Insights into the drivers and impact of climate change and climate change adaptation in the Eastern Cape, South Africa: the case of Amathole District Municipality
- Authors: Gwala, Lindokuhle
- Date: 2022-11
- Subjects: Climatic changes , Climatic factors , Global environmental change
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10353/27832 , vital:69945
- Description: Climate change is a threat to communal livestock production, causing increases in the rate and intensity of droughts, floods, pests and diseases, and thus subjecting communal livestock production to vulnerability. Communal farmers rely on rain-fed agriculture and are usually too poorly resourced to cope with the frequency of climate-related events that may be expected in the future. Response and adaptation is vital to ensure the sustainability of livestock production, particularly since it is the main source of survival in communal areas. The Eastern Cape Provincial Policy on Climate Change was introduced in 2010 to facilitate a coordinated approach that assists farmers to respond, adapt and mitigate climate change. The study examines the implementation of the policy to identify farmers’ perceptions of the response rate prior to, during and after climate change disasters. In addition, the study establishes the characteristics of livestock production in the study area, seeking to ascertain how communal livestock farmers CLFs are adapting their practices to ensure sustainable livestock production in the face of climate change. In order to make informed decisions on coping strategies, farmers require access to information on climate change. The study therefore examines the communication channels used by farmers to access such information. Since food security is under threat, the study also assesses the effects of climate change on food security among the CLFs. Multi stage sampling was used to select 388 communal livestock farmers in three local municipalities in Amathole District Municipality. A cross-sectional survey was carried out in five communities randomly selected in the three local municipalities, with data collected by means of a semi-structured questionnaire. Descriptive statistics, inferential statistics, principal component analysis and regression methods were used to analyse the data. The study reveals that communal livestock farmers perceive a poor response rate from extension services before, during and after disasters, and that they have poor access to support materials. All of the respondents practised uncontrolled breeding, attributed to a lack of infrastructure such as fencing. Most farmers kept cattle for income generation. The major constraints of cattle production were diseases and pests. CLFs employed dipping, rotational grazing, water tanks, veld burning and the sale of animals as the main strategies to cope with climate change. CLFs access climate change information through multiple channels. The main sources were other farmers and media such as radio and television, although language barriers hampered full understanding of information conveyed about climate chnage. Farmer-to-farmer contact was a central aspect of the CLFs’ lives that could be better employed in the dissemination of climate change information. The results suggest a positive relationship between diversity of species kept and food security. Assets, the social safety net (mainly grants) and adaptive capacity indicators positively and significantly impacted households’ resilience to food insecurity. The study recommends that the capacity of communal livestock farmers on effective mitigating strategies be improved, making use of mass media; that more work be done by extension services to prepare farmers for adverse events and that relief materials disseminated during periods of disaster be tagged “national emergency” to speed up distribution and use. There is an urgent need for adequate and timely provision of climate change information that will help CLFs to make more effective use of their resources in the face of climate change. Agricultural extension services should address challenges associated with breeding practices, disaster response and adult illiteracy to promote better adaptive capacity and ensure food security among this vulnerable cohort. , Thesis (MSci) -- Faculty of Science and Agriculture, 2022
- Full Text:
- Date Issued: 2022-11
- Authors: Gwala, Lindokuhle
- Date: 2022-11
- Subjects: Climatic changes , Climatic factors , Global environmental change
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10353/27832 , vital:69945
- Description: Climate change is a threat to communal livestock production, causing increases in the rate and intensity of droughts, floods, pests and diseases, and thus subjecting communal livestock production to vulnerability. Communal farmers rely on rain-fed agriculture and are usually too poorly resourced to cope with the frequency of climate-related events that may be expected in the future. Response and adaptation is vital to ensure the sustainability of livestock production, particularly since it is the main source of survival in communal areas. The Eastern Cape Provincial Policy on Climate Change was introduced in 2010 to facilitate a coordinated approach that assists farmers to respond, adapt and mitigate climate change. The study examines the implementation of the policy to identify farmers’ perceptions of the response rate prior to, during and after climate change disasters. In addition, the study establishes the characteristics of livestock production in the study area, seeking to ascertain how communal livestock farmers CLFs are adapting their practices to ensure sustainable livestock production in the face of climate change. In order to make informed decisions on coping strategies, farmers require access to information on climate change. The study therefore examines the communication channels used by farmers to access such information. Since food security is under threat, the study also assesses the effects of climate change on food security among the CLFs. Multi stage sampling was used to select 388 communal livestock farmers in three local municipalities in Amathole District Municipality. A cross-sectional survey was carried out in five communities randomly selected in the three local municipalities, with data collected by means of a semi-structured questionnaire. Descriptive statistics, inferential statistics, principal component analysis and regression methods were used to analyse the data. The study reveals that communal livestock farmers perceive a poor response rate from extension services before, during and after disasters, and that they have poor access to support materials. All of the respondents practised uncontrolled breeding, attributed to a lack of infrastructure such as fencing. Most farmers kept cattle for income generation. The major constraints of cattle production were diseases and pests. CLFs employed dipping, rotational grazing, water tanks, veld burning and the sale of animals as the main strategies to cope with climate change. CLFs access climate change information through multiple channels. The main sources were other farmers and media such as radio and television, although language barriers hampered full understanding of information conveyed about climate chnage. Farmer-to-farmer contact was a central aspect of the CLFs’ lives that could be better employed in the dissemination of climate change information. The results suggest a positive relationship between diversity of species kept and food security. Assets, the social safety net (mainly grants) and adaptive capacity indicators positively and significantly impacted households’ resilience to food insecurity. The study recommends that the capacity of communal livestock farmers on effective mitigating strategies be improved, making use of mass media; that more work be done by extension services to prepare farmers for adverse events and that relief materials disseminated during periods of disaster be tagged “national emergency” to speed up distribution and use. There is an urgent need for adequate and timely provision of climate change information that will help CLFs to make more effective use of their resources in the face of climate change. Agricultural extension services should address challenges associated with breeding practices, disaster response and adult illiteracy to promote better adaptive capacity and ensure food security among this vulnerable cohort. , Thesis (MSci) -- Faculty of Science and Agriculture, 2022
- Full Text:
- Date Issued: 2022-11
Effects of climatic variability on maize productivity in South Africa from 1937-2018
- Awum Awum, Rudin https://orcid.org/ 0000-0002-8740-6163
- Authors: Awum Awum, Rudin https://orcid.org/ 0000-0002-8740-6163
- Date: 2022-03
- Subjects: Crops and climate , Climatic changes
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/21410 , vital:48576
- Description: Climate is an important factor in agricultural production. The world is facing climate change and variability, which result in high temperatures, low rainfall patterns, shortage of water and persistent droughts. Climate change poses a significant threat to South Africa’s water resources, food security, health, infrastructure, ecosystem services and biodiversity. Negative impacts of climate variability on agriculture, especially on maize the staple crop, will worsen the food security status of the nation as most of South Africa’s maize crop is produced in summer and highly depends on rainfall. This study attempted to assess the impact of climate on maize production in South Africa using secondary time series data for the period 1937 to 2018. Rainfall and temperature were used as proxies for climate variability. The Granger Causality Model was used to examine the causal linkages between climatic variables (temperature or rainfall) and maize output in South Africa for the study period. The major outcome of the analysis was that there is a two-way causal relationship between maize production and temperature. The results also indicated that there is uni-directional causality between maize yield and rainfall. Furthermore, the Variance Decomposition Model was used to forecast the relationship between climatic elements and maize production in South Africa. The result showed that all variables have an effect on maize yield, with temperature having the least effect. The last objective of the study was to profile the maize output trend for the period from 1937 to 2018. The main findings from the analysis indicate that maize production in South Africa has a general upward slope. The study recommends that the government should intensify the provision of irrigation systems for the farmers in the most vulnerable areas to mitigate the climate change. Government should also embark on massive campaigns using a variety of media to create the needed public awareness on climate change and its impact on food security. , Thesis (MSc) -- Faculty of Science and Agriculture, 2022
- Full Text:
- Date Issued: 2022-03
- Authors: Awum Awum, Rudin https://orcid.org/ 0000-0002-8740-6163
- Date: 2022-03
- Subjects: Crops and climate , Climatic changes
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/21410 , vital:48576
- Description: Climate is an important factor in agricultural production. The world is facing climate change and variability, which result in high temperatures, low rainfall patterns, shortage of water and persistent droughts. Climate change poses a significant threat to South Africa’s water resources, food security, health, infrastructure, ecosystem services and biodiversity. Negative impacts of climate variability on agriculture, especially on maize the staple crop, will worsen the food security status of the nation as most of South Africa’s maize crop is produced in summer and highly depends on rainfall. This study attempted to assess the impact of climate on maize production in South Africa using secondary time series data for the period 1937 to 2018. Rainfall and temperature were used as proxies for climate variability. The Granger Causality Model was used to examine the causal linkages between climatic variables (temperature or rainfall) and maize output in South Africa for the study period. The major outcome of the analysis was that there is a two-way causal relationship between maize production and temperature. The results also indicated that there is uni-directional causality between maize yield and rainfall. Furthermore, the Variance Decomposition Model was used to forecast the relationship between climatic elements and maize production in South Africa. The result showed that all variables have an effect on maize yield, with temperature having the least effect. The last objective of the study was to profile the maize output trend for the period from 1937 to 2018. The main findings from the analysis indicate that maize production in South Africa has a general upward slope. The study recommends that the government should intensify the provision of irrigation systems for the farmers in the most vulnerable areas to mitigate the climate change. Government should also embark on massive campaigns using a variety of media to create the needed public awareness on climate change and its impact on food security. , Thesis (MSc) -- Faculty of Science and Agriculture, 2022
- Full Text:
- Date Issued: 2022-03
A framework for communicating climate information to rural small-scale farmers in the Eastern Cape Province, South Africa using systems thinking approach
- Pindura, Tineyi Herbert https://orcid.org/0000-0001-7233-6222
- Authors: Pindura, Tineyi Herbert https://orcid.org/0000-0001-7233-6222
- Date: 2022-02
- Subjects: Farms, Small , Agriculture -- Environmental aspects , Climatic changes
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10353/22809 , vital:52784
- Description: In the Eastern Cape of South Africa, rural small-scale farmers live in uncertain times characterized by climate change and variability, which intensify social, political and financial inequalities. Therefore, there is a need to increase the understanding and interpretation of climate information to minimize crop production risk, reduce rural small-scale farmers’ vulnerability to climate, and maximize opportunities. Increasing the resilience among rural small-scale farmers requires appropriate and viable practical approaches. By using systems thinking approach (and the Raymond Mhlaba Municipality in the Eastern Cape as a study area), this research disseminates the complex nature of current climate information frameworks. Through inputs from farmers (through a Farmers Research Group methodology) and climate data, this thesis developed a new framework for communicating climate information (herein referred as the climate information communication systems framework) to rural small-scale farmers. The proposed climate information communication systems framework successfully integrates scientific and traditional knowledge. The framework constitutes certain stages, where the farming system and crop identification is the first stage. The second stage is the requirements stage, which has two relationships: scientist to crop requirements and farmers to crop requirements. At the scientist to crop requirements level, users will examine crop requirements by combining climatic data and crop simulations, while at the farmers to crop requirement, rural small-scale farmers use the scientific information to plan for the farming season. The farmer then decides the planting and growing period as well as the harvest timing. The study suggests that building links between rural small-scale farmers, extension officers, and scientists makes sharing information easier. The proposed climate information framework design illustrates that stakeholders now have a personal relationship with climate information transmission and can influence the different actions to reduce the effect of climate change unpredictability. The study established that climate change and variability influence recent agricultural output trends. Recent temperature and rainfall trends could have resulted in low crop productivity in the study area. The study also reflected that women are a critical resource in agriculture and rural economy and that middle-aged men and youth are less interested in farming. The findings also showed that women are more receptive to climate information; hence, to promote access and use of climate information remains vital to consider gender-specific aspects for appropriate decision-making in the agriculture sector. The study also recommends enhancing the Farmers Research Group technique. In the past, the scope of this methodology has been narrowly focused, and it must be explored to incorporate additional types of climate information and more debate about how climate is related to the decisions farmers for implementation or consideration. , Thesis (PhD) -- Faculty of Science and Agriculture, 2022
- Full Text:
- Date Issued: 2022-02
- Authors: Pindura, Tineyi Herbert https://orcid.org/0000-0001-7233-6222
- Date: 2022-02
- Subjects: Farms, Small , Agriculture -- Environmental aspects , Climatic changes
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10353/22809 , vital:52784
- Description: In the Eastern Cape of South Africa, rural small-scale farmers live in uncertain times characterized by climate change and variability, which intensify social, political and financial inequalities. Therefore, there is a need to increase the understanding and interpretation of climate information to minimize crop production risk, reduce rural small-scale farmers’ vulnerability to climate, and maximize opportunities. Increasing the resilience among rural small-scale farmers requires appropriate and viable practical approaches. By using systems thinking approach (and the Raymond Mhlaba Municipality in the Eastern Cape as a study area), this research disseminates the complex nature of current climate information frameworks. Through inputs from farmers (through a Farmers Research Group methodology) and climate data, this thesis developed a new framework for communicating climate information (herein referred as the climate information communication systems framework) to rural small-scale farmers. The proposed climate information communication systems framework successfully integrates scientific and traditional knowledge. The framework constitutes certain stages, where the farming system and crop identification is the first stage. The second stage is the requirements stage, which has two relationships: scientist to crop requirements and farmers to crop requirements. At the scientist to crop requirements level, users will examine crop requirements by combining climatic data and crop simulations, while at the farmers to crop requirement, rural small-scale farmers use the scientific information to plan for the farming season. The farmer then decides the planting and growing period as well as the harvest timing. The study suggests that building links between rural small-scale farmers, extension officers, and scientists makes sharing information easier. The proposed climate information framework design illustrates that stakeholders now have a personal relationship with climate information transmission and can influence the different actions to reduce the effect of climate change unpredictability. The study established that climate change and variability influence recent agricultural output trends. Recent temperature and rainfall trends could have resulted in low crop productivity in the study area. The study also reflected that women are a critical resource in agriculture and rural economy and that middle-aged men and youth are less interested in farming. The findings also showed that women are more receptive to climate information; hence, to promote access and use of climate information remains vital to consider gender-specific aspects for appropriate decision-making in the agriculture sector. The study also recommends enhancing the Farmers Research Group technique. In the past, the scope of this methodology has been narrowly focused, and it must be explored to incorporate additional types of climate information and more debate about how climate is related to the decisions farmers for implementation or consideration. , Thesis (PhD) -- Faculty of Science and Agriculture, 2022
- Full Text:
- Date Issued: 2022-02
The use of earth observation multi-sensor systems to monitor and model Pastures: a case of Savannah Grasslands in Hluvukani Village, Bushbuckridge Local Municipality, Mpumalanga Province, South Africa
- Nduku, Lwandile https://orcid.org/0000-0001-9168-4548
- Authors: Nduku, Lwandile https://orcid.org/0000-0001-9168-4548
- Date: 2022-01
- Subjects: Climatic changes , Grassland conservation
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/22578 , vital:52470
- Description: Grassland degradation associated with climate change and inappropriate grassland management has been characterized as a global environmental concern driving decreased grassland ecosystem's ecological functioning. More than 60% of South African grassland is degraded or permanently transformed to other land uses and nearly 2% properly conserved. Yet, grasslands are a major source of food for livestock grazing and provide material and non-material benefits to many livelihoods. Therefore, grassland above-ground biomass (AGB) estimation is crucial in planning and managing pastoral agriculture and the benefits derived from it. However, current grassland monitoring techniques used in rural smallholder livestock farms rely on conventional methods, which are destructive, labour-intensive, costly, and restricted to small areas. This study investigated the monitoring and modelling of protected grasslands biomass using current Earth observation systems (EOS), an approach, which is non-destructive, cost-effective, cover larger areas and is a time-saving alternative to conventional methods. Hence, the research objectives were: (i) to map the trends and advances in data and models used in the monitoring of grassland (pastures) with Earth observation systems, and (ii) to assess above-ground biomass estimation in semi-arid savannah grassland integrating Sentinel-1 and Sentinel-2 data with Machine-Learning. This goal was to assess if this approach could provide the requisite information, which could contribute to the long-term goal of developing a semi-automated system for data processing, and mapping grassland biomass to benefit local communities. For this investigation, it was crucial to understanding what research had achieved so far in this area of pasture management. An assessment of the Scopus database showed the recent developments in European Union (EU) programs and Sentinel missions, including statistical models and machine learning for monitoring grassland changes at multiple scales. However, Sentinel-1 and Sentinel-2 data, machine learning models, and variable importance techniques were applied for grassland AGB estimation. These techniques have been used in similar studies to determine optimum machine learning models, influential variables, and the capability of integrated Sentinel datasets for mapping grassland AGB, spatial distribution, and abundance. Results showed improved performance with the Random forest regression (RFR) model (R² of 34.7%, RMSE of 9.47 Mg and MAE of 7.68 Mg ). The study also observed optimum sensitivity of Difference Vegetation Index (DVI) and Enhanced Vegetation Index (EVI) in all three machine learning models for modelling grassland AGB estimation in the study area. A further, statistical comparison of all three machine learning models showed an insignificant difference in the predictive capacity for AGB in the study area with Gradient Boosting regression (GBR) model (R² of 27.7, RMSE of 9.97 Mg and MAE of 8.03 Mg ) and Extreme Gradient Boost Regression (XGBR) model (R² of 17.3%, RMSE of 10.66 Mg and MAE of 8.83 Mg ). The study revealed that an integration of Sentinel-1 and Sentinel-2 has improved capabilities for monitoring grassland AGB estimation. This research sheds light on the timely and cost-effective techniques for grassland management strategies to enhance or restore the ecological functioning of grassland ecosystems and promote community sustainability. , Thesis (MSc) -- Faculty of Science and Agriculture, 2022
- Full Text:
- Date Issued: 2022-01
- Authors: Nduku, Lwandile https://orcid.org/0000-0001-9168-4548
- Date: 2022-01
- Subjects: Climatic changes , Grassland conservation
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/22578 , vital:52470
- Description: Grassland degradation associated with climate change and inappropriate grassland management has been characterized as a global environmental concern driving decreased grassland ecosystem's ecological functioning. More than 60% of South African grassland is degraded or permanently transformed to other land uses and nearly 2% properly conserved. Yet, grasslands are a major source of food for livestock grazing and provide material and non-material benefits to many livelihoods. Therefore, grassland above-ground biomass (AGB) estimation is crucial in planning and managing pastoral agriculture and the benefits derived from it. However, current grassland monitoring techniques used in rural smallholder livestock farms rely on conventional methods, which are destructive, labour-intensive, costly, and restricted to small areas. This study investigated the monitoring and modelling of protected grasslands biomass using current Earth observation systems (EOS), an approach, which is non-destructive, cost-effective, cover larger areas and is a time-saving alternative to conventional methods. Hence, the research objectives were: (i) to map the trends and advances in data and models used in the monitoring of grassland (pastures) with Earth observation systems, and (ii) to assess above-ground biomass estimation in semi-arid savannah grassland integrating Sentinel-1 and Sentinel-2 data with Machine-Learning. This goal was to assess if this approach could provide the requisite information, which could contribute to the long-term goal of developing a semi-automated system for data processing, and mapping grassland biomass to benefit local communities. For this investigation, it was crucial to understanding what research had achieved so far in this area of pasture management. An assessment of the Scopus database showed the recent developments in European Union (EU) programs and Sentinel missions, including statistical models and machine learning for monitoring grassland changes at multiple scales. However, Sentinel-1 and Sentinel-2 data, machine learning models, and variable importance techniques were applied for grassland AGB estimation. These techniques have been used in similar studies to determine optimum machine learning models, influential variables, and the capability of integrated Sentinel datasets for mapping grassland AGB, spatial distribution, and abundance. Results showed improved performance with the Random forest regression (RFR) model (R² of 34.7%, RMSE of 9.47 Mg and MAE of 7.68 Mg ). The study also observed optimum sensitivity of Difference Vegetation Index (DVI) and Enhanced Vegetation Index (EVI) in all three machine learning models for modelling grassland AGB estimation in the study area. A further, statistical comparison of all three machine learning models showed an insignificant difference in the predictive capacity for AGB in the study area with Gradient Boosting regression (GBR) model (R² of 27.7, RMSE of 9.97 Mg and MAE of 8.03 Mg ) and Extreme Gradient Boost Regression (XGBR) model (R² of 17.3%, RMSE of 10.66 Mg and MAE of 8.83 Mg ). The study revealed that an integration of Sentinel-1 and Sentinel-2 has improved capabilities for monitoring grassland AGB estimation. This research sheds light on the timely and cost-effective techniques for grassland management strategies to enhance or restore the ecological functioning of grassland ecosystems and promote community sustainability. , Thesis (MSc) -- Faculty of Science and Agriculture, 2022
- Full Text:
- Date Issued: 2022-01
Investigating the feasibility of using remote sensing in index-based crop insurance for South Africa’s smallholder farming systems
- Masiza, Wonga https://orcid.org/0000-0002-6224-3812
- Authors: Masiza, Wonga https://orcid.org/0000-0002-6224-3812
- Date: 2021-10
- Subjects: Precision farming , Agricultural engineering , Climatic changes
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10353/23000 , vital:54890
- Description: Crop farming in Sub-Saharan Africa (SSA) is largely practiced by resource-poor farmers under rain-fed and unpredictable weather conditions. Since agriculture is the mainstay of SSA’s economy, the lack of improved and adapted agricultural technologies in this region sets back economic development and the fight against poverty. Overcoming this constraint and achieving the sustainable development goal to end poverty, requires innovative tools that can be used for weather risk management. One tool that has been gaining momentum recently is index-based crop insurance (IBCI). Since the launch of the first IBCI program in Africa around 2005, the number of IBCI programs has increased. Unfortunately, these programs are constrained by poor product design, basis risk, and low uptake of contracts. When these issues were first pointed-out in the earliest IBCI programs, many reports suggested satellite remote sensing (RS) as a viable solution. Hence, the first objective of this study was to assess how RS has been used in IBCI, the challenges RS faces, and potential contributions of RS that have not yet been meaningfully exploited. The literature shows that IBCI programs are increasingly adopting RS. RS has improved demarcation of unit areas of insurance and enabled IBCI to reach inaccessible areas that do not have sufficient meteorological infrastructure. However, the literature also shows that IBCI is still tainted by basis risk, which emanates from poor contract designs, the influence of non-weather factors on crop yields, imperfect correlations between satellite-based indices and crop yields, and the lack of historical data for calibration. Although IBCI reports cover vegetation and crop health monitoring, few to none cover crop type and crop area mapping. Furthermore, areas including high-resolution mapping, data fusion, microwave RS, machine learning, and computer vision have not been sufficiently tested in IBCI. The second objective of this study was to assess how RS and machine learning techniques can be used to enhance the mapping of smallholder crop farming landscapes. The findings show that machine learning ensembles and the combination of optical and microwave data can map a smallholder farming landscape with a maximum accuracy of 97.71 percent. The third objective was to identify factors that influence crop yields and crop losses in order to improve IBCI design. Results demonstrated that the pervasive notion that low yields in smallholder farms are related to rainfall is an oversimplification. Factors including fertilizer use, seed variety, soil properties, soil moisture, growing degree-days, management, and socioeconomic conditions are some of the most important factors influencing crop yields and crop losses in smallholder farming systems. This shows why IBCI needs to be part of a comprehensive risk management system that understands and approaches smallholder crop farming as complex by linking insurance with advisories and input supplies. Improved inputs and good farming practices could reduce the influence of non-weather factors on crop losses, and thereby reduce basis risk in weather-based index insurance (WII) contracts. The fourth objective of this study was to assess how well the combination of synthetic aperture radar (SAR) and optical indices estimate soil moisture. As stated earlier, soil moisture was found to be one of the most important factors affecting crop yields. Although this method better estimated soil moisture over the first half of the growing season, estimation accuracies were comparable to those found in studies that had used similar datasets (RMSE = 0.043 m3 m-3, MAE = 0.034 m3 m- 3). Further interrogation of interaction effects between the variables used in this study and consideration of other factors that affect SAR backscatter could improve the method. More importantly, incorporating high-resolution satellite-based monitoring of soil moisture into IBCI could potentially reduce basis risk. The fifth objective of this study was to develop an IBCI for smallholder crop farming systems. The proposed IBCI scheme covers maize and derives index thresholds from crop water requirements and satellite-based rainfall estimates. It covers rainfall deficits over the vegetative, mid-season, and late-season stages of maize growth. The key contribution of this system is the derivation of index thresholds from CWR and site-specific rainfall conditions. The widely used approach, which calibrates IBCI by correlating yields and rainfall, exposes contracts to basis risk because, by simply correlating yield and rainfall data, it overlooks the influence of non-weather factors on crop yields and losses. The proposed system must be linked or bundled with non-weather variables that affect crop yields. Effectively, this means that the insurance must be linked or bundled with advisories and input supplies to address the influence of non-weather factors on crop losses. This system also incorporates a crop area-mapping component, which was found to be lacking in many IBCI systems. In conclusion, an IBCI that is based on crop water requirements, which incorporates crop area mapping and links insurance with non-weather crop yield-determining factors, is potentially capable of improving crop insurance for smallholder farming systems. , Thesis (PhD) -- Faculty of Science and Agriculture, 2021
- Full Text:
- Date Issued: 2021-10
- Authors: Masiza, Wonga https://orcid.org/0000-0002-6224-3812
- Date: 2021-10
- Subjects: Precision farming , Agricultural engineering , Climatic changes
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10353/23000 , vital:54890
- Description: Crop farming in Sub-Saharan Africa (SSA) is largely practiced by resource-poor farmers under rain-fed and unpredictable weather conditions. Since agriculture is the mainstay of SSA’s economy, the lack of improved and adapted agricultural technologies in this region sets back economic development and the fight against poverty. Overcoming this constraint and achieving the sustainable development goal to end poverty, requires innovative tools that can be used for weather risk management. One tool that has been gaining momentum recently is index-based crop insurance (IBCI). Since the launch of the first IBCI program in Africa around 2005, the number of IBCI programs has increased. Unfortunately, these programs are constrained by poor product design, basis risk, and low uptake of contracts. When these issues were first pointed-out in the earliest IBCI programs, many reports suggested satellite remote sensing (RS) as a viable solution. Hence, the first objective of this study was to assess how RS has been used in IBCI, the challenges RS faces, and potential contributions of RS that have not yet been meaningfully exploited. The literature shows that IBCI programs are increasingly adopting RS. RS has improved demarcation of unit areas of insurance and enabled IBCI to reach inaccessible areas that do not have sufficient meteorological infrastructure. However, the literature also shows that IBCI is still tainted by basis risk, which emanates from poor contract designs, the influence of non-weather factors on crop yields, imperfect correlations between satellite-based indices and crop yields, and the lack of historical data for calibration. Although IBCI reports cover vegetation and crop health monitoring, few to none cover crop type and crop area mapping. Furthermore, areas including high-resolution mapping, data fusion, microwave RS, machine learning, and computer vision have not been sufficiently tested in IBCI. The second objective of this study was to assess how RS and machine learning techniques can be used to enhance the mapping of smallholder crop farming landscapes. The findings show that machine learning ensembles and the combination of optical and microwave data can map a smallholder farming landscape with a maximum accuracy of 97.71 percent. The third objective was to identify factors that influence crop yields and crop losses in order to improve IBCI design. Results demonstrated that the pervasive notion that low yields in smallholder farms are related to rainfall is an oversimplification. Factors including fertilizer use, seed variety, soil properties, soil moisture, growing degree-days, management, and socioeconomic conditions are some of the most important factors influencing crop yields and crop losses in smallholder farming systems. This shows why IBCI needs to be part of a comprehensive risk management system that understands and approaches smallholder crop farming as complex by linking insurance with advisories and input supplies. Improved inputs and good farming practices could reduce the influence of non-weather factors on crop losses, and thereby reduce basis risk in weather-based index insurance (WII) contracts. The fourth objective of this study was to assess how well the combination of synthetic aperture radar (SAR) and optical indices estimate soil moisture. As stated earlier, soil moisture was found to be one of the most important factors affecting crop yields. Although this method better estimated soil moisture over the first half of the growing season, estimation accuracies were comparable to those found in studies that had used similar datasets (RMSE = 0.043 m3 m-3, MAE = 0.034 m3 m- 3). Further interrogation of interaction effects between the variables used in this study and consideration of other factors that affect SAR backscatter could improve the method. More importantly, incorporating high-resolution satellite-based monitoring of soil moisture into IBCI could potentially reduce basis risk. The fifth objective of this study was to develop an IBCI for smallholder crop farming systems. The proposed IBCI scheme covers maize and derives index thresholds from crop water requirements and satellite-based rainfall estimates. It covers rainfall deficits over the vegetative, mid-season, and late-season stages of maize growth. The key contribution of this system is the derivation of index thresholds from CWR and site-specific rainfall conditions. The widely used approach, which calibrates IBCI by correlating yields and rainfall, exposes contracts to basis risk because, by simply correlating yield and rainfall data, it overlooks the influence of non-weather factors on crop yields and losses. The proposed system must be linked or bundled with non-weather variables that affect crop yields. Effectively, this means that the insurance must be linked or bundled with advisories and input supplies to address the influence of non-weather factors on crop losses. This system also incorporates a crop area-mapping component, which was found to be lacking in many IBCI systems. In conclusion, an IBCI that is based on crop water requirements, which incorporates crop area mapping and links insurance with non-weather crop yield-determining factors, is potentially capable of improving crop insurance for smallholder farming systems. , Thesis (PhD) -- Faculty of Science and Agriculture, 2021
- Full Text:
- Date Issued: 2021-10
Coastal urban climate change adaptation and disaster risk reduction assessment: the case of East London city, South Africa
- Busayo, Emmanuel Tolulope https://orcid.org/ 0000-0002-9274-2145
- Authors: Busayo, Emmanuel Tolulope https://orcid.org/ 0000-0002-9274-2145
- Date: 2021-05
- Subjects: Climate change mitigation , Climatic changes , Emergency management
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10353/20938 , vital:46756
- Description: The increasing incidences of climate change and its registered negative effects have disturbed the entire world, with the coastal areas being the worst hit. Given the fact that coastal areas are becoming centres of global population settlement. An attempt to explore climate change-related disasters and risks is an important aspect in building communities' adaptation and resilience, especially for the most vulnerable global south. Consequently, climate change adaptation (CCA) and disaster risk reduction (DRR) have become fundamentally linked to offering sustainable solutions to address climate change and related disaster risk problems witnessed frequently in recent years. However, the assessment of synergy between CCA and DRR for coastal areas remains fragmented, vague and limited, especially for Sub-Saharan Africa and thus the need for exploration. Furthermore, the urban populace and planning stakeholders are grappling with the challenges of seeking ways to integrate adaptation measures into human livelihoods and planning systems. Also, considering complex issues inhibiting sustainable planning, for example, poor communication of climate risks affecting coastal areas, little records of hazards disclosure and disaster history, inundation and/or sea level rise etc warranted further investigation. Accordingly, the synergies between CCA and DRR in addressing various climate change-related disaster risks, especially for the coastal areas and cities was explored in this study. To this end, given the complexity of CCA and DRR, trio-theories were adopted, which included Resilience Theory (RT), Social Vulnerability Theory (SVT) and Protective Motivation Theory (PMT) as the study’s theoretical underpinnings using East London Coastal City as a case study. Consequently, a multi-method approach was employed using a review of literature, bibliometric analysis, field survey, geographic information system (GIS), and remote sensing. The first objective reveals that there is a need for convergence and harmonisation of CCA and DRR policy, programme, and practice to improve sustainable planning outcomes. Accordingly, the study proposed the adoption of a problem analysis model (PAM) for place function sustainability and local or community level resilience building. The second objective revealed that the Sendai framework for disaster risk reduction has not been fully operationalised at the local and global scales. However, in South Africa, there are efforts to streamline DRR across manifold sectors through the Integrated Urban Development Framework (IUDF). Therefore, disaster risk managers and climate change adaptation stakeholders at the local level need to embrace the position of the SFDRR to possibly offer sound and sustainable results to the most vulnerable. In addition, a bibliometric analysis on climate change adaptation from 1996 – 2019 highlights the need for more African countries' engagement and cross-collaboration between developing and developed countries in CCA research to advance sustainable solutions and improve resilience. The third objective revealed the need for more awareness, flexibility, and adaptability among stakeholders at various levels as fundamental ingredients for CCA and DRR sustainable planning outcomes. The fourth objective highlighted that floods were recorded as the most predominant hydro-meteorological hazard (n=118, 81.9percent) in the East London, coastal city. Finally, the fifth objective portrayed that many communities, populace, buildings (types), and areas are exposed to flood disaster risks, especially, communities such as Nahoon Park Valley, Sunrise on Sea, Beacon Bay, Buffalo, Gonubie, and East London are among the most vulnerable. The study recommends that early action and warning systems should be adopted, and allocation proper building codes to boost awareness to reduce the potential flood disaster risks. Moreover, the study reveals the significance of local flood disaster risk mapping in advancing CCA and DRR to ensure the implementation of coherent spatial planning for sustainable planning outcomes. The overall lessons learnt from this study are vital in contributing to the attainment of the sustainable development goals (SDGs) such as goal 11: sustainable cities and communities, and goal 13: climate action, including the seven targets and four priorities for action of the Sendai framework at a local level. The study results are deemed critical in guiding city planners, decision-makers, disaster risk managers, local communities among others towards the development of a more resilient coastal community. In general, the study calls for the integration of CCA and DRR initiatives to be premised on PAM for sustainable planning outcomes to achieve sustainable development goals and reduction of fatalities from climate-related disasters. , Thesis (PhD) -- Faculty of Science and Agriculture, 2021
- Full Text:
- Date Issued: 2021-05
- Authors: Busayo, Emmanuel Tolulope https://orcid.org/ 0000-0002-9274-2145
- Date: 2021-05
- Subjects: Climate change mitigation , Climatic changes , Emergency management
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10353/20938 , vital:46756
- Description: The increasing incidences of climate change and its registered negative effects have disturbed the entire world, with the coastal areas being the worst hit. Given the fact that coastal areas are becoming centres of global population settlement. An attempt to explore climate change-related disasters and risks is an important aspect in building communities' adaptation and resilience, especially for the most vulnerable global south. Consequently, climate change adaptation (CCA) and disaster risk reduction (DRR) have become fundamentally linked to offering sustainable solutions to address climate change and related disaster risk problems witnessed frequently in recent years. However, the assessment of synergy between CCA and DRR for coastal areas remains fragmented, vague and limited, especially for Sub-Saharan Africa and thus the need for exploration. Furthermore, the urban populace and planning stakeholders are grappling with the challenges of seeking ways to integrate adaptation measures into human livelihoods and planning systems. Also, considering complex issues inhibiting sustainable planning, for example, poor communication of climate risks affecting coastal areas, little records of hazards disclosure and disaster history, inundation and/or sea level rise etc warranted further investigation. Accordingly, the synergies between CCA and DRR in addressing various climate change-related disaster risks, especially for the coastal areas and cities was explored in this study. To this end, given the complexity of CCA and DRR, trio-theories were adopted, which included Resilience Theory (RT), Social Vulnerability Theory (SVT) and Protective Motivation Theory (PMT) as the study’s theoretical underpinnings using East London Coastal City as a case study. Consequently, a multi-method approach was employed using a review of literature, bibliometric analysis, field survey, geographic information system (GIS), and remote sensing. The first objective reveals that there is a need for convergence and harmonisation of CCA and DRR policy, programme, and practice to improve sustainable planning outcomes. Accordingly, the study proposed the adoption of a problem analysis model (PAM) for place function sustainability and local or community level resilience building. The second objective revealed that the Sendai framework for disaster risk reduction has not been fully operationalised at the local and global scales. However, in South Africa, there are efforts to streamline DRR across manifold sectors through the Integrated Urban Development Framework (IUDF). Therefore, disaster risk managers and climate change adaptation stakeholders at the local level need to embrace the position of the SFDRR to possibly offer sound and sustainable results to the most vulnerable. In addition, a bibliometric analysis on climate change adaptation from 1996 – 2019 highlights the need for more African countries' engagement and cross-collaboration between developing and developed countries in CCA research to advance sustainable solutions and improve resilience. The third objective revealed the need for more awareness, flexibility, and adaptability among stakeholders at various levels as fundamental ingredients for CCA and DRR sustainable planning outcomes. The fourth objective highlighted that floods were recorded as the most predominant hydro-meteorological hazard (n=118, 81.9percent) in the East London, coastal city. Finally, the fifth objective portrayed that many communities, populace, buildings (types), and areas are exposed to flood disaster risks, especially, communities such as Nahoon Park Valley, Sunrise on Sea, Beacon Bay, Buffalo, Gonubie, and East London are among the most vulnerable. The study recommends that early action and warning systems should be adopted, and allocation proper building codes to boost awareness to reduce the potential flood disaster risks. Moreover, the study reveals the significance of local flood disaster risk mapping in advancing CCA and DRR to ensure the implementation of coherent spatial planning for sustainable planning outcomes. The overall lessons learnt from this study are vital in contributing to the attainment of the sustainable development goals (SDGs) such as goal 11: sustainable cities and communities, and goal 13: climate action, including the seven targets and four priorities for action of the Sendai framework at a local level. The study results are deemed critical in guiding city planners, decision-makers, disaster risk managers, local communities among others towards the development of a more resilient coastal community. In general, the study calls for the integration of CCA and DRR initiatives to be premised on PAM for sustainable planning outcomes to achieve sustainable development goals and reduction of fatalities from climate-related disasters. , Thesis (PhD) -- Faculty of Science and Agriculture, 2021
- Full Text:
- Date Issued: 2021-05
Monitoring the impact of deforestation on an aquatic ecosystem using remote sensing: a case study of the Mngazana mangrove forest in the eastern cape province.
- Authors: Madasa, Akhona
- Date: 2020-12
- Subjects: Remote sensing , Mangrove forests , Climatic changes
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/20815 , vital:46598
- Description: Coastal mangrove vegetation at Mngazana continues to be threatened and reduced periodically due to unmonitored harvesting. Covering an area of 148ha, the Mngazana mangrove forest remains unreserved, thus, research on the Mngazana mangroves is essential in order to monitor their state and sustainable management. Since in-situ monitoring of mangrove areas is both challenging and time-consuming, remote sensing technologies have been used to monitor these ecosystems. This study was carried out to monitor the impact of deforestation using ASTER satellite images over ten years: from 2008 - 2018. Validation was carried out by comparing classification results with the ground-referenced data, which yielded satisfactory agreement, with an overall accuracy of 94.64 percent and Kappa coefficient of 0.93 for 2008; and in 2009, the overall accuracy was 88.62 percent and a Kappa coefficient of 0.85. While the overall accuracy of 95.08 percent and a Kappa coefficient of 0.92 for 2016 and 2018 were observed, the overall accuracy of 93.58 percent and a Kappa coefficient of 0.91 was yielded. NDVI and SAVI indices were used as monitoring indicators. The results obtained in the study indicated that the canopy density of the mangrove forest remained unchanged in the years under investigation. However, insignificant changes in canopy density were identified between 2009 and 2016. , Thesis (MSc) (Applied Remote Sensing & GIS) -- University of Fort Hare, 2021
- Full Text:
- Date Issued: 2020-12
- Authors: Madasa, Akhona
- Date: 2020-12
- Subjects: Remote sensing , Mangrove forests , Climatic changes
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/20815 , vital:46598
- Description: Coastal mangrove vegetation at Mngazana continues to be threatened and reduced periodically due to unmonitored harvesting. Covering an area of 148ha, the Mngazana mangrove forest remains unreserved, thus, research on the Mngazana mangroves is essential in order to monitor their state and sustainable management. Since in-situ monitoring of mangrove areas is both challenging and time-consuming, remote sensing technologies have been used to monitor these ecosystems. This study was carried out to monitor the impact of deforestation using ASTER satellite images over ten years: from 2008 - 2018. Validation was carried out by comparing classification results with the ground-referenced data, which yielded satisfactory agreement, with an overall accuracy of 94.64 percent and Kappa coefficient of 0.93 for 2008; and in 2009, the overall accuracy was 88.62 percent and a Kappa coefficient of 0.85. While the overall accuracy of 95.08 percent and a Kappa coefficient of 0.92 for 2016 and 2018 were observed, the overall accuracy of 93.58 percent and a Kappa coefficient of 0.91 was yielded. NDVI and SAVI indices were used as monitoring indicators. The results obtained in the study indicated that the canopy density of the mangrove forest remained unchanged in the years under investigation. However, insignificant changes in canopy density were identified between 2009 and 2016. , Thesis (MSc) (Applied Remote Sensing & GIS) -- University of Fort Hare, 2021
- Full Text:
- Date Issued: 2020-12
Assessment of the human health implications of climate variability in East London, Eastern Cape, South Africa
- Orimoloye, Israel Ropo https://orcid.org/0000-0001-5058-2799
- Authors: Orimoloye, Israel Ropo https://orcid.org/0000-0001-5058-2799
- Date: 2018-05
- Subjects: Climatic changes , Global temperature changes
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10353/25360 , vital:64224
- Description: Impacts associated with climate variability and extreme heat are already obvious in varying degrees and expected to be disruptive in the near future across the globe especially in the urban regions. Urban areas have distinctive features that leave their residents and properties vulnerable to extreme climate events. Global temperatures continue to change, reaching new levels almost every year for the past two decades. However, even though the causes are debated it is evident that climate variability is real. Climate variability and disaster risk are threats to human health that adversely reinforce each other. Better knowledge on the association between climate change, variability and extreme weather-related illness is needed and can aid strategies to reduce vulnerabilities. The impacts of climate variability on the health of residents in East London (EL) area in the Eastern Cape Province, South Africa were explored through four interdependent research segments. The first section examined the climate variability and urban surface thermal characteristics implication on human health using Remote Sensing (RS) and Geographic Information System (GIS) techniques. Remote sensing was used to assess the Land Surface Temperature (LST) and estimated Radiation (R) of East London area from Landsat Thematic Mapper (TM) images for 1986, 1996, 2006 as well as from Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) for 2016 spanning a period of 30 years. Rapid urbanization and land cover changes in this area have contributed significantly to this drastic change in the natural land surface characteristics (increased land surface temperature and surface solar radiation). For instance, vegetation cover declined by about 358.812km2 while built-up areas increased by 175.473km2 during this period which correlates with the area thermal characteristics changes. Radiation levels also increased over the years with values exceeding the global solar radiation index. Exposure to increased surface radiation poses risks of heat stroke, skin cancer and heart disease to the local population. Consequently, this study provides pertinent information on human health sustainability and epidemiological case management. The second part explored past temperature and humidity trends (1986-2016) and projects future trends (2017-2030). The historical data of meteorological variables were obtained from the archives of the South African Weather Service and analyzed using the ordinary least square regression model in GRETL (GNU Regression Econometric and Time-series Library) statistical software. This study discovered a local consistency between models and the observations add to existing knowledge and this is crucial in knowing the shifts in climatic change as well as recognizing variability and its conflicting effects on human health, environment, agriculture, ecological sustainability and socioeconomic status in the region. The third segment assessed the potential impacts of climate variability on health using existing heat indices during the study period. The results demonstrated that in East London from 1986 to 2016 during summer and autumn (December to May) of various years exceeded high heat index values. It is obvious that summer and autumn months are more vulnerable to heat extreme and humidex. The humidex and Heat Index (HI) increased annually by 0.03percent and 0.9percent respectively throughout the study period. The increment in the various indices showed highly significant ill-health and environmental impacts on humans especially with prolonged exposure. The last segment appraised the association between climatic elements and epidemiological incidences of the study area between 2012 and 2016. The epidemiology incidences data were obtained from the archives of the Cecilia Makiwane Hospital in East London area and National Tertiary Service Grant (NTSG) database for the period. The results have showed that there exists significant effects of climate variability on the health of East London residents and these have been identified to have negative impacts on health of the people in the area. This study also revealed noticeable impacts of extreme heat on human health and a positive correlation between meteorological components (HI and temperature) and epidemiological cases (cardiovascular, skin cancer and diarrhea) during the study period. , Thesis (PhD) -- Faculty of Science and Agriculture, 2018
- Full Text:
- Date Issued: 2018-05
- Authors: Orimoloye, Israel Ropo https://orcid.org/0000-0001-5058-2799
- Date: 2018-05
- Subjects: Climatic changes , Global temperature changes
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10353/25360 , vital:64224
- Description: Impacts associated with climate variability and extreme heat are already obvious in varying degrees and expected to be disruptive in the near future across the globe especially in the urban regions. Urban areas have distinctive features that leave their residents and properties vulnerable to extreme climate events. Global temperatures continue to change, reaching new levels almost every year for the past two decades. However, even though the causes are debated it is evident that climate variability is real. Climate variability and disaster risk are threats to human health that adversely reinforce each other. Better knowledge on the association between climate change, variability and extreme weather-related illness is needed and can aid strategies to reduce vulnerabilities. The impacts of climate variability on the health of residents in East London (EL) area in the Eastern Cape Province, South Africa were explored through four interdependent research segments. The first section examined the climate variability and urban surface thermal characteristics implication on human health using Remote Sensing (RS) and Geographic Information System (GIS) techniques. Remote sensing was used to assess the Land Surface Temperature (LST) and estimated Radiation (R) of East London area from Landsat Thematic Mapper (TM) images for 1986, 1996, 2006 as well as from Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) for 2016 spanning a period of 30 years. Rapid urbanization and land cover changes in this area have contributed significantly to this drastic change in the natural land surface characteristics (increased land surface temperature and surface solar radiation). For instance, vegetation cover declined by about 358.812km2 while built-up areas increased by 175.473km2 during this period which correlates with the area thermal characteristics changes. Radiation levels also increased over the years with values exceeding the global solar radiation index. Exposure to increased surface radiation poses risks of heat stroke, skin cancer and heart disease to the local population. Consequently, this study provides pertinent information on human health sustainability and epidemiological case management. The second part explored past temperature and humidity trends (1986-2016) and projects future trends (2017-2030). The historical data of meteorological variables were obtained from the archives of the South African Weather Service and analyzed using the ordinary least square regression model in GRETL (GNU Regression Econometric and Time-series Library) statistical software. This study discovered a local consistency between models and the observations add to existing knowledge and this is crucial in knowing the shifts in climatic change as well as recognizing variability and its conflicting effects on human health, environment, agriculture, ecological sustainability and socioeconomic status in the region. The third segment assessed the potential impacts of climate variability on health using existing heat indices during the study period. The results demonstrated that in East London from 1986 to 2016 during summer and autumn (December to May) of various years exceeded high heat index values. It is obvious that summer and autumn months are more vulnerable to heat extreme and humidex. The humidex and Heat Index (HI) increased annually by 0.03percent and 0.9percent respectively throughout the study period. The increment in the various indices showed highly significant ill-health and environmental impacts on humans especially with prolonged exposure. The last segment appraised the association between climatic elements and epidemiological incidences of the study area between 2012 and 2016. The epidemiology incidences data were obtained from the archives of the Cecilia Makiwane Hospital in East London area and National Tertiary Service Grant (NTSG) database for the period. The results have showed that there exists significant effects of climate variability on the health of East London residents and these have been identified to have negative impacts on health of the people in the area. This study also revealed noticeable impacts of extreme heat on human health and a positive correlation between meteorological components (HI and temperature) and epidemiological cases (cardiovascular, skin cancer and diarrhea) during the study period. , Thesis (PhD) -- Faculty of Science and Agriculture, 2018
- Full Text:
- Date Issued: 2018-05
- «
- ‹
- 1
- ›
- »