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Assessing the integration of fourth industrial revolution technologies into drought early warning systems for agricultural disaster risk management

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dc.contributor.advisor Moeletsi, M. E.
dc.contributor.advisor Tsubo, M. (Mitsuru)
dc.contributor.author Masupha, Elisa Teboho
dc.date.accessioned 2024-11-27T10:44:17Z
dc.date.available 2024-11-27T10:44:17Z
dc.date.issued 2024-07-28
dc.identifier.uri https://hdl.handle.net/10500/31941
dc.description.abstract The agricultural sector, particularly in developing countries, is highly vulnerable to drought occurrences, which often result in substantial socio-economic losses. In southern Africa, drought frequency has tripled over the last six decades and is projected to worsen due to climate change. This underscores the critical importance of effective drought early warning systems (DEWS), essential for enhancing preparedness before a drought, facilitating decision-making during drought conditions, and enabling stakeholders to implement long-term risk reduction measures following a drought. Therefore, the present study aimed to develop a conceptual framework for a DEWS that integrates modern technologies to enhance its functionality and impact within the context of the fourth industrial revolution (4IR). The research adopted a conceptual approach, employing both empirical and non-empirical methods. These included qualitative analysis, a scoping review, the application of Toulmin’s model of argumentation, and a metric-based approach utilizing South Africa as a case study. Primary data were collected through purposive sampling, with key informants interviewed between June 2021 and April 2022 using a semi-structured questionnaire. Secondary data collection and analysis involved a rigorous process of Boolean keyword searches, bibliometric analysis, and content evaluation. The survey revealed positive advancements in the development of relevant policies such as the Disaster Management Act No. 57 of 2002, yet identified significant concerns regarding localized planning and implementation, particularly across provinces. Notably, there was a lack of tailored drought plans in five of the seven participating provinces and 68% of participants expressed uncertainty regarding the effectiveness of current measures. Main factors included disparities in resource allocation, early warnings not tailored to farmers’ characteristics, overreliance on government support, and utilizing manual methods for documenting field reports. To address these challenges, the study proposed a proactive approach including pre-disaster plans that emphasize mitigation, improving implementation efficiency, building capacity, and establishing formal review mechanisms to enhance the effectiveness of strategies. This approach will therefore establish a conducive environment essential for implementing innovative DEWS effectively. v © Masupha, ET, University of South Africa 2024 According to the bibliometric analysis, the integration of 4IR technologies into drought research began in 2015, with significant contributions from China and the United States. However, the relationships derived from the co-occurrence of keywords predominantly focused on data collection and processing, lacking comprehensive emphasis on all fundamental components of DEWS. Therefore, employing Toulmin’s model of argumentation, the study established applicable links between 4IR technologies and DEWS, resulting in the development of eight interconnected modules. Modules such as smart data networks and advanced analytics presented the use of the Internet of Things, robotics, and big data analytics to enhance real-time data collection and monitoring. The study revealed that artificial intelligence plays a significant role in generating accurate forecasts, tailored warning alerts, and enabling interactive communication. Furthermore, the use of extended reality can enable users to simulate drought scenarios, interact with data, and evaluate the impact of various measures for improved decision-making. The study further assessed South Africa’s current agricultural drought early warning system to determine its effectiveness and feasibility for adopting the 4IR-based DEWS framework. The findings revealed a significant gap in that while the system is proficient in quantifying and monitoring drought, it lacks prioritization of field reports, interactive mapping, and enhancing the response capabilities of agricultural stakeholders. Therefore, the implementation of smart weather stations, unmanned aerial vehicles, and connected wearable devices ensures a more localized and timely collection of data, including weather, crop, irrigation, and farm management activities, to generate tailored information. Moreover, transitioning from current static web pages to Web 3.0 functionalities can facilitate intelligent processing, dissemination, and decision-making among farmers, extension officers, cooperatives, researchers, and policymakers. However, overcoming limitations such as high initial costs, and limited technical expertise, is crucial. Thus, to fully realize the potential of this proposed framework and achieve a more effective, sustainable approach to agricultural drought management, further research, practical implementation, and policy amendments are essential. en
dc.format.extent 1 online resource (xiii, 154 leaves): illustrations (some color) en
dc.language.iso en en
dc.subject Agricultural drought monitoring en
dc.subject Artificial intelligence en
dc.subject Big data en
dc.subject Disaster response en
dc.subject Early warning systems en
dc.subject Fourth industrial revolution en
dc.subject Machine learning en
dc.subject Qualitative research en
dc.subject Internet of Things sensors en
dc.subject Unmanned aerial vehicles en
dc.subject UCTD
dc.subject SDG 9 Industry, Innovation and Infrastructure en
dc.title Assessing the integration of fourth industrial revolution technologies into drought early warning systems for agricultural disaster risk management en
dc.type Thesis en
dc.description.department College of Agriculture and Environmental Sciences en
dc.description.degree D. Phil. (Agriculture) en


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