De-agrarianization, re-agrarianization and local economic development: re-orientating livelihoods in African artisanal mining communities

This article contributes to the debate on livelihood diversification in rural sub-Saharan Africa, focusing specifically on the growing economic importance of artisanal and small-scale mining (ASM) in the region. The precipitous decline in the value of many export crops and the removal of subsidies on crucial inputs such as fertilizers have made smallholder production unviable, forcing many farmers to ‘branch out’ into non-farm activities to supplement their incomes. One of the more popular destinations for poor farmers is the low-tech ASM sector which, because of its low barriers to entry, has absorbed millions of rural Africans over the past two decades, the majority of whom are engaged in the extraction of near-surface mineral deposits located on concessions that have been demarcated to multinational corporations. The efforts made hitherto to control this illegal mining activity, both through force and regulation, however, have had little effect, forcing many of the region’s governments and private sector partners to ‘re-think’ their approaches. One strategy that has gained considerable attention throughout the region is intensified support for agrarian-orientated activities, many of which, despite the problems plaguing smallholder agricultural sector and challenges with making it more economically sustainable, are being lauded as appropriate ‘alternative’ sources of employment to artisanal mining. After examining where artisanal mining fits into the de-agrarianization ‘puzzle’ in sub-Saharan Africa, the article critiques the efficacy of ‘re-agrarianization’ as a strategy for addressing the region’s illegal mining problem. A case study of Ghana is used to shed further light on these issues.

Poverty and livelihood diversification in rural Liberia: exploring the linkages between artisanal diamond mining and smallholder rice production

This paper provides an account of the changing livelihood dynamics unfolding in diamond-rich territories of rural Liberia. In these areas, many farm families are using the rice harvested on their plots to attract and feed labourers recruited specifically to mine for diamonds. The monies accrued from the sales of all recovered stones are divided evenly between the family and hired hands, an arrangement which, for thousands of people, has proved to be an effective short-term buffer against poverty. A deepened knowledge of these dynamics could be an important step towards facilitating lasting development in Liberia’s highly-impoverished rural areas.

New advances in aircraft MRO services: data mining enhancement

Aircraft Maintenance, Repair and Overhaul (MRO) agencies rely largely on row-data based quotation systems to select the best suppliers for the customers (airlines). The data quantity and quality becomes a key issue to determining the success of an MRO job, since we need to ensure we achieve cost and quality benchmarks. This paper introduces a data mining approach to create an MRO quotation system that enhances the data quantity and data quality, and enables significantly more precise MRO job quotations. Regular Expression was utilized to analyse descriptive textual feedback (i.e. engineer’s reports) in order to extract more referable highly normalised data for job quotation. A text mining based key influencer analysis function enables the user to proactively select sub-parts, defects and possible solutions to make queries more accurate. Implementation results show that system data would improve cost quotation in 40% of MRO jobs, would reduce service cost without causing a drop in service quality.

Diamond mining, rice farming, and a ‘Maggi cube’: a viable survivial strategy in rural Liberia?

Since the conclusion of its 14-year civil war in 2003, Liberia has struggled economically. Jobs are in short supply and operational infrastructural services, such as electricity and running water, are virtually nonexistent. The situation has proved especially challenging for the scores of people who fled the country in the 1990s to escape the violence and who have since returned to re-enter their lives. With few economic prospects on hand, many have elected to enter the artisanal diamond mining sector, which has earned notoriety for perpetuating the country's civil war. This article critically reflects on the fate of these Liberians, many of whom, because of a lack of government support, finances, manpower and technological resources, have forged deals with hired labourers to work artisanal diamond fields. Specifically, in exchange for meals containing locally grown rice and a Maggi (soup) cube, hired hands mine diamondiferous territories, splitting the revenues accrued from the sales of recovered stones amongst themselves and the individual ‘claimholder’ who hired them. Although this cycle—referred to here as ‘diamond mining, rice farming and a Maggi cube’—helps to buffer against poverty, few of the parties involved will ever progress beyond a subsistence level

Improving aircraft maintenance, repair, and overhaul: A novel text mining approach

Aircraft Maintenance, Repair and Overhaul (MRO) feedback commonly includes an engineer’s complex text-based inspection report. Capturing and normalizing the content of these textual descriptions is vital to cost and quality benchmarking, and provides information to facilitate continuous improvement of MRO process and analytics. As data analysis and mining tools requires highly normalized data, raw textual data is inadequate. This paper offers a textual-mining solution to efficiently analyse bulk textual feedback data. Despite replacement of the same parts and/or sub-parts, the actual service cost for the same repair is often distinctly different from similar previously jobs. Regular expression algorithms were incorporated with an aircraft MRO glossary dictionary in order to help provide additional information concerning the reason for cost variation. Professional terms and conventions were included within the dictionary to avoid ambiguity and improve the outcome of the result. Testing results show that most descriptive inspection reports can be appropriately interpreted, allowing extraction of highly normalized data. This additional normalized data strongly supports data analysis and data mining, whilst also increasing the accuracy of future quotation costing. This solution has been effectively used by a large aircraft MRO agency with positive results.