OntoCSA: A Climate-Smart Agriculture Ontology

Introduction

Climate change poses serious threats to the environment and will negatively impacts human beings, especially agricultural yields. Many threats such as higher carbon dioxide levels, an increase in atmospheric average temperature, varied rainfall patterns, and others can affect agriculture (Raza et al., 2019). In turn, agriculture affects climate change through greenhouse gas emissions and injudicious agricultural practices by humans (Smith & Gregory, 2013). Climate smart agriculture (CSA) is a set of strategies that was introduced in 2010 to tackle the effect of climate change in agriculture (Amin et al., 2015; FAO, 2017). It is a new way of practicing agriculture through the implementation of various techniques that increase productivity in a sustainable manner to achieve food security, provide sustainable solutions and reduce the contributions of agriculture to climate change (Lipper et al., 2014; Amin et al., 2015).

CSA is a knowledge intensive domain that requires innovative and “science-based solutions” (Torquebiau et al., 2018; Lewis & Rudnic, 2019) from multidisciplinary and collaborative research (Lipper et al., 2014; Andrieu et al., 2019; Ardakani et al., 2019; Torquebiau et al., 2018; Lewis & Rudnic, 2019). It is a knowledge intensive field in that, many actors from different disciplines such as scientists, academics, farmers, growers, land managers, agro foresters, livestock keepers, fishers, resource managers, NGOs, and policymakers/stakeholders need to collaborate for its successful development and adoption (Lipper et al., 2014; Torquebiau et al., 2018; Lewis & Rudnic, 2019). In such a context, Information and Communication Technologies (ICTs) can play a vital role for the modeling, automation, access, reuse and sharing of information/data for finding suitable solutions as well as sharing of best practices. One suitable ICTs solution for the CSA domain is the use of Semantic Web technologies including ontology, RDFS (Resource Description Framework Schema) and OWL, to formalize the CSA domain in machine-readable formats that can be integrated into the web for the storage, open and automated access and sharing of CSA information/data. RDFS and OWL are two standard languages for representing ontologies; they provide features that enable the representation of ontologies in a machine-readable format to be automatically queried and reasoned to infer new knowledge over the web (Antoniou et al., 2005; Domingue et al., 2011).

An ontology represents a knowledge domain formally, through its entities, concepts, objects and the relationships between them (Busse et al., 2015). Ontology represents common and shared terminologies of a domain (Nidhi et al., 2021) in both conceptual and machine-readable forms, and, thereby, provides a shared comprehension of the representation of information to the community of peoples as well as enables automated processing of knowledge in the domain. Ontology enables the integration of subsystems of a domain and facilitates their interoperability (Taye, 2010; Roussey et al., 2010; Goldstein et al., 2019; Nidhi et al., 2021)). In fact, the subsystems of a domain that use the same ontology process the same terminologies, which may facilitate their integration and interoperability. Alternatively, if a separate ontology has been developed for each subsystem, the terminologies in the ontologies of subsystems can be matched to integrate the subsystems (Goldstein et al., 2019).