Assessing the impact of access to climate services on smallholder farmers’ cropping decisions and household food security in Elundini Municipality, Eastern Cape province
- Authors: Yanga-Inkosi, Nocezo
- Date: 2023-11
- Subjects: Climatic changes , Food security -- Climatic factors , Crops and climate
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/28222 , vital:73913
- Description: Climate variability has significant implications for crop production and overall food security. Climate services, which provide tailored and localised climate information, have the potential to enhance farmers' understanding of climate patterns and support informed decision-making. The purpose of the study was to assess the impact of climate services on smallholder farmers’ cropping decisions and household food security. The study adopted a cross-sectional household survey on 217 smallholder crop farming households from Elundini Municipality. Structured questionnaires and face-to-face interviews were used to collect the required data from the farmers. Descriptive analysis was used to identify the types of climate service accessed by smallholder crop farmers in the study area. The probit model was used to examine factors influencing smallholder crop farmers’ access to specific climate services. The household dietary diversity score was used to measure household food security among smallholder crop farmers. The propensity score matching model was used to assess the impact of accessing specific climate services on cropping decisions and household food security. The results indicated that most farmers 77 percent in the study area had access to climate services. The results further showed that many farmers had access to short-term weather forecast 79 percent with very few accessing seasonal forecast 22 percent. The results also revealed that access to both short-term weather and seasonal forecasts is positively influenced by ownership of mobile phones and access to extension services. Similarly, access to short-term weather forecasts is positively influenced by age, monthly income, ownership of radio, timely climate information, and perceiving that climate change has negative effects on crop production. Land size, knowledge of climate change, and climate services accuracy are positive and significant factors influencing access to seasonal forecasts. Most of the smallholder farming households in the study area had higher dietary diversity scores 66 percent. Moreover, access to short-term weather and seasonal forecasts has a positive and significant impact on cropping decisions and household dietary diversity scores. The study concludes that climate services improve cropping decisions and household food security among smallholder crop farmers in Elundini Municipality. The study recommends that there should be investments in awareness programmes that will educate farmers about the importance of climate services and how to acquire and interpret both weather and seasonal forecasts. Stakeholders interested in improving smallholder farmers’ access to climate services should consider whether farmers own smart phones and have access to extension services, arable land, knowledge of climate change, and if the climate services are accurate for smallholder farmers. In addition, to improve crop production and household food security in the face of climate change, access to climate services by smallholder farmers should be considered. , Thesis (MSc) -- Faculty of Science and Agriculture, 2023
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- Authors: Yanga-Inkosi, Nocezo
- Date: 2023-11
- Subjects: Climatic changes , Food security -- Climatic factors , Crops and climate
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/28222 , vital:73913
- Description: Climate variability has significant implications for crop production and overall food security. Climate services, which provide tailored and localised climate information, have the potential to enhance farmers' understanding of climate patterns and support informed decision-making. The purpose of the study was to assess the impact of climate services on smallholder farmers’ cropping decisions and household food security. The study adopted a cross-sectional household survey on 217 smallholder crop farming households from Elundini Municipality. Structured questionnaires and face-to-face interviews were used to collect the required data from the farmers. Descriptive analysis was used to identify the types of climate service accessed by smallholder crop farmers in the study area. The probit model was used to examine factors influencing smallholder crop farmers’ access to specific climate services. The household dietary diversity score was used to measure household food security among smallholder crop farmers. The propensity score matching model was used to assess the impact of accessing specific climate services on cropping decisions and household food security. The results indicated that most farmers 77 percent in the study area had access to climate services. The results further showed that many farmers had access to short-term weather forecast 79 percent with very few accessing seasonal forecast 22 percent. The results also revealed that access to both short-term weather and seasonal forecasts is positively influenced by ownership of mobile phones and access to extension services. Similarly, access to short-term weather forecasts is positively influenced by age, monthly income, ownership of radio, timely climate information, and perceiving that climate change has negative effects on crop production. Land size, knowledge of climate change, and climate services accuracy are positive and significant factors influencing access to seasonal forecasts. Most of the smallholder farming households in the study area had higher dietary diversity scores 66 percent. Moreover, access to short-term weather and seasonal forecasts has a positive and significant impact on cropping decisions and household dietary diversity scores. The study concludes that climate services improve cropping decisions and household food security among smallholder crop farmers in Elundini Municipality. The study recommends that there should be investments in awareness programmes that will educate farmers about the importance of climate services and how to acquire and interpret both weather and seasonal forecasts. Stakeholders interested in improving smallholder farmers’ access to climate services should consider whether farmers own smart phones and have access to extension services, arable land, knowledge of climate change, and if the climate services are accurate for smallholder farmers. In addition, to improve crop production and household food security in the face of climate change, access to climate services by smallholder farmers should be considered. , Thesis (MSc) -- Faculty of Science and Agriculture, 2023
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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
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- 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
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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:
- 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
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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:
- 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
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An exploratory study of the impacts of climate variability on food production availability and access in Chivi district 6, Zimbabwe
- Authors: Gwindi, Raphael
- Date: 2013
- Subjects: Climatic changes , Food security , Agricultural development projects
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/26696 , vital:65852
- Description: The impacts of climate variability have been of global concern for many years. These impacts are affecting economic, social, cultural, agricultural, health and political structures in different countries. Although the impacts of climate variability on agricultural production are being experienced globally, it is generally accepted that developing countries are the worst affected due to a variety of reasons. Given the high susceptibility of developing countries to climate variability, this study maps and analyses the impacts of climate variability on agricultural production, food production, availability and access in Chivi District, Zimbabwe. The study aimed at finding out experiences, so it used a qualitative approach. The study uses in-depth and focus group discussions to collect data. Chivi district is experiencing erratic weather patterns which are impacting agricultural production in general and food production in particular. Consequently, food availability and access is on the decline in the district. Even though smallholder farmers have devised coping and adaptation strategies, this is not sufficient to help them fully deal with the impacts of climate variability. This is due to their limited assets, inadequate technology and climate information among other things. In an attempt to assist these smallholder farmers cope and adapt to the impacts of climate variability, NGOs and Government Departments have instituted a number of community interventions. This assistance includes agricultural extension services, farming input support and provision of climate change information and a lot of other things. In view of these findings, the study recommends the universal adoption and growing of small grain drought resistant crops in climate variability affected Chivi. It further recommends that farmers adopt conservation agriculture, get into partnerships and co-operatives to practice irrigation gardening where those without water sources provide equipment, labour and knowledge. The study also recommends that more climate science research be conducted in Zimbabwe by both NGOs and Government Departments. Furthermore, government and NGOs should provide more support for farmers in the form of climate change related training, knowledge and technology transfer among other things. , Thesis (MSoc) -- Faculty of Social Sciences and Humanities, 2013
- Full Text:
- Authors: Gwindi, Raphael
- Date: 2013
- Subjects: Climatic changes , Food security , Agricultural development projects
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/26696 , vital:65852
- Description: The impacts of climate variability have been of global concern for many years. These impacts are affecting economic, social, cultural, agricultural, health and political structures in different countries. Although the impacts of climate variability on agricultural production are being experienced globally, it is generally accepted that developing countries are the worst affected due to a variety of reasons. Given the high susceptibility of developing countries to climate variability, this study maps and analyses the impacts of climate variability on agricultural production, food production, availability and access in Chivi District, Zimbabwe. The study aimed at finding out experiences, so it used a qualitative approach. The study uses in-depth and focus group discussions to collect data. Chivi district is experiencing erratic weather patterns which are impacting agricultural production in general and food production in particular. Consequently, food availability and access is on the decline in the district. Even though smallholder farmers have devised coping and adaptation strategies, this is not sufficient to help them fully deal with the impacts of climate variability. This is due to their limited assets, inadequate technology and climate information among other things. In an attempt to assist these smallholder farmers cope and adapt to the impacts of climate variability, NGOs and Government Departments have instituted a number of community interventions. This assistance includes agricultural extension services, farming input support and provision of climate change information and a lot of other things. In view of these findings, the study recommends the universal adoption and growing of small grain drought resistant crops in climate variability affected Chivi. It further recommends that farmers adopt conservation agriculture, get into partnerships and co-operatives to practice irrigation gardening where those without water sources provide equipment, labour and knowledge. The study also recommends that more climate science research be conducted in Zimbabwe by both NGOs and Government Departments. Furthermore, government and NGOs should provide more support for farmers in the form of climate change related training, knowledge and technology transfer among other things. , Thesis (MSoc) -- Faculty of Social Sciences and Humanities, 2013
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