Rob Knapen

Analysis of Big Data technologies for use in agro-environmental science

Recent developments like the movements of open access and open data and the unprecedented growth of data, which has come forward as Big Data, have shifted focus to methods to effectively handle such data for use in agro-environmental research. Big Data technologies, together with the increased use of cloud based and high performance computing, create new opportunities for data intensive science in the multi-disciplinary agro-environmental domain. A theoretical framework is presented to structure and analyse data-intensive cases and is applied to three case studies, together covering a broad range of technologies and aspects related to Big Data usage. The case studies indicate that most persistent issues in the area of data-intensive research evolve around capturing the huge heterogeneity of interdisciplinary data and around creating trust between data providers and data users. It is therefore recommended that efforts from the agro-environmental domain concentrate on the issues of variety and veracity. A theoretical framework is presented to frame and analyse Big Data use cases.Three case studies related to agro-environmental modelling, covering the range of Big Data characteristics are analysed.Most persistent issues in agro-environmental science concern variety and veracity.Approaches to deal with variety and veracity issues are presented.

Harmonization and translation of crop modeling data to ensure interoperability

The Agricultural Model Intercomparison and Improvement Project (AgMIP) seeks to improve the capability of ecophysiological and economic models to describe the potential impacts of climate change on agricultural systems. AgMIP protocols emphasize the use of multiple models; consequently, data harmonization is essential. This interoperability was achieved by establishing a data exchange mechanism with variables defined in accordance with international standards; implementing a flexibly structured data schema to store experimental data; and designing a method to fill gaps in model-required input data. Researchers and modelers are able to use these tools to run an ensemble of?models on a single, harmonized dataset. This allows them to compare models directly, leading ultimately to model improvements. An important outcome is the development of a platform that facilitates researcher collaboration from many organizations, across many countries. This would have been very difficult to achieve without the AgMIP data interoperability standards described in this paper. Heterogeneous data can be harmonized and translated to multiple model formats.The ICASA data standards provide an extensible data structure and ontology.JSON structures provide a flexible, efficient means of handling heterogeneous data.DOME functions enable a consistent means of providing missing or inadequate data.Data provenance is maintained from data sources through simulated model outputs.

Improving Access to Big Data in Agriculture and Forestry Using Semantic Technologies

To better understand and manage the interactions of agriculture and natural resources, for example under current increasing societal demands and climate changes, agro-environmental research must bring together an ever growing amount of data and information from multiple science domains. Data that is inherently large, multi-dimensional and heterogeneous, and requires computational intensive processing. Thus, agro-environmental researchers must deal with specific Big Data challenges in efficiently acquiring the data fit to their job while limiting the amount of computational, network and storage resources needed to practical levels. Automated procedures for collection, selection, annotation and indexing of data and metadata are indispensable in order to be able to effectively exploit the global network of available scientific information. This paper describes work performed in the EU FP7 Trees4Future and SemaGrow projects that contributes to development and evaluation of an infrastructure that allows efficient discovery and unified querying of agricultural and forestry resources using Linked Data and semantic technologies.

A Web-Based Software System for Model Integration in Impact Assessments of Agricultural and Environmental Policies

The SEAMLESS consortium develops a computerized and integrated framework (SEAMLESS-IF) to assess the impacts on environmental and economic sustainability of a wide range of policies and technological improvements across a number of scales. In SEAMLESS-IF, different type of models are linked into model chains, where each model uses the outputs of another model as its inputs and ultimately indicators are calculated. This type of integrated modelling requires interoperability, which is the ability of two or more systems or components to exchange information and to use the information that has been exchanged.