Irrigation schemes determined based on statistical analysis of field trials are usually only applicable to specific soils and meteorological environments. It is difficult to quantitatively analyze the impact of irrigation strategies on the growth of jujube trees. In order to realize the quantitative analysis of the influence of temperature, light and water resources on the growth of fruit trees, WOrld FOod Studies (WOFOST) model parameters were calibrated to simulate the jujube tree growth and water migration process. Firstly, the observed data obtained from field trials in 2016 and 2017 were used to calibrate the phenology development, initialization, green leaf, CO2 assimilation, dry matter partitioning, respiration, and water use parameters of the WOFOST model. Secondly, the time series of total above-ground biomass, leaf area index (LAI) and soil moisture content in field trials were dynamically simulated, and accuracy verification and analysis were also performed. Finally, the maximum LAI, yield, actual evapotranspiration (ETa) and water use efficiency (WUE) data of 55 orchards were employed to evaluate the performance of the calibrated model at the county scale. The results showed that the coefficient of determination R2 of TAGP simulated in the field test area was between 0.92 and 0.98, and the normalized root mean square error (NRMSE) was between 8.7% and 20.5%, the R2 of simulated LAI ranged from 0.79 to 0.97, and the NRMSE ranged from 8.3% to 21.1%. The R2 of the simulated soil moisture content was between 0.29 and 0.75, and the NRMSE ranged from 4.1% and 6.1%. The model could well simulate the time series of jujube tree growth dynamics and soil moisture content changes. At the county scale, the R2 between the simulated and measured maximum LAI were 0.64 and 0.78, and the NRMSE were 13.3% and 10.7% in 2016 and 2017, respectively. The simulated yield showed R2 value of 0.48 and 0.60, and NRMSE of 12.1% and 11.9%, respectively. RMSE of the simulated versus measured ETa were 36.1 mm (7.9%) and 30.8 mm (7.4%), respectively. The model also showed high WUE simulation accuracy (10%<NRMSE<20%) with RMSE values of 0.23 and 0.28 kg/m3 in 2016 and 2017, respectively. In short, WOFOST model achieved accurate simulation of jujube tree growth and water transport at the field and county scales, which may provide new ideas for the quantitative and mechanism analysis of the coupled effects of soil, weather, irrigation management and jujube tree growth.
Climate change requires crop adaptation. Plantint at the suitable date is a key management technology to promote crop yield and address the impact of climate change. Wheat is one of the most important staple crops in China. Huang-Huai-Hai and Jiang-Huai regions are high-quality and high-quantity planting areas for wheat. To deal with the adverse effects of climate change and promote the winter wheat yield in Huang-Huai-Hai and Jiang-Huai regions, the optimum sowing date was identified by creating a wheat simulation with DSSAT CERES-Wheat model. The simulation experiment was designed with 51 management inputs of sowing date and 4 climate scenarios (RCPs) under baseline period (1985－2004) and 40 years in future for three representative stations in the study region. The optimum sowing data of winter wheat was corresponding to the simulation set with highest yield in each site. The characters of changes in climate factors during the growth period and the optimum sowing date among the different period were detected, and the yield increase planted at the optimum sowing date was quantified. The results showed that, in the future, the climate during winter wheat growth period showed a trend of warming and drying would shorten the growth period. The optimum sowing date would be postponed with the rise of temperature, and the decrease of latitude in all periods and under various climate scenarios. Relative to the baseline period, the maximum delay days of the optimal sowing date increased from north to south during 2030s, which were 5 days, 8 days and 13 days at the three representative stations, respectively. The optimum sowing times in 2050s were delayed in different degrees compared with that in 2030s. The largest postponed days at each station was at the RCP8.5 scenario in 2050s. Adopting the management of optimum planting date could mitigate climatic negative effects and was in varying degrees of yield increasing effect at three sites. The smallest increase occurred in Huang-Huai-Hai north region, while Huang-Huai-Hai south region and Jiang-Huai region had the relatively higher yield increasement about 2%－4%. Therefore, the present study demonstrated an effective management of optimum sowing date to promote winter wheat yield under climate change in Huang-Huai-Hai and Jiang-Huai regions.
Crop models involve complex plant processes, which can be built in different scales of space and time, from molecule, cell, organ, tissue, individual to stand in space and from second to year in time. Based on different research requirements, switching the model scales can make the applicability of the model more extensive and flexible. How to switch the crop model from stand level to organ level is the content of this research. The DSSAT software (stand level) and functional-structural plant model 'GreenLab' (organ level) were chosen to explore the possibility to switch the crop model from stand to organ level. The DSSAT can simulate the growth and development processes of crops in detail according to the growth period by taking the data of weather, soil, crop management, and observational data as input. The GreenLab can simulate the growth and development and their interaction of crops by considering plant structure, and the model parameters can be estimated according to the measurements. In this study, the experimental data contains two parts: the measurements of four maize cultivars with two treatments (irrigated and rainfed) in DSSAT, and the simulations including the weights of leaves, internodes and fruits per day using DSSAT based on the measurements. The simulation results of DSSAT were used to calibrate the parameters of the environmental (E), sink strength (Po), and remobilization (kb and ki) in GreenLab, and to compute the weights of leaves, internodes and fruits for each phytomer. The simulation results of the GreenLab model were compared and analyzed with the experimental data and the simulations of DSSAT. The consistency of calculation results could be an indicator to explore the method of building an interface between different-scale crop models, and to compare the characteristics of different models. The results showed that the GreenLab model could reproduce the simulation data of the DSSAT and the measurement data, including the leaf area index (LAI) and the total weight of the plants, and further could compute the biomass for each organ (leaf, internode and fruit), and the biomass distribution among organs, the biomass production (Q), the demand (D) and the ratio between Q and D during the growth. Therefore, the detailed information of organ growth and development could be reproduced and the 3D structures of plant could be given. Finally, the advantages and application fields of different-scale model integration were discussed.
