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How2QnD: Design and Construction of a Game-Style, Environmental Simulation Engine and Interface Using UML, XML, and Java

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Advances in Modeling Agricultural Systems

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 25))

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Abstract

Within wicked environmental challenges, problems that exist in the nexus of environmental science and environmental values, neatly and elegantly optimized solutions are difficult to find and rarely accepted by stakeholders. Different role players must explore the challenge adaptively and through viewpoints to contribute to their understanding of the situation and to learn about the dynamics and values of other relevant stakeholders. The Questions and Decisions (QnD) system (Kiker, G.A., et al., Springer Science, 2006, 11, 151–186) was created to provide an effective and efficient tool to integrate ecosystem, management, economics, and sociopolitical factors into a user-friendly game/model framework. QnD is written in object-oriented Java and can be deployed in stand-alone or Web-based (browser-accessed) modes. The QnD model links spatial components within geographic information system (GIS) files to the abiotic (climatic) and biotic interactions that exist in an environmental system. QnD can be used in a rigorous modeling role to mimic system elements obtained from scientific data or it can be used to create a “cartoon” style depiction of the system to promote greater learning and discussion from decision participants. Elephant and vegetation dynamics in Africa provide an excellent example of a wicked environmental challenge as conservation objectives and societal values (both local and international) often have conflicting goals concerning appropriate elephant densities and population control options in protected areas. In attempting to capture many dynamic aspects of elephant–vegetation relationships, previous models depicting the savanna ecosystem of the Kruger National Park (KNP), South Africa, can become quite complex and demanding in terms of detailed parameter inputs. Therefore, the purpose of this modeling project was to create a simplified, management-focused, visual simulation of the KNP in order to chart future elephant, tree, and grass scenarios. QnD:EleSim has been designed to spatially simulate elephant–vegetation dynamics in 195 areas at 10-km resolution at a monthly time-step. As the effects of elephant populations on the tree–grass equilibrium of the savanna are documented, future management decisions can be advised after analysis of potential scenarios.

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Acknowledgments

Special thanks to the following people/organizations that allowed for the development of this model to be made possible: Dr. Judith Kruger, Dr. Rina Grant, and Dr Harry Biggs of the South Africa National Parks for supplying climate, soils, vegetation, animal census, fire, and other KNP data sets. In addition, their guidance and friendship are truly appreciated. The KNP Elephant Modeling Group (chaired by Prof. Robert Slotow and Dr. Robert Scholes) for coordination of elephant modeling research activities. The South African Weather Bureau for provision of climate data. Prof. David Saah of the University of San Francisco for his input and comments. Finally, the University of Florida Center for Precollegiate Education and Training for organizing and supporting this research opportunity for Mr. Thummalapalli.

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Authors and Affiliations

  1. Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA

    Gregory A. Kiker

Authors
  1. Gregory A. Kiker
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  2. Rohit Thummalapalli
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Technical Appendix

Technical Appendix

This section contains a sample list of objects used in the Grass CLocalComponent (CGrass) object. The CGrass object exists within each of the 195 grid cells. In addition, the four woody vegetation objects (CSeedlings, CSaplings, CShrubs, and CTrees), potential fire objects (CFire), and elephant objects (CElephantHerd and CTaggedElephant) are also present in grid cells. They have a similar DData/PProcess object structure as CGrass, but they are not listed in this appendix. Overall in QnD:EleSim, approximately 7 CLocalComponent objects, 23 Process objects, 88 SubProcess objects, and hundreds of DData objects were programmed into each of the 195 CSpatialUnit/CHabitat combinations via the XML input files.

Grass Object (CGrass) DData and PProcesses

DData

DBiomass

DProductivity

DNewAreaCovered

DAreaCover

DGrassBiomassSenesced

DBiomassPerUnitArea

DBaseProductivity

DWetSeasonSenescence

DNewGrassBiomass

DGrassValue1

DDrySeasonSenescence

DRelativeAreaCoveredBySmallWoodyPlants

DGrassValue2

DGrassCrowdingCoefficient

 

DGrassValue3

DMaxBiomass

 

DGrassValue4

DRelativeGrazingIntensity

 

DGrassValue5

DBiomassAdded

 

DGrassValue6

DAreaCovered

 

Process/SubProcesses/DData

PProcess: PWetSeasonProcessesCalculateGrassAreaCoveredAndBiomass

 PSubProcess: PIfWetSeason (Type = PIfEquals)

  If (Global.DWetSeason == 1) Then continue to next PSubProcess -- Else Go To    Next Process

 PSubProcess: PCalcProductivity (Type = PMultiplyValue)

  CGrass.DProductivity = Global.GridArea x CGrass.DBaseProductivity

 PSubProcess: PCalcMonthlyProductivity (Type = PDivideValue)

  CGrass.DProductivity = CGrass.DBaseProductivity / Global.DSix

 PSubProcess: PCalcAvailableAreaCover (Type = PSubtractValue)

  CGrass.DGrassValue1 = Global.DOne – CSeedling.DRelativeAreaCover –Csapling.DRelativeAreaCover -- CShrub.DRelativeAreaCover

 PSubProcess: PCalcInverseOfElephantGrazingIntensity (Type = PSubtractValue)

  CGrass.DGrassValue2 = Global.DOne – CElephantHerd..DRelativeGrazingRate

 PSubProcess: PCalcInverseOfCrowdingIntensity (Type = PSubtractValue)

  CGrass.DGrassValue3 = Global.DOne – CSapling.DCrowdingCoefficient –Cshrub.DCrowdingCoefficient

 PSubProcess: PCollectingTerms (Type = PMultiplyValue)

  CGrass.DAreaCovered =HomeSpatialUnit.DLocalRainfall xCGrass.DGrassValue1 x CGrass.DGrassValue2 xCGrass.DGrassValue3

 PSubProcess: PCalculateGrassSenesced (Type = PMultiplyValue)

  CGrass. DGrassBiomassSenesced= CGrass. DWetSeasonSenescence xCGrass.DGrassBiomass

PProcess: PCalculateDrySeasonGrassSenescence

 PSubProcess: PIfDrySeason (Type = PIfEquals)

  If (Global.DWetSeason == 0) Then continue to next PSubProcess -- Else Go To     Next Process

 PSubProcess: PSetNewBiomassToZero (Type = PSetValue)

  CGrass. DNewGrassBiomass = Global.DZero

 PSubProcess: PCalcInverseOfElephantGrazingIntensity (Type = PSubtractValue)

  CGrass.DGrassValue2 =Global.DOne –CElephantHerd.DRelativeGrazingRate

 PSubProcess: PCalculateFireIgnition (Type = PSubtractValue)

  CGrass.DGrassValue6 = Global.DOne – CFire.DFireIgnition

 PSubProcess: PCalculateGrassFireLoss (Type = PMultiplyValue)

  CGrass. DNewGrassAreaCovered =Grass.DGrassValue6 xCGrass.DAreaCovered

 PSubProcess: PCalculateDrySeasonGrassSenescence (Type = PMultiplyValue)

  CGrass. DGrassBiomassSenesced=CGrass. DDrySeasonSenescence x CGrass. DGrassValue6 x CGrass.DGrassBiomass

PProcess: PCalculateFinalGrassBiomass

 PSubProcess: PCalculateFinalGrassGrowth (Type = PAddValue)

  CGrass. DGrassBiomass =CGrass. DGrassBiomass + CGrass. DGrassBiomassSenesced + CGrass.DNewGrassBiomass

 PSubProcess: PCalculateNewBiomassPerUnitArea (Type = PMultiplyValue)

  CGrass. DBiomassPerUnitArea= CGrass.DGrassBiomass / Global.DGridArea

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Kiker, G.A., Thummalapalli, R. (2009). How2QnD: Design and Construction of a Game-Style, Environmental Simulation Engine and Interface Using UML, XML, and Java. In: Advances in Modeling Agricultural Systems. Springer Optimization and Its Applications, vol 25. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-75181-8_6

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