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Journal of Integrative Agriculture  2023, Vol. 22 Issue (5): 1381-1395    DOI: 10.1016/j.jia.2022.08.018
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Novel models for simulating maize growth based on thermal time and photothermal units: Applications under various mulching practices

LIAO Zhen-qi1, ZHENG Jing1, 2, FAN Jun-liang1#, PEI Sheng-zhao1, DAI Yu-long1, ZHANG Fu-cang1, LI Zhi-jun1

1 Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, P.R.China

2 Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, P.R.China

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摘要  玉米(Zeamays L.)是三大粮食作物之一,是碳水化合物的重要来源,对保障世界粮食安全具有重要意义。株高(H)、茎粗(SD)、叶面积指数(LAI)和干物质(DM)是影响玉米产量的重要生长参数。然而,作物生长模型中很少考虑温度和光照对玉米生长的综合效应。基于2015-2018年的大田试验数据,本文提出了10个基于修正Logistic生长方程(Mlog)和Mitscherlich生长方程(Mit)的玉米生长模型,用于模拟不同覆盖措施下玉米H、SD、LAI和DM。以累积生长度日(AGDD)、太阳热单位(HTU)、光热单位(PTU)或我们首次提出的光周期热单位(PPTU)作为模型的单一驱动因素; AGDD进一步结合累积实际日照时数(ASS)、我们首次提出的累积光周期响应(APR)或累积最大可能日照时数(ADL)作为模型的双重驱动因素。采用七个统计指标和一个全局性能指数评估模型模拟效果。研究结果表明,与不覆盖相比,三种覆盖方式显着提高了玉米的生长速率和生长峰值。在四个单因素驱动模型中,MlogPTU模型的整体性能最优,其次为MlogAGDD模型。MlogPPTU模型在模拟SD和LAI方面优于MlogAGDD模型。在所有十个模型中,MlogAGDD-APR模型的整体性能最优,其次为MlogAGDD-ASS模型。具体而言,MlogAGDD-APR模型模拟H和LAI效果最好,而MlogAGDD-ADL和MlogAGDD-ASS模型分别模拟SD和DM效果最好。综上所述,以AGDD和APR、ASS或ADL为双驱动因素的修正Logistic生长方程在模拟玉米生长方面优于常规的以AGDD为单驱动因子的修正Logistic生长模型。


Maize (Zea mays L.) is one of the three major food crops and an important source of carbohydrates for maintaining food security around the world.  Plant height (H), stem diameter (SD), leaf area index (LAI) and dry matter (DM) are important growth parameters that influence maize production.  However, the combined effect of temperature and light on maize growth is rarely considered in crop growth models.  Ten maize growth models based on the modified logistic growth equation (Mlog) and the Mitscherlich growth equation (Mit) were proposed to simulate the H, SD, LAI and DM of maize under different mulching practices based on experimental data from 2015–2018.  Either the accumulative growing degree-days (AGDD), helio thermal units (HTU), photothermal units (PTU) or photoperiod thermal units (PPTU, first proposed here) was used as a single driving factor in the models; or AGDD was combined with either accumulative actual solar hours (ASS), accumulative photoperiod response (APR, first proposed here) or accumulative maximum possible sunshine hours (ADL) as the dual driving factors in the models.  The model performances were evaluated using seven statistical indicators and a global performance index.  The results showed that the three mulching practices significantly increased the maize growth rates and the maximum values of the growth curves compared with non-mulching.  Among the four single factor-driven models, the overall performance of the MlogPTU Model was the best, followed by the MlogAGDD Model.  The MlogPPTU Model was better than the MlogAGDD Model in simulating SD and LAI.  Among the 10 models, the overall performance of the MlogAGDD–APR Model was the best, followed by the MlogAGDD–ASS Model.  Specifically, the MlogAGDD–APR Model performed the best in simulating H and LAI, while the MlogAGDD–ADL and MlogAGDD–ASS models performed the best in simulating SD and DM, respectively.  In conclusion, the modified logistic growth equations with AGDD and either APR, ASS or ADL as the dual driving factors outperformed the commonly used modified logistic growth model with AGDD as a single driving factor in simulating maize growth.

Keywords:  thermal time       accumulative growing degree-days       helio thermal units       photothermal units       growth model  
Received: 24 January 2022   Accepted: 17 March 2022

This study was funded by the National Natural Science Foundation of China (51879226) and the Chinese Universities Scientific Fund (2452020018).


About author:  #Correspondence FAN Jun-liang, E-mail:

Cite this article: 

LIAO Zhen-qi, ZHENG Jing, FAN Jun-liang, PEI Sheng-zhao, DAI Yu-long, ZHANG Fu-cang, LI Zhi-jun. 2023.

Novel models for simulating maize growth based on thermal time and photothermal units: Applications under various mulching practices . Journal of Integrative Agriculture, 22(5): 1381-1395.

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