JIA-2019-11

2522 ZHOU Tian-mei et al. Journal of Integrative Agriculture 2019, 18(11): 2521–2533 2014). Seedling quality is affected by multiple environmental factors during the growth phase in nurseries, particularly illumination and temperature (Li et al . 2012; Carvalho and Folta 2014; De Ron et al . 2016). Under suboptimal light and temperature, seedlings exhibit weak and elongated hypocotyls and lower chlorophyll contents which greatly affect their subsequent field performance. For example, low light intensity due to shading may increase the height of plug seedling and decrease the leaf numbers, resulting in spindly seedling (Yang et al . 2011) or reduction of seedling quality (Li et al . 2012). Similarly, a significant decrease in fresh/ dry mass and relative growth rate was observed in tomato seedlings at suboptimal temperatures (5–18°C) compared to seedlings grown at normal temperatures (14–30°C) (Liu G Y et al . 2017). However, production of healthy seedlings is challenging due to the lack of knowledge in systemic management of nursery environments. A crop growth simulation model, or crop model for short, is a powerful tool to quantitatively predict the dynamic processes of crop development with responses to the environmental conditions and nutrition (Mattsson 1996; Marcelis et al . 1998; Waring et al . 2016; Awaisa et al . 2017; Zhao et al . 2017; Chang et al . 2018; Bauer et al . 2019), which in turn can be used for greenhouse environment management, seedling quality and yield prediction, and production planning (Lentz 1998; Marcelis and Gijzen 1998; Xu et al . 2010). Currently, studies of crop simulation models were developed with characteristics of plant growth including leaf area, dry matter distribution, arrival dates of each phenophase, quality, and productivity (Mattsson 1996; Marcelis et al . 1998; Li et al . 2013; Zhang et al . 2015; Fan et al . 2016; Shi et al . 2016; Tan et al . 2016; Liu C et al . 2017). However, these simulation models mainly focused on the effect of a single environmental factor such as light or temperature (Bruggink 1992; Marcelis and Gijzen 1998; Kahlen and Stützel 2011), making these simulation models less accurate and practical in predicting plant growth and production in a greenhouse with interactive environmental conditions. It is also difficult to accurately evaluate seedling quality using a single index because most of the individual indexes, such as seedling height and fresh mass of seedlings are quantitative traits, which are greatly affected by environmental factors and have poor stability (Zhang et al . 1992, 2004; Huang et al. 2012). The composite index constituting multiple agronomic characters is stable, and can roundly summarize seedlings quality (Yang and Zhou 2010). Bai et al . (2014) adopted fuzzy comprehensive evaluation to identify seedling quality and calculated a composite evaluating index with all 17 single seedling quality indexes; but the composite index is too complex. Their results also showed that the correlation coefficients of [(Stem girth/Plant height)×Total fresh mass×10], [(Stem girth/Plant height)×Total dry mass×10], and ([Stem girth/ Plant height+(Above-ground dry mass/Underground part dry mass)]×Total dry mass)×10) closely related to the composite evaluating index, which might be used to evaluate seedlings quality (Bai et al . 2014). Meanwhile, results from Yang et al . (2013) and Liang et al. (2011) demonstrated that G value (Growth function: defined as ratio of whole plant dry weight to days of seedling) has good yield predictability, and that the root-shoot ration is able to reflect the growth coordination of seedlings. Both can be used to determine seedling quality. Based on these studies, and according to the rules of accuracy and easier determination, we assessed five composite indexes in this experiment, specifically, root-shoot ratio, G value, HI1=(Stem diameter/Plant height)×Dry mass of seedling, HI2=(Stem diameter/Plant height)×Fresh mass of seedling, and HI3=[(Stem diameter/Plant height)+(Dry mass of root/Dry mass of shoot)]×Dry mass of seedling. Up to now, few studies have focused on simulation models examining the relationship between TEP (thermal effectiveness and photosynthetically active radiation) and seedling quality in tomato and cabbage. In this study, to predict the comprehensive influence of temperature and illumination on seedling quality of tomato and cabbage in a greenhouse, these five composite indexes were used to establish a simulation model of seedling quality based on TEP. We further validated these models with the real test data. Finally, we obtained models for tomato (HI1=0.0009e 0.0308TEP −0.0015) and cabbage (HI1=0.0003e 0.0671TEP −0.0003), respectively. We believe that these models have a potential use for tomato and cabbage when temperature and light are involved in greenhouse management during the nursery production. 2. Materials and methods 2.1. Plant materials and growth conditions The tomato cultivars Jinlingmeiyu (cherry tomato) and Hezuo 903 (large-fruit tomato) were obtained from Jiangsu Academy of Agricultural Sciences and Shanghai Seed Industry Co., Ltd. (Shanghai, China), respectively; the cabbage cultivars Sugan 27 (late-maturing variety) and Bochun (early-maturing variety) were obtained from Jiangsu Academy of Agricultural Sciences. These cultivars were widely planted in Middle and Lower Yangtze River and other areas. This study was performed from September 2014 to June 2015 in a single-slope glass greenhouse at Nanjing Agricultural University, Nanjing. This greenhouse has an area of 40.5 m 2 (9 m in length and 4.5 m in width) and a 1.45-m high front-roof and a 2.65-m high back-roof, forming