Abstract: Correct parameters are the key prerequisite for the prediction of crop models. In this study, a series of important crop parameters including initial total crop dry weight (TDWI), specific leaf area (SLA), leaf area index at emergency (LAIEM), developing parameters and other physiological parameters were measured and determinated using the observation data and trialanderror method based on the experiment of different sowing dates for maize in Jinzhou during 2011-2015. The adaptability of those parameters in WOFOST model was evaluated by testing the simulation accuracy of growth periods, leaf area index (LAI) and the biomass of different organs of maize with the methods of correlation coefficient, relative error (RE) and root mean square error (RMSE). The results showed that mean simulation errors for antheses and mature stages with 80% and 75% of the total samples were 1.25 and 3.47 days, respectively. The explanatory abilities of simulation to observation for LAI and total aboveground production (TAGP) were 0.8412 and 0.8945, respectively. Besides, there were obvious interannual and sowing date differences for the model performances in simulating abovementioned variables and their simulation accuracy were lower in dry year. Specifically, the RMSEs of LAI and TAGP were 0.38 m²·m^-2 and 9.40 kg·hm^-2 in 2012 respectively when soil water content was under the suitable condition during the whole growing season, and were 0.44 m²·m^-2 and 22.65 kg·hm^-2 in 2015 when maize suffered longterm drought. Furthermore, the model performed worse when the sowing date was deviated from the normal range. The RMSEs of LAI and TAGP were 0.30 m²·m^-2 and 15.19 kg·hm^-2 for the optimum sowing date on 30 April and 0.43 m²·m^-2 and 25.66 kg·hm^-2 for the sowing date on 20 May. Our results provide an important reference for determining the crop model parameters of spring maize in northeast China.

Key words: PM_2.5, atmospheric particulate matter, laser particle size analyzer, Populus tomentosa, elution-weighingparticle sizeanalysis (EWPA).