https://orcid.org/0000-0002-1784-7502
Figures
Abstract
Climate change is projected to adversely impact the health and wellbeing of households particularly in Sub-Saharan Africa where social vulnerability is pervasive. While countries such as Ghana have intensified efforts to ensure effective and proactive adaptation to emerging climate impacts, it has also become apparent, in some instances, that levels of adaptive capacity within households and communities remain a critical determinant of the success of adaptation efforts. This is particularly the case in the poor and perennially vulnerable northern regions of Ghana where high incidence of stunting in children has highlighted the complex interlinkages that exists among climate change, food systems, household income levels, nutrition, and adaptive capacity. This is against the background that this is also the time that government and other development partners have intensified intervention actions to influence household adaptation to climate change and nutrition and health outcomes, particularly among children. Using the Karaga district of northern Ghana as a reference point, and employing the sustainable livelihoods framework, this study explores the link between household adaptive capacity to climate change and the nutritional needs of low-income households. The study finds a significant inverse relationship between household adaptive capacity and stunting in children under five years of age as an indicator of household nutritional needs. Additionally, the study also finds that agricultural practices like adopting new varieties, dry season farming, mulching, and intercropping could have positive influence on household nutrition if households have sufficient capacity to adopt such practices. The study, therefore, provides, critical insights into adaptive capacity measurement and its utility in the context of human systems. More importantly, the study also shows how carefully considered adaptation efforts can shape national policies on climate adaptation, adaptive capacity, nutrition, and health.
Citation: Issahaku D, Manteaw BO, Wrigley-Asante C (2023) Climate change and food systems: Linking adaptive capacity and nutritional needs of low-income households in Ghana. PLOS Clim 2(5): e0000154. https://doi.org/10.1371/journal.pclm.0000154
Editor: Anamika Barua, Indian Institute of Technology Guwahati, INDIA
Received: September 15, 2022; Accepted: January 17, 2023; Published: May 16, 2023
Copyright: © 2023 Issahaku et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data are in the manuscript.
Funding: This work was supported partially by the Open Society Foundation as part of a broader support for the Center for Climate Change and Sustainability Studies at the University of Ghana. However, the bulk of funding came from individual private funds from the co-authors. No other funding was received from any other source. The Open Society Foundation had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Climate change is expected to affect various aspects of human lives including health and wellbeing either as a direct result of extreme weather events, or through other indirect mechanisms [1–3]. Some of these impacts and disruptions are already underway and have become common realities in most communities across Africa where climate change impacts on food security have become a major concern for governments and development agencies [4,5]. Ghana is no exception to the impacts of climate change on food systems as various communities continue to navigate the challenges of food insecurity with attendant nutritional deficiencies [6,7].
This is particularly the case in the northern regions of Ghana where perennial vulnerability and entrenched poverty are being exacerbated by the emergent reality of climate change impacts [8–10]. As impacts become more pronounced and pervasive, food security or better still food insecurity has emerged as a front-line issue in most communities across Ghana, but even more so in poor and deprived communities [11,12]. Declining access to food as result of climate variability and change has also come with attendant food quality problems which, as is currently becoming clear, has significant challenges for the health of low-income families and households [4,13]. This, as indicated, is especially so in the deprived northern regions of the country, where a generally dry ecology and variability in both temperature and precipitation is causing nutritional deficiencies and challenges for many families [14].
Focusing specifically on the Karaga district of Northern Ghana, this study employs the Sustainable Livelihoods Framework (SLF) by Ellis [15] and DFID [16], as a vehicle to explore the link between household adaptive capacity and the nutritional needs of low-income households. This study explores how the adaptive capacity of low-income households serve as a barrier to meeting required nutritional needs of households. The context of this study is to explore how low-income farming households are coping with the climate challenge, in terms of their nutritional needs, given their low levels livelihood assets such as physical, social, financial, and human capitals and access to information. The livelihood assets levels of these households together with the household’s interaction with associated policies, institutions and processes is used as a measure of its adaptive capacity [17–19].
This is against the background that human nutrition intervention in Ghana’s northern regions has in recent years become a priority policy and practice concern. Malnutrition is approached primarily as a health issue. This has seen the Ghana Health Service and its development partners put in extra efforts to address the combined challenges of food access, food quality and other nutritional needs which are critical underlying issues that foster or prevent malnutrition and high incidences of severe stunting [20,21]. The reality remains that a lot of these programs as led by the Ghana health Services, have seen great success over the years. Rates of severe stunting, severe wasting and underweight have fallen considerably in successive years. Between 2008 and 2014, stunting decreased from 28% to 19%, wasting–from 9% to 5% and from 14% to 11% for underweight. Despite on-going efforts to improve nutritional status in local communities, high malnutrition rates persist in low-income households, raising concerns about the link between household adaptive capacity to climate change vulnerabilities and the ability of these households to meet required nutritional needs. Prevalence of stunting in the northern region in particular is still over 42%, falling by only 4% in six years (2008 to 2014) whilst wasting and underweight rates are at 7.9% and 23.6% respectively with underweight fallen by only 1.6% over the same period [22–24].
There seem to be a disconnect between current nutritional improvement efforts by government and partners and the levels of malnutrition and stunting among children in low-income households in some observed communities [23,25,26]. This seeming disconnects also raise concerns about the extent to which household’s climate change adaptive capacity influences household nutritional outcome. This is a necessary concern which needs to be carefully explored to throw more light on the current nutritional challenges facing low-income households in Ghana.
The sustainable livelihood framework considers that given a particular vulnerability context, the outcome of household livelihood is a function of access to livelihood assets and the use of these assets in a livelihood strategy. Vulnerability context includes climate change and variability as manifested in changes in seasonality, trends and incidences of shocks as a result of erratic rainfall patterns, floods and droughts [16,27]. Livelihood assets include physical, financial, social, and human capital coupled with appropriate information. Access to these assets and the presence of enabling policies, institutions and processes constitute the determinants of adaptive capacity where more access indicates a higher capacity to adopt to climate variability and a limited access means a low capacity to adopt [28–31].
Livelihood strategies adopted by households may be influenced by the assets possessed and are generally geared towards agricultural intensification, income diversification or migration [32–34]. However, households could employ several strategies either in succession or together. The study considers livelihood strategies as adaptation strategies; the effectiveness of which means better livelihood outcomes and the ineffectiveness or failure could result in poor livelihood outcomes. Livelihood outcome generally means improv ed wellbeing and resilience, improved food security, reduced vulnerability to climate change and variability [16,27]. The study uses stunting in children as an indicator of households’ nutritional wellbeing and needs. Fig 1. Shows an amended version of the sustainable livelihood framework for this study.
Source: Sustainable livelihoods’ framework.
The paper is structured as follows: it will begin with an account of Ghana’s vulnerability to climate change impacts and its relation to agriculture and food systems. This will be followed by a discussion on the uniqueness of northern Ghana from a socio-ecological and socio-economic perspectives to provide insights into the vulnerability context of the region. After this a discussion of the study design and the theoretical underpinnings, the findings, and conclusions.
Ghana’s vulnerability to climate change impacts and its relation to agriculture and food systems.
Methodology
This study was carried out in the Northern Region of Ghana with a focus on Karaga district. Karaga district is located in the semi-arid parts of Ghana between latitude 9⁰30’ south and 10⁰30’ north and longitude 0⁰ east and 45’west. East and West Mamprusi bounds Karaga to the north, Savelugu Municipality and Nanton districts to the west. The districts southern and eastern borders is with the Gushegu district. Karaga has a land area of about 3,119.3 square kilometers and is 94 kilometers from the regional capital, Tamale. \ The Karaga district experiences a general tropical climate typical of northern Ghana with one rainy season starting yearly in May and reaching its highest intensity by September of the same year. The rainy season is followed by a dry spell from September. Rainfall ranges between 900 and 1000milimeters annually. Temperature is generally high in this area reaching average up to 36°C or more in March and April. The months of November through to February experiences lower temperature typical of the harmattan [34].
Socio-cultural and economic context
Ghana’s northern region is located in the semi-arid areas of the country where mixed and rain-fed agriculture are particularly dominant [35]. This region, until the Savannah and North East regions were carved from it remained one of the poorest in the country with up to half of all its inhabitants classified as poor between the years 2012 to 2013 [36]. The region is also known for its high illiteracy and poverty rates as more than half of all inhabitants have never attended school [36]. The proportion of people in rural Northern region, above fifteen years of age, who have ever attended school is 46.8% of male and 27.7% of females [37]. This is the lowest across all regions of Ghana and for both sexes. The Karaga district is no exception to these illiteracy trends experienced in the region. Results of the Population and Housing census by the Ghana Statistical Service [38] indicates that literacy rate for females eleven year or older stood at 24.6% whilst that for males is at 19.9%. Also, according to the same source, of the population above three years of age, 65.7% have never been to school.
The population of Karaga district from the 2010 Population and housing census is 77,706 of which forty-eight percent are males and fifty-two percent are females. The population forms about thirty-one percent of the regional population. Majority of the people in this district are Ghanaian by birth (93.5%) and 1.1% have naturalized. The remaining 3.3% are however non-Ghanaians. A large proportion (82%) of the populace of Karaga still reside in rural areas [38]. There are 76,927 people living as part of 7,664 households in Karaga with an average household size of ten. This household size is greater than that of the regional average of eight. Children form the largest proportion of people in the household (47.9%) compared with the adults whilst spouses form just 8.4% of the household population. The extended family comprising of other sibling, their spouses and children, parents, and house helps, constitute the greater part of household population making up to 71.2%. Marriage rates are high in Karaga as 63.9% of the population above twelve years of age in the district are married [38].
Seventy-nine percent of the people in Karaga district above fifteen years of age are economically active. Of the 21% inactive group, 37.1% are engaged in domestic chores, 26.1% are in school and the 4.4% are either disabled or sick. Almost the entire active population (98.7%) is employed. Agriculture, forestry, and fishery engage 93.7% of the employed population. The public services and sales engage 2% of the working population, whilst 1.8% is into crafts hair dressing, masonry, fitting mechanics and other related endeavors. Majority (66.5%) of the population above fifteen years are self-employed with no employees of their own. About a quarter (25.2%) the same population contributes to family labour whilst 2.3% are casual workers.
Climate variability and extreme events in the northern regions
The most widely perceived changes in the climate of Northern Ghana include a reduced predictability of the rains and a generally drier weather [39]. The start of the peak rainy season has increasingly come later and the rainy season has seen less precipitation over the past five years when compared with the past decades [39]. Predictions of future climate for Northern Ghana generally points towards increasingly hot weather but with little agreement on the extent of the variation within and between seasons as projected by Asante & Amuakwa-Mensah, (2014) [40]. According to the same report, the trend for temperature (from 2010 to 2050) suggest elevation in all regions of Ghana with the highest in the Upper East, Upper West, and Northern regions.
Using various climate models and three emission scenarios, Stanturf et al., (2011) [32] studied changes in climate across Ghana and projected that the capital of the Northern Region of Ghana, Tamale, which also falls in the Guinea Savanna Zone may experience wet season temperature increase of 1.84 (± 0.46)°C by 2050 and 2.83 (± 0.91)°C by 2080. Dry season temperature rise for the same area will be 2.05 (± 0.75)°C by 2050 and 3.18 (± 1.18)°C by 2080. The long term impacts of climate change include, changing rainfall pattern which is exacerbating water insecurity and causing reduction in agricultural production and fish stocks; and the shifting of vector borne diseases [40]. This is against the background that the region has some of the highest poverty levels, with hot temperature, low rainfall and a single rainy season [41]. Laube et al., (2012) [42] are of the view that the changing climate in conjunction with degradation of the land has had significant ramifications for crop yields and crop failures in northern Ghana and has deepened poverty, impoverishment and conditions of vulnerability in the northern regions.
Food and nutrition security in the northern region of Ghana
Low food production, low fish stocks; shifting vector borne diseases, a general impoverishment along with other socioeconomic factors associated with climate change and variability all have negative implications for human nutrition and health in northern Ghana, contributing to malnutrition and disease of various forms at various level and as a function of food insecurity. Food security encapsulate three broad dimensions; food availability, a measure of the quantity of food that is either present or is expected to be present at a place and during a particular period of time; food access concerns the physical and economic ability of a group of people to obtain food at a particular time. Access also entails intra-household food distribution, income levels and access to markets; and food utilization concerns the ability to derive sufficient nourishment from the available food.
However, this definition was modified in the 2009 World Summit on Food Security. The access component was further elaborated to include physical, social, and economic aspects. A fourth dimension indicating food stability was added to the previous definition at the world food summit in 2009. In the situation where food security is not met the results is food insecurity. Food insecurity has been classified as seasonal, chronic or transitory [43]. Recent Demographic and health surveys in Ghana assess the nutritional status of children less than five using measures of height, weight along with their ages. Current trends show a marked reduction in the prevalence of underweight, wasting and stunting amongst under five-year-old children at the national level between the years 2003 and 2014 as shown in Fig 2.
About a third of children in the Northern region are stunted even though the country average is nineteen percent. Stunting refers to a condition of failure to reach optimal growth potential [44]. Stunting is also described by Cogill (2003) [45] as a deficit in length or height-for-age and a slowing in the growth of a neonate or child or generally a growth failure in the past. This condition at population level have been attributed to low socioeconomic status, early exposure to poor health conditions and inappropriate feeding practice [44]. Other factors include a history of insufficient protein and energy intake and infection [45].
Stunting in children may lead to small size in adulthood which is associated with relatively low physical capacity [44]. The condition has also been linked to negative cognitive and behavioral consequences even though the mechanisms are not fully understood [46]. This condition has also been described as an indicator of malnutrition in pregnancy and in the first two to five years of a person’s life [47]. Stunting is of particular interest to this study for being a good indicator food and nutrition insecurity experienced over a long period of time. Eliminating stunting is one of the principal indicators of the Sustainable Development Goal (SDG) two and reducing stunting is also among the World Health Alliance (WHA) targets. This makes stunting a major policy and program priority in most countries [48]. Stunting is also a robust measure since height and ages are not very sensitive to variation when compared to weight [2]. The WHO defines stunting as height or length measure which varies by more than two standard deviations below the median for children of a well-nourished population of that age and sex [44].
Study design, population and sampling
Study population and sampling.
A cross sectional quantitative study design is employed in this study. The study population included all households with at least one child below the age of five years in the Karaga district at the time of study. The unit of enquiry for this study therefore is the household. A two-stage cluster sampling procedure was employed for this study. Karaga district has twenty major communities at various locations. Five communities were randomly selected in the first stage of sampling as clusters for the study. Each of these five communities has several geographically bounded area units which served as clusters in the second stage of sampling. In each area unit a household is randomly selected by spinning a pen at a central location of the area unit and picking the house that the pen points at when it falls. The next house is then enumerated to establish if it lies found on the immediate left of the first until the required number of households were attained. This sampling approach is a modified version of that of Hatløy et al., (2000) [49].
For this study, a household was defined as “a person or group of related or unrelated persons who live together in the same housing unit, sharing the same housekeeping and cooking arrangements, and who acknowledge an adult male or female as the head of the household” [36] and a household should have been living in the community for more than five years. Households that did not meet these requirements were replaced. Two households were replaced in this study. Sample size for this study was determined using a formula quoted by Cross (2013) [50] for sample size determination of an infinite population which is applicable since the exact number of households with children under five is unknown. A total of 288 households with children under five years of age were selected for the study. This estimate was based on a 95% confidence interval, 5% precision and prevailing stunting rate of 25% for the poorest households in Ghana as reported by GDHS (2014) report [24].
The head or an adult member of the household with information regarding the assets of the household was interviewed in the survey. The height and age of one child (less than five years old) for each sampled household was determined using anthropometric measures. Where there was more than one child in the household, selection was done through balloting. A group of five experts in the field of rural livelihoods and climate adaptation were tasked to compare various indicators and sub-indicators using a pairwise comparison questionnaire. This group included two agriculture extension officers, a program officer each from one international and one local NGO and a project manager at a local NGO. Ten other key informants, two per community, were interviewed for the confirmation of various livelihood strategies practiced in the area. This consisted of five men and five women.
A cross sectional survey was conducted to study the adaptive capacity, livelihood strategies and the prevalence of stunting in selected households. This survey employed both structured and semi-structured questionnaire. The prevalence of stunting was measured using anthropometry. Height was measured for children above two years. Children were made to stand barefooted with head, back and ankles all touching the calibrated rule and feet slightly apart as prescribed by WHO/UNICEF [51]. Children’s heights were measured using the UNICEF’s Infant and Child Height and Length board. Children under twenty-four months or less than 85cm were measured lying down and measures were taken by a qualified health professional. Height-for-age z-scores were calculated using children’s heights and dates of birth. Dates of births were confirmed from birth certificates, weighing cards and other birth records. Data analysis was done using Microsoft excel and Statistical Package for Social Sciences (SPSS) version 25. Means, percentages, and frequencies of household socio-demographic characteristics are presented in tables. The binary logistic model with the cumulative distribution function was employed. The WHO Anthro software version 3.2.2 was also employed in the determination of the status of stunting in children under five years.
Analysis and discussion
Assessing household adaptive capacity
Household adaptive capacity was accessed in three steps; selection of indicators, assignment of weights to selected indicators and Calculation of household adaptive capacity index (HHACI). Adaptive capacity is an inherent property necessitating the employment of theoretical frameworks for its assessment [28]. The sustainable livelihoods approach by DFID, (1999) [16] and Ellis, (2000) [15] provided the theoretical framework for this assessment. This framework views livelihood outcomes as a function of access or ownership of livelihood. A concept which emanated from the entitlements approach by Nobel Laureate Amartya Sen [52]. The entitlements approach proposed that households that hold sufficient array of entitlements, assets or capabilities have more options in choosing adaptation strategies for coping during adversity and reduced their risk [27,29]. The sustainable livelihoods framework has been demonstrated to be applicable in the analysis of the capacity of farmers to adapt sustainable farming practices in Australia [30] and identifies five capitals that make up an individual or systems asset against a climatic adversity. These capitals: the physical, social, financial, and human along with access to information are used in the formulation of indices for this study.
Selection of indicators for adaptive capacity assessment
Several classes of indicators of adaptive capacity stems from the IPCC third assessment report and these have been adopted in various forms into several theoretical frameworks. The choice of indicators is however guided by theory and/or data availability [53]. Considering theory and available literature, the livelihood vulnerability index by Hahn et al., (2009) [54] and the Sustainable livelihood approaches could both be used in studying adaptive capacity. However, Hahn et al. (2009) [54] alludes that the Sustainable Livelihoods approach better addresses issues of sensitivity and adaptive capacity as a whole. Following the work of Nelson et al. (2010) [30] and Jakobsen (2011) [29], the study therefore adopts the Sustainable Livelihood Approach by DFID (1999) [16]. The following sections discuss indicators selected for the assessment of household adaptive capacity and their sources in literature. A detailed list of the selected indicators and scores is found in Table 1.
Indicators for human capital.
Human capital includes knowledge, skills, competence and physical capacity to work for a means of livelihood [55]. These are indicated by household literacy, household members with at least primary education, household dependency ratio and proportion of household members falling sick for up to a week in the past [28]. Piya (2012) [28] indicates human capital by considering highest qualification attained in the family, technical and vocational training attended by the family members and dependency ratio. For human resources, Defiesta & Rapera (2014) [17] used farm experience, education of household head, percentage of adults in household, and percentage of adults with primary education. For this study, human capital is indicated by farming experience and educational level of household head, number of household members with at least primary education and dependency ratio. A farmer with several decades of experience would be more conversant with the changes in weather patterns and therefore be in a better position to modify livelihoods accordingly. Households with better educated people would be in a better position to appreciate new technologies for adaptation than those others. Also, a household with a high dependency would be seriously disadvantaged since it has a relatively lower economic output.
Indicators for physical capital.
Physical capital encompasses the basic infrastructure and equipment needed for a livelihood and may include access to sanitation, water, energy, communications, energy and production equipment [56]. This asset category has the widest range of indicators including type of dwelling; possession of televisions, radio, solar panels, agricultural tools and equipment and access to irrigation service [28]. This same indicator is assessed using land holding and tenure, and number of machines owned [17]. Similar indicators for physical capital has also been referred to by other names by different studies. Abdul-Razak & Kruse (2017) [57] indicates infrastructure using access to irrigation, land holding and access to roads. Jakobsen (2011) [29] uses agricultural productive capital instead of physical capital and indicates this largely with landholding, number of cattle, yaks and buffalos owned. Physical capital for this study has been indicated by access to productive land, land holding, access to irrigation and ownership of farm machinery and equipment. A household with more productive land can greatly minimize the effect of climate variability since their crops are growing in a more conducive environment. Households that are capable of cultivating larger plots of land would be able to generate more income and make better saving against difficult years and can also buy productivity enhancing machines and equipment. Irrigation of farms would better insure against drought thereby reducing the probability of a total crop failure.
Indicators for financial capital.
Financial capital includes the capital base needed by a household for the adoption of a livelihood strategy and includes cash, credit, savings and other economic assets [55]. For this indicator, Defiesta & Rapera (2014) [17] employed remittances, value of animal units, receiving financial assistance from government and access to credit. Piya (2012) [28] on the other hand employed total household savings, possession of animals, having a salary or skilled job, livelihood diversification index, and gross per capita annual income. For this same indicator, Abdul-Razak & Kruse (2017) [57] only employed diversity of income sources, remittances and access to credit. Jakobsen, (2011) [29] also recognizes access to credit, number of ruminants per capita, household receiving public transfer like a pension payout and remittances as indicators of financial capital. Indicators included for household financial capital include remittances, possession of animals, access to credit and diversification of income. The selection of these indicators was based on the reliability and relative ease of data collection. These indicators all tend to either increase household income or safeguard against one income source failing to meet household needs.
Indicators for social capital.
Social capital refers to the claims, networks, relationships that are put into force by household when a collaborative effort is needed to pursue a livelihood strategy [55]. Abdul-Razak & Kruse (2017) [57] use access to family labor and participation in farmer and other community based organizations (FBOs/CBOs) to indicate social capital whilst Piya (2012) [28] indicates this with participation in CBOs and access to credit. However, Defiesta & Rapera (2014) [17] and Jakobsen (2011) [29] have not included social capital in their assessments. In this study, participation in FBOs and other CBOs and participation in social intervention such as subsidy programs was used to indicate social capital. The IPCC third assessment report includes equity as an indicator for adaptive capacity [1]. Positions held by family members in the community has been included since this enables a household more access to resources and information. Also, if households are able to get support in the form of farm inputs from government and other development agents, they will be in a better position to increase their productivity and asset generation.
Indicator for access to information.
Current research on assessment of adaptive capacity using the sustainable livelihoods framework have included information. Access to information, which is one of the indicators from the IPCC third assessment report, helps households to better plan and adopt a livelihood strategy. Awareness and training is one indicator used by Abdul-Razak & Kruse (2017) [57] in their assessment of adaptive capacity. Sub-indicators for this included, access to information. Information is also one of the indicators used by Defiesta & Rapera (2014) [17] and the sub-indicators for this are trainings on farming, receiving technical assistance, participation in farming organizations and number of sources of climate information. Access to information has been included in the assessment of adaptive capacity for this study. Technical assistance on the farm; access to climate and weather information and participation in technical trainings, as included for this study, would all enable a household to make better adaptation and livelihood decisions and enhance productivity. A more productive household will be able to increase their assets against environmental challenges.
Assignment of weights to indicators
The next step involves assigning weights to indicators and sub indicators. Early studies of adaptive capacity assigned equal weights to all indicators [53,58] whilst some recent studies assign weights to various indices by either employing expert judgment or other mathematical means such as Principal Component Analysis [17,28,57]. Since adaptive capacity is context specific, the study holds that the use of expert judgment in its assessment is invaluable. Five experts in the area of climate change and agricultural livelihoods applied rankings to various indicators and sub-indicators based on a pairwise comparison questionnaire. The ranking was analysed into assigned weights using the Analytical Hierarchy Process (AHP) as employed by Defiesta & Rapera, (2014) [17].
The AHP is a method for guided decision making developed by [59] and helps in converting expert qualitative judgment into quantitative measures. It has the advantage of identifying inconsistencies that may result in contradiction of rankings. Results are presented in Fig 3. below.
On a scale of zero to one, expert ranking of five adaptive capacity indicators using a pairwise comparison questionnaire and subsequent analysis using the analytical hierarchy process, assigned the highest weight (0.380) to financial capital indicating its relative importance in adaptation whilst assigning the lowest weights to social capital (0.080). This is corroborated by findings from Abdul-Razak & Kruse (2017) [57], who in a study of the adaptive capacity of farming households in northern Ghana had expert ranking put economic resources ahead of awareness and training, technology, infrastructure with social capital attaining the also lowest rank. Defiesta & Rapera (2014) [17] using similar methods also assigned the highest weight to financial capital but however did not include social capital in their study. Studies by Piya (2012) [28] using principal component analysis however places financial assets behind human and physical assets in a study of the adaptive capacity and adaptation strategies of farmers in Nepal.
Calculation of HHACI
HHACI is aggregated by summing the scores for individual sub-indices attained by a household from Table 1. with their weights considered. The aggregated HHACI is a continuous variable which lies between 0 and 1. Household adaptive capacity is classified into very low (0.0 to <0.25), low (0.25 to <0.5), high (0.5 to <0.75) and very high (0.75–1) ACI.
Findings
The study finds more than two-thirds (66.3%) of all households in Karaga to have low adaptive capacity as presented in Table 2. This is consistent with findings from Abdul-Razak & Kruse (2017) [57] who reported 66.25% of all farmers in the Karaga district to having low adaptive capacity. The mean adaptive capacity for Karaga was found to be 0.456 (or 45.6%) which is also low. There are however large adaptive capacity differences between households as indicated by relatively larger standard deviations of 0.098. Average performance of households in various indicators and sub indicators have been presented in Table 3.
Household livelihood strategies
Household income generation and adaptation strategies are combined in most rural setting and can be grouped into livelihood diversification, changes in agricultural practices and migration [32]. The adoption of these practices in 288 households are presented on Table 4.
Household nutritional needs
Stunting as a nutritional condition stem from prolonged inadequate intake of protein and energy, and frequent infection and/or illness [60,61]. Stunting can also be influenced by recurrent and chronic illness [24]. The study therefore considers the nutritional needs of a stunted person or group to include sufficient intake of quality protein and energy food, good environmental sanitation and better feeding and care practices in the household. The nutritional needs of children are also an indication of the nutritional needs of the household. Stunting is determined by comparing height-for-height z-scores of a child to a reference healthy population for the same age and sex as described in the De Onis (2006) [44] WHO growth standards.
For this study, a child with a height-for-age Z-score worse than minus two standard deviations from that of the reference healthy population is classified as stunted. The overall prevalence of stunting was found to be 39.2% which is similar to findings by de Jager et al. (2018) [62] who in a study of food and nutrition gaps in rural northern Ghana found 39.8% of children in Karaga to be stunted. The prevalence of stunting for Karaga is however lower than that for the region (43.8%). And, it was from findings that stunting was more prevalent in male children than in females which is consistent with findings from Wamani et al., (2007) [63] and that of the (GDHS, 2014) report [24].
Assessing the link between adaptive capacity and nutritional needs
The link between household adaptive capacity and nutritional needs was modeled using the binary logistic distribution function. The dependent variable for this model is child’s stunting status. Stunted and non-stunted are considered as binary outcomes. The predictor variables are child’s sex, age, community, the child’s HHACI and child’s household livelihood strategies. The study finds an inverse relationship between household adaptive capacity and stunting in children (P < 0.01). The coefficient of regression (-1.125) translates into an odd ration of 0.263. This indicates that a child from a household with a high adaptive capacity is about four times (3.8) more likely to be normal (non-stunted) than a child from a low adaptive capacity household. The model for this investigation was found to be significant at p < 0.01 and predicted more than two-thirds of the cases indicating a good fit of the model to the data. This suggest an inverse relationship between household adaptive capacity and stunting which is significant at 1% confidence. This is presented on Table 5.
The relationship between household adaptive capacity and good nutritional wellbeing observed in this study can be further explored through underlying relationships. First, through the relationship between adaptive capacity and food and/or nutrition security. Wright et al. (2012) [64] report that households with the lowest food security are also least adaptive to climate change. A study by Asante et al. (2012) [65] reports low adoption of modern productivity enhancing strategies by households with low adaptive capacity which may result in food insecurity. Food security is a precursor to good nutritional wellbeing. Another way of assessing adaptive capacity-nutrition link is through the performance of households in various indicators used in the study as presented in Table 3.
The biggest driver of low adaptive capacity for the study population is low financial capital of which only 37.9% was attained. This was largely due to low levels of remittance (4.8%) and ownership of smaller animals (36.3%). The significance of financial capital in fostering adaptation has been related in several studies [17,66,67]. Also, the role of financial and economic status in fostering food security and good nutritional wellbeing has been reported in some studies [68]. Human capital is one of the biggest drivers of variation in adaptive capacity as indicated on the table. Of the sub-indicators of human capital, low literacy level of household heads was the single most important factor contributing to low adaptive capacity with households attaining only 3.8% of the required score.
The link between levels of education and nutrition have been observed in several studies which generally show a direct relationship. [69–73]. Households were able to attain only 25.0% access to irrigation and 16.7% ownership of machinery or equipment which contributed most to low physical capital. Physical capital generally contributes to higher productivity in households leading to more income and a better economic status. Physical capital contributes to improved household security [71,73].
Other variables in the model were also found to contribute significantly to stunting and affects the relationship between household adaptive capacity and household nutritional needs. Children from household’s that adopted new varieties were more than two times (2.3) more likely to be normal (not-stunted) than children from households that did not (P < 0.05). New varieties of crops are generally bred to be more resistant to drought and pest attack and are a very important addition to climate adaptation efforts. Many households change the variety of crops due relatively poor performance over the years. Dry season farming generally involves the introduction of irrigation technology to enable the growing of crops during the dry season. Children from households that adopted dry season farming were six times (6.1) less likely to be stunted than children from households that do not practice dry season (p <0.05). The potential for dry season farming to increase household income and food availability would have a profound impact on household food security and nutrition [74]. Children from households who have adopted mulching were found to be seven times (7.4) less likely to be stunted than those from households that do not. Mulching is generally practiced in yam cultivation and in dry season farming.
The only livelihood diversification strategies found to be significantly related to stunting in children was the practice of crafts and related trade. This was found to have a direct relationship with stunting in children as indicated by the positive coefficient. A child from a household that practiced any such trades as hairdressing, fitting mechanics, building masonry etc. was 2.6 times more likely to be stunted than other children. This finding is contrary to that of Owusu et al. (2011) [75] who report that non-farm work is crucial to food security and poverty reduction in rural settings and since these two conditions are important determinants of household’s nutritional status, livelihood diversification was expected to have a significant relationship with nutritional status. However, Egyir et al. (2015) [76] also found that off-farm work had a negative impact on the adoption of modern productivity enhancing strategies. This negative impact could lead to low productivity and food insecurity. If the practice of such crafts and trades generates very little income, a household could face challenges with food security.
Conclusions
The assessment of adaptive capacity can have varied approaches and employ several groups of indicators and sub-indicators which are employed at various level ranging from global to household and individual levels. The reality, however, is that the adaptive capacity of rural and low-income farmers has generally been found to be low in most instances. Employing the class of indicators used in this study shows that the overall performance in various indicators were generally low for households in Karaga with the worst performances in financial, human, and physical capitals. Migration along with low remittance returns is the highest contributor to the low financial capital levels whilst ownership of smaller low value animal and low diversification in income also account for low financial capital. There is also the issue of low literacy rates at the household level which also contributes significantly to low human capital levels. Physical capital, as has become evident, is also significantly impacted by low access to irrigation and poor performance in terms of ownership of farm equipment and machinery. The above-mentioned indicators all independently have a negative effect on households’ food and nutrition security.
There is a wide range of livelihood strategies employed by households mainly to increase productivity and production rather than to adapt to climate change and variability. The most popular ones include increasing the variety of crops grown, keeping animals for security against crop failure, intercropping and preserving some of the most thriving crop as seed for the next season. The strategies mostly adopted often require low level of technology and capital cost. The least adopted practices include dry season farming, mulching, paid employment and the practice of vocational trades. These strategies on the other hand may require higher capital cost and technical know-how or additional labour to adopt. The prevalence rate of stunting among children under five years in Karaga is still very high with more boys stunted than girls. The study finds an inverse relationship between a household’s adaptive capacity and stunting the latter which is an indicator of household nutritional needs. Even though this relationship is significant at p < 0.01, it requires the interaction of the other predictors to be valid. This suggests that household adaptive capacity may be important for predicting nutritional needs of low-income households especially in the context of climate change. However, adaptive capacity should not be considered in isolation but as part of a broad spectrum of determinants of malnutrition and in effect, nutritional needs.
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