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
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
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:
- Date Issued: 2013
- 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:
- Date Issued: 2013
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