The Politics of Upgrading in Global Value Chains: The Case of Rwanda’s Coffee Sector

Behuria, Pritish

doi:10.2139/ssrn.3275240

Cited by 2 publications

(1 citation statement)

References 81 publications

(102 reference statements)

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“…13 Interviews, Rwanda Private Sector Federation (PSF), May 2013 and January 2015; annual reports. 14 A new strand of the GVC/GPN literature explicitly focuses on the state's role in GVC/GPNs (Smith, 2015;Horner, 2017;Mayer and Phillips, 2017;Alford and Phillips, 2018;Behuria, 2018c).…”

Section: Intellectual Trendsmentioning

confidence: 99%

African Development and the Marginalisation of Domestic Capitalists

Behuria

2019

SSRN Journal

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The revival of industrial policy discussions has operated in parallel to reports of increasing domestic wealth accumulation across the African continent. Regional and continent-wide industrialisation has begun to be rhetorically linked to discussions of regional common markets and through the African Continental Free Trade Area. Yet, there is barely any mention of integrating African capital into the African industrial policy agenda. Where such discussions have appeared, they have emerged through the 'Africapitalism' narrative, which ignores the role of the state and politics in supporting and sustaining domestic business groups. Instead, the re-imagination of industrial policy on the continent relies on foreign investors, particularly the relocation of Chinese industry to various parts of the continent. This paper has two core objectives. The first is to explain why the study of African capitalists-popular in the 1980s and 1990s-has remained relatively dormant since then. Dominant narratives-through neopatrimonalism and dependency-inspired arguments-have been pessimistic about the potential of African capitalists to deliver structural transformation. Gradually, these narratives, alongside intellectual trends within mainstream social science and African studies, have discouraged the study of politics of state-business relations in Africa. Yet African capitalists have become increasingly prominent in popular culture. Many of the wealthiest and most prominent capitalists have emerged through owning diversified business groups across the continent. This paper argues that more attention should be dedicated to the study of the politics of the emergence and sustenance of African diversified business groups (DBGs). To achieve this goal, a fluid categorisation of DBGs is introduced, building on Ben Ross Schneider's previous work. By examining three country case studies-Rwanda, Kenya and Tanzania-this paper highlights how a range of DBGs are emerging across three very different political contexts.

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Section: Intellectual Trendsmentioning

confidence: 99%

African Development and the Marginalisation of Domestic Capitalists

Behuria

2019

SSRN Journal

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Revolutionizing Coffee Farming: A Mobile App with GPS-Enabled Reporting for Rapid and Accurate On-Site Detection of Coffee Leaf Diseases Using Integrated Deep Learning

Hitimana,

Kuradusenge,

Sinayobye

et al. 2024

Software

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Coffee leaf diseases are a significant challenge for coffee cultivation. They can reduce yields, impact bean quality, and necessitate costly disease management efforts. Manual monitoring is labor-intensive and time-consuming. This research introduces a pioneering mobile application equipped with global positioning system (GPS)-enabled reporting capabilities for on-site coffee leaf disease detection. The application integrates advanced deep learning (DL) techniques to empower farmers and agronomists with a rapid and accurate tool for identifying and managing coffee plant health. Leveraging the ubiquity of mobile devices, the app enables users to capture high-resolution images of coffee leaves directly in the field. These images are then processed in real-time using a pre-trained DL model optimized for efficient disease classification. Five models, Xception, ResNet50, Inception-v3, VGG16, and DenseNet, were experimented with on the dataset. All models showed promising performance; however, DenseNet proved to have high scores on all four-leaf classes with a training accuracy of 99.57%. The inclusion of GPS functionality allows precise geotagging of each captured image, providing valuable location-specific information. Through extensive experimentation and validation, the app demonstrates impressive accuracy rates in disease classification. The results indicate the potential of this technology to revolutionize coffee farming practices, leading to improved crop yield and overall plant health.

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The Politics of Upgrading in Global Value Chains: The Case of Rwanda’s Coffee Sector

Cited by 2 publications

References 81 publications

African Development and the Marginalisation of Domestic Capitalists

African Development and the Marginalisation of Domestic Capitalists

Revolutionizing Coffee Farming: A Mobile App with GPS-Enabled Reporting for Rapid and Accurate On-Site Detection of Coffee Leaf Diseases Using Integrated Deep Learning

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