ABSTRACT
Climate change is projected to have an adverse impact on the health and wellbeing of households especially in sub-Saharan Africa. This could be through disruptions in food production and distribution systems leading to food insecurity or the proliferation of diseases vectors both of which contributes to the prevalence of malnutrition. The high prevalence of stunting in the northern parts of Ghana despite various nutrition and health interventions by government and other development partners over the years has led to the need to find out how the ability of households to adapt to climate change and variability affects its nutritional needs.
Using the sustainable livelihoods framework, the study aimed at exploring the link between adaptive capacity and household nutritional needs of low-income households in Karaga district using stunting in children as an indicator of nutritional needs. The study employed a quantitative design with a survey of two-hundred and eighty-eight households with children under five years old, ten key informant interviews and a five-expert ranking of indicators. A binary logistic model was used to study the link between adaptive capacity and household nutritional needs. The study finds a significant inverse relationship between adaptive capacity and household nutritional needs. The study therefore recommends the strengthening of education on best practices for infant and young child feeding practices, improvements in non-formal, technical and vocational training and the establishment of an irrigation scheme in Karaga to help increase the adaptive capacity of households and reduce the prevalence of stunting.
CHAPTER ONE INTRODUCTION
Background
Climate change is projected to affect various aspects of human endeavor including human health and wellbeing (IPCC, 2001). These negative impacts may be as a direct consequence of extreme weather events like floods, droughts and heat waves or through other mechanisms such as; the proliferation of disease pathogens; and disruptions in food production and distribution, conflict and migration (Burke et al., 2008). These disruptions could negatively affect human nutrition which is an essential precursor to good health thereby amplifying the vulnerability to low income households already struggling to improve their wellbeing.
Vulnerability is a state in which a system is liable to harm by virtue of being exposed to stresses resulting from environmental and social change (Adger, 2006). Vulnerability is a function of exposure of a system to external stimuli, sensitivity of that system to such a stimulus, and adaptive capacity. Exposure is the size and duration of a climate event such as changes in rainfall patterns, drought or flood whilst sensitivity is the extent to which systems are affected by these events (Thathsarani & Gunaratne, 2018). Adaptive capacity is the ability of human and other systems and institutions to adjust to potential adverse conditions by exploiting opportunities and responding to consequences(IPCC, 2014).
A system with low exposure and sensitivity coupled with a high adaptive capacity is less vulnerable (Smit & Wandel, 2006). A high adaptive capacity is reported to enhance the adoption of new technologies and adaptation strategies by households (Egyir, Ofori, Antwi, & Ntiamoa- Baidu, 2015). Therefore, in order to cope favorably with this changing climate, the adaptive
capacities of individuals, groups and institutions have to be determined and when found to be low, enhanced. Adaptive capacity building is therefore part of many interventions aimed at enhancing adaptation to climate change and variability.
Global hunger even though reducing considerably, still poses a great challenge to individual countries as every country faces one nutrition problem or another (FAO, 2017a). Nearly two billion people have deficiencies in various micronutrients whilst 52 million children are acutely malnourished (FAO, 2017b). The worlds undernutrition is concentrated mainly in Africa, Asia and Latin America (FAO, 2017a). Sub-Saharan African countries including Ghana are especially vulnerable due to a host of socio-economic and political constrains that reduces capacity of adaptation (Gregory, Ingram, & Brklacich, 2005).
The context of this study is to explore how low-income farming households are coping with the climate challenge given their levels livelihood assets such as physical, social, financial and human capitals and access to information. The households levels of these livelihood assets together with the households interaction with associated policies, institutions and processes is a measure of its adaptive capacity (Defiesta & Rapera, 2014; Lockwood, Raymond, Oczkowski, & Morrison, 2015; Thathsarani & Gunaratne, 2018).
Adaptive capacity facilitates the adoption of combinations of livelihood strategies aimed at increasing food production and adapting to climate change (Asante et al., 2012). Livelihood strategies generally include livelihood diversification, agricultural intensification and migration (Stunturf et al., 2011). The intended purpose for adopting a livelihood strategy is to achieve good livelihood outcomes including household food and nutrition security. Food security is said to be achieved when all people have access to enough safe and nutritious food to meet their dietary needs
and food preferences for an active and healthy life (FAO, 1996). This study employs stunting as an indicator for food security. Stunting is a deficit in length or height-for-age and a consequence of prolonged inadequate protein and energy intake (Cogill, 2003).
The study employs the sustainable livelihoods framework by DFID (2009) and Ellis (2000) which makes household nutritional status (a livelihood outcome) a function of adaptive capacity (resources and governance) and adaption strategy. The logit model with the cumulative distribution function is therefore used to model this relation.
Problem statement
Despite decades of nutrition intervention, the three Northern regions of Ghana have the highest rates of stunting and underweight in the country (GDHS, 2014). About a third of all children under five years of age in the Northern regions are stunted(GDHS, 2014). This trend continues in other malnutrition indicator such as wasting and underweight. Rates of wasting and underweight are at 7.9% and 23.6% respectively haven fallen only marginally in six years between the years 2008 and 2014(GDHS, 2014). The prevalence of underweight has fallen by only 1.6% over the same period (GDHS, 1999, 2005, 2009, 2014).
The three Northern regions, being among the poorest in the country, are also located in the semi-arid regions of Ghana. These regions are regarded as areas of high risk to climate impacts based on the small scale mixed farming systems dominant in these parts (Wossen & Berger, 2015).
Climate change exacerbates the problems of food insecurity already being experienced by low- income farming households (Wossen et al.,2014). The ability of households to cope with climate change and variability, which is termed adaptive capacity, is critical in enhancing resilience in the
face of climate change. Adaptive capacity therefore becomes one of the main driver influencing household vulnerability. Studying how a household’s climate change adaptive capacity influences household food and nutrition security should be of prime interest to the research community.
Literature generally rely on the consequences of climate related extreme weather and effects on agricultural production and productivity in trying to explain the link between climate change and human nutrition ( Panel et al., (2007). There is also limited research as to whether particular adaptation strategies results in positive outcomes at the household and population levels and this has motivated research into exploring the link between adaptive capacity and the capacity to take action for better outcomes (Elrick-Barr, Preston, Thomsen, & Smith, 2014).
Following a growing interest in the need to progress beyond the inferential assumptions regarding the nature and applications of adaptive capacity for fostering adaptation Elrick-Barr, Preston, Thomsen, & Smith, (2014) identifies two dimensions to adaptive capacity research; one focusing on how behavior and choices are modified by adaptive capacity whilst the other focuses on reducing vulnerability. In line with these two dimensions, this study aims to provide empirical evidence by exploring the link between adaptive capacity and nutrition needs of low-income households using the Sustainable Livelihoods Framework.
Research objectives
This study is generally to examine link between climate change adaptive capacity and the nutrition needs of low-income households in the Northern Region of Ghana.
The study specifically aims to;
- Assess the adaptive capacity of low-income farming households in the Karaga District
- Study the prevalence of stunting as indicated by low height-for-age in the Karaga District.
- Explore the effects of adaptive capacity on the nutritional needs of low-income households.
Hypotheses
Based on the above, the study proposes the following hypotheses;
Objective 3:
𝐻𝑜1: There is no significant association between adaptive capacity and the nutritional needs of low-income households.
𝐻11: There is a significant relationship between adaptive capacity and the nutritional needs of low-income households.
Objective 3
𝐻𝑜2: There is no significant association between livelihood strategy and the nutritional needs of low-income households.
𝐻12: There is a significant association between livelihood strategy and the nutritional needs of low-income households.
Significance of the study
The results of this study provide empirical evidence towards finding the linkages between household adaptive capacity and household nutritional needs in the face of climate change. It aims at putting climate change considerations into nutrition and health intervention planning and project design with the hope of delivering higher implementation efficiency and more positive impacts.
Results of this study contributes towards a more robust approach to household adaptive capacity assessment by contributing to the empirical knowledge on the use of assigned weights to aggregated scores for various sub-indices.
