Rule-based management for simulation in agricultural decision support systems

Abstract Rule-based management systems can offer the farmer or consultant the opportunity to better approximate current or proposed management options, especially as they relate to dynamic conditions in farm fields and across the whole farm. Most existing attempts at rule-based management within simulation-oriented agricultural decision support systems (DSS) involve limited extension of fixed management dates in response to environmental conditions, or involve rules for implementing limited management events such as fertilizer applications or irrigations. A comprehensive rule-based management system for agricultural DSS was developed that allows simulated management events to occur in response to flexible producer-defined rules and to weather and management induced changes in the soil–crop system over time and space. The system provides a simple, English-based rules language, a rules development editor, and software to parse and interpret these rules and provide linkages to application DSS software packages such as Great Plains Framework for Agricultural Resource Management (GPFARM). Rules can be quickly and easily developed that cover management activities for individual management units (MUs) or groups of MUs. Feedback to the simulation package at each time step provides for generation of site- and time-specific management events across the application. Tests of the rule-based system on: wheat ( Triticum aestivum L.), corn ( Zea mays L.), fallow and wheat–fallow crop rotations used in eastern Colorado showed that management events were simulated within the correct time windows and in the proper sequence. Dates for simulated events varied as expected across each rotational cycle as a function of temporal conditions. Additional work is anticipated to allow dynamic calculation of event attributes, capture and implementation of producer time priorities, and a simplified menu system for the rule editor.

[1]  Michael A. Driscoll,et al.  An expert system for the hydraulic analysis of microirrigation systems , 1993 .

[2]  Fergus L. Sinclair,et al.  Acquiring qualitative knowledge about complex agroecosystems. Part 2: Formal representation , 1998 .

[3]  J. G. Kroes Managing nitrogen for groundwater quality and farm profitability , 1994 .

[4]  Bruce K. Wylie,et al.  Using climate/weather data with the NLEAP model to manage soil nitrogen , 1994 .

[5]  Jim E. Greer,et al.  Explaining and justifying recommendations in an agriculture decision support system , 1994 .

[6]  M. J. Shaffer,et al.  Coordinated farm and research management (COFARM) data system for soils and crops , 1984 .

[7]  F. Papy,et al.  Modelling decision-making processes for annual crop management , 1998 .

[8]  John R. Williams,et al.  A modeling approach to determining the relationship between erosion and soil productivity [EPIC, Erosion-Productivity Impact Calculator, mathematical models] , 1984 .

[9]  T. Schumacher,et al.  Simulating the Effects of Erosion on Corn Productivity , 1995 .

[10]  N. D. Stone,et al.  Pink bollworm control in southwestern desert cotton: III. Strategies for control: An economic simulation study , 1986 .

[11]  Fergus L. Sinclair,et al.  Acquiring qualitative knowledge about complex agroecosystems. Part 1: Representation as natural language , 1998 .

[12]  M. J. Shaffer,et al.  Simulating resource competition in multispecies agricultural plant communities , 1993 .

[13]  N. Arumugam,et al.  Expert system applications in irrigation management: an overview , 1997 .

[14]  Liwang Ma,et al.  Manure Management in an Irrigated Silage Corn Field: Experiment and Modeling , 1998 .

[15]  T. F. Weaver,et al.  Indigenous knowledge and fertilizer strategies in Leyte, Philippines: implications for research and demonstration trials , 1992 .

[16]  H Lemmon,et al.  Comax: An Expert System for Cotton Crop Management , 1986, Science.

[17]  M. Al‐Kaisi,et al.  Simulating the Impact of Management Practices on Nitrous Oxide Emissions , 1998 .

[18]  Richard E. Plant,et al.  Knowledge Based Systems in Agriculture , 1991 .