An Exploratory Study on Agriculture Ontology: A Global Perspective

Jebaraj, Johannah; Sathiaseelan, J. G. R.

doi:10.23956/ijarcsse/v7i6/0148

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…Agricultural ontologies seek to solve such incompatibilities by establishing a shared vocabulary across different datasets, permitting easier aggregation, comparison, and analysis while ensuring that data are machine readable and interoperable across platforms (Baker et al, 2019). There are a few widely accepted agri-food ontologies already in existence, and many more under development (Arnaud et al, 2020; Jebaraj and Sathiaseelan, 2017). Some ontologies map crop-specific breeding traits, such as the Oat Ontology developed by the public–private research partnership Oat Global at the University of Minnesota; some are geographically anchored, such as the nationally oriented ontologies developed by the Agriculture Ontology Service Research Group in the Chinese Academy of Agriculture Sciences; while others catalog agronomic practices and techniques, such as CGIAR’s Agronomy Ontology (AgrO).…”

Section: Open Data For Agricultural Development: Elements Of Data Col...mentioning

confidence: 99%

Setting data free: The politics of open data for food and agriculture

Fairbairn

Kish

2023

New Media & Society

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Open data is increasingly being promoted as a route to achieve food security and agricultural development. This article critically examines the promotion of open agri-food data for development through a document-based case study of the Global Open Data for Agriculture and Nutrition (GODAN) initiative as well as through interviews with open data practitioners and participant observation at open data events. While the concept of openness is striking for its ideological flexibility, we argue that GODAN propagates an anti-political, neoliberal vision for how open data can enhance agricultural development. This approach centers values such as private innovation, increased production, efficiency, and individual empowerment, in contrast to more political and collectivist approaches to openness practiced by some agri-food social movements. We further argue that open agri-food data projects, in general, have a tendency to reproduce elements of “data colonialism,” extracting data with minimal consideration for the collective harms that may result, and embedding their own values within universalizing information infrastructures.

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Section: Open Data For Agricultural Development: Elements Of Data Col...mentioning

confidence: 99%

Setting data free: The politics of open data for food and agriculture

Fairbairn

Kish

2023

New Media & Society

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“…This is witnessed in recent reviews of the use of semantic technologies in agriculture (Jebaraj & Sathiaseelan, 2017;Drury et al, 2019) and a recent review and evaluation of existing agricultural ontologies (Goldstein et al, 2019). Many studies have specifically reported on the use of ontologies in agriculture (Hu et al, 2011;Kim et al, 2013;Dineshkumar et al, 2014;Harika & Bhargav, 2015;Sivamani et al, 2016;Jebaraj & Sathiaseelan, 2017;Ngo et al, 2018;Goldstein et al, 2019;Nidhi et al, 2021;Jachimczyk et al, 2021) and climate change (Diop & Lo, 2013;Pileggi & Lamia, 2020) domains. Kim et al (2013), Harika and Bhargav (2015) and Sivamani et al (2016) developed context aware ontologies for seamless services access in vertical farming.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although the semantic modelling of agriculture has attracted researchers in recent years (Ramar & Gurunathan, 2017;Jebaraj & Sathiaseelan, 2017;Goldstein et al, 2019;Drury et al, 2019;Kessler et al, 2021;Roussey et al, 2021;Abayomi-Alli et al, 2021) and has resulted in the development of ontologies for agriculture (Hu et al, 2011;Kim et al, 2013;Jebaraj & Sathiaseelan, 2017;Ngo et al, 2018;Arnaud et al, 2020;Jachimczyk et al, 2021;Nidhi et al, 2021), no research to date has focused on building a knowledge base ontology for the CSA domain. This study attempts to fill this gap through the development of a Climate Smart Agriculture Ontology (OntoCSA).…”

Section: Introductionmentioning

confidence: 99%

OntoCSA

Fonou-Dombeu

Naidoo

Ramnanan

et al. 2021

International Journal of Agricultural and Environmental Information Systems

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The modelling of agriculture with ontologies has been of interest to many authors in the past years. However, no research, currently, has focused on building a knowledge base ontology for the Climate Smart Agriculture (CSA) domain. This study attempts to fill this gap through the development of a Climate Smart Agriculture Ontology (OntoCSA). Information was gathered from secondary sources including websites, published research articles and reports as well as related ontologies, to formalize the OntoCSA ontology in Description Logics (DLs). The OntoCSA ontology was developed in Web Ontology Language (OWL) with Protégé. Furthermore, the OntoCSA ontology was successfully validated with the HermiT reasoner within Protégé. The resulting OntoCSA ontology is a machine-readable model of CSA that can be leveraged in web-based applications for the storage, open and automated access and sharing of CSA information/data, for research and dissemination of best practices

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“…Researchers have developed different models in order to explain the farming behavior (Dury et al, 2012;Jones et al, 2017a). According to the common ground of these models, the fivestage farming decision process includes (a) the domain recognition, (b) product characterization, (c) farm management, (d) limiting factors control, and (e) post-production evaluation (Antle et al, 2017;Kyaruzi et al, 2019;Jebaraj & Sathiaseelan, 2017). Figure 1 depicts the framework for FAABB as advocated in literature (Kyaruzi et al, 2019b).…”

Section: Farming As a Business Stagesmentioning

confidence: 99%

“…These difficult concepts and relationships have been modelled using Ontologies in various capacities in different countries. A review of the various ontology-based applications developed specifically for the domain of agriculture is presented by Jebaraj and Sathiaseelan (2017). The authors in this work also discuss various agriculture ontologies created by specific developing countries.…”

Section: Agricultural Ontology Generator Componentmentioning

confidence: 99%

A novel framework for the use of mobile applications in inducing smallholder-farmers’ response to farming as a business via benchmarking

Kyaruzi¹

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Farming as a business (FAAB) is currently acknowledged as the best route out of poverty for the majority of rural poor farmers in developing countries like Tanzania. Supporting farmers to participate in FAAB translates into assisting them to go through a farming life cycle of five interrelated stages namely: Agricultural domains recognition, farm characterization, simulation of predictive solutions, identification of limiting factors, and post production evaluation. Managing FAAB processes, resources and products, requires benchmarking as its analytical tool; hence, the concept of farming as a business via benchmarking (FAABB). Supporting a farmer to achieve FAABB is the primary role of an Extension Officer (EO). Since FAABB is a data-intensive activity, computational and cognitive limitations of an EO decrease quality and efficiency and increase time spent as well as costs related to facilitating smallholder farmers to achieve FAABB. Several research efforts have demonstrated that mobile apps bring in significant capabilities for helping EOs deal with the challenges associated with FAABB. However, in Tanzania, data capture and codification are the two greater obstacles in developing useful mobile applications, than gaps in conceptual theories or available methods for FAABB. This research takes advantage of available technologies to develop a mobile framework for FAABB that embeds data capture and codification services to support rapid development of domain specific m-apps. The main objective of this research is, therefore, to develop a mobile framework for FAABB (m-FFAABB) that facilitates knowledge capture and codification for rapid development and use of m-apps that induce farmers‟ response to FAABB. The research adopted a Design Science Research (DSR) through Soft System Methodology (SSM). In the reported work, the framework was designed and two corresponding prototypes were developed and evaluated to show the applicability of m-FFAABB. The data collected during the experiments show that the mobile apps developed through the m-FFAABB are useful, well integrated and easy to use. Moreover, statistical analysis of the results indicates that the framework reduces time, costs, and intellectual effort of the EOs.

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An Exploratory Study on Agriculture Ontology: A Global Perspective

Cited by 4 publications

References 12 publications

Setting data free: The politics of open data for food and agriculture

Setting data free: The politics of open data for food and agriculture

OntoCSA

A novel framework for the use of mobile applications in inducing smallholder-farmers’ response to farming as a business via benchmarking

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