annual rainfall variability, and extreme temperatures reaching up to 45°C. The most well-documented consequences include recurrent droughts and floods 17 , alongside ongoing land and natural resource degradation, which result in substantial economic and ecological losses 18 . The adverse effects of climate change have significantly exacerbated food and nutritional insecurity, affecting over 40% of Niger’s population, and have led to the widespread destruction of livelihoods 15,18–20 . Current and projected climate impacts 8,21 compound pre-existing challenges, including insecurity, health crises, low education levels, inadequate infrastructure, and the limited capacity of agricultural producers. These producers are often constrained by low livelihood diversification, restricted access to technical guidance, and minimal political support 22–24 . Such limitations contribute to low adoption rates of climate change adaptation strategies, with farmers often resorting to survival-focused approaches that prioritize low-risk, low-yield inputs 16,23,25 . This convergence of crises and climatic stressors poses a significant obstacle to achieving the government’s objectives for poverty reduction and economic and social development, as outlined in national policies and strategies 21,24,26 . It also undermines Niger’s commitment to reducing carbon emissions by 35% by 2030, in line with its Nationally Determined Contribution 20 . Despite substantial investments and alignment of national strategies with the needs of the agricultural sector, these policy documents acknowledge a critical gap: the lack of reliable information on the agricultural sector, which hinders progress towards their goals. Notably, while various studies have analyzed the impacts of climate change on adaptation strategies in sub-Saharan Africa, Niger has received disproportionately little attention 23 . This study aims to provide reliable information by analyzing the processes determining adaptation through the calculation of an adaptive capacity index for producers in the Kollo and N'Dounga localities in the Tillabéri region, and Guidan Roumdji and Gadabedji localities in the Maradi region ( Fig. 1 ). These localities, frequently identified as highly vulnerable to climate change, are situated within two agroecological zones with significant agricultural activity: the Sudan-Sahel and Sahel-Sahara zones, which are the focus of this study. Adaptive capacity in this study is conceptualized through five components or assets: financial capital, social capital, human capital, physical capital, and natural capital. The 21 indicators representing these components are summarized in Supplementary Table 1 . The analytical framework and methodology used to calculate the adaptive capacity index were adopted from the literature ( Supplementary Fig. 1 ). Unlike previous studies that aggregate indicators directly into a composite index, this study introduces a refined approach. Indicators were first aggregated into components, and components were then combined into an overall adaptive capacity index, considering the relative importance of elements at each aggregation level. This methodology not only avoids the limitations of direct aggregates of indicators into an adaptive capacity index – which can lead to inappropriate policy recommendations 27 – but also enables a more nuanced analysis of adaptive capacity. The results provide valuable insights into the adaptive capacities of farmers and the factors shaping their livelihood strategies. Policy recommendations are made for enhancing low-scoring assets 11 . Data were checked for normality, and descriptive statistics were employed to examine the socio-economic characteristics of farm households. Additional statistical tests assessed differences in adaptive capacity between households and across communities 28 . By employing a consistent methodological and analytical framework tailored to the realities of producers in these regions, this study contributes both to the growing body of literature on adaptive capacity and to informed decision-making in Niger. Results Scores and levels of adaptive capacity of farm households The indicators and components of adaptive capacity were compared and weighted using the Analytic Hierarchy Process (AHP) methodology. Prior to weighting, Cronbach's alpha (α) was calculated to assess the reliability of the indicators in measuring adaptive capacity, while Consistency Ratios (CRs) were computed to evaluate the consistency of weightings derived from expert judgments. The estimated Cronbach's alpha (α = 0.7093) indicated an acceptable level of reliability for the 21 selected indicators. All CRs were ≤ 0.1, confirming the consistency and acceptability of the derived weightings. Indicators were aggregated into components, which were further combined into a single adaptive capacity index ( \(\:{ACI}_{i}\) ) using an additive approach. The resulting \(\:{ACI}_{i}\) was categorized into three equal-interval groups: a farm household has a low level of adaptive capacity if 0 < \(\:{ACI}_{i}\) ≤ 0.33, moderate level if 0.34 ≤ \(\:{ACI}_{i}\) ≤ 0.66, and high level if 0.67 ≤ \(\:{ACI}_{i}\) ≤ 1. Overall, 37.28% of surveyed households exhibited low adaptive capacity, 59.17% had moderate capacity, and only 3.55% achieved a high adaptive capacity level ( Fig. 2 ). This indicates an uneven distribution of adaptive capacity among households, with the majority displaying moderate capacity for adaptation to climate change, while very few demonstrated high adaptive capacity. Figure 3 highlights that the most significant disparities between producers with low and moderate/high adaptive capacity levels were observed in the accessibility and availability of natural capital, human capital, and financial capital. Notably, producers with low adaptive capacity recorded a near-zero score in human capital. Similarly, differences in these capitals were evident between households with moderate and high adaptive capacity, with human capital displaying the greatest variation. These results suggest that the producers from the study sites were characterized by a very limited capacity to diversify their livelihoods, hence their vulnerability to climate-related events. This aligns with existing literature demonstrating the critical role of livelihood capitals in explaining the adaptive capacity of agricultural households 11 , 12 , 27 , 30 . Additionally, the observed low proportion of households with high adaptive capacity mirrors trends commonly reported in sub-Saharan African countries 31 , 32 . The calculated component scores and the site-specific categorization of the adaptive capacity index shown in Supplementary Tables 2 and 3 reveal that while access to and utilization of adaptive capacity assets varied significantly across household locations and adaptation levels, no significant association was found between producers’ adaptation levels and the selected study sites. A similar trend has been observed among Australian wheat growers, whose average adaptive capacity index showed no significant variation by location 33 . This lack of association may stem from the methodology or indicators used to compute the index 27 . Alternatively, it could reflect the reliance of producers in Niger on autonomous adaptation strategies, rooted in traditional and endogenous knowledge historically employed to manage climatic risks 23 , 34 . Such responses tend to be limited and exhibit minimal variation across communities, largely due to limited means of subsistence and political support 23 , 35 . Moreover, the selected study sites have been frequent targets of adaptation and resilience projects. However, these initiatives often implement standardized actions across multiple localities, failing to address climate threats specific to individual communities. This generalized approach may explain the observed inconsistency in fostering localized and tailored adaptation strategies 36 . The statistically significant differences in components across study sites provide valuable insights for decision-making. Notably, there were no significant differences in access to and use of social capital among the localities surveyed, although scores for this asset were relatively high. This reflects the deep-rooted importance of social ties – such as networks, reciprocity, solidarity, and cooperation – in shaping farmers' daily activities within the African rural context, regardless of location. These social connections serve as vital risk-sharing mechanisms, helping communities mitigate or recover from climate-related shocks 37 . Conversely, human capital scores were uniformly low across all localities. This finding aligns with similar studies in Africa and Asia, which frequently highlight human capital deficits as a major constraint to adaptive capacity 30 , 37 – 41 . In Niger, national strategic documents focusing on the agricultural sector 21 , 24 , 26 emphasize the urgent need to strengthen human capital through targeted training and agricultural advisory services while addressing persistent gender inequalities. These results underscore the necessity for tailored interventions aimed at bolstering this critical asset. Farmers in Gadabédji performed relatively better in access to and use of natural capital compared to other sites but exhibited notably low scores in physical and human capital. The elevated natural capital score is attributed to the unique environmental features of the locality, which includes the Réserve Total de Faune de Gadabédji (RTFG) – a UNESCO Biosphere Reserve since 2017 (see UNESCO’s World Network of Biosphere Reserves). This area, characterized by its biodiversity and rich grazing resources, plays a pivotal role in both conservation and pastoral livelihoods. Despite the high and moderate contribution of natural and financial capital, respectively, Gadabédji farm households were unable to make efficient use of resources due to very low human and physical capital scores. Furthermore, while financial capital in Gadabédji was rated moderate, it was insufficient to facilitate access to essential physical assets such as irrigation infrastructure or necessary farm equipment during the farming process. In contrast, farmers in N’Dounga, Kollo, and Guidan Roumdji relied on a combination of moderate levels of financial capital, physical capital, and natural capital, with uniformly low levels of human capital. Some statistically significant differences in asset distribution were observed among these sites. These findings emphasize the importance of site-specific approaches to building adaptive capacity. Comparative analyses revealed key factors that can inform decision-making, helping to prioritize interventions that address the most critical deficiencies in each locality. Comparative analysis of household adaptive capacity indicator scores This section draws on the statistical results presented in Table 1 , with a focus on unravelling and visualizing the contributions of individual indicators to each asset. A series of figures were developed to provide a clearer understanding of these contributions. The following analysis focuses on financial capital, natural capital, and human capital, consistent with the findings highlighted in the previous section. They emerged as particularly noteworthy by revealing potential significant differences among farmers displaying high, moderate and low adaptive capacity. Figure 4 illustrates that financial capital was predominantly composed of low indicator scores among surveyed households. However, statistical analyses revealed that adaptive capacity improved significantly with increases in the number of household income sources (FC1), farm workers (FC2), and animal units (FC5). These findings suggest that households with more diversified income sources and resources are better positioned to enhance their adaptive capacity. For instance, households with more than two income sources (30.61%) were more likely to achieve a moderate level of adaptive capacity. Likewise, households with larger number of farm workers and larger number of animal units were more likely to better adapt to climate shocks. Specifically, households with more than three farm workers (40.52%) and over ten animal units (29.45%) were more likely to acquire a moderate adaptive capacity. In contrast, there was no significant relationship between access to formal and informal credit (FC3) and access to public subsidies (FC4) with the level of adaptive capacity, with near-zero contribution scores (0.0009 and 0.005, respectively). These results highlight systemic challenges, including the lack of financial institutions in rural Niger and the limited effectiveness of small-scale social safety net programs 16 , 35 , 42 , 43 . The findings underscore the critical role of income diversification, labor availability, and livestock ownership in enhancing adaptive capacity, while also pointing to the need for improved rural credit systems and targeted subsidy programs to address these gaps. Like financial capital, household human capital was predominantly composed of low-scoring indicators ( Fig. 5 ). Statistical analyses revealed that adaptive capacity increases with education levels (HC1), with households lacking formal education exhibiting low adaptive capacity. These households constituted the majority (74%) of the surveyed population. Regarding the indicator "household's head farming experience" (HC2), it was anticipated that more experienced farmers, acting as independent decision-makers, would adapt better to climate change. While most surveyed farmers were relatively experienced – only 15% reported farming experience of ten years or less – Spearman's correlation tests indicated a very weak and non-significant relationship between years of farming experience and adaptive capacity. In contrast, the "access to agricultural advice" indicator (HC3) made a positive, albeit minimal, contribution to adaptive capacity (0.038). Only 30% of households surveyed (102 out of 338) reported access to farm advisory services, and these households demonstrated a higher average adaptive capacity score than those without access. These findings highlight the critical need for improving education and expanding access to agricultural advisory services to enhance human capital and adaptive capacity among rural households. The average scores achieved by the natural capital indicators, shown in Fig. 6 , were also low. Natural capital comprised indicators such as soil fertility (NC1), household’s plot size (NC2), experience of natural hazards on farm plots (NC3), and number of tree types on household farms (NC4). Among these, all but the experience of natural hazards showed a significant and positive correlation with households' adaptive capacity, as households with prior exposure to natural hazards often exhibited lower adaptive capacity. In contrast, larger farm sizes, greater tree variety, and more fertile soils were associated with higher adaptive capacity. However, over half of the surveyed farmers (around 54%) reported farming on infertile soils, highlighting a key limitation. Prior research by Fosu-Mensah et al. (2012) emphasized the critical role of soil fertility in shaping farmers’ adaptive capacity, as it influences decisions about adaptation practices in response to climate risks. Unexpectedly, households with experience of natural hazards (NC3) on their farms exhibited lower adaptive capacity compared to those without such experiences. This finding challenges the conventional understanding that exposure to climatic stressors can motivate risk-reducing adaptation strategies, such as crop residue management 44 , 45 . In this study, while 99% of households perceived changes in climatic parameters (e.g., insufficient or erratic rainfall, prevalence of hot days), only 40% reported experiencing natural disasters on their farms. These households displayed lower adaptive capacity scores, likely due to existing vulnerabilities such as low education levels, limited access to agricultural advice, and financial constraints, which exacerbate the impacts of shocks and reduce resilience. This underscores the urgent need for targeted external support to assist disaster-affected farmers in building adaptive capacity. Furthermore, households in flood-prone areas, especially in the Tillabéri region, often settle on unsuitable lands near riverbanks, increasing their exposure to flooding. 46 . Regarding the "number of tree types on household farms" (NC4), the estimates showed that farmers who planted and managed a variety of trees, particularly woody species, demonstrated higher adaptability scores. Trees played a crucial role in reducing flood impacts, resisting pest attacks, and providing additional household income, underscoring their importance as a strategy for enhancing adaptive capacity 47 , 48 . Discussion This study was aimed to assess the adaptive capacity of farming households in Kollo and N’Dounga (Tillabéri region) as well as Gadabédji and Guidan Roumdji (Maradi region) in Niger. The findings highlight critical disparities in asset portfolios among households, with low and moderate adaptive capacity associated with limited diversity and unbalanced portfolios, respectively. Diversification and balance between different assets are essential for farm households in the pursuit of livelihood strategies, enabling livelihood substitution strategies in the face of shocks and ensuring efficient use of resources 49 . The study revealed that low and moderate adaptive capacity households lacked critical human capital, particularly education and skills, which are essential for accessing information, technologies, and off-farm income opportunities. In Niger, remarkably low levels of education, particularly in rural areas 16 , have regularly been cited among the main constraints to effective adaptation to the adverse effects of climate change and improved agricultural productivity 8 , 23 , 34 , 50 . This situation has not enabled farmers to maximize agricultural production by using all available resources to better adapt to climate change 31 , 51 , 52 . In addition, a low level of education limits producers' ability to engage in off-farm, better-paid, activities and thus increase their income and subsistence activities 53 . It also emerged that these households did not receive sufficient access to agricultural advice through extension services to make informed choices on effective adaptation strategies and better manage climate change-related risks through climate information 47 , 54 . Farmers' access to agricultural extension services has been structurally low in Niger 34 , whereas it had a positive and significant effect on technical efficiency for farm households in Uganda 55 . Households with moderate adaptive capacity tended to diversify financial capital through multiple income sources, farm labor, and animal units. Households with a diversified portfolio of activities, including other jobs into non-agricultural activities like fishing, handicrafts, animal husbandry and petty trade, were more likely to recover from the negative effects of climate change. In Niger, several studies showed the positive and significant impact, in the short and medium term, of income diversification on climate change resilience and the well-being of farming households (particularly vulnerable ones) and food security 8 , 16 , 23 . In addition, households with more workers are more likely to use new technologies and more labor-intensive measures and are therefore likely to be more effective in adapting to the effects of climate change 37 , 56 . Similar to the findings in the northern region of Ghana 56 , labor shortage in Niger can be mainly attributed to the out-migration of active men, particularly young people. This affects farming operations and crop yields when many do not return, with important implications for food security in these communities. Moreover, according to the World Bank report 57 , if no concrete action is taken on climate and development, internal climate change induced migration will affect 86 million people in sub-Saharan Africa by 2050, with effects more accentuated for poor and climate-vulnerable populations like those in Niger. Animal units have traditionally been an important financial resource for households in Niger for the provision of insurance mechanisms and supporting adaptive measures such as manure spreading due to the greater availability of manure 58 . It is often admitted that most rural livelihoods depend on natural resources, such as farm size. This suggest that farmers with larger farms had greater adaptive capacity 30 , 39 . These farmers are more likely to adopt new high-yield agricultural technologies, such as mixed cropping 31 . The results exhibited a significant association between the farm size and the type of cropping system used on the farms, suggesting that households with larger farms adopted mixed cropping. They are also often considered as wealthy households who can afford to buy the necessary inputs 31 . As a result, for smallholders to adopt integrated farming systems, external support will be needed. This external support is also a necessary factor in improving the soil fertility of household farms, some 54% of which reported producing on non-fertile soils. The study sites reflect the characteristics of Niger, for very few producers have access to modern inputs such as chemical fertilizers and pesticides, and they mostly either use organic inputs (mainly manure) or do not use any soil improvement products 16 . While soil improvement practices have long-term benefits, their upfront costs pose challenges for resource-poor households 58 . These findings, combined with the poverty conditions of farm households who generally position themselves in a short-term perspective to decision-making, suggest that policy interventions to improve input availability – through subsidies or market facilitation – are urgently needed. The results suggest several policy implications. There is an urgent need for targeted interventions to strengthen the financial, human and natural capital of farming households, except for households residing in Gadabéji. Given that this locality had a high level of natural capital, interventions should target both financial and natural capital. Policymakers should give priority to programs that promote income diversification, improve education levels and increase farm size and soil fertility through access to modern inputs and soil improvement practices. Collaboration with agricultural research and advisory institutions such as RECA ( Réseau National des Chambres d’Agriculture du Niger ), Regional Center AGRHYMET (regional training and application center for agrometeorology and operational hydrology), INRAN ( Institut National de la Recherche Agronomique du Niger ) and the Plateforme Paysanne (civil society organization bringing together farmers' organizations and active in all areas of the rural sector in Niger) will be crucial to the coordinated and effective implementation of policy actions. To effectively support farmers, it is essential to implement tailored programs that consider the specific needs and climatic conditions of target populations, while capitalizing on households' indigenous knowledge as ''indigenous knowledge is the backbone of successful climate change adaptation in agriculture '' 59 . The results of this study provide a basis for the development of comprehensive and context-specific policies that strengthen the adaptive capacity and well-being of farm households in Niger. Future research should adopt dynamic approaches to capture the evolving nature of adaptive capacity and integrate vulnerability and social justice considerations to ensure inclusive and effective policy outcomes. Methods Data Multi-stage sampling was used to select the farmers in our sample. At the first stage, the regions of Maradi and Tillabéri were purposively selected to represent the country's agroecological zones: the Sudan-Sahel zone, covering the central part of Tillabéri and the southern part of Maradi, and the Sahel-Sahara zone in northern Tillabéri ( Fig. 1 ). These regions were chosen based on their agricultural importance and high vulnerability to the impacts of climate change 15 , 46 , 60 , 61 . Cochran’s sampling method 62 was used to determine the number of farmers to interview in the survey. This led to a sample of 196 for the Tillabéri region and 138 for the Maradi region, for a total of 334. To account for non-responses and missing data, the sample size was slightly overestimated to 343 farmers. However, due to insecurity in Tillabéri, the target sample size for that region could not be reached, and the gap was filled by increasing the sample size in Maradi. At the second stage, departments within the selected regions were identified. The selection was guided by agricultural production levels (measured using local agricultural statistics, such as crop production and land under cultivation) and vulnerability to climate change (assessed through reports on drought and flood impacts, as well as indices for food insecurity and poverty) 15 , 46 , 60 , 61 . Based on these criteria, Kollo was selected from the Tillabéri region, while Guidan Roumdji and Bermo were selected from the Maradi region. In the third stage, specific communities within these departments were chosen using a proportional-to-size sampling technique 63 . Within the Kollo department, Kollo, a town and urban commune, and N’Dounga, a village and rural commune, were selected. In the Maradi region, Guidan-Roumdji, a town and urban commune, and Gadabedji, a rural village, were selected. Farmers were then randomly selected within these locations to complete a pre-established and pre-tested questionnaire. Index construction This section outlines the method used to construct an adaptive capacity index for Niger farmers, a key element of the theoretical framework developed for conducting this study (see Supplementary Fig. 1 ). The process follows validation by 11 key experts (see Supplementary Table 11 for experts’ affiliation), selected from institutions responsible for climate change adaptation issues, who reviewed the 21 indicators used to calculate the index. These experts were carefully chosen from a list of 19 previously interviewed, based on their in-depth knowledge of climate change, its impacts on farming households in Niger, and adaptation policies. Furthermore, they were required to have strong familiarity with at least two of the four selected study sites. The construction of the index followed several steps, as outlined below: Analysis of the internal consistency of indicators: Cronbach's Alpha Coefficient (1951) The goal of this analysis is to assess the extent to which the selected set of indicators is sufficient and appropriate for measuring the same underlying concept targeted by the index. The Cronbach's alpha coefficient (α) was estimated using the following equation: \(\:\alpha\:=\:\left(\frac{I}{I-1}\right)\frac{\sum\:_{i\ne\:j}cov\left({x}_{i},{x}_{j}\right)}{var\left({x}_{o}\right)}\) ; i,j = 1,…,N Where N indicates the number of individuals considered, \(\:I\) the number of indicators selected, \(\:{x}_{o}\) = \(\:\sum\:_{n=1}^{N}{x}_{j}\) the sum of all individual indicators. A high Cronbach's alpha, or equivalently a high "reliability", indicates that the individual indicators reliably measure the latent concept. It is further compared to Nunnally's threshold of 0.7, which is considered an acceptable reliability threshold 64 , 65 . In the context of our study, the estimated Cronbach's alpha is 0.7093, indicating an acceptable level of reliability for the 21 selected indicators. Normalization of indicators: the Min-Max method Prior to any data aggregation, normalization is required to make variables comparable. In this work, the Min-Max method was used, as it can be used with all weighting systems and for all aggregation systems 65 . This method converts indicator values to an identical interval [0, 1]. The values of each indicator are normalized according to the formula: $$\:{I}_{i}=\:\frac{{x}_{i}-\:{x}_{min}}{{x}_{max}-\:{x}_{min}}$$ Where \(\:{x}_{i}\) is the specific value of the indicator to be transformed, \(\:{x}_{min}\) and \(\:{x}_{max}\) respectively the smallest and largest value of the concerned indicator, and \(\:{I}_{i}\) is the new normalized value. This step is followed by the aggregation of the normalized indicators, multiplied by their respective weightings based on a specific methodology. Weighting indicators using the AHP (Analytic Hierarchy Process) method The aim of the weighting system is to assign relative importance to each criterion or component in the composite index aggregation. A robust weighting system must be informed, explicit, transparent, and reliable to support adequate policy formulation 64 , 66 , 67 . Weighting approaches based on expert judgement often contrasted with equal weighting systems and those based on data. Expert judgment-based weighting is considered a conventional and appropriate method when experts possess in-depth knowledge of the national adaptation policy and the selected sites 67 . Additionally, because the components of adaptive capacity are closely linked to livelihoods, experts can more easily assign weights to them 68 . In this study, the experts involved in the validation of the adaptive capacity indicators were asked to assign weights using the Analytic Hierarchy Process (AHP), developed by Saaty (1990). AHP is especially useful for solving problems involving multidimensional latent concepts, as it structures complex problems into simpler hierarchies and groups 69 Furthermore, AHP includes a consistency measure to ensure the accuracy of expert judgments 64 , 65 , 70 . Weights are therefore derived through a systematic process rather than being arbitrarily assigned. The AHP process was applied in the following steps: Step 1: Pairwise comparison of adaptive capacity indicators and components. In this step, experts conducted pairwise comparisons of indicators and components to determine the relative importance of each criterion compared with other. This approach simplifies the decision-making process by making comparisons more accurate 69 . The relative importance is expressed on an ordinal scale ranging from 1 to 9 71 , where a value of 1 indicates equal importance and higher values represent increasing importance. The comparisons resulted in a series of squared reciprocal matrices, known as pairwise comparison matrices and represented by Supplementary Tables 5 to 10 . For example, regarding financial capital, the experts considered that the number of sources of income for a farming household is six times greater than the number of farmers in the household. Step 2: The pairwise comparison matrices were used to calculate the weights of indicators and components using the eigenvector method, employing the power method 66,69 . It involves squaring the matrix, then dividing the sum of each row by the sum of all the matrix elements. The resulting column vector contains the normalized values and approximates the eigenvector. This step is repeated until the eigenvector values no longer change. These resulting values were taken as the weights of the indicators and components. Step 3: A third step was carried out to check the consistency of the expert judgments used to construct the pairwise comparison matrix and assign weights. Weights only make sense if derived from a consistent matrix 64,65 . To this end, the Consistency Ratio (CR) suggested by Saaty 71 is calculated using the following formula: $$\:CR=\:\frac{CI}{RI}\:$$ Where RI is the random index, and CI is the consistency index. CI is calculated according to the formula: $$\:CI=\:\frac{\left({\lambda\:}_{max}-n\right)}{n-1}$$ Where \(\:n\) represents the number of indicators considered (also equal to the order of the pairwise comparison matrix), and \(\:{\lambda\:}_{max}\) the maximum eigenvalue of the calculated matrix. The random index RI is defined as the consistency of the randomly generated pairwise comparison matrix, which depends on the number of elements (indicators or components). The RI value is derived from the random index table proposed by Saaty. The estimated CRs are shown in Supplementary Tables 5 to 10 , indicating the consistency of the pairwise comparison matrices and the weights assigned. A degree of inconsistency not exceeding 0.10 is considered acceptable 66 , 70 , hence the acceptability of our various results obtained. Aggregating indicators into components and components into a composite index of ACI adaptability Once the indicators have been standardized and weighted, they are integrated into the five (5) corresponding components, which are in turn aggregated into a composite index. The additive method was used for aggregation, as it is simple, independent of outlier and compensatory, meaning that low values in one indicator can be compensated by higher values in another 64 , 65 . This method is also appropriate for the AHP method which belongs to the group of compensatory aggregation methods 64 , 65 . The additive method is commonly used in livelihood studies where different types of capital, such as natural or financial capital, can be substituted for one another to increase adaptive capacity 72 . The indicators were aggregated into each corresponding component using the following formula: $$\:{CP}_{ij}=\:\sum\:_{q=1}^{21}{w}_{q}{I}_{ijq}$$ with \(\:\sum\:_{q=1}^{21}{w}_{q}=1\) , q = 1,…,21, i = 1,…,N et j = 1,…,5 Where \(\:{CP}_{ij}\) captures the capital (asset or component) j of the adaptive capacity of farm household i , \(\:{w}_{q}\) the weight of indicator q , and \(\:{I}_{ijq}\) the normalized indicator of the corresponding component j . Then, the components were aggregated into a composite index of farm household adaptive capacity, following the same aggregation method and through the formula: $$\:{ACI}_{i}=\:\sum\:_{j=1}^{5}{w}_{j}{CP}_{ij}\:$$ with \(\:\sum\:_{j=1}^{5}{w}_{j}=1\) Where \(\:{ACI}_{i}\) represents the composite index of adaptive capacity of farm household i, \(\:{w}_{j}\) the weight assigned to component j. The index scores obtained are between 0 and 1. The composite index scores range from 0 to 1. For easier interpretation, the index scores were categorized into three groups at equal intervals: low (0 < \(\:{ACI}_{i}\) ≤ 0.33), moderate (0.34 ≤ \(\:{ACI}_{i}\) ≤ 0.66), and high (0.67 ≤ \(\:{ACI}_{i}\) ≤ 1), following the categorization method by Datta and Behera 48 . Finally, data were checked for normality, and descriptive statistics were applied to analyze the socio-economic characteristics of farm households. Additional statistical tests were conducted to test for differentiated adaptive capacity between farm households and communities 28 . Declarations All participants provided informed consent before participating in the study. Participants were informed about the voluntary nature of their participation, their right to withdraw at any time, and the confidentiality of their responses. No personally identifiable information, such as names or addresses, was recorded or published. The research received formal ethical approval from WASCAL Climate Change Economics at Cheikh Anta Diop University (UCAD), Senegal. No objections were raised regarding the implementation of the study in its present form. Acknowledgements The authors gratefully acknowledge the support provided by the West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), funded by the German Federal Ministry of Education and Research (BMBF). Sanoussi Ibrahim Oumarou’s PhD research, including his fieldwork in Niger and his research exchange at the University of Kassel, Germany, was funded by WASCAL as part of the Climate Change Economics Graduate Research Program at Cheikh Anta Diop University in Dakar, Senegal. His fieldwork was conducted in collaboration with Abdou Moumouni University (UAM) in Niamey, Niger. The authors also acknowledge the project “Greenhouse Gas Determination in West Africa’s Agricultural Landscapes” (GreenGaDe), funded under the BMBF WASCAL II – Research Call (grant number: 01LG2078B). Through this project, Sanoussi Ibrahim Oumarou was connected to the project partners, including UAM in Niger and the University of Kassel in Germany, enabling collaboration and supporting his fieldwork, research exchange, and the collaborative work presented in this paper. Author information Contributions S.I.O. conceptualized and designed the study; conducted the literature review to inform the methodological framework and indicator selection; led data collection in Niger, including multi-stage sampling, survey implementation, and engagement with local experts; performed data analysis, including the construction and validation of the adaptive capacity index; developed visualizations; and drafted the manuscript. R.H. contributed to the conceptualization and design of the study and survey; co-conducted the literature review; provided detailed input on data analysis and interpretation; participated in virtual consultations during data collection to provide guidance and support; coordinated project management, including research exchanges and manuscript preparation; drafted sections of the manuscript; developed additional visualizations; and provided iterative feedback and critical revisions throughout the manuscript development process. I.T.D. and R.S. supervised the research, providing intellectual guidance on the study design, data analysis, and overall research framework. Both reviewed and provided feedback on the manuscript. All authors reviewed and approved the final manuscript. Corresponding author Correspondence to Sanoussi Ibrahim Oumarou Ethics declarations Competing interests The authors declare no competing interests. References Baroudy E, World Bank Group (2022) G5 SAHEL REGION. Country climate and development report . (: at https://reliefweb.int/report/burkina-faso/g5-sahel-region-country-climate-and-development-report#::text = The G5 Sahel countries combined,and land degradation by 2030.> IPCC Summary for Policymakers. Climate Change 2021: The Physical Science Basis. Contribution of Working Group Ito the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (2021) 10.1017/9781009157896.001 IPCC Summary for Policymakers. Global Warming of 1.5°C. An IPCC Special Report on the impactsof global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (2018) IPCC Summary for Policymakers. Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (2022) UNEP Adaptation Gap Report 2022: Too Little, Too Slow - Climate adaptation failure puts world at risk - Executive Summary . (United Nations Environment Programme, Nairobi: (2022) at < Nairobi. https://www.unep.org/adaptation-gap-report-2022 Adger W et al (2007) Assessment of adaptation practices, options, constraints and capacity. Climate change 2007: impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change 717–743 at https://www.researchgate.net/publication/313769934_Assessment_of_adaptation_practices_options_constraints_ and_capacity_Climate_change_2007_impacts_adaptation_and_vulnerability Adger WN, Vincent K (2005) Uncertainty in adaptive capacity. CR Geosci 337:399–410 Röhrig F et al (2022) Climate Risk Analysis for Identifying and Weighing Adaptation Strategies in Niger’s Agricultural Sector. (A report prepared by the Potsdam Institute for Climate Impact Research (PIK) in cooperation with the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ), 154 pp.: 10.48485/PIK.2022.002 UNEP The gathering storm - Adapting to climate change in a post-pandemic world. (United Nations Environment Programme (UNEP) Nairobi: 2021).at < https://www.unep.org/adaptation-gap-report-2021 Zougmoré R et al (2016) Toward climate-smart agriculture in West Africa: a review of climate change impacts, adaptation strategies and policy developments for the livestock, fishery and crop production sectors. Agric Food Secur 5 Abdul-Razak M, Kruse S (2017) The adaptive capacity of smallholder farmers to climate change in the Northern Region of Ghana. Clim Risk Manage 17:104–122 Chepkoech W, Mungai NW, Stöber S, Lotze-Campen H (2020) Understanding adaptive capacity of smallholder African indigenous vegetable farmers to climate change in Kenya. Clim Risk Manage 27:100204 Vincent K (2007) Uncertainty in adaptive capacity and the importance of scale. Glob Environ Change 17:12–24 Engle NL (2011) Adaptive capacity and its assessment. Glob Environ Change 21:647–656 INS (2017) Enquête Conjointe sur la Vulnérabilité à l’Insécurité Alimentaire des Ménages au Niger. Institut National de la, Statistique (INS) WB Priorities for Ending Poverty and Boosting Shared Prosperity. Systematic Country Diagnostic . (The World Bank, Report No. 115661-NE: (2017) BM ÉVALUATION DES RISQUES DU (2013) SECTEUR AGRICOLE AU NIGER: De la Réaction Aux Crises à la Gestion des Risques à Long Terme. LA BANQUE MONDIALE, RAPPORT NUMÉRO, pp 74322–NE CNEDD Plan National d’Adaptation aux Changements Climatiques . (République du Niger, Cabinet du Premier Ministre, Conseil National de l’Environnement pour un Développement Durable: (2022) DTDA Profil du marché du travail du Niger – 2021/2022 . (Danish Trade Union Development (DTDA), Agence Syndicale Danoise de Coopération au Développement, Agenda mondial pour le Travail Décent (ATD): 2022).at < https://www.ulandssekretariatet.dk/wp-content/uploads/2021/11/LMP-Niger-2021-Final-Francais.pdf ME/LCD Contribution Déterminée au Niveau National . (République du Niger, Ministère de l’Environnement et de la Lutte contre la Désertification: (2021) ME/LCD CNEDD, HE3N & MAG/EL (2020) Stratégie et Plan National d’Adaptation face aux changements climatiques dans le secteur Agricole: SPN2A 2020–2035. Version finale . (République du Niger Agbegnido MK, ADAMOU MM (2018) Etude diagnostique du dispositif de Formation Agricole et Rurale du Niger . (République du Niger: at https://reca-niger.org/IMG/pdf/-9.pdf Asfaw S, Pallante G, Palma A (2018) Diversification Strategies and Adaptation Deficit: Evidence from Rural Communities in Niger. World Dev 101:219–234 M/P Plan de Développement Economique et Social (PDES) 2022–2026. Volume I: Diagnostic stratégique . (République du Niger, Ministère du Plan: (2022) Lalou R, Sultan B, Muller B, Ndonky A (2019) Does climate opportunity facilitate smallholder farmers’ adaptive capacity in the Sahel? Palgrave Commun 5 M/P Stratégie de Développement Durable et de Croissance Inclusive. Niger 2035: Un pays et un peuple prospères. Tome - II : Scénarii, Vision et Orientations Stratégiques . (République du Niger, Ministère du Plan: (2017) Zanmassou YC, Al-Hassan RM, Mensah-Bonsu A, Osei-Asare YB, Igue CB (2020) Assessment of smallholder farmers’ adaptive capacity to climate change: Use of a mixed weighting scheme. J Environ Manage 276:111275 Choden K, Keenan RJ, Nitschke CR (2020) An approach for assessing adaptive capacity to climate change in resource dependent communities in the Nikachu watershed, Bhutan. Ecol Ind 114:106293 Kamara AY et al (2023) Assessment of the impact of crop management strategies on the yield of early-maturing maize varieties in the drylands of Niger Republic: Application of the DSSAT-CERES-Maize model. Heliyon 9:e17829 Jamshidi O et al (2020) Adaptive capacity of smallholder farmers toward climate change: evidence from Hamadan province in Iran. Climate Dev 12:923–933 Egyir IS, Ofori K, Antwi G, Ntiamoa-Baidu Y (2015) Adaptive Capacity and Coping Strategies in the Face of Climate Change: A Comparative Study of Communities around Two Protected Areas in the Coastal Savanna and Transitional Zones of Ghana. J Sustainable Dev 8 Mekonnen Z, Kassa H (2019) Living with Climate Change: Assessment of the Adaptive Capacities of Smallholders in Central Rift Valley, Ethiopia. Am J Clim Change 08:205–227 Bryan BA et al (2015) What Actually Confers Adaptive Capacity? Insights from Agro-Climatic Vulnerability of Australian Wheat. PLoS ONE 10:e0117600 Assoumana BT, Ndiaye M, Puije GVD, Diourte M, Gaiser T (2016) Comparative Assessment of Local Farmers’ Perceptions of Meteorological Events and Adaptations Strategies: Two Case Studies in Niger Republic. J Sustainable Dev 9:118 Gambo Boukary A, Diaw A, Wünscher T (2016) Factors Affecting Rural Households’ Resilience to Food Insecurity in Niger. Sustainability 8:181 Tiepolo M, Bacci M, Braccio S (2018) Multihazard Risk Assessment for Planning with Climate in the Dosso Region. Niger Clim 6:67 Yaméogo T, Fonta W, Wünscher T (2018) Can Social Capital influence Smallholder Farmers’ Climate-Change Adaptation Decisions? Evidence from Three Semi-Arid Communities in Burkina Faso, West Africa. Social Sci 7:33 Antwi-Agyei P, Dougill AJ, Fraser EDG, Stringer LC (2013) Characterising the nature of household vulnerability to climate variability: empirical evidence from two regions of Ghana. Environ Dev Sustain 15:903–926 Defiesta G, Rapera CL (2014) Measuring Adaptive Capacity of Farmers to Climate Change and Variability: Application of a Composite Index to an Agricultural Community in the Philippines. J Environ Sci Manage 17:48–62 Gbetibouo GA, Ringler C, Hassan R (2010) Vulnerability of the South African farming sector to climate change and variability: An indicator approach. Nat Resour Forum 34:175–187 Mogomotsi PK, Sekelemani A, Mogomotsi GE (2020) J. Climate change adaptation strategies of small-scale farmers in Ngamiland East, Botswana. Clim Change 159:441–460 Aker JC, del Ninno C, Dorosh PA, Mulder-Sibanda M, Razmara S (2009) Niger: Food Security and Safety Nets Stoeffler Q, Mills B, Stoeffler Q, Mills B (2014) Households’ investments in durable and productive assets in Niger: quasi-experimental evidences from a cash transfer project. 10.22004/AG.ECON.170212 Asfaw S, Lipper L (2015) & others Adaptation to climate change and its impacts on food security: Evidence from Niger. August 9–14, 2015, Milan, Italy (2015).at https://ideas.repec.org/p/ags/iaae15/225667.html Shackleton S, Ziervogel G, Sallu S, Gill T, Tschakert P (2015) Why is socially-just climate change adaptation in sub-Saharan Africa so challenging? A review of barriers identified from empirical cases. WIREs Clim Change 6:321–344 Tarchiani V et al (2016) Risque et adaptation climatique dans la région Tillabéri, Niger. (Editions L’Harmattan Ariom TO et al (2022) Climate-Smart Agriculture in African Countries: A Review of Strategies and Impacts on Smallholder Farmers. Sustainability 14:11370 Datta P, Behera B (2022) Assessment of adaptive capacity and adaptation to climate change in the farming households of Eastern Himalayan foothills of West Bengal, India. Environ Challenges 7:100462 Nelson R et al (2010) The vulnerability of Australian rural communities to climate variability and change: Part II-Integrating impacts with adaptive capacity. Environ Sci &mathsemicolon Policy 13:18–27 Asfaw S, Battista FD, Lipper L (2016) Agricultural Technology Adoption under Climate Change in the Sahel: Micro-evidence from Niger. J Afr Econ 25:637–669 Deressa TT, Hassan RM, Ringler C, Alemu T, Yesuf M (2009) Determinants of farmers’ choice of adaptation methods to climate change in the Nile Basin of Ethiopia. Glob Environ Change 19:248–255 Singh S (2020) Farmers’ perception of climate change and adaptation decisions: A micro-level evidence from Bundelkhand Region, India. Ecol Ind 116:106475 WB An Assessment of Employment in Niger. (World Bank Group (2017) Fosu-Mensah BY, Vlek PLG, MacCarthy DS (2012) Farmers’ perception and adaptation to climate change: a case study of Sekyedumase district in Ghana. Environ Dev Sustain 14:495–505 Kansiime MK, van Asten P, Sneyers K (2018) Farm diversity and resource use efficiency: Targeting agricultural policy interventions in East Africa farming systems. NJAS: Wageningen J Life Sci 85:32–41 Antwi-Agyei P, Dougill AJ, Stringer LC, Codjoe S (2018) N. A. Adaptation opportunities and maladaptive outcomes in climate vulnerability hotspots of northern Ghana. Clim Risk Manage 19:83–93 Rigaud K (2018) Groundswell: se préparer aux migrations climatiques internes. (Washington, Banque mondiale.: at https://www.banquemondiale.org/fr/news/infographic/2018/03/19/groundswell—preparing-for-internal-climate-migration Shikuku KM et al (2017) Smallholder farmers’ attitudes and determinants of adaptation to climate risks in East Africa. Clim Risk Manage 16:234–245 Dinesh D, Vermeulen S (2016) Climate change adaptation in agriculture: practices and technologies . (CCAFS INFO NOTE MAG Rapport définitif de l’enquête sur les productions irriguées 2020–2021 . (République du Niger, Ministère de l’Agriculture, Secrétariat Général, Direction des Statistiques: (2021) OCHA Niger - Région de Maradi Analyse situationnelle trimestrielle . (Le Bureau des Nations Unies pour la Coordination des Affaires Humanitaires, OCHA: 2021).at < https://www.unocha.org/niger Cochran WG (1977) Sampling Techniques. John Wiley & Sons, Inc. Skinner CJ (2016) Probability Proportional to Size (PPS) Sampling. Wiley StatsRef: Statistics Reference Online, © 2014–2016 John Wiley & Sons, Ltd. 1–5 10.1002/9781118445112.stat03346.pub2 Nardo M, Saisana M, Saltelli A, Tarantola S Tools for Composite Indicators Building . (EUR 21682 EN. JRC31473: 2005).at https://publications.jrc.ec.europa.eu/repository/handle/JRC31473 OECD Handbook on constructing composite indicators. 158 (OECD (2008) Gan X et al (2017) When to use what: Methods for weighting and aggregating sustainability indicators. Ecol Ind 81:491–502 Greco S, Ishizaka A, Tasiou M, Torrisi G (2018) On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Soc Indic Res 141:61–94 Below TB et al (2012) Can farmers’ adaptation to climate change be explained by socio-economic household-level variables? Glob Environ Change 22:223–235 Ishizaka A, Nemery P (2013) Multi-criteria Decision Analysis Methods and Software, vol 310. Wiley, Ltd Giri S, Lathrop RG, Obropta CC (2020) Climate change vulnerability assessment and adaptation strategies through best management practices. J Hydrol 580:124311 Saaty TL (1990) How to make a decision: The analytic hierarchy process. Eur J Oper Res 48:9–26 Jacobs B, Nelson R, Kuruppu N, Leith P (2015) An adaptive capacity guide book: Assessing, building and evaluating the capacity of communities to adapt in a changing climate . (Southern Slopes Climate Change Adaptation Research Partnership (SCARP), University of Technology Sydney and University of Tasmania. Hobart, Tasmania Table Table 1 Comparison of groups based on adaptative capacity Adaptive capacity components Indicators Clusters Mean/Median ACI scores Percentage of the distribution* t value F value Financial Capital (FC) Sources of income (FC1) 1 0.22 18.08 345.7 *** 2 0.37 49.85 More than 2 sources 0.56 30.61 Farm worker on HH plots (FC2) 1 0.30 29.45 39.07 *** 2 0.39 17.2 3 0.43 22.45 More than 3 workers 0.49 29.45 Access to credit (FC3) No access 0.40 97.67 -1.17 Access 0.50 0.87 Access to public subsidy (FC4) No access 0.40 91.55 -0.94 Access 0.43 7 Animal ownership (FC5) 0 0.31 20.99 38.97 *** 1–5 0.34 25.95 6–10 0.39 11.08 More than 10 animals 0.49 40.52 Human Capital (HC) Level of education (HC1) Informal / none 0.37 72.59 20.73 *** Primary 0.46 13.12 Secondary 0.53 11.08 Tertiary 0.51 1.75 Access to agricultural advice (HC3) No access 0.36 68.8 -7.23 *** Access 0.49 29.74 Natural Capital (NC) Soil fertility (NC1) Not fertile 0.35 53.94 -7.75 *** Fertile 0.46 44.61 HH plot size (NC2) <=2 ha 0.34 37.9 21.10 *** <=4 ha 0.38 20.99 6 ha 0.49 18.66 HH experience with natural hazards (NC3) No 0.42 58.6 2.23*** Yes 0.37 39.94 Number of tree types in the main farm of the HH (NC4) 0 type 0.31 2.92 3.34* 1 type 0.38 26.53 2 types 0.38 24.49 3 types 0.41 15.74 More than 3 types 0.44 28.86 Particulars Correlation coefficient Significance HH farming experience (HC2) 0.0386 0.48 *0.05 level of significance; **0.01 level of significance, ***0.001 level of significance. * The remaining 1.46% relate to non-respondent in the database. Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryinformationAdaptivecapacityNiger.docx Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5866839","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":411532252,"identity":"13bf57b4-1b96-4d30-9543-f169d3a5a290","order_by":0,"name":"Sanoussi Ibrahim Oumarou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBACNhDxgMGCRwLMraiRY2DgAbHk8GtJYJCAajlzzBiqxRi/VUAtDGAtjG3MiQ2EtPCxtz/dANQiI9ne/ky6sI0tfcPxswcffGAwyMfpMJ4zZjdADpMGMqRnnJPJ3XAmL9lwBoOBZQMuLRI5bGAtckCGNE8ZW+6GAzlm0jwMfwxw2iKR/gyiRf75M2keNuZ0g/NvQFoM8GhJgDpMggGoso05weBGDgEtYL8YSPBI9uQYW/OcOWY488YbY8MZBri1yAMD6saHCht7iePHH97mqaiR5zufY/jgQwVuLRCALK1wAF2EIJBvIEX1KBgFo2AUjAQAACnfSsDd0LvtAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0009-0057-7647","institution":"WASCAL Graduate Research Program on Climate Change Economics, Cheikh Anta Diop University","correspondingAuthor":true,"prefix":"","firstName":"Sanoussi","middleName":"Ibrahim","lastName":"Oumarou","suffix":""},{"id":411532253,"identity":"60fe148d-1bd6-4f22-9dd1-121d27aff964","order_by":1,"name":"Roman Hinz","email":"","orcid":"https://orcid.org/0000-0002-2829-2338","institution":"Kassel Institute for Sustainability, University of Kassel","correspondingAuthor":false,"prefix":"","firstName":"Roman","middleName":"","lastName":"Hinz","suffix":""},{"id":411532254,"identity":"eafe749b-a9d6-4521-bb49-006489c0016d","order_by":2,"name":"Ibrahima Thione Diop","email":"","orcid":"","institution":"Faculty of Economics and Management Sciences, Cheikh Anta Diop University","correspondingAuthor":false,"prefix":"","firstName":"Ibrahima","middleName":"Thione","lastName":"Diop","suffix":""},{"id":411532255,"identity":"53db66a6-18e5-462f-9980-4d7e0f42a211","order_by":3,"name":"Rüdiger Schaldach","email":"","orcid":"https://orcid.org/0000-0003-2513-9773","institution":"University of Kassel","correspondingAuthor":false,"prefix":"","firstName":"Rüdiger","middleName":"","lastName":"Schaldach","suffix":""}],"badges":[],"createdAt":"2025-01-20 14:50:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5866839/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5866839/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77770296,"identity":"f1ea196a-3ab0-46b9-b5d1-7b44fee786af","added_by":"auto","created_at":"2025-03-05 10:42:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":101957,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area overview. The top left corner highlights Niger's location in Africa. The main map displays Niger, including its agroecological zones and administrative boundaries (regions), with the study regions Tillabéri and Maradi and the respective study sites indicated. Enlarged views of the Tillabéri and Maradi regions show the study departments and precise locations of the study sites: Kollo (\u003cstrong\u003eKo\u003c/strong\u003e) and N’Dounga (\u003cstrong\u003eNd\u003c/strong\u003e) in the Kollo department, Guidan Roumdji (\u003cstrong\u003eGr\u003c/strong\u003e) in the Guidan Roumdji department, and Gadabedji (\u003cstrong\u003eGa\u003c/strong\u003e) in the Bermo department. Agroecological zones are based on previous work\u003csup\u003e29\u003c/sup\u003e, and administrative boundaries, including country, region, and department levels, are sourced from publicly available data sources (GADM maps and data and Humanitarian Data Exchange (HDX), OCHA services, Niger Data Grid).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5866839/v1/cde3a32080b863b092367e61.png"},{"id":77769277,"identity":"f0638326-2297-4bff-9095-10789acaf2bc","added_by":"auto","created_at":"2025-03-05 10:33:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16838,"visible":true,"origin":"","legend":"\u003cp\u003eOverall percentage distribution of farmers’ adaptive capacity on the selected sites (n = 338, excluding the 5 nonresponse).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5866839/v1/2969842fdb0b1e60ea5c8375.png"},{"id":77770297,"identity":"25b40f1f-59cd-4110-b42f-04ec7d46855b","added_by":"auto","created_at":"2025-03-05 10:42:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33201,"visible":true,"origin":"","legend":"\u003cp\u003eAdaptive capacity levels across components scores. Average scores of components reported the x-axis.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5866839/v1/996ecc60e3f8c83f96fa46b1.png"},{"id":77769282,"identity":"2b1ee2d9-d016-45d9-a42a-420ec50b8442","added_by":"auto","created_at":"2025-03-05 10:33:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30416,"visible":true,"origin":"","legend":"\u003cp\u003eScores of indicators of financial capital component. FC1: Number of income sources FC2: Number of farm workers; FC3: Access to formal and/or informal credit; FC4: Access to public subsides; FC5: Number of animals owned by the household.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5866839/v1/b6919c1c337a68f96f772d5e.png"},{"id":77769280,"identity":"9d73f5a5-70ce-4785-b2f5-17d38ff40754","added_by":"auto","created_at":"2025-03-05 10:33:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":12868,"visible":true,"origin":"","legend":"\u003cp\u003eScores of indicators of human capital component. HC1: Level of education; HC2: HH farming experience; HC3: Access to agricultural advice.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5866839/v1/ddd8264ad1a59334d9762452.png"},{"id":77770395,"identity":"098f264f-3cab-40ca-b2d3-7a171ca3d03a","added_by":"auto","created_at":"2025-03-05 10:49:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":18171,"visible":true,"origin":"","legend":"\u003cp\u003eScores of indicators of natural capital component. NC1: Soil fertility; NC2: HH plot size; NC3: HH experience of natural hazards; NC4: Number of tree types on the HH farm.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5866839/v1/464a698154cb6bf2f23ffa30.png"},{"id":77771692,"identity":"f0b0b28f-987b-4cfa-99cc-ecb42a296bf3","added_by":"auto","created_at":"2025-03-05 11:06:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1165003,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5866839/v1/91300b0e-e731-43ff-aeb1-068e838aebf6.pdf"},{"id":77770293,"identity":"7e517f22-5e08-4e94-88b5-a72a8b10d849","added_by":"auto","created_at":"2025-03-05 10:42:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":115136,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryinformationAdaptivecapacityNiger.docx","url":"https://assets-eu.researchsquare.com/files/rs-5866839/v1/72a6466cd0ca1134e7ca4e95.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Measuring adaptive capacity: An index-based approach for farmers in Niger","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change is a pervasive threat that exacerbates poverty and inequality\u003csup\u003e1\u0026ndash;5\u003c/sup\u003e, posing far-reaching consequences for global efforts to improve population well-being. Livelihoods in agriculture-dependent economies are particularly vulnerable to climate risks, as numerous studies have shown\u003csup\u003e6\u0026ndash;10\u003c/sup\u003e. However, the resources underpinning these livelihoods \u0026ndash; such as labor and crop diversification within economic portfolios \u0026ndash; can play a critical role in addressing current and future climate challenges\u003csup\u003e11,12\u003c/sup\u003e. Consequently, assessing the adaptive capacity of agricultural producers, especially in vulnerable countries, has become increasingly urgent\u003csup\u003e2\u0026ndash;5\u003c/sup\u003e. Reliable information is essential for developing and implementing effective adaptation policies, highlighting the need for robust assessments to guide decision-making. Adaptive capacity is a critical attribute as it reflects a system\u0026rsquo;s ability to mobilize resources to respond, recover and/or maintain its functions in the face of stresses and shocks\u003csup\u003e13\u003c/sup\u003e. It emphasizes the strengths of a system that can reduce the biophysical and socio-economic vulnerabilities associated with climate change\u003csup\u003e14\u003c/sup\u003e. It identifies key strategic factors that can act as levers for developing and implementing effective adaptation measures, particularly in contexts of resource scarcity.\u003c/p\u003e\n\u003cp\u003eIn Sahelian countries like Niger, even minor changes in climatic parameters can have significant negative impacts on producers, particularly those with limited adaptive capacity. From the first vulnerability assessments\u003csup\u003e7\u003c/sup\u003e up to now (see ND-GAIN Index, 2022), Niger has consistently been ranked among the most vulnerable and least prepared countries globally to adapt to climate change. With two-thirds of the country located in the Sahara zone, the area suitable for agricultural production is limited\u003csup\u003e8,15\u003c/sup\u003e. Agriculture in Niger is predominantly rain-fed and heavily dependent on climatic conditions. Approximately 80% of the population is employed in the agricultural sector, with nearly 83% of these workers living in rural areas. Agriculture contributes around 40% to the country\u0026apos;s GDP, making poverty reduction and community well-being heavily reliant on the sector\u0026rsquo;s performance\u003csup\u003e16\u003c/sup\u003e. However, agricultural productivity remains strikingly low and is regularly threatened by factors such as rainfall deficits, inter- and intra-annual rainfall variability, and extreme temperatures reaching up to 45\u0026deg;C. The most well-documented consequences include recurrent droughts and floods\u003csup\u003e17\u003c/sup\u003e, alongside ongoing land and natural resource degradation, which result in substantial economic and ecological losses\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe adverse effects of climate change have significantly exacerbated food and nutritional insecurity, affecting over 40% of Niger\u0026rsquo;s population, and have led to the widespread destruction of livelihoods\u003csup\u003e15,18\u0026ndash;20\u003c/sup\u003e. Current and projected climate impacts\u003csup\u003e8,21\u003c/sup\u003e compound pre-existing challenges, including insecurity, health crises, low education levels, inadequate infrastructure, and the limited capacity of agricultural producers. These producers are often constrained by low livelihood diversification, restricted access to technical guidance, and minimal political support\u003csup\u003e22\u0026ndash;24\u003c/sup\u003e. Such limitations contribute to low adoption rates of climate change adaptation strategies, with farmers often resorting to survival-focused approaches that prioritize low-risk, low-yield inputs\u003csup\u003e16,23,25\u003c/sup\u003e. This convergence of crises and climatic stressors poses a significant obstacle to achieving the government\u0026rsquo;s objectives for poverty reduction and economic and social development, as outlined in national policies and strategies\u003csup\u003e21,24,26\u003c/sup\u003e. It also undermines Niger\u0026rsquo;s commitment to reducing carbon emissions by 35% by 2030, in line with its Nationally Determined Contribution\u003csup\u003e20\u003c/sup\u003e.\u0026nbsp; Despite substantial investments and alignment of national strategies with the needs of the agricultural sector, these policy documents acknowledge a critical gap: the lack of reliable information on the agricultural sector, which hinders progress towards their goals. Notably, while various studies have analyzed the impacts of climate change on adaptation strategies in sub-Saharan Africa, Niger has received disproportionately little attention\u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis study aims to provide reliable information by analyzing the processes determining adaptation through the calculation of an adaptive capacity index for producers in the Kollo and N\u0026apos;Dounga localities in the Tillab\u0026eacute;ri region, and Guidan Roumdji and Gadabedji localities in the Maradi region (\u003cem\u003eFig. 1\u003c/em\u003e). These localities, frequently identified as highly vulnerable to climate change, are situated within two agroecological zones with significant agricultural activity: the Sudan-Sahel and Sahel-Sahara zones, which are the focus of this study. Adaptive capacity in this study is conceptualized through five components or assets: financial capital, social capital, human capital, physical capital, and natural capital. The 21 indicators representing these components are summarized in \u003cem\u003eSupplementary Table 1\u003c/em\u003e. The analytical framework and methodology used to calculate the adaptive capacity index were adopted from the literature (\u003cem\u003eSupplementary Fig. 1\u003c/em\u003e). Unlike previous studies that aggregate indicators directly into a composite index, this study introduces a refined approach. Indicators were first aggregated into components, and components were then combined into an overall adaptive capacity index, considering the relative importance of elements at each aggregation level. This methodology not only avoids the limitations of direct aggregates of indicators into an adaptive capacity index \u0026ndash; which can lead to inappropriate policy recommendations\u003csup\u003e27\u003c/sup\u003e \u0026ndash; but also enables a more nuanced analysis of adaptive capacity. The results provide valuable insights into the adaptive capacities of farmers and the factors shaping their livelihood strategies. Policy recommendations are made for enhancing low-scoring assets\u003csup\u003e11\u003c/sup\u003e. Data were checked for normality, and descriptive statistics were employed to examine the socio-economic characteristics of farm households. Additional statistical tests assessed differences in adaptive capacity between households and across communities\u003csup\u003e28\u003c/sup\u003e. By employing a consistent methodological and analytical framework tailored to the realities of producers in these regions, this study contributes both to the growing body of literature on adaptive capacity and to informed decision-making in Niger.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eScores and levels of adaptive capacity of farm households\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe indicators and components of adaptive capacity were compared and weighted using the Analytic Hierarchy Process (AHP) methodology. Prior to weighting, Cronbach's alpha (α) was calculated to assess the reliability of the indicators in measuring adaptive capacity, while Consistency Ratios (CRs) were computed to evaluate the consistency of weightings derived from expert judgments. The estimated Cronbach's alpha (α = 0.7093) indicated an acceptable level of reliability for the 21 selected indicators. All CRs were ≤ 0.1, confirming the consistency and acceptability of the derived weightings. Indicators were aggregated into components, which were further combined into a single adaptive capacity index (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ACI}_{i}\\)\u003c/span\u003e\u003c/span\u003e) using an additive approach. The resulting \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ACI}_{i}\\)\u003c/span\u003e\u003c/span\u003e was categorized into three equal-interval groups: a farm household has a low level of adaptive capacity if 0 \u0026lt; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ACI}_{i}\\)\u003c/span\u003e\u003c/span\u003e ≤ 0.33, moderate level if 0.34 ≤ \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ACI}_{i}\\)\u003c/span\u003e\u003c/span\u003e ≤ 0.66, and high level if 0.67 ≤ \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ACI}_{i}\\)\u003c/span\u003e\u003c/span\u003e ≤ 1.\u003c/p\u003e\u003cp\u003eOverall, 37.28% of surveyed households exhibited low adaptive capacity, 59.17% had moderate capacity, and only 3.55% achieved a high adaptive capacity level (\u003cem\u003eFig.\u0026nbsp;2\u003c/em\u003e). This indicates an uneven distribution of adaptive capacity among households, with the majority displaying moderate capacity for adaptation to climate change, while very few demonstrated high adaptive capacity. Figure\u0026nbsp;3 highlights that the most significant disparities between producers with low and moderate/high adaptive capacity levels were observed in the accessibility and availability of natural capital, human capital, and financial capital. Notably, producers with low adaptive capacity recorded a near-zero score in human capital. Similarly, differences in these capitals were evident between households with moderate and high adaptive capacity, with human capital displaying the greatest variation. These results suggest that the producers from the study sites were characterized by a very limited capacity to diversify their livelihoods, hence their vulnerability to climate-related events. This aligns with existing literature demonstrating the critical role of livelihood capitals in explaining the adaptive capacity of agricultural households\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Additionally, the observed low proportion of households with high adaptive capacity mirrors trends commonly reported in sub-Saharan African countries\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe calculated component scores and the site-specific categorization of the adaptive capacity index shown in \u003cem\u003eSupplementary Tables\u0026nbsp;2 and 3\u003c/em\u003e reveal that while access to and utilization of adaptive capacity assets varied significantly across household locations and adaptation levels, no significant association was found between producers’ adaptation levels and the selected study sites. A similar trend has been observed among Australian wheat growers, whose average adaptive capacity index showed no significant variation by location\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This lack of association may stem from the methodology or indicators used to compute the index\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Alternatively, it could reflect the reliance of producers in Niger on autonomous adaptation strategies, rooted in traditional and endogenous knowledge historically employed to manage climatic risks\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Such responses tend to be limited and exhibit minimal variation across communities, largely due to limited means of subsistence and political support\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Moreover, the selected study sites have been frequent targets of adaptation and resilience projects. However, these initiatives often implement standardized actions across multiple localities, failing to address climate threats specific to individual communities. This generalized approach may explain the observed inconsistency in fostering localized and tailored adaptation strategies\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe statistically significant differences in components across study sites provide valuable insights for decision-making. Notably, there were no significant differences in access to and use of social capital among the localities surveyed, although scores for this asset were relatively high. This reflects the deep-rooted importance of social ties – such as networks, reciprocity, solidarity, and cooperation – in shaping farmers' daily activities within the African rural context, regardless of location. These social connections serve as vital risk-sharing mechanisms, helping communities mitigate or recover from climate-related shocks\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Conversely, human capital scores were uniformly low across all localities. This finding aligns with similar studies in Africa and Asia, which frequently highlight human capital deficits as a major constraint to adaptive capacity\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan additionalcitationids=\"CR38 CR39 CR40\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e–\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In Niger, national strategic documents focusing on the agricultural sector\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e emphasize the urgent need to strengthen human capital through targeted training and agricultural advisory services while addressing persistent gender inequalities. These results underscore the necessity for tailored interventions aimed at bolstering this critical asset.\u003c/p\u003e\u003cp\u003eFarmers in Gadabédji performed relatively better in access to and use of natural capital compared to other sites but exhibited notably low scores in physical and human capital. The elevated natural capital score is attributed to the unique environmental features of the locality, which includes the Réserve Total de Faune de Gadabédji (RTFG) – a UNESCO Biosphere Reserve since 2017 (see UNESCO’s World Network of Biosphere Reserves). This area, characterized by its biodiversity and rich grazing resources, plays a pivotal role in both conservation and pastoral livelihoods. Despite the high and moderate contribution of natural and financial capital, respectively, Gadabédji farm households were unable to make efficient use of resources due to very low human and physical capital scores. Furthermore, while financial capital in Gadabédji was rated moderate, it was insufficient to facilitate access to essential physical assets such as irrigation infrastructure or necessary farm equipment during the farming process. In contrast, farmers in N’Dounga, Kollo, and Guidan Roumdji relied on a combination of moderate levels of financial capital, physical capital, and natural capital, with uniformly low levels of human capital. Some statistically significant differences in asset distribution were observed among these sites.\u003c/p\u003e\u003cp\u003eThese findings emphasize the importance of site-specific approaches to building adaptive capacity. Comparative analyses revealed key factors that can inform decision-making, helping to prioritize interventions that address the most critical deficiencies in each locality.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparative analysis of household adaptive capacity indicator scores\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis section draws on the statistical results presented in \u003cem\u003eTable\u0026nbsp;1\u003c/em\u003e, with a focus on unravelling and visualizing the contributions of individual indicators to each asset. A series of figures were developed to provide a clearer understanding of these contributions. The following analysis focuses on financial capital, natural capital, and human capital, consistent with the findings highlighted in the previous section. They emerged as particularly noteworthy by revealing potential significant differences among farmers displaying high, moderate and low adaptive capacity.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFigure\u0026nbsp;4\u003c/em\u003e illustrates that financial capital was predominantly composed of low indicator scores among surveyed households. However, statistical analyses revealed that adaptive capacity improved significantly with increases in the number of household income sources (FC1), farm workers (FC2), and animal units (FC5). These findings suggest that households with more diversified income sources and resources are better positioned to enhance their adaptive capacity. For instance, households with more than two income sources (30.61%) were more likely to achieve a moderate level of adaptive capacity. Likewise, households with larger number of farm workers and larger number of animal units were more likely to better adapt to climate shocks. Specifically, households with more than three farm workers (40.52%) and over ten animal units (29.45%) were more likely to acquire a moderate adaptive capacity. In contrast, there was no significant relationship between access to formal and informal credit (FC3) and access to public subsidies (FC4) with the level of adaptive capacity, with near-zero contribution scores (0.0009 and 0.005, respectively). These results highlight systemic challenges, including the lack of financial institutions in rural Niger and the limited effectiveness of small-scale social safety net programs\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The findings underscore the critical role of income diversification, labor availability, and livestock ownership in enhancing adaptive capacity, while also pointing to the need for improved rural credit systems and targeted subsidy programs to address these gaps.\u003c/p\u003e\u003cp\u003eLike financial capital, household human capital was predominantly composed of low-scoring indicators (\u003cem\u003eFig.\u0026nbsp;5\u003c/em\u003e). Statistical analyses revealed that adaptive capacity increases with education levels (HC1), with households lacking formal education exhibiting low adaptive capacity. These households constituted the majority (74%) of the surveyed population. Regarding the indicator \"household's head farming experience\" (HC2), it was anticipated that more experienced farmers, acting as independent decision-makers, would adapt better to climate change. While most surveyed farmers were relatively experienced – only 15% reported farming experience of ten years or less – Spearman's correlation tests indicated a very weak and non-significant relationship between years of farming experience and adaptive capacity. In contrast, the \"access to agricultural advice\" indicator (HC3) made a positive, albeit minimal, contribution to adaptive capacity (0.038). Only 30% of households surveyed (102 out of 338) reported access to farm advisory services, and these households demonstrated a higher average adaptive capacity score than those without access. These findings highlight the critical need for improving education and expanding access to agricultural advisory services to enhance human capital and adaptive capacity among rural households.\u003c/p\u003e\u003cp\u003eThe average scores achieved by the natural capital indicators, shown in \u003cem\u003eFig.\u0026nbsp;6\u003c/em\u003e, were also low. Natural capital comprised indicators such as soil fertility (NC1), household’s plot size (NC2), experience of natural hazards on farm plots (NC3), and number of tree types on household farms (NC4). Among these, all but the experience of natural hazards showed a significant and positive correlation with households' adaptive capacity, as households with prior exposure to natural hazards often exhibited lower adaptive capacity. In contrast, larger farm sizes, greater tree variety, and more fertile soils were associated with higher adaptive capacity. However, over half of the surveyed farmers (around 54%) reported farming on infertile soils, highlighting a key limitation. Prior research by Fosu-Mensah et al. (2012) emphasized the critical role of soil fertility in shaping farmers’ adaptive capacity, as it influences decisions about adaptation practices in response to climate risks.\u003c/p\u003e\u003cp\u003eUnexpectedly, households with experience of natural hazards (NC3) on their farms exhibited lower adaptive capacity compared to those without such experiences. This finding challenges the conventional understanding that exposure to climatic stressors can motivate risk-reducing adaptation strategies, such as crop residue management\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. In this study, while 99% of households perceived changes in climatic parameters (e.g., insufficient or erratic rainfall, prevalence of hot days), only 40% reported experiencing natural disasters on their farms. These households displayed lower adaptive capacity scores, likely due to existing vulnerabilities such as low education levels, limited access to agricultural advice, and financial constraints, which exacerbate the impacts of shocks and reduce resilience. This underscores the urgent need for targeted external support to assist disaster-affected farmers in building adaptive capacity. Furthermore, households in flood-prone areas, especially in the Tillabéri region, often settle on unsuitable lands near riverbanks, increasing their exposure to flooding.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Regarding the \"number of tree types on household farms\" (NC4), the estimates showed that farmers who planted and managed a variety of trees, particularly woody species, demonstrated higher adaptability scores. Trees played a crucial role in reducing flood impacts, resisting pest attacks, and providing additional household income, underscoring their importance as a strategy for enhancing adaptive capacity\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study was aimed to assess the adaptive capacity of farming households in Kollo and N’Dounga (Tillabéri region) as well as Gadabédji and Guidan Roumdji (Maradi region) in Niger. The findings highlight critical disparities in asset portfolios among households, with low and moderate adaptive capacity associated with limited diversity and unbalanced portfolios, respectively. Diversification and balance between different assets are essential for farm households in the pursuit of livelihood strategies, enabling livelihood substitution strategies in the face of shocks and ensuring efficient use of resources\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe study revealed that low and moderate adaptive capacity households lacked critical human capital, particularly education and skills, which are essential for accessing information, technologies, and off-farm income opportunities. In Niger, remarkably low levels of education, particularly in rural areas\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, have regularly been cited among the main constraints to effective adaptation to the adverse effects of climate change and improved agricultural productivity\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. This situation has not enabled farmers to maximize agricultural production by using all available resources to better adapt to climate change\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. In addition, a low level of education limits producers' ability to engage in off-farm, better-paid, activities and thus increase their income and subsistence activities\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. It also emerged that these households did not receive sufficient access to agricultural advice through extension services to make informed choices on effective adaptation strategies and better manage climate change-related risks through climate information\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Farmers' access to agricultural extension services has been structurally low in Niger\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, whereas it had a positive and significant effect on technical efficiency for farm households in Uganda\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHouseholds with moderate adaptive capacity tended to diversify financial capital through multiple income sources, farm labor, and animal units. Households with a diversified portfolio of activities, including other jobs into non-agricultural activities like fishing, handicrafts, animal husbandry and petty trade, were more likely to recover from the negative effects of climate change. In Niger, several studies showed the positive and significant impact, in the short and medium term, of income diversification on climate change resilience and the well-being of farming households (particularly vulnerable ones) and food security\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In addition, households with more workers are more likely to use new technologies and more labor-intensive measures and are therefore likely to be more effective in adapting to the effects of climate change\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Similar to the findings in the northern region of Ghana\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, labor shortage in Niger can be mainly attributed to the out-migration of active men, particularly young people. This affects farming operations and crop yields when many do not return, with important implications for food security in these communities. Moreover, according to the World Bank report\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, if no concrete action is taken on climate and development, internal climate change induced migration will affect 86\u0026nbsp;million people in sub-Saharan Africa by 2050, with effects more accentuated for poor and climate-vulnerable populations like those in Niger. Animal units have traditionally been an important financial resource for households in Niger for the provision of insurance mechanisms and supporting adaptive measures such as manure spreading due to the greater availability of manure\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIt is often admitted that most rural livelihoods depend on natural resources, such as farm size. This suggest that farmers with larger farms had greater adaptive capacity\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. These farmers are more likely to adopt new high-yield agricultural technologies, such as mixed cropping\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The results exhibited a significant association between the farm size and the type of cropping system used on the farms, suggesting that households with larger farms adopted mixed cropping. They are also often considered as wealthy households who can afford to buy the necessary inputs\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. As a result, for smallholders to adopt integrated farming systems, external support will be needed. This external support is also a necessary factor in improving the soil fertility of household farms, some 54% of which reported producing on non-fertile soils. The study sites reflect the characteristics of Niger, for very few producers have access to modern inputs such as chemical fertilizers and pesticides, and they mostly either use organic inputs (mainly manure) or do not use any soil improvement products\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. While soil improvement practices have long-term benefits, their upfront costs pose challenges for resource-poor households\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. These findings, combined with the poverty conditions of farm households who generally position themselves in a short-term perspective to decision-making, suggest that policy interventions to improve input availability – through subsidies or market facilitation – are urgently needed.\u003c/p\u003e\u003cp\u003eThe results suggest several policy implications. There is an urgent need for targeted interventions to strengthen the financial, human and natural capital of farming households, except for households residing in Gadabéji. Given that this locality had a high level of natural capital, interventions should target both financial and natural capital. Policymakers should give priority to programs that promote income diversification, improve education levels and increase farm size and soil fertility through access to modern inputs and soil improvement practices. Collaboration with agricultural research and advisory institutions such as RECA (\u003cem\u003eRéseau National des Chambres d’Agriculture du Niger\u003c/em\u003e), Regional Center AGRHYMET (regional training and application center for agrometeorology and operational hydrology), INRAN (\u003cem\u003eInstitut National de la Recherche Agronomique du Niger\u003c/em\u003e) and the \u003cem\u003ePlateforme Paysanne\u003c/em\u003e (civil society organization bringing together farmers' organizations and active in all areas of the rural sector in Niger) will be crucial to the coordinated and effective implementation of policy actions. To effectively support farmers, it is essential to implement tailored programs that consider the specific needs and climatic conditions of target populations, while capitalizing on households' indigenous knowledge as \u003cem\u003e''indigenous knowledge is the backbone of successful climate change adaptation in agriculture\u003c/em\u003e''\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. The results of this study provide a basis for the development of comprehensive and context-specific policies that strengthen the adaptive capacity and well-being of farm households in Niger. Future research should adopt dynamic approaches to capture the evolving nature of adaptive capacity and integrate vulnerability and social justice considerations to ensure inclusive and effective policy outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eData\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMulti-stage sampling was used to select the farmers in our sample. At the first stage, the regions of Maradi and Tillabéri were purposively selected to represent the country's agroecological zones: the Sudan-Sahel zone, covering the central part of Tillabéri and the southern part of Maradi, and the Sahel-Sahara zone in northern Tillabéri (\u003cem\u003eFig.\u0026nbsp;1\u003c/em\u003e). These regions were chosen based on their agricultural importance and high vulnerability to the impacts of climate change\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Cochran’s sampling method\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e was used to determine the number of farmers to interview in the survey. This led to a sample of 196 for the Tillabéri region and 138 for the Maradi region, for a total of 334. To account for non-responses and missing data, the sample size was slightly overestimated to 343 farmers. However, due to insecurity in Tillabéri, the target sample size for that region could not be reached, and the gap was filled by increasing the sample size in Maradi. At the second stage, departments within the selected regions were identified. The selection was guided by agricultural production levels (measured using local agricultural statistics, such as crop production and land under cultivation) and vulnerability to climate change (assessed through reports on drought and flood impacts, as well as indices for food insecurity and poverty)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Based on these criteria, Kollo was selected from the Tillabéri region, while Guidan Roumdji and Bermo were selected from the Maradi region. In the third stage, specific communities within these departments were chosen using a proportional-to-size sampling technique\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Within the Kollo department, Kollo, a town and urban commune, and N’Dounga, a village and rural commune, were selected. In the Maradi region, Guidan-Roumdji, a town and urban commune, and Gadabedji, a rural village, were selected. Farmers were then randomly selected within these locations to complete a pre-established and pre-tested questionnaire.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIndex construction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis section outlines the method used to construct an adaptive capacity index for Niger farmers, a key element of the theoretical framework developed for conducting this study (see \u003cem\u003eSupplementary Fig.\u0026nbsp;1\u003c/em\u003e). The process follows validation by 11 key experts (see \u003cem\u003eSupplementary Table\u0026nbsp;11\u003c/em\u003e for experts’ affiliation), selected from institutions responsible for climate change adaptation issues, who reviewed the 21 indicators used to calculate the index. These experts were carefully chosen from a list of 19 previously interviewed, based on their in-depth knowledge of climate change, its impacts on farming households in Niger, and adaptation policies. Furthermore, they were required to have strong familiarity with at least two of the four selected study sites.\u003c/p\u003e\u003cp\u003eThe construction of the index followed several steps, as outlined below:\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of the internal consistency of indicators: Cronbach's Alpha Coefficient (1951)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe goal of this analysis is to assess the extent to which the selected set of indicators is sufficient and appropriate for measuring the same underlying concept targeted by the index. The Cronbach's alpha coefficient (α) was estimated using the following equation:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:=\\:\\left(\\frac{I}{I-1}\\right)\\frac{\\sum\\:_{i\\ne\\:j}cov\\left({x}_{i},{x}_{j}\\right)}{var\\left({x}_{o}\\right)}\\)\u003c/span\u003e \u003c/span\u003e ; \u003cem\u003ei,j = 1,…,N\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWhere N indicates the number of individuals considered, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:I\\)\u003c/span\u003e\u003c/span\u003e the number of indicators selected, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{o}\\)\u003c/span\u003e\u003c/span\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{n=1}^{N}{x}_{j}\\)\u003c/span\u003e\u003c/span\u003e the sum of all individual indicators. A high Cronbach's alpha, or equivalently a high \"reliability\", indicates that the individual indicators reliably measure the latent concept. It is further compared to Nunnally's threshold of 0.7, which is considered an acceptable reliability threshold\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. In the context of our study, the estimated Cronbach's alpha is 0.7093, indicating an acceptable level of reliability for the 21 selected indicators.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNormalization of indicators: the Min-Max method\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrior to any data aggregation, normalization is required to make variables comparable. In this work, the Min-Max method was used, as it can be used with all weighting systems and for all aggregation systems\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. This method converts indicator values to an identical interval [0, 1]. The values of each indicator are normalized according to the formula:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{I}_{i}=\\:\\frac{{x}_{i}-\\:{x}_{min}}{{x}_{max}-\\:{x}_{min}}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the specific value of the indicator to be transformed, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{min}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{max}\\)\u003c/span\u003e\u003c/span\u003e respectively the smallest and largest value of the concerned indicator, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{I}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the new normalized value. This step is followed by the aggregation of the normalized indicators, multiplied by their respective weightings based on a specific methodology.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWeighting indicators using the AHP (Analytic Hierarchy Process) method\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe aim of the weighting system is to assign relative importance to each criterion or component in the composite index aggregation. A robust weighting system must be informed, explicit, transparent, and reliable to support adequate policy formulation\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Weighting approaches based on expert judgement often contrasted with equal weighting systems and those based on data. Expert judgment-based weighting is considered a conventional and appropriate method when experts possess in-depth knowledge of the national adaptation policy and the selected sites\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Additionally, because the components of adaptive capacity are closely linked to livelihoods, experts can more easily assign weights to them\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. In this study, the experts involved in the validation of the adaptive capacity indicators were asked to assign weights using the Analytic Hierarchy Process (AHP), developed by Saaty (1990). AHP is especially useful for solving problems involving multidimensional latent concepts, as it structures complex problems into simpler hierarchies and groups\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e Furthermore, AHP includes a consistency measure to ensure the accuracy of expert judgments\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Weights are therefore derived through a systematic process rather than being arbitrarily assigned. The AHP process was applied in the following steps:\u003c/p\u003e\u003cp\u003e\u003cb\u003eStep 1: Pairwise comparison of adaptive capacity indicators and components.\u003c/b\u003e In this step, experts conducted pairwise comparisons of indicators and components to determine the relative importance of each criterion compared with other. This approach simplifies the decision-making process by making comparisons more accurate\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. The relative importance is expressed on an ordinal scale ranging from 1 to 9\u003csup\u003e71\u003c/sup\u003e, where a value of 1 indicates equal importance and higher values represent increasing importance. The comparisons resulted in a series of squared reciprocal matrices, known as pairwise comparison matrices and represented by \u003cem\u003eSupplementary Tables\u0026nbsp;5 to 10\u003c/em\u003e. For example, regarding financial capital, the experts considered that the number of sources of income for a farming household is six times greater than the number of farmers in the household.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStep 2:\u003c/strong\u003e The pairwise comparison matrices were used to calculate the weights of indicators and components using the eigenvector method, employing the power method\u003csup\u003e66,69\u003c/sup\u003e. It involves squaring the matrix, then dividing the sum of each row by the sum of all the matrix elements. The resulting column vector contains the normalized values and approximates the eigenvector. This step is repeated until the eigenvector values no longer change. These resulting values were taken as the weights of the indicators and components.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStep 3:\u003c/strong\u003e A third step was carried out to check the consistency of the expert judgments used to construct the pairwise comparison matrix and assign weights. Weights only make sense if derived from a consistent matrix\u003csup\u003e64,65\u003c/sup\u003e. To this end, the Consistency Ratio (CR) suggested by Saaty\u003csup\u003e71\u003c/sup\u003e is calculated using the following formula:\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:CR=\\:\\frac{CI}{RI}\\:$$\u003c/div\u003e \u003c/div\u003e\u003cp\u003eWhere RI is the random index, and CI is the consistency index. CI is calculated according to the formula:\u003c/p\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:CI=\\:\\frac{\\left({\\lambda\\:}_{max}-n\\right)}{n-1}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e represents the number of indicators considered (also equal to the order of the pairwise comparison matrix), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}_{max}\\)\u003c/span\u003e\u003c/span\u003e the maximum eigenvalue of the calculated matrix. The random index RI is defined as the consistency of the randomly generated pairwise comparison matrix, which depends on the number of elements (indicators or components). The RI value is derived from the random index table proposed by Saaty. The estimated CRs are shown in \u003cem\u003eSupplementary Tables\u0026nbsp;5 to 10\u003c/em\u003e, indicating the consistency of the pairwise comparison matrices and the weights assigned. A degree of inconsistency not exceeding 0.10 is considered acceptable\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, hence the acceptability of our various results obtained.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAggregating indicators into components and components into a composite index of ACI adaptability\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOnce the indicators have been standardized and weighted, they are integrated into the five (5) corresponding components, which are in turn aggregated into a composite index. The additive method was used for aggregation, as it is simple, independent of outlier and compensatory, meaning that low values in one indicator can be compensated by higher values in another\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. This method is also appropriate for the AHP method which belongs to the group of compensatory aggregation methods\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. The additive method is commonly used in livelihood studies where different types of capital, such as natural or financial capital, can be substituted for one another to increase adaptive capacity\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe indicators were aggregated into each corresponding component using the following formula:\u003c/p\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{CP}_{ij}=\\:\\sum\\:_{q=1}^{21}{w}_{q}{I}_{ijq}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewith \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{q=1}^{21}{w}_{q}=1\\)\u003c/span\u003e\u003c/span\u003e, q = 1,…,21, i = 1,…,N et j = 1,…,5\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CP}_{ij}\\)\u003c/span\u003e\u003c/span\u003e captures the capital (asset or component) \u003cem\u003ej\u003c/em\u003e of the adaptive capacity of farm household \u003cem\u003ei\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{w}_{q}\\)\u003c/span\u003e\u003c/span\u003ethe weight of indicator \u003cem\u003eq\u003c/em\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{I}_{ijq}\\)\u003c/span\u003e\u003c/span\u003e the normalized indicator of the corresponding component \u003cem\u003ej\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eThen, the components were aggregated into a composite index of farm household adaptive capacity, following the same aggregation method and through the formula:\u003c/p\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:{ACI}_{i}=\\:\\sum\\:_{j=1}^{5}{w}_{j}{CP}_{ij}\\:$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewith \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{j=1}^{5}{w}_{j}=1\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ACI}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the composite index of adaptive capacity of farm household i, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{w}_{j}\\)\u003c/span\u003e\u003c/span\u003e the weight assigned to component j. The index scores obtained are between 0 and 1.\u003c/p\u003e\u003cp\u003eThe composite index scores range from 0 to 1. For easier interpretation, the index scores were categorized into three groups at equal intervals: low (0 \u0026lt; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ACI}_{i}\\)\u003c/span\u003e\u003c/span\u003e ≤ 0.33), moderate (0.34 ≤ \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ACI}_{i}\\)\u003c/span\u003e\u003c/span\u003e ≤ 0.66), and high (0.67 ≤ \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ACI}_{i}\\)\u003c/span\u003e\u003c/span\u003e ≤ 1), following the categorization method by Datta and Behera\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFinally, data were checked for normality, and descriptive statistics were applied to analyze the socio-economic characteristics of farm households. Additional statistical tests were conducted to test for differentiated adaptive capacity between farm households and communities\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003eAll participants provided informed consent before participating in the study. Participants were informed about the voluntary nature of their participation, their right to withdraw at any time, and the confidentiality of their responses. No personally identifiable information, such as names or addresses, was recorded or published.\u003c/p\u003e\n \u003cp\u003eThe research received formal ethical approval from WASCAL Climate Change Economics at Cheikh Anta Diop University (UCAD), Senegal. No objections were raised regarding the implementation of the study in its present form.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the support provided by the West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), funded by the German Federal Ministry of Education and Research (BMBF). Sanoussi Ibrahim Oumarou\u0026rsquo;s PhD research, including his fieldwork in Niger and his research exchange at the University of Kassel, Germany, was funded by WASCAL as part of the Climate Change Economics Graduate Research Program at Cheikh Anta Diop University in Dakar, Senegal. His fieldwork was conducted in collaboration with Abdou Moumouni University (UAM) in Niamey, Niger. The authors also acknowledge the project \u0026ldquo;Greenhouse Gas Determination in West Africa\u0026rsquo;s Agricultural Landscapes\u0026rdquo; (GreenGaDe), funded under the BMBF WASCAL II \u0026ndash; Research Call (grant number: 01LG2078B). Through this project, Sanoussi Ibrahim Oumarou was connected to the project partners, including UAM in Niger and the University of Kassel in Germany, enabling collaboration and supporting his fieldwork, research exchange, and the collaborative work presented in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS.I.O.\u003c/strong\u003e conceptualized and designed the study; conducted the literature review to inform the methodological framework and indicator selection; led data collection in Niger, including multi-stage sampling, survey implementation, and engagement with local experts; performed data analysis, including the construction and validation of the adaptive capacity index; developed visualizations; and drafted the manuscript. \u003cstrong\u003eR.H.\u003c/strong\u003e contributed to the conceptualization and design of the study and survey; co-conducted the literature review; provided detailed input on data analysis and interpretation; participated in virtual consultations during data collection to provide guidance and support; coordinated project management, including research exchanges and manuscript preparation; drafted sections of the manuscript; developed additional visualizations; and provided iterative feedback and critical revisions throughout the manuscript development process. \u003cstrong\u003eI.T.D.\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eR.S.\u0026nbsp;\u003c/strong\u003esupervised the research, providing intellectual guidance on the study design, data analysis, and overall research framework. Both reviewed and provided feedback on the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Sanoussi Ibrahim Oumarou\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaroudy E, World Bank Group (2022) \u003cem\u003eG5 SAHEL REGION. Country climate and development report\u003c/em\u003e. (: at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u0026thinsp;https://reliefweb.int/report/burkina-faso/g5-sahel-region-country-climate-and-development-report#::text\u0026thinsp;=\u0026thinsp;The\u003c/span\u003e\u003cspan address=\"http://\u0026thinsp;https://reliefweb.int/report/burkina-faso/g5-sahel-region-country-climate-and-development-report#::text\u0026thinsp;=\u0026thinsp;The\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e G5 Sahel countries combined,and land degradation by 2030.\u0026amp;gt\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPCC Summary for Policymakers. Climate Change 2021: The Physical Science Basis. Contribution of Working Group Ito the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (2021) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/9781009157896.001\u003c/span\u003e\u003cspan address=\"10.1017/9781009157896.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPCC Summary for Policymakers. Global Warming of 1.5\u0026deg;C. An IPCC Special Report on the impactsof global warming of 1.5\u0026deg;C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (2018)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPCC Summary for Policymakers. Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNEP \u003cem\u003eAdaptation Gap Report 2022: Too Little, Too Slow - Climate adaptation failure puts world at risk - Executive Summary\u003c/em\u003e. (United Nations Environment Programme, Nairobi: (2022) at \u0026lt;\u0026thinsp;Nairobi. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unep.org/adaptation-gap-report-2022\u003c/span\u003e\u003cspan address=\"https://www.unep.org/adaptation-gap-report-2022\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdger W et al (2007) Assessment of adaptation practices, options, constraints and capacity. Climate change 2007: impacts, adaptation and vulnerability. \u003cem\u003eContribution of working group II to the fourth assessment report of the intergovernmental panel on climate change\u003c/em\u003e 717\u0026ndash;743 at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u0026thinsp;https://www.researchgate.net/publication/313769934_Assessment_of_adaptation_practices_options_constraints_ and_capacity_Climate_change_2007_impacts_adaptation_and_vulnerability\u003c/span\u003e\u003cspan address=\"http://\u0026thinsp;https://www.researchgate.net/publication/313769934_Assessment_of_adaptation_practices_options_constraints_ and_capacity_Climate_change_2007_impacts_adaptation_and_vulnerability\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdger WN, Vincent K (2005) Uncertainty in adaptive capacity. CR Geosci 337:399\u0026ndash;410\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026ouml;hrig F et al (2022) Climate Risk Analysis for Identifying and Weighing Adaptation Strategies in Niger\u0026rsquo;s Agricultural Sector. (A report prepared by the Potsdam Institute for Climate Impact Research (PIK) in cooperation with the Deutsche Gesellschaft f\u0026Atilde;\u0026frac14;r Internationale Zusammenarbeit (GIZ) GmbH on behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ), 154 pp.: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.48485/PIK.2022.002\u003c/span\u003e\u003cspan address=\"10.48485/PIK.2022.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNEP The gathering storm - Adapting to climate change in a post-pandemic world. (United Nations Environment Programme (UNEP) Nairobi: 2021).at \u0026lt;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u0026thinsp;https://www.unep.org/adaptation-gap-report-2021\u003c/span\u003e\u003cspan address=\"http://\u0026thinsp;https://www.unep.org/adaptation-gap-report-2021\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZougmor\u0026eacute; R et al (2016) Toward climate-smart agriculture in West Africa: a review of climate change impacts, adaptation strategies and policy developments for the livestock, fishery and crop production sectors. Agric Food Secur 5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdul-Razak M, Kruse S (2017) The adaptive capacity of smallholder farmers to climate change in the Northern Region of Ghana. Clim Risk Manage 17:104\u0026ndash;122\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChepkoech W, Mungai NW, St\u0026ouml;ber S, Lotze-Campen H (2020) Understanding adaptive capacity of smallholder African indigenous vegetable farmers to climate change in Kenya. Clim Risk Manage 27:100204\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVincent K (2007) Uncertainty in adaptive capacity and the importance of scale. Glob Environ Change 17:12\u0026ndash;24\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngle NL (2011) Adaptive capacity and its assessment. Glob Environ Change 21:647\u0026ndash;656\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eINS (2017) Enqu\u0026ecirc;te Conjointe sur la Vuln\u0026eacute;rabilit\u0026eacute; \u0026agrave; l\u0026rsquo;Ins\u0026eacute;curit\u0026eacute; Alimentaire des M\u0026eacute;nages au Niger. Institut National de la, Statistique (INS)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWB \u003cem\u003ePriorities for Ending Poverty and Boosting Shared Prosperity. Systematic Country Diagnostic\u003c/em\u003e. (The World Bank, Report No. 115661-NE: (2017)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBM \u0026Eacute;VALUATION DES RISQUES DU (2013) SECTEUR AGRICOLE AU NIGER: De la R\u0026eacute;action Aux Crises \u0026agrave; la Gestion des Risques \u0026agrave; Long Terme. LA BANQUE MONDIALE, RAPPORT NUM\u0026Eacute;RO, pp 74322\u0026ndash;NE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCNEDD \u003cem\u003ePlan National d\u0026rsquo;Adaptation aux Changements Climatiques\u003c/em\u003e. (R\u0026eacute;publique du Niger, Cabinet du Premier Ministre, Conseil National de l\u0026rsquo;Environnement pour un D\u0026eacute;veloppement Durable: (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDTDA \u003cem\u003eProfil du march\u0026eacute; du travail du Niger \u0026ndash;\u0026thinsp;2021/2022\u003c/em\u003e. (Danish Trade Union Development (DTDA), Agence Syndicale Danoise de Coop\u0026eacute;ration au D\u0026eacute;veloppement, Agenda mondial pour le Travail D\u0026eacute;cent (ATD): 2022).at \u0026lt;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u0026thinsp;https://www.ulandssekretariatet.dk/wp-content/uploads/2021/11/LMP-Niger-2021-Final-Francais.pdf\u003c/span\u003e\u003cspan address=\"http://\u0026thinsp;https://www.ulandssekretariatet.dk/wp-content/uploads/2021/11/LMP-Niger-2021-Final-Francais.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eME/LCD \u003cem\u003eContribution D\u0026eacute;termin\u0026eacute;e au Niveau National\u003c/em\u003e. (R\u0026eacute;publique du Niger, Minist\u0026egrave;re de l\u0026rsquo;Environnement et de la Lutte contre la D\u0026eacute;sertification: (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eME/LCD CNEDD, HE3N \u0026amp; MAG/EL (2020) \u003cem\u003eStrat\u0026eacute;gie et Plan National d\u0026rsquo;Adaptation face aux changements climatiques dans le secteur Agricole: SPN2A 2020\u0026ndash;2035. Version finale\u003c/em\u003e. (R\u0026eacute;publique du Niger\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgbegnido MK, ADAMOU MM (2018) \u003cem\u003eEtude diagnostique du dispositif de Formation Agricole et Rurale du Niger\u003c/em\u003e. (R\u0026eacute;publique du Niger: at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u0026thinsp;https://reca-niger.org/IMG/pdf/-9.pdf\u003c/span\u003e\u003cspan address=\"http://\u0026thinsp;https://reca-niger.org/IMG/pdf/-9.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsfaw S, Pallante G, Palma A (2018) Diversification Strategies and Adaptation Deficit: Evidence from Rural Communities in Niger. World Dev 101:219\u0026ndash;234\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM/P \u003cem\u003ePlan de D\u0026eacute;veloppement Economique et Social (PDES) 2022\u0026ndash;2026. Volume I: Diagnostic strat\u0026eacute;gique\u003c/em\u003e. (R\u0026eacute;publique du Niger, Minist\u0026egrave;re du Plan: (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLalou R, Sultan B, Muller B, Ndonky A (2019) Does climate opportunity facilitate smallholder farmers\u0026rsquo; adaptive capacity in the Sahel? Palgrave Commun 5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM/P \u003cem\u003eStrat\u0026eacute;gie de D\u0026eacute;veloppement Durable et de Croissance Inclusive. Niger 2035: Un pays et un peuple prosp\u0026egrave;res. Tome - II : Sc\u0026eacute;narii, Vision et Orientations Strat\u0026eacute;giques\u003c/em\u003e. (R\u0026eacute;publique du Niger, Minist\u0026egrave;re du Plan: (2017)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZanmassou YC, Al-Hassan RM, Mensah-Bonsu A, Osei-Asare YB, Igue CB (2020) Assessment of smallholder farmers\u0026rsquo; adaptive capacity to climate change: Use of a mixed weighting scheme. J Environ Manage 276:111275\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoden K, Keenan RJ, Nitschke CR (2020) An approach for assessing adaptive capacity to climate change in resource dependent communities in the Nikachu watershed, Bhutan. Ecol Ind 114:106293\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamara AY et al (2023) Assessment of the impact of crop management strategies on the yield of early-maturing maize varieties in the drylands of Niger Republic: Application of the DSSAT-CERES-Maize model. Heliyon 9:e17829\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJamshidi O et al (2020) Adaptive capacity of smallholder farmers toward climate change: evidence from Hamadan province in Iran. Climate Dev 12:923\u0026ndash;933\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgyir IS, Ofori K, Antwi G, Ntiamoa-Baidu Y (2015) Adaptive Capacity and Coping Strategies in the Face of Climate Change: A Comparative Study of Communities around Two Protected Areas in the Coastal Savanna and Transitional Zones of Ghana. J Sustainable Dev 8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMekonnen Z, Kassa H (2019) Living with Climate Change: Assessment of the Adaptive Capacities of Smallholders in Central Rift Valley, Ethiopia. Am J Clim Change 08:205\u0026ndash;227\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBryan BA et al (2015) What Actually Confers Adaptive Capacity? Insights from Agro-Climatic Vulnerability of Australian Wheat. PLoS ONE 10:e0117600\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAssoumana BT, Ndiaye M, Puije GVD, Diourte M, Gaiser T (2016) Comparative Assessment of Local Farmers\u0026rsquo; Perceptions of Meteorological Events and Adaptations Strategies: Two Case Studies in Niger Republic. J Sustainable Dev 9:118\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGambo Boukary A, Diaw A, W\u0026uuml;nscher T (2016) Factors Affecting Rural Households\u0026rsquo; Resilience to Food Insecurity in Niger. Sustainability 8:181\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiepolo M, Bacci M, Braccio S (2018) Multihazard Risk Assessment for Planning with Climate in the Dosso Region. Niger Clim 6:67\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYam\u0026eacute;ogo T, Fonta W, W\u0026uuml;nscher T (2018) Can Social Capital influence Smallholder Farmers\u0026rsquo; Climate-Change Adaptation Decisions? Evidence from Three Semi-Arid Communities in Burkina Faso, West Africa. Social Sci 7:33\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAntwi-Agyei P, Dougill AJ, Fraser EDG, Stringer LC (2013) Characterising the nature of household vulnerability to climate variability: empirical evidence from two regions of Ghana. Environ Dev Sustain 15:903\u0026ndash;926\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDefiesta G, Rapera CL (2014) Measuring Adaptive Capacity of Farmers to Climate Change and Variability: Application of a Composite Index to an Agricultural Community in the Philippines. J Environ Sci Manage 17:48\u0026ndash;62\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGbetibouo GA, Ringler C, Hassan R (2010) Vulnerability of the South African farming sector to climate change and variability: An indicator approach. Nat Resour Forum 34:175\u0026ndash;187\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMogomotsi PK, Sekelemani A, Mogomotsi GE (2020) J. Climate change adaptation strategies of small-scale farmers in Ngamiland East, Botswana. Clim Change 159:441\u0026ndash;460\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAker JC, del Ninno C, Dorosh PA, Mulder-Sibanda M, Razmara S (2009) Niger: Food Security and Safety Nets\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStoeffler Q, Mills B, Stoeffler Q, Mills B (2014) Households\u0026rsquo; investments in durable and productive assets in Niger: quasi-experimental evidences from a cash transfer project. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.22004/AG.ECON.170212\u003c/span\u003e\u003cspan address=\"10.22004/AG.ECON.170212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsfaw S, Lipper L (2015) \u0026amp; others Adaptation to climate change and its impacts on food security: Evidence from Niger. August 9\u0026ndash;14, 2015, Milan, Italy (2015).at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u0026thinsp;https://ideas.repec.org/p/ags/iaae15/225667.html\u003c/span\u003e\u003cspan address=\"http://\u0026thinsp;https://ideas.repec.org/p/ags/iaae15/225667.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShackleton S, Ziervogel G, Sallu S, Gill T, Tschakert P (2015) Why is socially-just climate change adaptation in sub-Saharan Africa so challenging? A review of barriers identified from empirical cases. WIREs Clim Change 6:321\u0026ndash;344\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTarchiani V et al (2016) Risque et adaptation climatique dans la r\u0026eacute;gion Tillab\u0026eacute;ri, Niger. (Editions L\u0026rsquo;Harmattan\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAriom TO et al (2022) Climate-Smart Agriculture in African Countries: A Review of Strategies and Impacts on Smallholder Farmers. Sustainability 14:11370\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDatta P, Behera B (2022) Assessment of adaptive capacity and adaptation to climate change in the farming households of Eastern Himalayan foothills of West Bengal, India. Environ Challenges 7:100462\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson R et al (2010) The vulnerability of Australian rural communities to climate variability and change: Part II-Integrating impacts with adaptive capacity. Environ Sci \u0026amp;ampmathsemicolon Policy 13:18\u0026ndash;27\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsfaw S, Battista FD, Lipper L (2016) Agricultural Technology Adoption under Climate Change in the Sahel: Micro-evidence from Niger. J Afr Econ 25:637\u0026ndash;669\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeressa TT, Hassan RM, Ringler C, Alemu T, Yesuf M (2009) Determinants of farmers\u0026rsquo; choice of adaptation methods to climate change in the Nile Basin of Ethiopia. Glob Environ Change 19:248\u0026ndash;255\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh S (2020) Farmers\u0026rsquo; perception of climate change and adaptation decisions: A micro-level evidence from Bundelkhand Region, India. Ecol Ind 116:106475\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWB An Assessment of Employment in Niger. (World Bank Group (2017)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFosu-Mensah BY, Vlek PLG, MacCarthy DS (2012) Farmers\u0026rsquo; perception and adaptation to climate change: a case study of Sekyedumase district in Ghana. Environ Dev Sustain 14:495\u0026ndash;505\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKansiime MK, van Asten P, Sneyers K (2018) Farm diversity and resource use efficiency: Targeting agricultural policy interventions in East Africa farming systems. NJAS: Wageningen J Life Sci 85:32\u0026ndash;41\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAntwi-Agyei P, Dougill AJ, Stringer LC, Codjoe S (2018) N. A. Adaptation opportunities and maladaptive outcomes in climate vulnerability hotspots of northern Ghana. Clim Risk Manage 19:83\u0026ndash;93\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRigaud K (2018) \u003cem\u003eGroundswell: se pr\u0026eacute;parer aux migrations climatiques internes.\u003c/em\u003e (Washington, Banque mondiale.: at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u0026thinsp;https://www.banquemondiale.org/fr/news/infographic/2018/03/19/groundswell\u0026mdash;preparing-for-internal-climate-migration\u003c/span\u003e\u003cspan address=\"http://\u0026thinsp;https://www.banquemondiale.org/fr/news/infographic/2018/03/19/groundswell\u0026mdash;preparing-for-internal-climate-migration\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShikuku KM et al (2017) Smallholder farmers\u0026rsquo; attitudes and determinants of adaptation to climate risks in East Africa. Clim Risk Manage 16:234\u0026ndash;245\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDinesh D, Vermeulen S (2016) \u003cem\u003eClimate change adaptation in agriculture: practices and technologies\u003c/em\u003e. (CCAFS INFO NOTE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMAG \u003cem\u003eRapport d\u0026eacute;finitif de l\u0026rsquo;enqu\u0026ecirc;te sur les productions irrigu\u0026eacute;es 2020\u0026ndash;2021\u003c/em\u003e. (R\u0026eacute;publique du Niger, Minist\u0026egrave;re de l\u0026rsquo;Agriculture, Secr\u0026eacute;tariat G\u0026eacute;n\u0026eacute;ral, Direction des Statistiques: (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOCHA \u003cem\u003eNiger - R\u0026eacute;gion de Maradi Analyse situationnelle trimestrielle\u003c/em\u003e. (Le Bureau des Nations Unies pour la Coordination des Affaires Humanitaires, OCHA: 2021).at \u0026lt;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u0026thinsp;https://www.unocha.org/niger\u003c/span\u003e\u003cspan address=\"http://\u0026thinsp;https://www.unocha.org/niger\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCochran WG (1977) Sampling Techniques. John Wiley \u0026amp; Sons, Inc.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkinner CJ (2016) Probability Proportional to Size (PPS) Sampling. \u003cem\u003eWiley StatsRef: Statistics Reference Online, \u0026copy; 2014\u0026ndash;2016 John Wiley \u0026amp; Sons, Ltd.\u003c/em\u003e 1\u0026ndash;5 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/9781118445112.stat03346.pub2\u003c/span\u003e\u003cspan address=\"10.1002/9781118445112.stat03346.pub2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNardo M, Saisana M, Saltelli A, Tarantola S \u003cem\u003eTools for Composite Indicators Building\u003c/em\u003e. (EUR 21682 EN. JRC31473: 2005).at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u0026thinsp;https://publications.jrc.ec.europa.eu/repository/handle/JRC31473\u003c/span\u003e\u003cspan address=\"http://\u0026thinsp;https://publications.jrc.ec.europa.eu/repository/handle/JRC31473\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOECD Handbook on constructing composite indicators. 158 (OECD (2008)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGan X et al (2017) When to use what: Methods for weighting and aggregating sustainability indicators. Ecol Ind 81:491\u0026ndash;502\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreco S, Ishizaka A, Tasiou M, Torrisi G (2018) On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Soc Indic Res 141:61\u0026ndash;94\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelow TB et al (2012) Can farmers\u0026rsquo; adaptation to climate change be explained by socio-economic household-level variables? Glob Environ Change 22:223\u0026ndash;235\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshizaka A, Nemery P (2013) Multi-criteria Decision Analysis Methods and Software, vol 310. Wiley, Ltd\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiri S, Lathrop RG, Obropta CC (2020) Climate change vulnerability assessment and adaptation strategies through best management practices. J Hydrol 580:124311\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaaty TL (1990) How to make a decision: The analytic hierarchy process. Eur J Oper Res 48:9\u0026ndash;26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacobs B, Nelson R, Kuruppu N, Leith P (2015) \u003cem\u003eAn adaptive capacity guide book: Assessing, building and evaluating the capacity of communities to adapt in a changing climate\u003c/em\u003e. (Southern Slopes Climate Change Adaptation Research Partnership (SCARP), University of Technology Sydney and University of Tasmania. Hobart, Tasmania\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eComparison of groups based on adaptative capacity\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdaptive capacity components\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eIndicators\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eClusters\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eMean/Median ACI scores\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003ePercentage of the distribution*\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003et value\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eF value\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cdiv class=\"SimplePara\"\u003eFinancial Capital (FC)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cdiv class=\"SimplePara\"\u003eSources of income (FC1)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.22\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e18.08\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e345.7\u003csup\u003e***\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.37\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e49.85\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eMore than 2 sources\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.56\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e30.61\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cdiv class=\"SimplePara\"\u003eFarm worker on HH plots (FC2)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.30\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e29.45\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e39.07\u003csup\u003e***\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.39\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e17.2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.43\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e22.45\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eMore than 3 workers\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.49\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e29.45\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAccess to credit (FC3)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo access\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.40\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e97.67\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e-1.17\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eAccess\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.50\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.87\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAccess to public subsidy (FC4)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo access\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.40\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e91.55\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.94\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eAccess\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.43\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e7\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAnimal ownership (FC5)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.31\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e20.99\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e38.97\u003csup\u003e***\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u0026ndash;5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.34\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e25.95\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u0026ndash;10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.39\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e11.08\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eMore than 10 animals\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.49\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e40.52\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cdiv class=\"SimplePara\"\u003eHuman Capital (HC)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eLevel of education (HC1)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eInformal / none\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.37\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e72.59\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e20.73\u003csup\u003e***\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ePrimary\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.46\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e13.12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eSecondary\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.53\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e11.08\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eTertiary\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.51\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.75\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAccess to agricultural advice (HC3)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo access\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.36\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e68.8\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e-7.23\u003csup\u003e***\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eAccess\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.49\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e29.74\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"12\" rowspan=\"13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNatural Capital (NC)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSoil fertility (NC1)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eNot fertile\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.35\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e53.94\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e-7.75\u003csup\u003e***\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eFertile\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.46\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e44.61\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eHH plot size (NC2)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;=2 ha\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.34\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e37.9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e21.10\u003csup\u003e***\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;=4 ha\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.38\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e20.99\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;=6 ha\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.44\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e20.99\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026gt;\u0026thinsp;6 ha\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.49\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e18.66\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eHH experience with natural hazards (NC3)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.42\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e58.6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.23***\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.37\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e39.94\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cdiv class=\"SimplePara\"\u003eNumber of tree types in the main farm of the HH (NC4)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0 type\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.31\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.92\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.34*\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 type\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.38\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e26.53\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2 types\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.38\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e24.49\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3 types\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.41\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e15.74\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eMore than 3 types\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.44\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e28.86\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eParticulars\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eCorrelation coefficient\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eSignificance\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHH farming experience (HC2)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.0386\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.48\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cp\u003e*0.05 level of significance; **0.01 level of significance, ***0.001 level of significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003eThe remaining 1.46% relate to non-respondent in the database.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5866839/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5866839/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn Niger, a Sahelian country characterized by resource scarcity, even minor climatic variations can severely impact agricultural households. This study develops an adaptive capacity index using a robust framework that integrates an in-depth literature review with site-specific parameters. Primary data from 343 households in highly vulnerable agricultural regions were complemented by qualitative insights from expert interviews and workshops. The analysis revealed significant disparities in household asset portfolios, with many households lacking critical elements, particularly education and skills for accessing information, technologies, and off-farm income opportunities. Households with moderate adaptive capacity were more likely to engage in diversified off-farm activities, which significantly enhanced their ability to adapt to climate change. Insights from this analysis, combined with comparative analysis of factors that can serve as policy levers, provide a foundation for evidence-based interventions. The proposed index-based approach offers valuable insights for addressing climate vulnerabilities and strengthening resilience in Niger's agricultural sector.\u003c/p\u003e","manuscriptTitle":"Measuring adaptive capacity: An index-based approach for farmers in Niger","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-05 10:33:54","doi":"10.21203/rs.3.rs-5866839/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6f1176c4-f5c3-460b-8844-2430de9d0b16","owner":[],"postedDate":"March 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":43880769,"name":"Earth and environmental sciences/Environmental social sciences/Climate-change adaptation"},{"id":43880770,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts/Governance"},{"id":43880771,"name":"Scientific community and society/Social sciences/Decision making"},{"id":43880772,"name":"Scientific community and society/Social sciences/Economics"},{"id":43880773,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2025-03-05T10:33:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-05 10:33:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5866839","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5866839","identity":"rs-5866839","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}