@article{O’Donoghue:240703,
      recid = {240703},
      author = {O’Donoghue, Cathal and McKinstry, Alistair and Green,  Stuart and Fealy, Reamonn and Heanue, Kevin and Ryan, Mary  and Connolly, Kevin and Desplat, J.C. and Horan, Brendan},
      title = {A Blueprint for a Big Data Analytical Solution to Low  Farmer Engagement with Financial Management},
      journal = {International Food and Agribusiness Management Review},
      address = {2016-06-15},
      number = {1030-2016-83143},
      series = {Volume 19},
      pages = {24},
      month = {Jun},
      year = {2016},
      note = {The IFAMR is published quarterly my IFAMA. For more  information visit: www.ifama.org.},
      abstract = {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.},
      url = {http://ageconsearch.umn.edu/record/240703},
      doi = {https://doi.org/10.22004/ag.econ.240703},
}