Models of Dry Matter Production and Yield Formation for the Protected Tomato

  • Yuli Chen
  • Zhiyou Zhang
  • Yan Liu
  • Yan Zhu
  • Hongxin Cao
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 368)


[Objective] In order to quantify the yield formation of protected tomato, [Method] the field experiments on varieties and fertilizer were conducted in 2009 and 2010, and cultivars: (B1) American mole 1 (early maturing), (B2) Chaoshijifanqiedawang (late maturing), and (B3) American 903 (medium maturing) were adopted; The models of dry matter production and yield formation for protected tomato were built by analyzing the relationships between yield and the number of fruit letting and the mean fruit weight, between yield and biomass and the economic coefficient at harvest, and between the mean fruit weight and economic coefficient and biomass of different varieties and fertilizer levels in accordance with the theory of yield formation. Independent experiments data was used to validate the models. [Result] The results showed that root mean squared error (RMSE), mean absolute error (Xde), and the determined coefficient (R2) between the simulated and measured values of dry matter production was 363.135kg/ha (n=63), 79.016kg/ha, and 0.900, respectively, and RMSE, Xde, and R2 between the simulated and measured values of yield based on yield components was 186.842g per plant (n=36), -1.069g per plant, and 0.854, respectively, and RMSE, Xde, and R2 between the simulated and measured values of yield based on economic coefficient was 137.302g per plant (n=27), 21.170g per plant, and 0.785, respectively. [Conclusion] It indicated that the dry matter production and yield formation under different varieties and fertilizer levels for protected tomato could be well simulated by these models.


protected tomato dry matter production yield formation biomass economic coefficient models 


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Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Yuli Chen
    • 1
    • 2
  • Zhiyou Zhang
    • 1
    • 2
  • Yan Liu
    • 2
  • Yan Zhu
    • 1
  • Hongxin Cao
    • 2
  1. 1.College of AgronomyNanjing Agricultural UniversityNanjingP.R. China
  2. 2.Institute of Agricultural Economy and Information/Engineering Research Center for Digital AgricultureJiangsu Academy of Agricultural SciencesNanjingP.R. China

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