Abstract
Agricultural modeling has long suffered from fragmentation in model implementation. Many models are developed, there is much redundancy, models are often poorly coupled, model component re-use is rare, and it is frequently difficult to apply models to generate real solutions for the agricultural sector. To improve this situation, we argue that an open, self-sustained, and committed community is required to co-develop agricultural models and associated data and tools as a common resource. Such a community can benefit from recent developments in information and communications technology (ICT). We examine how such developments can be leveraged to design and implement the next generation of data, models, and decision support tools for agricultural production systems. Our objective is to assess relevant technologies for their maturity, expected development, and potential to benefit the agricultural modeling community. The technologies considered encompass methods for collaborative development and for involving stakeholders and users in development in a transdisciplinary manner.Our qualitative evaluation suggests that as an overall research challenge, the interoperability of data sources, modular granular open models, reference data sets for applications and specific user requirements analysis methodologies need to be addressed to allow agricultural modeling to enter in the big data era. This will enable much higher analytical capacities and the integrated use of new data sources. Overall agricultural systems modeling needs to rapidly adopt and absorb state-of-the-art data and ICT technologies with a focus on the needs of beneficiaries and on facilitating those who develop applications of their models. This adoption requires the widespread uptake of a set of best practices as standard operating procedures.
Highlights
Information and computer technology (ICT) is changing at a rapid pace
The next generation” modeling community (NextGen) tool used by Jan must be able to combine or be combined with existing data about localized conditions with farm-scale models to predict the viability of using the new varieties
Jan obtains information from the farmer to input into his smart phone, which has NextGen apps that were developed for the farming systems of his region and that help him determine combinations of system components that might best fit specific farm situations
Summary
Information and computer technology (ICT) is changing at a rapid pace. Digital technologies allow people to connect across the globe at high speeds at any time (Gartner, 2016). A knowledge chain is a set of linked steps by which data are processed into information, knowledge and wisdom as used in decision making. This perspective postulates that data comprise a raw material that, when combined with description and quality attributes, leads to information. Based on the data in the infrastructure, applications targeted at end-users serve information and knowledge, e.g. a yield forecast to a supply chain manager; effects on farm income of a policy change; estimates of disease related crop damage to a farmer. Design of the application chains must consider the end-users, but the full spectrum of users of NextGen ICT infrastructure including primary data collectors, database professionals, software developers, modelers and the end-users of knowledge and information. Recommendations for steps that need to be taken to enable knowledge and application chains to be created for the generation of agricultural knowledge products
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