基于Cropgro-Tomato模型的温室番茄生长模拟

doi:10.19640/j.cnki.jtau.2025.04.007

Journal of Tianjin Agricultural University ›› 2025, Vol. 32 ›› Issue (4): 47-53.doi: 10.19640/j.cnki.jtau.2025.04.007

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Simulation of greenhouse tomato growth based on Cropgro-Tomato model

Wang Yikun, Zheng Zhiwei^{Corresponding Author}, Zhang Ni, Dou Jingjing

College of Water Conservancy Engineering, Tianjin Agricultural University, Tianjin 300392, China

Received:2024-01-25 Published:2025-09-02

Abstract

Abstract: To optimize the irrigation system for greenhouse tomato in Tianjin, combined with crop modeling, this study used EFAST to complete the sensitivity analysis of the model output results under different irrigation treatments（60%θf, 70%θf, 80%θf, θf: field capacity）to the model input variety parameters based on the soil, weather and field management data of greenhouse tomato in Tianjin from 2021 to 2022. Parameter correction and validation of the Cropgro-Tomato model were carried out with reference to the results of the sensitivity analysis, measured phenology, LAI, aboveground dry matter mass and yield data of tomato from 2020 to 2022. Using the validated model to simulate of the dry matter mass, yield of tomato under different irrigation treatments（W1-W7: 50%θf, 55%θf, 60%θf, 65%θf, 70%θf, 75%θf, 80%θf）, the scores were comprehensively evaluated by principal component analysis. The results showed that: the most sensitive variety parameter for anthesis days, maturity days, LAI, aboveground dry matter mass, and fresh weight yield were EMFL, and the model has high simulation accuracy for tomato phenology, LAI, aboveground dry matter mass, and yield. The yield and irrigation water use efficiency of the simulation results were maximum at W6 and W3 treatments with 83.7 t/hm² and 33.55 kg/m³, respectively; the treatment with the highest scores was W5, and the lowest one was W1. It showed that the optimal irrigation treatment is the irrigation lower limit of 70%θf-75%θf.

Key words: greenhouse tomato, cropgro-tomato model, EFAST, global sensitivity analysis, fresh fruit yield, principal component analysis

CLC Number:

S626
S641.2

Wang Yikun, Zheng Zhiwei, Zhang Ni, Dou Jingjing. Simulation of greenhouse tomato growth based on Cropgro-Tomato model[J]. Journal of Tianjin Agricultural University, 2025, 32(4): 47-53.

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Simulation of greenhouse tomato growth based on Cropgro-Tomato model

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