GHGFarm: a software tool to estimate and reduce net-greenhouse gas emission from farms in Canada

Decision support software tools and computer simulation models help us to better understand the structure and functioning of agricultural ecosystems - how to better manage them in a sustainable way. They assist in the synthesis of interdisciplinary knowledge amongst stake-holders, automation of meta-analyses, communication and outreach. In this paper, we showcase the GHGFarm software tool for estimating and reducing net-greenhouse gas emission from farms. This software tool provides a capability for scientists, policy makers, and agricultural producers to collectively quantify, interpret and compare alternative farm management scenarios, thereby encouraging the adoption of longer-term sustainable farm practices. Farmers can characterize their multi-enterprise operations and management practices in a flexible way. Reduction scenarios are generated from current scientific knowledge and emission algorithms available (International Panel on Climate Change, IPCC), modified with Canadian-specific emission factors, local practices, topography, and climate conditions. We discuss the software development approach, highlighting past and present challenges within the broader context of empirical software engineering and agricultural extension. We identify the need for a better resolved multi-user stream/flow design, enhancement of the graphical user interface, expanded sensitivity testing and quantification of uncertainty.

[1]  Laurent Gauthier,et al.  SAGE: An object-oriented framework for the construction of farm decision support systems , 1996 .

[2]  B. M. Petersen,et al.  Evaluating nitrogen taxation scenarios using the dynamic whole farm simulation model FASSET , 2003 .

[3]  R. L. McCown,et al.  Changing systems for supporting farmers' decisions: problems, paradigms, and prospects , 2002 .

[4]  D. K. Lovett,et al.  A systems approach to quantify greenhouse gas fluxes from pastoral dairy production as affected by management regime , 2006 .

[5]  Mark A. Miller,et al.  Applying operation research methods to identify complex government farm program participation decisions , 1990 .

[6]  James M. Gibbons,et al.  Modelling uncertainty in greenhouse gas emissions from UK agriculture at the farm level , 2006 .

[7]  Fabrizio Mazzetto,et al.  MEACROS: a tool for multi-criteria evaluation of alternative cropping systems , 2003 .

[8]  David J. Parsons,et al.  A message system to integrate diverse programs and databases in a farm decision support system , 1993 .

[9]  Jørgen E. Olesen,et al.  Modelling greenhouse gas emissions from European conventional and organic dairy farms , 2006 .

[10]  Niels Halberg,et al.  Farm level environmental indicators; are they useful?: An overview of green accounting systems for European farms , 2005 .

[11]  Hee-Woong Kim,et al.  A balanced thinking-feelings model of information systems continuance , 2007, Int. J. Hum. Comput. Stud..

[12]  Ward N. Smith,et al.  A proposed approach to estimate and reduce net greenhouse gas emissions from whole farms , 2006 .

[13]  James C. Ascough,et al.  Computer use in agriculture: an analysis of Great Plains producers☆ , 1999 .

[14]  M. K. van Ittersum,et al.  ROTAT, a tool for systematically generating crop rotations , 2003 .

[15]  Changsheng Li,et al.  A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity , 1992 .

[16]  Jetse J. Stoorvogel,et al.  Integration of computer-based models and tools to evaluate alternative land-use scenarios as part of an agricultural systems analysis , 1995 .

[17]  Stef Proost,et al.  An integrated decision support framework for the prediction and evaluation of efficiency, environmental impact and total social cost of domestic and international forestry projects for greenhouse gas mitigation: description and case studies. , 2005 .

[18]  Brian Keating,et al.  DAM EA$Y—software for assessing the costs and benefits of on-farm water storage based production systems , 2003 .

[19]  Judith Segal,et al.  When Software Engineers Met Research Scientists: A Case Study , 2005, Empirical Software Engineering.

[20]  Steve Frolking,et al.  A model of nitrous oxide evolution from soil driven by rainfall events: 2. Model applications , 1992 .

[21]  J. W. King,et al.  A trend analysis of computing in agricultural extension , 1994 .

[22]  J. Olesen,et al.  Mitigation of greenhouse gas emissions in European conventional and organic dairy farming , 2006 .

[23]  Evan Thomson,et al.  UNEForm: a powerful feed formulation spreadsheet suitable for teaching or on-farm formulation , 2001 .

[24]  Martin Kaltschmitt,et al.  Disaggregated greenhouse gas emission inventories from agriculture via a coupled economic-ecosystem model , 2006 .

[25]  Holger Meinke,et al.  Increasing profits and reducing risks in crop production using participatory systems simulation approaches , 2001 .

[26]  M. J Shaffer,et al.  Rule-based management for simulation in agricultural decision support systems , 1998 .

[27]  James W. Jones,et al.  The DSSAT cropping system model , 2003 .

[28]  Peter E. Hildebrand,et al.  The dynamic North Florida dairy farm model: A user-friendly computerized tool for increasing profits while minimizing N leaching under varying climatic conditions , 2005 .

[29]  Yu Zhang,et al.  A simulation model linking crop growth and soil biogeochemistry for sustainable agriculture , 2002 .

[30]  C. Solano,et al.  MIS support for pasture and nutrition management of dairy farms in tropical countries , 1996 .