The chaos in calibrating crop models: Lessons learned from a multi-model calibration exercise.
Daniel WallachTaru PalosuoPeter J. ThorburnZvi HochmanEmmanuelle GourdainFety AndrianasoloSenthold AssengBruno BassoSamuel BuisNeil CroutCamilla DibariBenjamin DumontRoberto FerriseThomas GaiserCecile GarciaSebastian GaylerAfshin GhahramaniSantosh HiremathSteven HoekHeidi HoranGerrit HoogenboomMingxia HuangMohamed JablounPer-Erik JanssonQi JingEric JustesKurt Christian KersebaumAnne KlosterhalfenMarie LaunayElisabet LewanQunying LuoBernardo MaestriniHenrike MielenzMarco MoriondoHasti Nariman ZadehGloria PadovanJørgen Eivind OlesenArne PoydaEckart PriesackJohannes W. M. PullensBudong QianNiels SchützeVakhtang SheliaAmir SouissiXenia SpeckaAmit Kumar SrivastavaTommaso StellaThilo StreckGiacomo TrombiEvelyn WallorJing WangTobias K. D. WeberLutz WeihermüllerAllard de WitThomas WöhlingLiujun XiaoChuang ZhaoYan ZhuSabine J. SeidelPublished in: Environ. Model. Softw. (2021)
Keyphrases
- lessons learned
- probabilistic model
- experimental data
- classification models
- hybrid model
- computational model
- mathematical model
- parametric models
- statistical model
- linear model
- parameter estimation
- metamodel
- computational models
- statistical models
- future directions
- prior knowledge
- modeling framework
- analytical model
- generic model
- multiple models
- accurate models
- bayesian framework
- bayesian networks
- cooperative
- markov random field
- learning algorithm
- similarity measure
- objective function
- linear models
- d objects
- autoregressive
- particle swarm optimization
- monte carlo simulation
- learning models
- random fields
- design process
- pose estimation
- process model