A Blueprint for a Big Data Analytical Solution to Low Farmer Engagement with Financial Management

As the market environment for farming has become more complicated, the need for farmer engagement in financial management has increased. However, financial management decisions need to consider individual farm environmental conditions. This paper discusses the design of a new big-data based analytical solution for low farmer engagement in financial management—a Farm Financial Information System (FARMFIS). Using a pastoral based livestock system as the case study, the methodology required to develop this predictive Information System is described. Building upon real-time weather, satellite grass growth and soil information, a local setting and a bio-physical model of weather and market changes on farm level economic outcomes are utilized. The aim is to use the back-end framework described here to develop decision support tools for farmers to provide benchmark information in relation to the financial and technical attributes to a similar top, middle or bottom one-third performing farm. This information can help farmers engage more meaningfully in their own management decisions, technologies, and practices.


Editor(s):
IFAMR, IFAMA
Issue Date:
Jun 15 2016
Publication Type:
Journal Article
DOI and Other Identifiers:
(ISSN #: 1559-2448) (Other)
PURL Identifier:
http://purl.umn.edu/240703
Published in:
International Food and Agribusiness Management Review, Volume 19, Special Issue A
Page range:
131-153
Total Pages:
24
JEL Codes:
Q12
Note:
The IFAMR is published quarterly my IFAMA. For more information visit: www.ifama.org.
Series Statement:
Volume 19
Issue A




 Record created 2017-04-01, last modified 2017-08-29

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