Considering farmers' situated knowledge of using agricultural decision support systems (AgriDSS) to Foster farming practices: The case of CropSAT

Considering farmers' situated knowledge of using agricultural decision support systems (AgriDSS)... Precision agriculture is an important part of the sustainable intensification of agriculture, where information and communications technology and other technologies are necessary, but not sufficient for sustainable farming systems. The technology must fit into farmers' practice and be handled by their experienced-based, situated knowledge in order to contribute to increased sustainability in their farming. This study analysed the relationship between farmers' experience-based situated knowledge and the use of agricultural decision support systems in order to develop care by farmers in their practice. The theoretical framework of distributed cognition was used as a lens when investigating and analysing farmers' use of an agricultural decision support system called CropSAT developed for calculation of variable rate application files for nitrogen fertilisation from satellite images. In the case study, the unit of analysis was broadened to the whole socio-technical system of farmers' decision-making and learning, including other people and different kinds of tools and artefacts. The results revealed that social contexts could support farmers' development of cognitive strategies for use of agricultural decision support systems, e.g. CropSAT, and could thus facilitate decision-making and learning through development of enhanced professional vision that hopefully may increase farmers' situated knowledge and care in PA. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Agricultural Systems Elsevier

Considering farmers' situated knowledge of using agricultural decision support systems (AgriDSS) to Foster farming practices: The case of CropSAT

12 pages
Read Article

Loading next page...
 
/lp/elsevier/considering-farmers-situated-knowledge-of-using-agricultural-decision-dhoxE2hhU0
Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier Ltd
ISSN
0308-521x
DOI
10.1016/j.agsy.2017.10.004
Publisher site
See Article on Publisher Site

Abstract

Precision agriculture is an important part of the sustainable intensification of agriculture, where information and communications technology and other technologies are necessary, but not sufficient for sustainable farming systems. The technology must fit into farmers' practice and be handled by their experienced-based, situated knowledge in order to contribute to increased sustainability in their farming. This study analysed the relationship between farmers' experience-based situated knowledge and the use of agricultural decision support systems in order to develop care by farmers in their practice. The theoretical framework of distributed cognition was used as a lens when investigating and analysing farmers' use of an agricultural decision support system called CropSAT developed for calculation of variable rate application files for nitrogen fertilisation from satellite images. In the case study, the unit of analysis was broadened to the whole socio-technical system of farmers' decision-making and learning, including other people and different kinds of tools and artefacts. The results revealed that social contexts could support farmers' development of cognitive strategies for use of agricultural decision support systems, e.g. CropSAT, and could thus facilitate decision-making and learning through development of enhanced professional vision that hopefully may increase farmers' situated knowledge and care in PA.

Journal

Agricultural Systems – Elsevier

Published: Jan 1, 2018

References

  • Rekommendationer för gödsling och kalkning 2017. Jordbruksverket. JO15:19
    Albertsson, B.; Börling, K.; Kvarmo, P.; Listh, U.; Malgeryd, J.; Stenberg, M.
  • Envisioning a Future Decision Support System for Requirements Engineering
    Alenljung, B.
  • Interruptions in the wild: Portraying the handling of interruptions in manufacturing from a distributed cognition lens
    Andreasson, R.; Lindblom, J.; Thorvald, P.
  • Tool use and collaborative work in dock assembly: a distributed cognition perspective
    Andreasson, R.; Lindblom, J.; Thorvald, P.
  • IT as enabler of sustainable farming
    Aubert, B.A.; Schroeder, A.; Grimaudo, J.
  • Experiential science; towards an integration of implicit and reflected practitioner-expert knowledge in the scientific development of organic farming
    Baars, T.
  • A Complicated Chain of Circumstances: Decision-making in the New Zealand Wool Supply Chains
    Bradford, L.E.A.
  • Using thematic analysis in psychology
    Braun, V.; Clarke, V.
  • Situated cognition and the culture of learning
    Brown, J.S.; Collins, A.
  • Situated Cognition: On Human Knowledge and Computer Representations
    Clancey, J.W.
  • Being there – Putting Brain, Body and World Together Again
    Clark, A.
  • Activity theory, cognitive ergonomics and distributed cognition: three views of a transport company
    Decortis, F.; Noirfalise, S.; Saudelli, B.
  • What Computers Still can't Do – A Critique of Artificial Reason
    Dreyfus, H.L.
  • Expertise in real world contexts
    Dreyfus, H.L.; Dreyfus, S.E.
  • Networks of practice for co-construction of agricultural decision support systems: case studies of precision dairy farms in Australia
    Eastwood, C.R.; Chapman, D.F.; Paine, M.S.
  • Sustainable intensification in agriculture: Premises and policies
    Garnett, T.; Appleby, M.C.; Balmford, A.; Bateman, I.J.; Benton, T.G.; Bloomer, P.; Burlingame, B.; Dawkins, M.; Dolan, L.; Fraser, D.; Herrero, M.; Hoffmann, I.; Smith, P.; Thornton, P.K.; Toulmin, C.; Vermeulen, S.J.; Godfray, H.C.J.
  • Professional vision
    Goodwin, C.
  • Seeing as situated activity
    Goodwin, C.; Goodwin, M.H.
  • Distributed cognition in the heart room: How situation awareness arises from coordinated communications during cardiac surgery
    Hazlehurst, B.; McMullen, C.K.; Gorman, P.N.
  • Technology and social interaction: the emergence of ‘workplace studies
    Heath, C.; Knoblauch, H.; Luff, P.
  • Emerging consensus on desirable characteristics of tools to support farmers' management of climate risk in Australia
    Hochman, Z.; Carberry, P.S.
  • Farmers and researchers: How can collaborative advantages be created in participatory research and technology development?
    Hoffmann, V.; Probst, K.; Christinck, A.
  • Distributed cognition: toward a new foundation for human-computer interaction research
    Hollan, J.; Hutchins, E.; Kirsh, D.
  • Cognition in the Wild
    Hutchins, E.
  • A conceptual framework for guiding the participatory development of agricultural decision support systems
    Jakku, E.; Thorburn, P.J.
  • Choices, Values, and Frames
    Kahneman, D.; Tversky, A.
  • What farmers know: experiential knowledge and care in vine growing
    Krzywoszynska, A.
  • Communication for Rural Innovation. Rethinking Agricultural Extension
    Leeuwis, C.
  • Rethinking communication in innovation processes
    Leeuwis, C.; Aarts, N.
  • Embodied Social Cognition
    Lindblom, J.
  • Lantbrukares beslutsfattande och lantbruksrådgivning en förstudie (DEMIPROF)
    Lindblom, J.; Lundström, C.
  • Manufacturing in the wild - viewing human based assembly through the lens of distributed cognition
    Lindblom, J.; Thorvald, P.
  • Decision-making in agriculture - farmers' lifeworld in theory and practice
    Lindblom, J.; Rambusch, J.; Ljung, M.; Lundström, C.
  • Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies
    Lindblom, J.; Lundström, C.; Ljung, M.; Jonsson, A.
  • Workplace Studies
    Luff, P.; Hindmarsh, J.; Heath, C.
  • Adoption of the agricultural decision support system CropSAT in extension: incentives and needs
    Lundström, C.; Lindblom, J.; Ljung, M.
  • Wither agricultural DSS?
    Matthews, K.B.; Schwarz, G.; Buchan, K.; Rivington, M.; Miller, D.
  • Changing systems for supporting farmers' decisions
    McCown, R.L.
  • Re-inventing model-based decision support with Australian dryland farmers: changing intervention concepts during 17 years of action research
    McCown, R.L.; Carberry, P.S.; Hochman, Z.; Dalgliesh, N.P.; Foale, M.A.
  • Care in Practice
    Mol, A.; Moser, I.; Pols, J.
  • Farmers and computers
    Nitsch, U.
  • From Diffusion of Innovations to Mutual Learning: The Changing Role of the Agricultural Advisory Services
    Nitsch, U.
  • The Reinvention of Decision-Making
    Orasanu, J.; Connolly, T.
  • The Psychology of Judgement and Decision-Making
    Plous, S.
  • Decision Support Systems: Concepts and Resources for Managers
    Power, D.J.
  • Lack of consideration for end-users during the design of agronomic models: a review
    Prost, L.; Cerf, M.; Jeuffroy, M.-H.
  • A modified soil adjusted vegetation index
    Qi, J.G.; Chehbouni, A.; Huete, A.R.; Kerr, Y.H.; Sorooshian, S.
  • HCI Theory: Classical, Modern, and Contemporary
    Rogers, Y.
  • Research in the Wild
    Rogers, Y.; Marshall, P.
  • Extension Science
    Röling, N.G.
  • Addressing the implementation problem in agricultural decision support systems
    Rossi, V.; Salinari, F.; Poni, S.; Caffi, T.; Bettati, T.
  • What can be learned about the adaptation process of farming systems to climate dynamics using crop models?
    Schlindwein, S.L.; Eulenstein, F.; Lana, M.; Sieber, S.; Boulanger, J.-P.; Guevara, E.; Meira, S.; Gentile, E.; Bonatti, M.
  • Producing nitrogen (N) uptake maps in winter wheat by combining proximal crop measurements with Sentinel-2 and DMC satellite images in a decision support system for farmers
    Söderström, M.; Piikki, K.; Stenberg, M.; Stadig, H.; Martinsson, J.
  • Internet of food and farm 2020
    Sundmaeker, H.; Verdouw, C.; Wolfert, S.; Pérez Freire, L.
  • Agricultural decision support system facilitating co-learning
    Thorburn, P.J.; Jakku, E.; Webster, A.J.; Everingham, Y.L.
  • Decision Support and Business Intelligence Systems
    Turban, E.; Aronson, J.E.; Liang, J.E.; Sharda, R.
  • Effect of a participatory approach on the successful development of agricultural decision support systems: the case of Pigs2win
    Van Meensel, J.; Lauwers, L.; Kempen, I.; Dessein, J.; van Huylenbroeck, G.
  • Big data in smart farming – a review
    Wolfert, S.; Ge, L.; Verdouw, C.; Bogaardt, M.-J.

You’re reading a free preview. Subscribe to read the entire article.

Try 2 weeks free now

DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

or browse the journals available

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off

Sign up
for free
Start 14 day
Free Trial