The Semantic Web as a Platform Against Risk and Uncertainty in Agriculture

In this article, we discuss existing literature on DSS in agriculture, on DSS that use data available in the Semantic Web, and on Semantic Web initiatives focusing on agriculture information. Our goal is to assess the readiness of the Semantic Web as a platform to empower DSS that can keep risk and uncertainty in agriculture under control. Key agricultural activities targeted by DSS reported in literature are nutrient management, insect and pest management, land use and planning, environmental change and forecasting, and water and drought management. The most relevant use of Semantic Web in DSS, is in data analysis, as a means of making DSS more intelligent. There are initiatives to produce vocabularies and semantic repositories in the domain of agriculture. However, data and models are still isolated in specific domain repositories, and interoperability is still weak.IFIP Advances in Information and Communication Technology, vol. 506.Laboratorio de Investigación y Formación en Informática Avanzad

The Semantic Web as a Platform Against Risk and Uncertainty in Agriculture

In this article, we discuss existing literature on DSS in agriculture, on DSS that use data available in the Semantic Web, and on Semantic Web initiatives focusing on agriculture information. Our goal is to assess the readiness of the Semantic Web as a platform to empower DSS that can keep risk and uncertainty in agriculture under control. Key agricultural activities targeted by DSS reported in literature are nutrient management, insect and pest management, land use and planning, environmental change and forecasting, and water and drought management. The most relevant use of Semantic Web in DSS, is in data analysis, as a means of making DSS more intelligent. There are initiatives to produce vocabularies and semantic repositories in the domain of agriculture. However, data and models are still isolated in specific domain repositories, and interoperability is still weak.IFIP Advances in Information and Communication Technology, vol. 506.Laboratorio de Investigación y Formación en Informática Avanzad

Towards an agriculture knowledge ecosystem :A social life network for farmers in Sri Lanka

We have developed and successfully trialled a Social Life Network (SLN); a Mobile Based Information System to support farming activities in Sri Lanka. It provides information required to support activities such as crop selection and cultivation planning in the context of farmer, farm location, season and task being performed. The system also provides a facility for farmers to sell farming related products and services to other farmers. The final system architecture evolved through a series of iterative relevance and design cycles based on Design Science Research methodology. In the first relevance cycle we identified farmer information needs, their current decision making patterns, and some possible ways to enhance their decision making process. In the first design cycles we developed the initial prototype to visualise a possible solution and in subsequent cycles a crop ontology to reorganise published crop information that would be queried in context and processes to empower farmers. Next we went through 2 cycles of creating functional prototypes, field testing with farmers and improving these to arrive at the final system. We noted that this system can enhance the flow of information in the agriculture domain by aggregating or disaggregating information produced by some stakeholders to be consumed by others. Based on this observation the overall architecture was reconceptualised as a Digital Knowledge Ecosystem

Towards An Agriculture Information Ecosystem

Stakeholders of a domain in their day today activities generate information which is a valuable resource. To obtain full value of this information it should reach right people at the right time. To investigate how this can be achieved we developed an information flow model for agriculture domain by mapping information needed by stakeholders to information generated by others using set of aggregation and disaggregation operators. We found majority of information needs of stakeholders can be fulfilled by applying these operators to information produced by some other stakeholders thus creating a direct benefit to encourage sharing information. This information flow model had many similarities to biological ecosystems where nutrient cycles and energy flows are replaced by information flows. Based on this information ecosystem model we are developing a mobile based information system for farmers in Sri Lanka. Like biological ecosystems information ecosystems will also need time to grow and become sustainable

The Semantic Web as a Platform Against Risk and Uncertainty in Agriculture

In this article, we discuss existing literature on DSS in agriculture, on DSS that use data available in the Semantic Web, and on Semantic Web initiatives focusing on agriculture information. Our goal is to assess the readiness of the Semantic Web as a platform to empower DSS that can keep risk and uncertainty in agriculture under control. Key agricultural activities targeted by DSS reported in literature are nutrient management, insect and pest management, land use and planning, environmental change and forecasting, and water and drought management. The most relevant use of Semantic Web in DSS, is in data analysis, as a means of making DSS more intelligent. There are initiatives to produce vocabularies and semantic repositories in the domain of agriculture. However, data and models are still isolated in specific domain repositories, and interoperability is still weak.IFIP Advances in Information and Communication Technology, vol. 506.Laboratorio de Investigación y Formación en Informática Avanzad

Unearthing farmers' information seeking contexts and challenges in digital, local and industry environments

The information seeking contexts of Australian grain and cotton growers were explored as they undertook self-directed learning to make farming system changes. This investigation provided insights into information seeking and what constitutes ‘information’ that supported learning. Growers' information seeking contexts were individual, personalised, situated within experiential practices, bounded by locales, and facilitated by social practices. Farmers are agents who must personalise both information content and processes to produce relevant meanings and to progress their own learning agendas and pathways. Information seeking in online, local, and industry environments highlighted differences between available content and farmers' individual information needs. Information and communications systems that facilitate and empower individual farmer knowledge processes and onfarm outcomes are a necessary strategy of agricultural development

Context based content aggregation for Social Life Networks

Better decisions can be made in the profession of the users if they can filter out the relevant information from all the available information sources. The mass availability of the mobile devices has enabled the users to quickly access timely information from any location. The aim of this work is to identify a suitable way to provide timely information in context by capturing contextual information through the mobile device, to support the activities of the user. The context model tries to identify the context of the user by identifying the task being performed by the user. The system is aware of the information need and the information source for each task of the user and the relevant information is filtered out of the information source, by using the users context. The context model was designed and tested for the farming domain, to support the livelihood activities of the farmer, by extending the concepts of Social Life Networks. Social Life Networks aggregates information from various sensors on a mobile phone, other published data sources and micro blogs such as Twitter to detect evolving situations and make that information available to the users in real time. This initial prototype was evaluated with a sample of farmers to check usefulness of provided information and usability of the application in order to support their day to day decision making process. The sample group strongly endorsed the various aspects of the proto-type application and provided valuable insights for improvement. The current application is a specific instance of the SLN project and we plan to create more application for SLN to test and refine the context models

Designing a farmer centred ontology for social life network

Rapid adoption of mobile phones has vastly improved access to information. Yet finding the information within the context in which information is required in a timely manner is a challenge. To investigate some of the underlying farmer centric research challenges a large International Collaborative Research Project to develop mobile based information systems for people in developing countries has been launched. One major sub project is to develop a Social Life Network; a mobile based information system for farmers in Sri Lanka. Lack of timely information with respect to their preferences and needs to support farming activities is creating many problems for farmers in Sri Lanka. For instance, farmers need agricultural information within the context of location of their farm land, their economic condition, their interest and beliefs, and available agricultural equipment. As a part of this project we investigated how we can create a knowledge repository of agricultural information to respond to user queries taking into account the context in which the information is needed. Because of the complex nature of the relationships among various concepts we selected an ontological approach that supports first order logic to create the knowledge repository. We first identified set of questions that reflect various motivation scenarios. Next we created a model to represent user context. Then we developed a novel approach to derive the competency questions incorporating user context. These competency questions were used to identify the concepts, relationships and axioms to develop the ontology. Initial system was trialled with a group of farmers in Sri Lanka. There was universal agreement among the farmers participated in the field trial to varying degree (strongly agree, agree, moderately agree) to the question "All information for the crop selection stage is provided"