Deliberative processes for comprehensive evaluation of agroecological models. A review

详细信息    查看全文

• 作者：Gianni Bellocchi (1)
  Mike Rivington (2)
  Keith Matthews (2)
  Marco Acutis (3)
  
  1. Grassland Ecosystem Research Unit ; French National Institute for Agricultural Research ; 5 Chemin de Beaulieu ; 63039 ; Clermont-Ferrand ; France
  2. The James Hutton Institute ; Craigiebuckler ; Aberdeen ; AB15 8QH ; UK
  3. Department of Agricultural and Environmental Sciences鈥擯roduction ; Landscape ; Agroenergy ; University of Milan ; Milan ; Italy
  
• 关键词：Component ; oriented programing ; Deliberative approach ; Modeling ; Model evaluation ; Multiple metrics ; Stakeholders
• 刊名：Agronomy for Sustainable Development
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：35
• 期：2
• 页码：589-605
• 全文大小：2,642 KB
• 参考文献：1. Aaslyng, JM, Lund, JB, Ehler, N, Rosenqvist, E (2003) IntelliGrow: a greenhouse component-based climate control system. Environ Model Softw 18: pp. 657-666 CrossRef
  2. Acutis, M, Confalonieri, R (2006) Optimization algorithms for calibrating cropping systems simulation models. A case study with simplex-derived methods integrated in the WARM simulation environment. Italian Journal of Agrometeorology 11: pp. 26-34
  3. Acutis M, Confalonieri R, Genovese G, Donatelli M, Rodolfi M, Mariani L, Bellocchi G, Trevisiol P, Gusberti D, Sacco D (2006) WARM: a new model for rice simulation. In: Fotyma M., Kaminska B. (eds) Proceedings of the 9th European Society for Agronomy Congress, 6鈥? September, Warsaw, pp 259鈥?60
  4. Alexandrov, GA, Ames, D, Bellocchi, G, Bruen, M, Crout, N, Erechtchoukova, M, Hildebrandt, A, Hoffman, F, Jackisch, C, Khaiter, P, Mannina, G, Matsunaga, T, Purucker, ST, Rivington, M, Samaniego, L (2011) Technical assessment and evaluation of environmental models and software. Environ Model Softw 26: pp. 328-336 CrossRef
  5. Argent, RM (2004) An overview of model integration for environmental applications鈥攃omponents, frameworks and semantics. Environ Model Softw 19: pp. 219-234 CrossRef
  6. Argent, RM (2004) Concepts, methods and applications in environmental model integration. Environ Model Softw 19: pp. 217 CrossRef
  7. Argent, RM (2005) A case study of environmental modelling and simulation using transplantable components. Environ Model Softw 20: pp. 1514-1523 CrossRef
  8. Argent, RM, Voinov, A, Maxwell, T, Cuddy, SM, Rahman, JM, Seaton, S, Vertessy, RA, Braddock, RD (2006) Comparing modelling frameworks鈥攁 workshop approach. Environ Model Softw 21: pp. 895-910 CrossRef
  9. Asseng, S, Ewert, F, Rosenzweig, C, Jones, JW, Hatfield, JL, Ruane, A, Boote, KJ, Thorburn, P, R枚tter, RP, Cammarano, D, Brisson, N, Basso, B, Martre, P, Aggarwal, PK, Angulo, C, Bertuzzi, P, Biernath, C, Doltra, J, Gayler, S, Goldberg, R, Grant, R, Heng, L, Hooker, JE, Hunt, LA, Ingwersen, J, Izaurralde, RC, Kersebaum, KC, M眉ller, C, Naresh Kumar, S, Nendel, C, O鈥橪eary, G, Olesen, JE, Osborne, TM, Palosuo, T, Priesack, E, Ripoche, D, Semenov, MA, Shcherbak, I, Steduto, P, St枚ckle, CO, Stratonovitch, P, Streck, T, Supit, I, Travasso, M, Tao, F, Waha, K, Wallach, D, White, JW, Wolf, J (2013) Uncertainties in simulating wheat yields under climate change. Nature Clim Change 3: pp. 827-832 CrossRef
  10. Bair, ES (1994) Model (in)validation鈥攁 view from courtroom. Ground Water 32: pp. 530-531 CrossRef
  11. Balci, O (1997) Principles of simulation model validation, verification, and testing. Trans Soc Comput Simul Int 14: pp. 3-12
  12. Balci O, Ormsby WF (2002) Expanding our horizons in verification, validation, and accreditation research and practice. In: Y眉cesan E, Chen C-H, Snowdon JL, Charnes JM (eds) Proceedings of 2002 Winter Simulation Conference, 8鈥?1 December, San Diego, pp 653鈥?63. doi:10.1109/WSC.2002.1172944
  13. Bassu, S, Brisson, N, Durand, JL, Boote, K, Lizaso, J, Jones, JW, Rosenzweig, C, Ruane, AC, Adam, M, Baron, C, Basso, B, Biernath, C, Boogaard, H, Conijn, S, Corbeels, M, Deryng, D, Sanctis, G, Gayler, S, Grassini, P, Hatfield, J, Hoek, S, Izaurralde, C, Jongschaap, R, Kemanian, AR, Kersebaum, KC, Kim, SH, Kumar, NS, Makowski, D, M眉ller, C, Nendel, C, Priesack, E, Pravia, MV, Sau, F, Shcherbak, I, Tao, F, Teixeira, E, Timlin, D, Waha, K (2014) How do various maize crop models vary in their responses to climate change factors?. Global Change Biol 20: pp. 2301-2320 CrossRef
  14. Bellocchi, G, Confalonieri, R, Donatelli, M (2006) Crop modelling and validation: integration of IRENE_DLL in the WARM environment. Italian Journal of Agrometeorology 11: pp. 35-39
  15. Bellocchi, G, Rivington, M, Donatelli, M, Matthews, KB (2010) Validation of biophysical models: issues and methodologies. A review. Agron Sustain Dev 30: pp. 109-130 CrossRef
  16. Bellocchi G, Rivington M, Acutis M (2014) Deliberative processes for comprehensive evaluation of agro-ecological models. FACCE MACSUR Mid鈥恡erm Scientific Conference, 鈥淎chievements, Activities, Advancement,鈥?01鈥?4 April, Sassari. http://ocs.macsur.eu/index.php/Hub/Mid-term/paper/view/193. Accessed 06 November 2014
  17. Bellocchi G, Rivington M, Acutis M (2014) Protocol for model evaluation. FACCE MACSUR Reports 2(1): D-C1.3. http://ojs.macsur.eu/index.php/Reports/article/view/D-L2.2. Accessed 06 November 2014
  18. Bennett, ND, Croke, BFW, Guariso, G, Guillaume, JHA, Hamilton, SH, Jakeman, AJ, Marsili-Libelli, S, Newham, LTH, Norton, JP, Perrin, C, Pierce, SA, Robson, B, Seppelt, R, Voinov, AA, Fath, BA, Andreassian, V (2013) Characterising performance of environmental models. Environ Model Softw 40: pp. 1-20 CrossRef
  19. Bergez, J-E, Debaeke, P, Deumier, J-M, Lacroix, B, Leenhardt, D, Leroy, P, Wallach, D (2001) MODERATO: an object-oriented decision tool for designing maize irrigation schedules. Ecol Model 137: pp. 43-60 CrossRef
  20. Bergez, J-E, Chabrier, P, Gary, C, Jeuffroy, MH, Makowski, D, Quesnel, G, Ramat, E, Raynal, H, Rousse, N, Wallach, D, Debaeke, P, Durand, P, Duru, M, Dury, J, Faverdin, P, Gascuel-Odoux, C, Garcia, F (2013) An open platform to build, evaluate and simulate integrated models of farming and agro-ecosystems. Environ Model Softw 39: pp. 39-49 CrossRef
  21. Bernstein, PA, Bergstraesser, T, Carlson, J, Pal, S, Sanders, P, Shutt, D (1999) Microsoft repository version 2 and the open information model. Inf Syst 24: pp. 71-98 CrossRef
  22. Boe J (2007) Changement global et cycle hydrologique: une 茅tude de r茅gionalisation sur la France. PhD thesis, University Paul Sabatier(in French)
  23. Booch, G, Rumbaugh, J, Jacobson, I (1999) The unified modeling language user guide. Addison-Wesley, Reading
  24. Bredehoeft, JD, Konikow, LF (1993) Ground water models: validate or invalidate. Ground Water 3: pp. 178-179 CrossRef
  25. Bregaglio S, Donatelli M, Confalonieri R, De Mascellis R, Acutis M (2012) Comparing modelling solutions at submodel level: a case on soil temperature simulation. In: Seppelt R, Voinov AA, Lange S, Bankamp D (eds) International Environmental Modelling and Software Society (iEMSs), 2012 International Congress on Environmental Modelling and Software, Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig. http://www.iemss.org/sites/iemss2012//proceedings/D3_1_0851_Bregaglio_et_al.pdf. Accessed 06 November 2014
  26. Caminiti, JE (2004) Catchment modelling鈥攁 resource manager鈥檚 perspective. Environ Model Softw 19: pp. 991-997 CrossRef
  27. Carberry, PS, Hochman, Z, McCown, RL, Dalgliesh, NP, Foale, MA, Poulton, PL, Hargreaves, JNG, Hargreaves, DMG, Cawthray, S, Hillcoat, N, Robertson, MJ (2002) The FARMSCAPE approach to decision support: farmers鈥? advisers鈥? researchers鈥?monitoring, simulation, communication and performance evaluation. Agr Syst 74: pp. 141-177 CrossRef
  28. Carozzi, M, Bregaglio, S, Scaglia, B, Bernardoni, E, Acutis, M, Confalonieri, R (2013) The development of a methodology using fuzzy logic to assess the performance of cropping systems based on a case study of maize in the Po Valley. Soil Use Manage 29: pp. 576-585 CrossRef
  29. Chopin P, Blazy J-M, Dore T (2014) Indicators for the assessment of the sustainability level of agricultural landscapes. In: Pep贸 P, Csajb贸k J (eds) Proceedings of the 13th Congress of the European Society for Agronomy, 25鈥?9 August, Debrecen, pp 149鈥?50. http://www.esa2014.hu/doc/esa2014_proceedings.pdf
  30. IPCC (Intergovernmental Panel on Climate Change) (2013) IPCC 5th Assessment Report 鈥淐limate Change 2013: the Physical Science Basis. University Press, Cambridge. http://www.ipcc.ch/report/ar5/wg1/#.Uk7O1xBvCVq. Accessed 06 November 2014
  31. Colomb B, Carof M, Aveline A, Bergez J-E (2013) Stockless organic farming: strengths and weaknesses evidenced by a multicriteria sustainability assessment model. Agron Sustain Dev 33:593鈥?08. doi:10.1007/s13593-012-0126-5
  32. Confalonieri, R, Acutis, M, Donatelli, M, Bellocchi, G, Mariani, L, Boschetti, M, Stroppiana, D, Bocchi, S, Vidotto, F, Sacco, D, Grignani, C, Ferrero, A, Genovese, G (2005) WARM: a scientific group on rice modelling. Italian Journal of Agrometeorology 2: pp. 54-60
  33. Confalonieri, R, Acutis, M, Bellocchi, G, Donatelli, M (2009) Multi-metric evaluation of the models WARM, CropSyst, and WOFOST for rice. Ecol Model 220: pp. 1395-1410 CrossRef
  34. Confalonieri, R, Bregaglio, S, Acutis, M (2010) A proposal of an indicator for quantifying model robustness based on the relationship between variability of errors and of explored conditions. Ecol Model 221: pp. 960-964 CrossRef
  35. Creighton, J The use of values: public participation in the planning process. In: Daneke, GA, Garcia, MW, Priscoli, JD eds. (1983) Public involvement and social impact assessment. Westview Press, Boulder, pp. 143-160
  36. Criscuolo L, Donatelli M, Bellocchi G, Acutis M (2007) Component and software application for model output evaluation. In: Donatelli M, Hatfield J, Rizzoli AE (eds) Farming Systems Design 2007, Int. Symposium on Methodologies on Integrated Analysis on Farm Production Systems, September 10鈥?2, Catania, Vol 2, pp 211鈥?12
  37. Dietz, T, Stern, PC, Rycroft, RW (1989) Definition of conflict and the legitimation of resources: the case of environmental risk. Sociol Forum 4: pp. 47-69 CrossRef
  38. Donatelli M, Rizzoli AE (2007) A design for framework-independent model components of biophysical systems. In: Donatelli M, Hatfield J, Rizzoli AE (eds) Farming Systems Design 2007, International Symposium on Methodologies on Integrated Analysis on Farm Production Systems, September 10鈥?2, Catania, Vol 2, pp 208鈥?09
  39. Donatelli M, Omicini A, Fila G, Monti C (2004) Targeting reusability and replaceability of simulation models for agricultural systems. In: Jacobsen SE., Jensen CR, Porter JR (eds) Proceedings of the 8th European Society for Agronomy Congress, 11鈥?5 July, Copenhagen, pp 237鈥?38
  40. Donatelli M, Carlini L, Bellocchi G, Colauzzi M (2005) CLIMA: a component-based weather generator. In: Zerger A, Argent RN (eds) MODSIM 2005 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 12鈥?5 December, Melbourne, pp 627鈥?33
  41. Donatelli M, Cerrani I, Fanchini D, Fumagalli D., Rizzoli AE (2012) Enhancing model reuse via component-centered modeling frameworks: the vision and example realizations. In: Seppelt R, Voinov AA, Lange S, Bankamp D (eds.) International Environmental Modelling and Software Society (iEMSs), 2012 International Congress on Environmental Modelling and Software, Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig. http://www.iemss.org/sites/iemss2012//proceedings/D3_1_0847_Donatelli_et_al.pdf. Accessed 06 November 2014
  42. Donatelli M, Bregaglio S, Confalonieri R, De Mascellis R, Acutis M (2014) A generic framework for evaluating hybrid models by reuse and composition鈥攁 case study on soil temperature simulation. Env Modell Softw. doi:http://dx.doi.org/10.1016/j.envsoft.2014.04.011
  43. Dryzek, J (2000) Deliberative democracy and beyond: liberals, critics, contestations (Oxford Political Theory). Oxford University Press, New York
  44. Ethier, GJ, Livingston, NJ (2004) On the need to incorporate sensitivity to CO2 transfer conductance into the Farquhar-von Caemmerer鈥揃erry leaf photosynthesis model. Plant Cell Environ 27: pp. 137-153 CrossRef
  45. Fearon, JD Deliberation as discussion. In: Elster, J eds. (1998) Deliberative democracy. Cambridge University Press, Cambridge, pp. 44-68 CrossRef
  46. Fila, G, Bellocchi, G, Acutis, M, Donatelli, M (2003) IRENE: a software to evaluate model performance. Eur J Agron 18: pp. 369-372 CrossRef
  47. Fila, G, Bellocchi, G, Donatelli, M, Acutis, M (2003) IRENE_DLL: a class library for evaluating numerical estimates. Agron J 95: pp. 1330-1333 CrossRef
  48. Fila, G, Donatelli, M, Bellocchi, G (2006) PTFIndicator: an IRENE_DLL-based application to evaluate estimates from pedotransfer functions by integrated indices. Environ Model Softw 21: pp. 107-110 CrossRef
  49. Fila, G, Lena, B, Gardiman, M, Storchi, P, Tomasi, D, Silvestroni, O, Pitacco, A (2014) Calibration and validation of grapevine budburst models using growth-room experiments as data source. Agr Forest Meteorol 160: pp. 69-79 CrossRef
  50. French, S, Geldermann, J (2005) The varied contexts of environmental decision problems and their implications for decision support. Environ Sci Policy 8: pp. 378-391 CrossRef
  51. Girot P, Ehrhart C, Oglethorpe J (2012) Integrating community and ecosystem-based approaches in climate change adaptation responses. Ecosystems & Livelihood Adaptation Network Report. http://www.careclimatechange.org/files/adaptation/ELAN_IntegratedApproach_150412.pdf. Accessed 06 November 2014
  52. Glasow PA, Pace DK (1999) SIMVAL鈥?9: making VV&A effective and affordable workshop. The Simulation Validation Workshop 1999, January 26鈥?9, Laurel
  53. Gliessman, SR (2007) Agroecology: the ecology of sustainable food systems. CRC Press, Boca Raton
  54. Hamilton, MA (1991) Model validation: an annotated bibliography. Communications Stat-Theor M 20: pp. 2207-2266 CrossRef
  55. Hutchins, MG, Urama, K, Penning, E, Icke, J, Dilks, C, Bakken, T, Perrin, C, Saloranta, T, Candela, L, Kamari, J (2006) The BMW model evaluation tool: a guidance document. Archiv f眉r Hydrologie: Large Rivers Supplement 17: pp. 23-48
  56. Jakeman, AJ, Letcher, RA, Norton, JP (2006) Ten iterative steps in development and evaluation of environmental models. Environ Model Softw 21: pp. 602-614 CrossRef
  57. Keating, BA, Gaydon, D, Huth, NI, Probert, ME, Verburg, K, Smith, CJ, Bond, W (2002) Use of modelling to explore the water balance of dryland farming systems in the Murray-darling basin, Australia. Eur J Agron 18: pp. 159-169 CrossRef
  58. Keating, BA, Carberry, PS, Hammer, GL, Probert, ME, Robertson, MJ, Holzworth, D, Huth, NI, Hargreaves, JNG, Meinke, H, Hochman, Z, McLean, G, Verburg, K, Snow, V, Dimes, JP, Silburn, M, Wang, E, Brown, S, Bristow, KL, Asseng, S, Chapman, S, McCown, RL, Freebairn, DM (2003) An overview of APSIM, a model designed for farming systems simulation. Eur J Agron 18: pp. 267-288 CrossRef
  59. Kolkman MJ, van der Veen A (2006) Without a common mental model a DSS makes no sense (a new approach to frame analysis using mental models). In: Voinov A, Jakeman AJ, Rizzoli AE (eds) Proceedings of the 3rd Biennial Meeting of the International Environmental Modelling and Software Society (iEMSs), July 9鈥?3, Burlington. http://www.iemss.org/iemss2006/papers/s10/140_Kolkman_1.pdf. Accessed 11 June 2014
  60. Konikow, LF, Bredehoeft, JD (1992) Ground water models cannot be validated. Adv Water Resour 15: pp. 75-83 CrossRef
  61. Landry, M, Oral, M (1993) In search of a valid view of model validation for operations research. Eur J Oper Res 66: pp. 161-167 CrossRef
  62. Laniak, GF, Olchin, G, Goodall, J, Voinov, A, Hill, M, Glynn, P, Whelan, G, Geller, G, Quinn, N, Blind, M, Peckham, S, Reaney, S, Gaber, N, Kennedy, R, Hughes, A (2013) Integrated environmental modeling: a vision and roadmap for the future. Environ Model Softw 39: pp. 3-23 CrossRef
  63. Li T, Hasegawa T, Yin X, Zhu Y, Boote K, Adam M, Bregaglio S, Buis S, Confalonieri R, Fumoto T, Gaydon D, Marcaida III M, Nakagawa H, Oriol P, Ruane AC, Ruget F, Singh B, Singh U, Tang L, Tao F, Wilkens P, Yoshida H, Zhang Z, Bouman B (2014) Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Global Change Biol, in press. doi:10.1111/gcb.12758
  64. Liu, S, Sheppard, A, Kriticos, D, Cood, D (2011) Incorporating uncertainty and social values in managing invasive alien species: a deliberative multi-criteria evaluation approach. Biol Invasions 13: pp. 2323-2337 CrossRef
  65. Lizaso JI (2014) Improving crop models: incorporating new processes, new approaches, and better calibrations. In: Pep贸 P, Csajb贸k J (eds) Proceedings of the 13th Congress of the European Society for Agronomy, 25鈥?9 August, Debrecen, pp 5鈥?0. http://www.esa2014.hu/doc/esa2014_proceedings.pdf. Accessed 06 November 2014
  66. Maiorano, A, Cerrani, I, Fumagalli, D, Donatelli, M (2014) New biological model to manage the impact of climate warming on maize corn borers. Agron Sustain Dev 34: pp. 609-621 CrossRef
  67. Matthews, R (2006) The People and Landscape Model (PALM): towards full integration of human decision-making and biophysical simulation models. Ecol Model 4: pp. 329-343 CrossRef
  68. Matthews, KB, Buchan, K, Sibbald, AR, Craw, S (2006) Combining deliberative and computer-based methods for multi-objective land-use planning. Agr Syst 87: pp. 18-37 CrossRef
  69. Matthews, KB, Rivington, M, Buchan, K, Miller, DG (2008) Communicating climate change consequences for land use, Technical Report on Science Engagement, Grant No 42/07 2007-08. Macaulay Institute, Aberdeen
  70. Matthews, KB, Schwarz, G, Buchan, K, Rivington, M, Miller, D (2008) Wither agricultural DSS?. Comput Electron Agr 61: pp. 149-159 CrossRef
  71. Matthews, KB, Rivington, M, Blackstock, K, McCrum, G, Buchan, K, Miller, DG (2011) Raising the bar?鈥攖he challenges of evaluating the outcomes of environmental modelling and software. Environ Model Softw 26: pp. 247-257 CrossRef
  72. Matthews KB, Miller DG, Warden-Johnson D (2013) Supporting agricultural policy鈥攖he role of scientists and analysts in managing political risk. In: Piantadosi J, Anderssen RS, Boland J (eds) MODSIM 2013 International Congress on Modelling and Simulation, 1鈥? December, Adelaide, pp 2152鈥?158
  73. McCown, RL (2002) Changing systems for supporting farmers鈥?decisions: problems, paradigms, and prospects. Agr Syst 74: pp. 179-220 CrossRef
  74. McCown, RL (2002) Locating agricultural decision support systems in the troubled past and socio-technical complexity of models for management. Agr Syst 74: pp. 11-25 CrossRef
  75. McCown RL, Hochman Z, Carberry PS (2005) In search of effective simulation-based intervention in farm management. In: Zerger A, Argent RM (eds) MODSIM 2005 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 12鈥?5 December, Melbourne, pp 232鈥?38
  76. Meinke, H, Baethgen, WE, Carberry, PS, Donatelli, M, Hammer, GL, Selvaraju, R, St枚ckle, CO (2001) Increasing profits and reducing risks in crop production using participatory systems simulation approaches. Agr Syst 70: pp. 493-513 CrossRef
  77. Mijatovi膰, D, Oudenhoven, F, Eyzaguirre, P, Hodgkin, T (2013) The role of agricultural biodiversity in strengthening resilience to climate change: towards an analytical framework. Int J Agr Sustain 11: pp. 95-107 CrossRef
  78. Olesen, HR, Chang, JC (2010) Consolidating tools for model evaluation. Int J Environ Pollut 40: pp. 175-183 CrossRef
  79. Oreskes, N (1998) Evaluation (not validation) of quantitative models. Environ Health Persp 106: pp. 1453-1460 CrossRef
  80. Papajorgji, P, Beck, HW, Braga, JL (2004) An architecture for developing service-oriented and component-based environmental models. Ecol Model 179: pp. 61-76 CrossRef
  81. Petts, J (2001) Evaluating the effectiveness of deliberative processes: waste management case-studies. J Environ Plan Manage 44: pp. 207-226 CrossRef
  82. Raupach, MR, Finnigan, JJ (1988) Single layer models of evaporation from plant canopies are incorrect, but useful, whereas multilayer models are correct, but useless: discussion. Aust J Plant Physiol 15: pp. 705-716 CrossRef
  83. Rauschmayer, F, Wittmer, H (2006) Evaluating deliberative and analytical methods for the resolution of environmental conflicts. Land Use Policy 23: pp. 108-122 CrossRef
  84. Reynolds, JF, Acock, B (1997) Modularity and genericness in plant and ecosystems models. Ecol Model 94: pp. 7-16 CrossRef
  85. Reynolds, JF, Ford, ED (1999) Multi-criteria assessment of ecological process models. Ecology 80: pp. 538-553 CrossRef
  86. Richter, K, Atzberger, C, Hank, TB, Mauser, W (2012) Derivation of biophysical variables from Earth observation data: validation and statistical measures. J Appl Remote Sens 6: pp. 063557 CrossRef
  87. Ritter, A, Mu帽oz-Carpena, R (2013) Performance evaluation of hydrological models: statistical significance for reducing subjectivity in goodness-of-fit assessments. J Hydrol 480: pp. 33-45 CrossRef
  88. Rizzoli, AE, Davis, JR, Abel, DJ (1998) A model management system for model integration and re-use. Decis Support Syst 4: pp. 127-144 CrossRef
  89. Rotmans, J, Asselt, MBA (2001) Uncertainty in integrated assessment modelling: a labyrinthic path. Integr Assess 2: pp. 43-55 CrossRef
  90. Rykiel, EJ (1996) Testing ecological models: the meaning of validation. Ecol Model 90: pp. 229-244 CrossRef
  91. Sargent RG (2001) Verification, validation and accreditation of simulation models. In: Peters BA, Smith JS, Medeiros DJ, Rohrer MW (eds) Proceedings of 2001 Winter Simulation Conference, December 10鈥?3, Arlington, pp 106鈥?14
  92. Schneider, SH CO2, climate and society: a brief overview. In: Chen, RS, Boulding, E, Schneider, SH eds. (1983) Social science research and climate change: an interdisciplinary appraisal. Reidel, Boston, pp. 9-15 CrossRef
  93. Spitzeck, H, Hansen, EG (2010) Stakeholder governance: how stakeholders influence corporate decision making corporate governance. Int J Bus soc 10: pp. 378-391
  94. Steduto, P, Hsiao, T, Raes, D, Fereres, E (2009) AquaCrop鈥攖he FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron J 101: pp. 426-437 CrossRef
  95. Szypersky, C, Gruntz, D, Murer, S (2002) Component software鈥攂eyond object-oriented programming. Addison-Wesley, London
  96. Tanner, CB, Sinclair, TR Efficient water use in crop production: research or re-search?. In: Taylor, HMJ, Sinclair, TR eds. (1983) Limitations to efficient water use in crop production. American Society of Agronomy, Madison, pp. 1-27
  97. Tedeschi, LO (2006) Assessment of the adequacy of mathematical models. Agr Syst 89: pp. 225-247 CrossRef
  98. Teng枚, M, Brondizio, ES, Elmqvist, T, Malmer, P, Spierenburg, M (2014) Connecting diverse knowledge systems for enhanced ecosystem governance: the multiple evidence base approach. Ambio 43: pp. 579-591 CrossRef
  99. Timothy, B (1997) An introduction to object-oriented programming. Addison-Wesley, Reading
  100. Van Ittersum MK (2003) Modelling cropping systems鈥攈ighlights of the symposium and preface to the special issues. Eur J Agron 18:189鈥?91. doi:10.1016/S1161-0301(02)00095-3
  101. Van Ittersum MK (2006) Integrated assessment of agriculture and environmental policies: towards a computerised framework for the EU (SEAMLESS-IF). In: Voinov A (ed) Proceedings of the 3rd Biennial Meeting of the International Environmental Modelling and Software Society (iEMSS), July 9鈥?3, Burlington. http://www.iemss.org/iemss2006/papers/s10/280_vanIttersum_1.pdf. Accessed 11 June 2014
  102. Vanclay, JK (1994) Modelling forest growth and yield. CAB International, Wallingford
  103. Vanclay, JK, Skovsgaard, JP (1997) Evaluating forest growth models. Ecol Model 98: pp. 1-12 CrossRef
  104. Vischel, T, Pegram, G, Sinclair, S, Wagner, W, Bartsch, A (2007) Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa. Hydrol Earth Syst Sci Discuss 4: pp. 2273-2306 CrossRef
  105. Voinov, A, Bousquet, F (2010) Modelling with stakeholders. Environ Model Softw 25: pp. 1268-1281 CrossRef
  106. Woodbury, PB, Beloin, RM, Swaney, DP, Gollands, BE, Weinstein, DA (2002) Using the ECLPSS software environment to build a spatially explicit component-based model of ozone effects on forest ecosystems. Ecol Model 150: pp. 211-238 CrossRef
  
• 刊物主题：Agriculture; Soil Science & Conservation; Sustainable Development;
• 出版者：Springer Paris
• ISSN：1773-0155

文摘

The use of biophysical models in agroecology has increased in the last few decades for two main reasons: the need to formalize empirical knowledge and the need to disseminate model-based decision support for decision makers (such as farmers, advisors, and policy makers). The first has encouraged the development and use of mathematical models to enhance the efficiency of field research through extrapolation beyond the limits of site, season, and management. The second reflects the increasing need (by scientists, managers, and the public) for simulation experimentation to explore options and consequences, for example, future resource use efficiency (i.e., management in sustainable intensification), impacts of and adaptation to climate change, understanding market and policy responses to shocks initiated at a biophysical level under increasing demand, and limited supply capacity. Production concerns thus dominate most model applications, but there is a notable growing emphasis on environmental, economic, and policy dimensions. Identifying effective methods of assessing model quality and performance has become a challenging but vital imperative, considering the variety of factors influencing model outputs. Understanding the requirements of stakeholders, in respect of model use, logically implies the need for their inclusion in model evaluation methods. We reviewed the use of metrics of model evaluation, with a particular emphasis on the involvement of stakeholders to expand horizons beyond conventional structured, numeric analyses. Two major topics are discussed: (1) the importance of deliberative processes for model evaluation, and (2) the role computer-aided techniques may play to integrate deliberative processes into the evaluation of agroecological models. We point out that (i) the evaluation of agroecological models can be improved through stakeholder follow-up, which is a key for the acceptability of model realizations in practice, (ii) model credibility depends not only on the outcomes of well-structured, numerically based evaluation, but also on less tangible factors that may need to be addressed using complementary deliberative processes, (iii) comprehensive evaluation of simulation models can be achieved by integrating the expectations of stakeholders via a weighting system of preferences and perception, (iv) questionnaire-based surveys can help understand the challenges posed by the deliberative process, and (v) a benefit can be obtained if model evaluation is conceived in a decisional perspective and evaluation techniques are developed at the same pace with which the models themselves are created and improved. Scientific knowledge hubs are also recognized as critical pillars to advance good modeling practice in relation to model evaluation (including access to dedicated software tools), an activity which is frequently neglected in the context of time-limited framework programs.