Models Calibration and Evaluation

TY - CHAP

T1 - Models Calibration and Evaluation

AU - Ahmed, Mukhtar

AU - Ahmed, Shakeel

AU - Raza, Muhammad Ali

AU - Kumar, Uttam

AU - Ansar, Muhammad

AU - Shah, Ghulam Abbas

AU - Parsons, David

AU - Hoogenboom, Gerrit

AU - Palosuo, Taru

AU - Seidel, Sabine J.

PY - 2020

Y1 - 2020

N2 - Calibration of crop model is standard practice, and it involves estimation of crop parameters based upon observed field data. It is the process of estimation of unknown parameters using practical observations. It is generally carried out manually by adjusting the parameters of the model. It consists of choosing the accurate numbers of coefficients that play a significant role in the adjustment of soil nitrogen, soil organic carbon, soil phosphorus, crop growth, phenological development, biomass accumulation, dry-matter partitioning, nutrients uptake, grain dry weight, grain numbers, grain yield, grain nitrogen (N) at maturity and protein content. A minimum data set (MDS) is required for the calibration of the model. A number of different steps could be used to calibrate the crop model. The initial step involves running of the model with default crop parameters and comparison of simulation outcomes with the observed data set. Afterwards, crop parameters are adjusted to have good agreement with observed and simulated data. It starts with phenology, then vegetative growth and biomass, afterwards yield components and finally yield. Optimization tools such as generalized likelihood uncertainty estimation (GLUE) and Markov chain Monte Carlo (MCMC) can be used for the calibration. Finally, evaluation of models needs to be performed with independent data set. The quantification of calibration and evaluation goodness should be evaluated by different skill scores such as root-mean-squared error (RMSE)/root-mean-square deviation (RMSD), relative RMSE (RRMSE) or normalized objective function (NOF), root-mean-square deviation-systematic error (RMSDse), root-mean-square deviation-non-systematic error (RMSDnse), mean absolute error (MAE), mean bias error (ME), coefficient of determination (R2), Nash-Sutcliffe modelling efficiency (EF) test, maximum difference (MD) and D index (index of agreement). These skill scores confirm the calibrated model performances under different sets of scenarios.

AB - Calibration of crop model is standard practice, and it involves estimation of crop parameters based upon observed field data. It is the process of estimation of unknown parameters using practical observations. It is generally carried out manually by adjusting the parameters of the model. It consists of choosing the accurate numbers of coefficients that play a significant role in the adjustment of soil nitrogen, soil organic carbon, soil phosphorus, crop growth, phenological development, biomass accumulation, dry-matter partitioning, nutrients uptake, grain dry weight, grain numbers, grain yield, grain nitrogen (N) at maturity and protein content. A minimum data set (MDS) is required for the calibration of the model. A number of different steps could be used to calibrate the crop model. The initial step involves running of the model with default crop parameters and comparison of simulation outcomes with the observed data set. Afterwards, crop parameters are adjusted to have good agreement with observed and simulated data. It starts with phenology, then vegetative growth and biomass, afterwards yield components and finally yield. Optimization tools such as generalized likelihood uncertainty estimation (GLUE) and Markov chain Monte Carlo (MCMC) can be used for the calibration. Finally, evaluation of models needs to be performed with independent data set. The quantification of calibration and evaluation goodness should be evaluated by different skill scores such as root-mean-squared error (RMSE)/root-mean-square deviation (RMSD), relative RMSE (RRMSE) or normalized objective function (NOF), root-mean-square deviation-systematic error (RMSDse), root-mean-square deviation-non-systematic error (RMSDnse), mean absolute error (MAE), mean bias error (ME), coefficient of determination (R2), Nash-Sutcliffe modelling efficiency (EF) test, maximum difference (MD) and D index (index of agreement). These skill scores confirm the calibrated model performances under different sets of scenarios.

KW - Calibration

KW - Estimation

KW - Minimum data set

KW - Optimization tools

KW - Phenology

KW - Skills scores

UR - http://www.scopus.com/inward/record.url?scp=85150094945&partnerID=8YFLogxK

M3 - Book chapter

SP - 151

EP - 178

BT - Systems Modeling

PB - Springer

ER -