Evidence map›Paper›PMID 28701813›Full record

ArticleAgricultural systems2017

Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology.

Sander J C Janssen, Cheryl H Porter, Andrew D Moore, Ioannis N Athanasiadis, Ian Foster, James W Jones, John M Antle

Abstract read
In one paragraph

Article in Agricultural systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Lessons in implementing open farm data for diverse stakeholders at Harper Adams Future Farm.Animal frontiers : the review magazine of animal agriculture · 2026
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  18. Shared Socio-economic Pathways for European agriculture and food systems: The Eur-Agri-SSPs.Global environmental change : human and policy dimensions · 2020
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sander J C JanssenWageningen University and Research Centre, Postbus 47, 6700AA Wageningen, Netherlands.
Cheryl H PorterAgricultural & Biological Engineering, University of Florida, PO Box 110570, Gainesville, FL 32611, USA.
Andrew D MooreCSIRO Agriculture & Food, GPO Box 1600, Canberra, ACT, 2601, Australia.
Ioannis N AthanasiadisWageningen University and Research Centre, Postbus 47, 6700AA Wageningen, Netherlands.
Ian FosterComputation Institute, University of Chicago, IL, USA.
James W JonesAgricultural & Biological Engineering, University of Florida, PO Box 110570, Gainesville, FL 32611, USA.
John M AntleOregon State University, Corvallis, OR, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Agricultural modelsBig dataICTLinked dataOpen scienceSensingVisualization

Identifiers

PMID28701813
PMCPMC5485661

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.