Abstract:Recent developments in the field of technology have led to a renewed interest in the field of smart agriculture. The current smart agricultural system produces and depends on large amounts of data, yet, it is hard to process the vast amounts of data using traditional data analysis systems. Big Data technologies have attracted much attention among researchers due to their potential to handle large amounts of data. Thus, for numerous possibilities and powerful data processing capabilities, Big Data continues to … Show more
“…The non-agile approach to data has been widely used and validated to collect accurate information on agriculture in complex environments including those characterized by small farm size, remote plots, multiple cropping systems, and poorly demarcated land boundaries [28,29]. According to Sourav et al (2020) [30], large volumes of data are generated using traditional systems for data collection in agricultural contexts, but it is challenging to process the data using traditional data analysis. Non-agile approaches are disadvantaged by the costs associated with both the data gathering and the data analysis in the data generation process.…”
Section: Non-agile Data Versus Agile Datamentioning
Having reliable and timely or ongoing field data from development projects or supply chains is a perennial challenge for decision makers. This is especially true for those operating in rural areas where traditional data gathering and analysis approaches are costly and difficult to operate while typically requiring so much time that their findings are useful mostly as learning after the fact. A series of innovations that we refer to as Agile Data are opening new frontiers of timeliness, cost, and accuracy. They are leveraging a range of technological advances to do so. This paper explores the differences between traditional and agile approaches and offers insights into costs and benefits by drawing on recent field research in agriculture conducted by diverse institutions such as the World Bank (WB), World Food Program (WFP), United States Agency for International Development (USAID), and the Committee on Sustainability Assessment (COSA). The evidence collected in this paper about agile approaches—including those relying on internet and mobile-based data collection—contributes to define a contemporary dimension of data and analytics that can contribute to more optimal decision-making. Providing a theoretical, applied, and empirical foundation for the collection and use of Agile Data can offer a means to improve the management of development initiatives and deliver new value, as participants or beneficiaries are better informed and can better respond to a fast-changing world.
“…The non-agile approach to data has been widely used and validated to collect accurate information on agriculture in complex environments including those characterized by small farm size, remote plots, multiple cropping systems, and poorly demarcated land boundaries [28,29]. According to Sourav et al (2020) [30], large volumes of data are generated using traditional systems for data collection in agricultural contexts, but it is challenging to process the data using traditional data analysis. Non-agile approaches are disadvantaged by the costs associated with both the data gathering and the data analysis in the data generation process.…”
Section: Non-agile Data Versus Agile Datamentioning
Having reliable and timely or ongoing field data from development projects or supply chains is a perennial challenge for decision makers. This is especially true for those operating in rural areas where traditional data gathering and analysis approaches are costly and difficult to operate while typically requiring so much time that their findings are useful mostly as learning after the fact. A series of innovations that we refer to as Agile Data are opening new frontiers of timeliness, cost, and accuracy. They are leveraging a range of technological advances to do so. This paper explores the differences between traditional and agile approaches and offers insights into costs and benefits by drawing on recent field research in agriculture conducted by diverse institutions such as the World Bank (WB), World Food Program (WFP), United States Agency for International Development (USAID), and the Committee on Sustainability Assessment (COSA). The evidence collected in this paper about agile approaches—including those relying on internet and mobile-based data collection—contributes to define a contemporary dimension of data and analytics that can contribute to more optimal decision-making. Providing a theoretical, applied, and empirical foundation for the collection and use of Agile Data can offer a means to improve the management of development initiatives and deliver new value, as participants or beneficiaries are better informed and can better respond to a fast-changing world.
“…1 ). This involves truly cross disciplinary innovation efforts for example, genetic modifications to increase crops’ resilience 5 , different antibacterial strategies to improve food security 6 , sensors 7 and big data advances for precision farming 8 , artificial intelligence (AI) and block chain technologies to manage food supply 9 , advancement in material science to develop functional packaging materials 10 , tissue engineering in the development of alternative protein sources 11 etc. Fig.…”
Harnessing the potential of considerable food security efforts requires the ability to translate them into commercial applications. This is particularly true for alternative protein sources and startups being on the forefront of innovation represent the latest advancements in this field.
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