A crop growth model, integrating genotype, environment, and management factor, was developed to serve as an analytical tool to study the influence of these factors on crop growth, production, and agricultural planning.  A major challenge of model application is the optimization and calibration of a considerable number of parameters.  Sensitivity analysis (SA) has become an effective method to identify the importance of various parameters.  In this study, the extended Fourier Amplitude Sensitivity Test (EFAST) approach was used to evaluate the sensitivity of the DSSAT-CERES model output responses of interest to 39 crop genotype parameters and six soil parameters.  The outputs for the SA included grain yield and quality (take grain protein content (GPC) as an indicator) at maturity stage, as well as leaf area index, aboveground biomass, and aboveground nitrogen accumulation at the critical process variables.  The key results showed that: (1) the influence of parameter bounds on the sensitivity results was slight and less than the impacts from the significance of the parameters themselves; (2) the sensitivity parameters of grain yield and GPC were different, and the sensitivity of the interactions between parameters to GPC was greater than those between the parameters to grain yield; and (3) the sensitivity analyses of some process variables, including leaf area index, aboveground biomass, and aboveground nitrogen accumulation, should be performed differently.  Finally, some parameters, which improve the model’s structure and the accuracy of the process simulation, should not be ignored when maturity output as an objective variable is studied.

Crop modelling can facilitate researchers’ ability to understand and interpret experimental results, and to diagnose yield gaps. In this paper, the Decision Support Systems for Agrotechnology Transfer 4.6 (DSSAT) model together with the CENTURT soil model were employed to investigate the effect of low nitrogen (N) input on wheat (Triticum aestivum L.) yield, grain N concentration and soil organic carbon (SOC) in a long-term experiment (19 years) under a wheat-maize (Zea mays L.) rotation at Changping, Beijing, China.  There were two treatments including N0 (no N application) and N150 (150 kg N ha^–1) before wheat and maize planting, with phosphorus (P) and potassium (K) basal fertilizers applied as 75 kg P₂O₅ ha^–1 and 37.5 kg K₂O ha^–1, respectively.  The DSSAT-CENTURY model was able to satisfactorily simulate measured wheat grain yield and grain N concentration at N0, but could not simulate these parameters at N150, or SOC in either N treatment.  Model simulation and field measurement showed that N application (N150) increased wheat yield compared to no N application (N0).  The results indicated that inorganic fertilizer application at the rates used did not maintain crop yield and SOC levels.  It is suggested that if the DSSAT is calibrated carefully, it can be a useful tool for assessing and predicting wheat yield, grain N concentration, and SOC trends under wheat-maize cropping systems.

Crop models can be useful tools for optimizing fertilizer management for a targeted crop yield while minimizing nutrient losses. In this paper, the parameters of the decision support system for agrotechnology transfer (DSSAT)-CERES-Maize were optimized using a new method to provide a better simulation of maize (Zea mays L.) growth and N uptake in response to different nitrogen application rates. Field data were collected from a 5 yr field experiment (2006-2010) on a Black soil (Typic hapludoll) in Gongzhuling, Jilin Province, Northeast China. After cultivar calibration, the CERES-Maize model was able to simulate aboveground biomass and crop yield of in the evaluation data set (n-RMSE=5.0-14.6%), but the model still over-estimated aboveground N uptake (i.e., with E values from -4.4 to -21.3 kg N ha-1). By analyzing DSSAT equation, N stress coefficient for changes in concentration with growth stage (CTCNP2) is related to N uptake. Further sensitivity analysis of the CTCNP2 showed that the DSSAT model simulated maize nitrogen uptake more precisely after the CTCNP2 coefficient was adjusted to the field site condition. The results indicated that in addition to calibrating 6 coefficients of maize cultivars, radiation use efficiency (RUE), growing degree days for emergence (GDDE), N stress coefficient, CTCNP2, and soil fertility factor (SLPF) also need to be calibrated in order to simulate aboveground biomass, yield and N uptake correctly. Independent validation was conducted using 2008-2010 experiments and the good agreement between the simulated and the measured results indicates that the DSSAT CERES-Maize model could be a useful tool for predicting maize production in Northeast China.