Optimal irrigation and fertilizer amounts based on multi-level fuzzy comprehensive evaluation of yield, growth and fruit quality on cherry tomato
Introduction
The accurate and efficient usage of water and fertilizer has become a major concern over the past decade (Mutambara et al., 2016). In 2015, the Ministry of Agriculture of China, in conjunction with the eight departments of the Chinese government, released the national agricultural sustainable development plan for 2015–2030 (China, 2015). The plan emphasized the need for economical efficient water and fertilizer management in agricultural production in order to ensure crop yield and quality. Uncontrolled irrigation and blind fertilization result in serious water waste, low fertilizer utilization, groundwater pollution and soil salinization, consequently constituting a considerable proportion of production costs (Thompson et al., 2007; Min et al., 2011). Thus, over the past decade, attention has been focused on the rapid development of water-saving irrigation strategies as well as a method for precise fertilizer applications in greenhouse crop management (Parry et al., 2005; Topcu et al., 2007).
Cherry tomato is a high-grade, popular horticultural crop due to its high soluble solid content, and unique aroma and taste (Beckles, 2012; Liu et al., 2018). Water and fertilizer are two important factors that ensure the yield, growth and quality of cherry tomatoes (Brauman et al., 2013; Duncan et al., 2018). Efficient irrigation ensures the growth of crops, particularly as crop sensitivity to moisture content directly affects its physiological metabolic processes (Bajji et al., 2001; Christmann et al., 2007). When irrigation is lacking, the chemical environment of the roots of the crop will change, thus altering the yield and quality of the aboveground fruit (Coyago-Cruz et al., 2019; Hou et al., 2019). Meanwhile, an excessive water supply will weaken the active oxygen metabolism of the crop, which will consequently negatively affect the yield and quality of the fruit (Lin et al., 2004).
N, P and K fertilizers are the most basic three elements required by crops, and are pivotal for the successful implementation of nutrient management practices in crop production systems (Han et al., 2011). Dordas and Sioulas (2008) found that increasing N can affect yield, photosynthetic efficiency, and the physiology of safflower (Carthamus tinctorius L.) under rainfed conditions. Rehim et al. (2012) used Freundlich model to evaluated P application methods, and found that increasing the P rate up to 104 kg/ha improved the grain yield. Wang et al. (2009) used pot experiment to determine the effects of K fertilizer on the taste compounds of fresh tomato fruits, and observed that the concentration of titratable acidity increased with K supply. Increasing the amount of fertilization was found to increase the content of Vitamin C (VC) and nitrate in the fruit, however it also reduced the TSSC content, thus deteriorating the taste of the fruit. Thus, the optimal water and fertilizer amounts vary with indicator, and must be balanced in order to optimize the integrated growth of cherry tomato.
Numerous techniques have been applied to evaluate the growth of crops. For example, the use of questionnaires, grey relational analysis (GRA), game theory and principal components analysis (PCA), have been widely used in the evaluation of crop growth and development (Wei et al., 2018; Zhou et al., 2018; Jiang et al., 2019). However, some methods are influenced by human factors, while others base their evaluations on raw data, resulting in uncertainties in the final evaluation results (Wang et al., 2011; Zhong et al., 2017). Furthermore, the optimal amounts of water and fertilizer for cherry tomato vary with indicator (Mori et al., 2008; Zeng et al., 2018). Therefore, it is necessary to coordinate the response of multiple indicators of cherry tomato growth with water and fertilizer management. The multi-level fuzzy comprehensive evaluation (MFCE) framework is a fuzzy algorithm that combines qualitative and quantitative data to increase precision, thus obtaining broader and more accurate evaluation results compared to the aforementioned methods. Yao et al. (2019) proposed a fuzzy system based on eight environmental conditions of induced landslide disasters, whereby five landslide risk factors from a multi-level analysis system were employed for the MFCE system. Zhou et al. (2014) used a multi-level, multi-factor and non-structural fuzzy optimum decision model for the optimal selection of compact heat exchangers (including six design projects and eight influencing factors). The model was able to successfully cover the majority of the effective factors and determine the optimal design projects. The application of MFCE can achieve robust results due its superiority in dealing with multiple indicators.
Based on the limitations in the current literature, the current study has the following aims: (1) To establish a scientific evaluation system for the yield, growth and quality of cherry tomatoes through the multilevel fuzzy evaluation method, and to determine the integrated growth of cherry tomato in different seasons; (2) to explore the response of integrated cherry tomato growth to several single factors, and the interaction of two and three factors in different seasons; and (3) to determine the optimal water and fertilizer combination for the integrated growth of cherry tomato over seasons. The optimal amounts of irrigation and fertilization were obtained based on the comprehensive consideration of multiple indicators from different factors. This strategy is able to overcome the bias of the results from single-type indicators or single evaluation methods, and can support the decision system of cherry tomato production in northwestern China.
Section snippets
Experimental site and crop
The experiment was conducted in a greenhouse over two consecutive growing seasons (2018 March–July, denoted as 2018S; and 2018 August–December, denoted 2018 F) in the Northwest A&F University campus (latitude N34°16′, longitude E108°02′, altitude 450 m) of Yangling, Shaanxi Province, China. Based on the Koppen climate classification, this region is categorized as a cold temperate zone (Dwa) and experiences dry winters. The greenhouse was made of a steel frame (100 m × 17 m) with a double-layer
Multilevel fuzzy comprehensive evaluation of cherry tomato
For the multilevel fuzzy comprehensive evaluation analysis of cherry tomato, the weight of factor (ai) in the first layer and the weight of subfactor (wij) in the second layer were calculated using AHP and the entropy method, respectively (Table 6). These two sets of weights were then merged together. The fuzzy evaluation values under all treatments are reported in Table 7. The higher the fuzzy evaluation value of the treatment, the better the overall growth of the treated cherry tomato.
Discussion
Much research has evaluated single indicators via subjective or objective techniques. However, the comprehensive evaluation of multiple indicators is lacking. More importantly, the results of these studies only obtained the best treatment among all the treatments, no further modeling to analyze, which led to the results could not be accurate enough since the treatment is only an interval (Wang et al., 2015, 2017a; Hou et al., 2019; Keabetswe et al., 2019). In the current study, in order to
Conclusion
Numerous indicators can be applied in order to evaluate the growth of cherry tomatoes, yet for different single indicators, the optimal solution is different. We comprehensively evaluated cherry tomato yield, growth, and nutritional and taste quality using an integrated growth model based on MFCE, which subsequently increased the accuracy of the amount of irrigation and fertilizers. We identified irrigation to play the most important role in the integrated growth of cherry tomato in 2018S,
Declaration of Competing Interest
The authors declare that there are no conflicts of interest.
Acknowledgments
This work was supported by the Key Research and Development Program of Shaanxi Province in China (2018TSCXL-NY-05-03), the China Agriculture Research System (CARS-23-C07), the Xi'an Science and Technology Program in China(2017050NC/NY011(2)) and the Key project for Innovation in Production, Education and Research of Yangling in China(2017CXY-07).
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