Francis, J.Zuwarimwe, J.Kori, Dumisani2021-06-292021-06-292021-06-23Kori, Dumisani (2021) Developing a framework for estimating adaptation cost to climate variabilityand change for maize farmers in resettlement areas of Chirumanzu District, Zimbabwe. University of Venda, South Africa. <http://hdl.handle.net/11602/1678>http://hdl.handle.net/11602/1678PhDRDVInstitute for Rural DevelopmentSeasonal variation and long-term change in climate have a direct impact on the form, scale, spatial and temporal effects on maize farming. Adaptation is key to reducing the effects. Several costs accompany the adaptation process. However, there is no universally accepted method for estimating the costs. In Zimbabwe, this issue is of major interest to smallholder maize farmers operating in resettlement areas, hereafter called A1 maize farmers. The methods in use currently are complex and A1 maize farmers cannot comprehend them easily. Moreover, existing methods focus on direct costs that are easy to quantify in monetary terms. However, literature on indirect costs, which are difficult to measure quantitatively and attach a monetary value, is scarce. The current study was designed to fill the gaps highlighted above through developing a framework for estimating adaptation cost to climate variability and change for A1 maize farmers in Chirumanzu District using a bottom-up approach. The four objectives formulated, corresponded to the steps taken in developing the framework, viz.: 1) to identify adaptation measures adopted by A1 maize farmers in Chirumanzu; 2) to establish cost elements for adaptation measures adopted; 3) to develop a typology of the cost elements; and 4) to conceptualise and operationalise the cost categories and variables. Measures were created for each of the latent constructs, leading to the formulation of a context-specific framework for estimating adaptation cost. Smallholder maize farmers in resettlement areas of Chirumanzu who had been adapting to climate variability and change, for at least five years and still had operational adaptation systems during the time of the study, constituted the population. Four out of the nine resettlement wards were selected using heterogeneous or maximum variation purposive sampling. Key informants from the respective wards were selected to participate in interviews. Homogenous purposive sampling was used to select A1 maize farmers in the sampled wards. Data were collected through semi-structured and key informant interviews to corroborate and triangulate findings. Interviews were audio recorded concurrently with note taking. Audio-recorded interviews were transcribed verbatim and textual data was prepared for Computer Aided Qualitative Data Analysis. Data was analysed using a thematic content analysis. Network diagrams and code-document tables were used to present results. Hierarchical Cluster Analysis was used to classify and categorise identified cost elements using the squared Euclidean Distance and Between-Groups Linkage methods while developing a typology. Results were presented using agglomeration schedules, scree plots and dendrograms. The quality of the resulting clusters was tested using the Silhouette measure of cohesion and separation. Path analysis using the Structural Equation Modelling was conducted to identify relationships between adaptation cost variables and categories. Principles of quantification of theoretical constructs including conceptualisation, operationalisation and attribute development were used to develop the framework for estimating adaptation cost to climate variability and change. Adaptation among A1 maize farmers was found to be climate-driven. Variations and changes in climate were the push factors for adaptation. The A1 maize farmers adopted various adaptation measures that varied from one farmer to another and among wards, indicating farmer heterogeneity. Farmers adopted measures out of desperation, aiming to restore loses by investing in adaptation measures and incurred costs. One hundred and nineteen cost elements were associated with adaptation measures adopted. Cost elements revealed the scourge that climate variability and change inflicted on the A1 maize farmers. The cost elements were classified into six distinct cost categories, namely impact, psychological, implementation, unintended, social and associated burden. Categories formulated go beyond the much recognised financial components of adaptation costs introducing non-financial and/ or indirect cost components. A typology with an extended continuum of adaptation cost variables for each of the established categories was formulated. The cost variables and categories were used to develop a framework for estimating adaptation cost. The framework comprises of three hypothesised frameworks, three evaluation tools and three adaptation cost equations for pre, during and post-adaptation phases, a total adaptation cost equation and a summated rating scale. The summated rating scale determines the sustainability and desirability of adaptation activities. Results of the current study form the basis for sustainable adaptation decision-making in smallholder farming. The framework developed in the study provides smallholder maize farmers, policy makers and researchers with a tool that may enable the sustainable designing, implementation and evaluation of action plans, policies and methods in the face of climate variability and change.1 online resource (xix, 223 leaves ) : color illustrationsenUniversity of VendaAdaptation cost elementsAgrarian and land reformMaizeResettlementSmallholder farmersSustainable adaptation630.2515096891Farms, Small -- ZimbabweLand settlement patterns -- ZimbabweAgricultural colonies -- ZimbabweClimatic changes -- ZimbabweClimatology -- ZimbabweFarmers -- ZimbabweLand settlement -- ZimbabweLand use, Rural -- ZimbabweThesisKori D. Developing a framework for estimating adaptation cost to climate variabilityand change for maize farmers in resettlement areas of Chirumanzu District, Zimbabwe. []. , 2021 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/1678Kori, D. (2021). <i>Developing a framework for estimating adaptation cost to climate variabilityand change for maize farmers in resettlement areas of Chirumanzu District, Zimbabwe</i>. (). . Retrieved from http://hdl.handle.net/11602/1678Kori, Dumisani. <i>"Developing a framework for estimating adaptation cost to climate variabilityand change for maize farmers in resettlement areas of Chirumanzu District, Zimbabwe."</i> ., , 2021. http://hdl.handle.net/11602/1678TY - Thesis AU - Kori, Dumisani AB - Seasonal variation and long-term change in climate have a direct impact on the form, scale, spatial and temporal effects on maize farming. Adaptation is key to reducing the effects. Several costs accompany the adaptation process. However, there is no universally accepted method for estimating the costs. In Zimbabwe, this issue is of major interest to smallholder maize farmers operating in resettlement areas, hereafter called A1 maize farmers. The methods in use currently are complex and A1 maize farmers cannot comprehend them easily. Moreover, existing methods focus on direct costs that are easy to quantify in monetary terms. However, literature on indirect costs, which are difficult to measure quantitatively and attach a monetary value, is scarce. The current study was designed to fill the gaps highlighted above through developing a framework for estimating adaptation cost to climate variability and change for A1 maize farmers in Chirumanzu District using a bottom-up approach. The four objectives formulated, corresponded to the steps taken in developing the framework, viz.: 1) to identify adaptation measures adopted by A1 maize farmers in Chirumanzu; 2) to establish cost elements for adaptation measures adopted; 3) to develop a typology of the cost elements; and 4) to conceptualise and operationalise the cost categories and variables. Measures were created for each of the latent constructs, leading to the formulation of a context-specific framework for estimating adaptation cost. Smallholder maize farmers in resettlement areas of Chirumanzu who had been adapting to climate variability and change, for at least five years and still had operational adaptation systems during the time of the study, constituted the population. Four out of the nine resettlement wards were selected using heterogeneous or maximum variation purposive sampling. Key informants from the respective wards were selected to participate in interviews. Homogenous purposive sampling was used to select A1 maize farmers in the sampled wards. Data were collected through semi-structured and key informant interviews to corroborate and triangulate findings. Interviews were audio recorded concurrently with note taking. Audio-recorded interviews were transcribed verbatim and textual data was prepared for Computer Aided Qualitative Data Analysis. Data was analysed using a thematic content analysis. Network diagrams and code-document tables were used to present results. Hierarchical Cluster Analysis was used to classify and categorise identified cost elements using the squared Euclidean Distance and Between-Groups Linkage methods while developing a typology. Results were presented using agglomeration schedules, scree plots and dendrograms. The quality of the resulting clusters was tested using the Silhouette measure of cohesion and separation. Path analysis using the Structural Equation Modelling was conducted to identify relationships between adaptation cost variables and categories. Principles of quantification of theoretical constructs including conceptualisation, operationalisation and attribute development were used to develop the framework for estimating adaptation cost to climate variability and change. Adaptation among A1 maize farmers was found to be climate-driven. Variations and changes in climate were the push factors for adaptation. The A1 maize farmers adopted various adaptation measures that varied from one farmer to another and among wards, indicating farmer heterogeneity. Farmers adopted measures out of desperation, aiming to restore loses by investing in adaptation measures and incurred costs. One hundred and nineteen cost elements were associated with adaptation measures adopted. Cost elements revealed the scourge that climate variability and change inflicted on the A1 maize farmers. The cost elements were classified into six distinct cost categories, namely impact, psychological, implementation, unintended, social and associated burden. Categories formulated go beyond the much recognised financial components of adaptation costs introducing non-financial and/ or indirect cost components. A typology with an extended continuum of adaptation cost variables for each of the established categories was formulated. The cost variables and categories were used to develop a framework for estimating adaptation cost. The framework comprises of three hypothesised frameworks, three evaluation tools and three adaptation cost equations for pre, during and post-adaptation phases, a total adaptation cost equation and a summated rating scale. The summated rating scale determines the sustainability and desirability of adaptation activities. Results of the current study form the basis for sustainable adaptation decision-making in smallholder farming. The framework developed in the study provides smallholder maize farmers, policy makers and researchers with a tool that may enable the sustainable designing, implementation and evaluation of action plans, policies and methods in the face of climate variability and change. DA - 2021-06-23 DB - ResearchSpace DP - Univen KW - Adaptation cost elements KW - Agrarian and land reform KW - Maize KW - Resettlement KW - Smallholder farmers KW - Sustainable adaptation LK - https://univendspace.univen.ac.za PY - 2021 T1 - Developing a framework for estimating adaptation cost to climate variabilityand change for maize farmers in resettlement areas of Chirumanzu District, Zimbabwe TI - Developing a framework for estimating adaptation cost to climate variabilityand change for maize farmers in resettlement areas of Chirumanzu District, Zimbabwe UR - http://hdl.handle.net/11602/1678 ER -