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Chinese Journal of Applied Ecology ›› 2018, Vol. 29 ›› Issue (6): 2017-2027.doi: 10.13287/j.1001-9332.201806.012

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Parameter estimation and verification of DSSAT-CROPGRO-Tomato model under different irrigation levels in greenhouse.

ZHAO Zi-long1, LI Bo1*, FENG Xue2, YAO Ming-ze1, XIE Ying1, XING Jing-wei1, LI Chang-xin1   

  1. 1College of Water Conservancy, Shenyang Agricultural University, Shenyang 110866, China;
    2College of Science, Shenyang Agricultural University, Shenyang 110866, China
  • Received:2017-09-22 Revised:2018-03-07 Online:2018-06-18 Published:2018-06-18
  • Supported by:

    This work was supported by the National Natural Science Foundation of Liaoning Province (2015020770).

Abstract: Based on the greenhouse experiment in Shenyang, the growth, development, and yield formation of tomato under different irrigation levels were simulated by growth model DSSAT-CROPGRO-Tomato. The optimal scheme of parameter estimation and model validation was determined. There were four treatments in this experiment. Irrigation upper limit of whole growth season was set as field capacity, while the lower limit was 50% (W1), 60% (W2), 70% (W3), and 80% of field capacity (CK), respectively. The relevant genetic coefficients were estimated by DSSAT-GLUE, a program package for parameter estimation in DSSAT. The differences between simulated and observed values of phenological phase, canopy height, shoot dry matter, tomato fresh mass, leaf area index (LAI), and soil moisture were analyzed to determine the accuracy of simulation. The results showed that the estimated value of genetic parameter of tomato (thermal time for final pod load appeared greater variability under optimal genetic coefficient of tomato, PODUR) had large variability, with the coefficient of variation being 11.5%. When the CROPGRO-Tomato model was applied to the greenhouse in different regions, the PODUR should be estimated adequately. Otherwise, the accuracy of simulation would be affected. In the process of model application, the observation data of sufficient irrigation treatment should be selected for estimating genetic parameters, which could improve the simulation precision. The absolute relative error and standard root mean square error were 8.7% and 10.5%, respectively. The simulation results of LAI and soil moisture showed that the higher the irrigation level was, the higher accuracy of simulation was. By leave-one-out cross validation, the overall error validation ranged from 10.5% to 12.5%. Our results indicated that the growth, development, and yield formation of tomato could be accurately simulated by DSSAT CROPGRO-Tomato model under different irrigation conditions in Shenyang greenhouse.