Multivariate analysis between meteorological factor and fruit quality of Fuji apple at different locations in China

ZHANG Qiang , ZHOU Bei-bei, LI Min-ji, WEI Qin-ping, HAN Zhen-hai

1 Key Laboratory of Biology and Genetic Improvement of Horticultural Crop (Nutrition and Physiology) Sciences, Ministry of Agriculture/College of Horticulture, China Agricultural University, Beijing 100193, P.R.China

2 Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (North China), Ministry of Agriculture/Institute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, P.R.China

1. Introduction

China is the largest apple producing and consuming country in the world and had the top apple planting area and yield worldwide (FAO 2015). Fuji is the major apple cultivar in China, accounting for more than 70% of China’s apple planting acreage (Zhanget al. 2016, 2017). Fruit quality is the result of a complex interaction of management and environmental factors (Melke and Fetene 2014). Among factors that affect fruit productivity, meteorological conditions are important environmental factors affecting apple quality,but these factors are difficult to regulate (Duanet al. 2014;Chandet al. 2016; Qu and Zhou 2016b).

Numerous studies have examined the effects of meteorological factors on apple quality and quantity. Kays(1999) showed that appropriate light (intensity and quality),temperature and water availability do influence postharvest eating fruit quality. Summer temperature and light conditions also influence the size and quality of apples as the fruits develop from April to June. Specifically, high (＞26°C) or low temperatures (≤15°C) during the flowering phase reduced apple weight (Forshey 1990). Moreover, temperature differences between day and night play an important role in fruit sugar, acidity, and vitamin C content (Liet al. 2011;Lakatoset al. 2012). Light (intensity and quality), rainfall,and relatively wet conditions influence apple final size, eating quality, and volatile emissions of apples (Kays 1999; Armelleet al. 2005; Lachapelleeet al. 2013).

Partial least-squares regression (PLSR) has been used to study the relationship of fruit quality and the mineral nutrients in the leaves and fruits of fruit trees (Caseroet al. 2004). PLSR is a new multivariate statistical method to solve multicollinearity problems. This method mainly focuses on regression modeling of multiple independent variables using multiple dependent variables, especially when the variables are highly related to each other (Wanget al. 2000; Woldet al. 2001; Gao 2002). PLSR was also used to predict fruit quality and the optimal harvest time of apples (Mendozaet al. 2011; Bertoneet al. 2012) and to analyze the relationship between soil nutrient and fruit quality(Zhouet al. 2016). Moreover, PLSR was used to predict total nitrogen, total phosphorus, total potassium, and trace elements content in apple leaves on the basis of spectral analysis (Xing and Chang 2008, 2009).

The current study was performed to screen the main meteorological factors (temperature, precipitation,temperature difference between day and night, relative humidity, and sunshine percentage) affecting Fuji apple quality attributes (fruit weight, fruit shape index, firmness,soluble solid content, titratable acid (TA) content, and skin color area), and to optimize these factors for high-quality Fuji apples. Results of this study will provide resources for a reasonable regional layout and planting of Fuji apple in China.

2. Materials and methods

2.1. Sampling site

From 2010 to 2011, production regions in 51 counties of 11 regions in China were selected based on the geographical location and the ‘Advantageous Regional Pattern Plans for Apples in China from 2008 to 2015 (https://wenku.baidu.com/view/06decc19227916888486d70d.html)’. Three orchards were selected in each county, where Fuji was main apple cultivar with tree ages of 15–20 yr, free growing spindle or small canopy tree shapes, and high and stable yield consecutively for 5 yr with yield of 30 000-45 000 kg ha-1.Each orchard had an area of over 0.6 ha. The geographical locations of the sampling counties are shown in Table 1.

2.2. Fruit sampling and quality analysis

At the end of October to early November in 2010 and 2011,10 fruits were taken from the canopy at the southeast and southwest ends of each orchard at 1.5 m above the ground for each tree. Approximately 90 fruits were picked from each orchard. Then, the samples were placed in plastic bags and transferred to the laboratory in an insulated box filled with ice packs. In each orchard, 30 apples were randomly selected to measure the individual fruit length,diameter, and length/diameter (L/D) using a vernier caliper(Harbin Measuring & Cutting Tool Group Co., Ltd., China).A balance with accuracy of 0.001 was used to weigh the fruit mass. Fruit firmness was assessed on the opposite sides of the fruit with a GY-1 Fruit Firmness Tester (Mudanjiang Machinery Reserach Institute, China). Soluble solid content(SSC, °Brix) was measured with a digital refractometer(Atago RS-5000). Acid concentration was measured by titrating the juice with 0.1 mol L-1NaOH and expressed as a percentage of acid.

Table 1 Geographical location of the 51 sampling counties in 11 regions of China

2.3. Meteorological data collection

The meteorological data used in this study were obtained from the National Meteorological Information Center, China,and 306 uniformly distributed stations near the orchard sites were chosen. An interpolation method was used to calculate the mean annual temperature, annual total precipitation,monthly mean, minimum, and maximum temperatures,temperature difference between day and night, total precipitation, relative humidity, and sunshine percentage from April to October at each orchard in 2010 and 2011.

2.4. Statistical approach

A total of 153 samples were collected from different orchards every year, and each sample was repeatedly collected in 2010 and 2011. Statistical analysis was performed using SAS 9.2 Software (World Headquarters SAS Institute Inc., USA) to perform duplicate collinearity diagnosis,PLSR, variable selection, and model establishment of the relationship between fruit quality attributes and meteorological factors. Linear programming was performed using LINGO 10.0 Software (Lindo System Inc. USA) to determine the optimum meteorological factors for highquality Fuji apples.

3. Results

3.1. Basic information of meteorological factors and fruit qualities

Table 2 illustrates the variations among the average values in meteorological factors and fruit qualities in the different apple planting regions. The mean annual temperatures of 153 apple orchards in China ranged from 5.5 to 18.0°C with a mean value of 11.5°C, and the maximum temperature of the orchard was 3.3 times higher than that of the minimum one. The average annual precipitations ranged from 167 to 1 121 mm, with mean value of 620 mm. Monthly mean, maximum, and minimum temperatures from April to October were 19.0, 14.5, and 24.5°C, respectively. The temperature differences between day and night during the same period ranged from 4.6 to 13.7°C, with a mean value of 10.0°C. Relative humidity and sunshine percentages during the same period ranged from 51.9 to 84.1% and from 32.9 to 69.7%, respectively, with mean values of 67.8 and 48.1%, respectively. Thus, the meteorological factors varied significantly among the different apple producing counties in China.

Fruit quality consists of many external and intrinsic attributes. Intrinsic features include key external attributes,such as color, shape, size, and freedom from defects.Internal attributes include texture, sweetness, acidity, aroma,flavor, and nutritional value (Hewett 2006). Fruit weights ranged from 175.5 to 362.6 g with a mean value of 260.9 g.Fruit shape index is the L/D ratio of the fruit. The mean,minimum, and maximum fruit shape indices of Fuji apple in China were 0.86, 0.76, and 0.94, respectively. Fruit firmness ranged from 7.2 to 12.0 kg cm−2with a mean of 8.8 kg cm–2.SSC varied significantly among the different counties, with mean, minimum, and maximum values of 13.8, 11.0 and 18.6 °Brix, respectively, for all orchards. TA content (%)ranged from 0.14 to 0.33% with a mean of 0.21%. The highest SSC/TA value was 2.5 times greater than that of the lowest one.

3.2. Determining the main meteorological factors affecting fruit qualities

Meteorological factors, including the mean annual temperature (x1), the annual total precipitation (x2), the mean temperature from April to October (x3), the minimum temperature from April to October (x4), the maximum temperature from April to October (x5), temperature difference between day and night (x6), the total precipitation from April to October (x7), the relative humidity from April to October (x8), and the sunshine percentage from April to October (x9), were treated as independent variables (Zhuet al. 2001; Duanet al. 2014). Fruit quality attributes,namely, fruit weight (y1), fruit shape index (y2), fruit firmness(y3), SSC (y4), TA (y5), SSC/TA ratio (y6), and skin color area(y7), were treated as dependent variables. PLSR was used to analyze the model effect loading and weight on the impact of meteorological factors on fruit quality (Fig. 1). Model effect loading indicates the importance of the potential overall impact of the different independent variables on dependent variables,while the model effect weight indicates the proportion of the potential overall impact of the different independent variables on dependent variables (Zhouet al. 2016). Effect loadings and weights of the various meteorological factors on fruit quality were consistent with each other. The mean, minimum,and the maximum temperatures from April to October had high and positive effect loadings and weights on fruit quality,followed by sunshine percentage during the same period,the mean annual temperature, the temperature difference between day and night, and the total precipitation during the same period. The annual total precipitation and relative humidity from April to October had negative loadings and weights on fruit quality.

Table 2 Basic information of the meteorological factors and fruit qualities

The relationship between meteorological factors and fruit quality was complex. Thus, the total effect of meteorological factors on fruit quality was difficult to determine. To clearly demonstrate the importance of the effects of different meteorological factors on fruit quality attributes, the variable importance for projection (VIP) values for the impacts of the different meteorological factors on fruit weight, fruit shape index, fruit firmness, SSC, TA, SSC/TA ratio, and skin color area were calculated (Fig. 2). The VIP values reflect the importance of all independent variables in explaining the variation in the dependent variables. These values also indicate the strength and direction of the impact of each variable in the PLS model, with 0.8 to 1.0 often used as threshold for determining importance (Woldet al. 2001;Luedelinget al. 2013). In this study, a VIP value of 1.0 was used as the threshold to screen the main meteorological factors affecting fruit qualities. The effects of meteorological factors on the different fruit quality attributes varied. The meteorological factors affecting on fruit mass from the largest to the smallest were the annual total precipitation, the mean annual temperature, and the minimum temperature and the mean temperature from April to October with VIP values of 2.59, 1.30, 1.12, and 1.03, respectively. In contrast, meteorological factors affecting SSCs from the largest to the smallest were the annual total precipitation,the minimum temperature from April to October, the mean temperature from April to October, temperature difference between day and night, and the mean annual temperature with VIP values of 1.73, 1.70, 1.41, 1.30, and 1.17,respectively. In the same way, we could determine the order of the main meteorological factors affecting fruit shape index,fruit firmness, TA content, SSC/TA ratio, and skin color area.

Fig. 1 Model effect loadings and weights of the meteorological factors on fruit qualities. x1–x9 denote the mean annual temperature, the annual total precipitation, the mean temperature from April to October, the minimum temperature from April to October, the maximum temperature from April to October, the temperature difference between day and night, the total precipitation from April to October, the relative humidity from April to October, and the sunshine percentage from April to October, respectively in 2010 and 2011.

Fig. 2 Variable importance for projection of main meteorological factors affecting fruit qualities. x1–x9 denote the mean annual temperature, the annual total precipitation, the mean temperature from April to October, the minimum temperature from April to October, the maximum temperature from April to October, the temperature difference between day and night, the total precipitation from April to October, the relative humidity from April to October, and the sunshine percentage from April to October, respectively in 2010 and 2011. SSC, soluble solid content; TA, titratable acidity.

3.3. Regression analysis of the relationship between meteorological factors and fruit quality attributes

The regression analysis were used to determine the VIP values and the positive and negative effects of meteorological factors on fruit quality indicators. A regression equation of the effects of these factors on fruit quality was established with the major meteorological factors as independent variables and fruit quality attributes as dependent variables (Table 3). The coefficients and symbols of the regression equations could explain the important degrees and positive and negative impacts of the effects of different meteorological factors on fruit qualities. Each characteristic of fruit quality was affected by the various meteorological factors. For example, fruit weight had a positive effect coefficient with the mean temperature from April to October and annual total precipitation but a negative effect coefficient with the mean annual temperature and the maximum temperature from April to October. SSC had the largest negative effect coefficient with mean temperature from April to October but a larger positive effect coefficient with the minimum temperature and temperature difference between day and night from April to October.

3.4. How to optimize meteorological factors for high-quality Fuji apples

To illustrate the effects of meteorological factors on fruit quality characteristics, linear programming equations were constructed using the regression analysis in Table 4. When solving for the maximum value of a certain fruit quality attribute, other fruit quality attributes were ensured to be of high quality, thus the restraint conditions for the mean fruit weight (y1), fruit shape index (y2), fruit firmness (y3), SSC(y4), TA content (y5), SSC/TA ratio (y6), and skin color area(y7) were greater than 250 g, 0.86, 8.8 kg cm−2, 13.8, 0.21,67.0, and 90.0%, respectively. The restraint ranges of the meteorological factors were the minimum and maximum values from 153 orchards, because meteorological factors could be same in apple producing areas. The linear programming equation of the maximum mean fruit weight is as follows:

Where,x1–x9denote the mean annual temperature,annual total precipitation, the mean temperature fromApril to October, the minimum temperature from April to October, the maximum temperature from April to October,temperature difference between day and night, total precipitation from April to October, relative humidity from April to October, and sunshine percentage from April to October, respectively in 2010 and 2011, and 5.5≤x1≤18.0,166≤x2≤1121, 13.3≤x3≤22.4, 7.8≤x4≤19.7, 19.5≤x5≤27.3,4.6≤x6≤13.7, 227≤x7≤1054, 51.8≤x8≤84.0, and 32.8≤x9≤70.0.

Table 3 Regression equation of meteorological factors affecting fruit qualities

Table 4 Proposed optimum meteorological factors for high-quality Fuji apple fruit

We used the same method to obtain the maximum values of fruit shape index, fruit firmness, SSC, TA, SSC/TA ratio, and skin color area when the mean fruit mass of a Fuji apple was ≥250 g (Table 4). The maximum temperature difference between day and night, and the total precipitation from April to October were all the minimum values at 19.5°C, all the maximum values at 13.7°C,and the minimum values at 227 mm, respectively, for all aspects of good fruit quality. These results indicate that regions with high temperature differences between day and night and low maximum temperatures from April to October could produce high-quality Fuji apples. Moreover,proposed optimum values of relative humidity and sunshine percentage from April to October were higher than the actual measured minimum values, indicating that suitable relative humidity (57.5–84.0%) and sunshine percentage(36.5–70.0%) from April to October were also important factors that affect fruit qualities. The SSC and skin color area in Fuji apples had the same requirements for the meteorological factors and obviously differed from other quality factors. The optimum meteorological factors for highquality Fuji apples were the mean annual temperature of 5.5–18.0°C; the annual total precipitation of 602–1 121 mm;and, from April to October, the mean temperature of 13.3–19.6°C, the minimum temperature of 7.8–18.5°C,the maximum temperature of 19.5°C, the temperature difference between day and night of 13.7°C, the total precipitation of 227 mm, relative humidity of 57.5–84.0%,and sunshine percentage of 36.5–70.0%.

4. Discussion

4.1. Multivariate analysis between fruit quality and meteorological factors

Most of the fruit quality characteristics of apples are affected not only by cultural practices but also by different climatic factors, such as temperature, rainfall, and relative humidity(Takashi and Hisashi 1988; Melke and Fetene 2014).Multivariate analysis between meteorological factors and fruit quality characteristics involve multicollinearity because of the strong linear correlation among meteorological factors (Weiet al. 1999, 2003). The classic leastsquares method and principal component method cannot satisfactorily solve this problem. Partial least-squares(PLSR) regression can effectively construct the regression model for cases like this, greatly improving the accuracy and reliability of the model (Wanget al. 2000). Zhuet al. (2001)used the index, logarithmic, linear, and polynomial fitting methods to select the main meteorological factors affecting different fruit quality characteristics in Shaanxi Province in China. Weiet al. (1999) used canonical correlation analysis to screen the meteorological factors that affect the fruit quality of Fuji apple. Linear stepwise regression, principal component analysis, and fuzzy comprehensive evaluation were also used to determine the main meteorological factors affecting apple quality factors and to evaluate the ecological factors of apple (Liet al. 2011; Zhanget al. 2011; Maudeet al. 2013). Caseroet al. (2004) used PLSR to study the relationship of the mineral nutrient elements in the leaves and fruits of Golden Smoothe apple and found that the fruit mineral elements affected the quality of the apple.In the current study, PLSR was used to screen the main meteorological factors affecting fruit quality, and a regression equation of the relationship between fruit qualities and major meteorological factors was established. Moreover, linear programming was used to optimize the meteorological factors to achieve high-quality Fuji apples.

4.2. Effects of meteorological factors on fruit quality attributes

Fruit quality is usually assessed by external, textural quality and eating quality; external quality features include size,shape, skin color, and lack of blemishes. Textural quality factors include firmness, crispness, juiciness, and mealiness,while eating quality or flavor depends upon sweetness,acidity, astringency, and aroma (Melke and Fetene 2014).Apple qualities are affected by meteorological factors, such as light, temperature, temperature difference between day and night, and relative humidity, and non-climatic factors such as soil types, fertility levels, management, and the interaction of these factors (Warringtonet al. 1999; Melke and Fetene 2014). The results in the present study showed that fruit qualities of Fuji apple were affected positively by the mean temperature, minimum temperature, maximum temperature and sunshine percentage in growing season(from April to October) and affected negatively by annual total precipitation and relative humidity during the growing season. These results are consistent with those of Sugiuraet al. (2013) who reported that the quality attributes are likely caused by higher temperatures during maturation period.Reductions in acid concentration and sugar metabolism in the fruit are apparently greatly affected by temperature(Takashi and Hisashi 1988). This result is contrary to Gaoet al. (2009) that fruit quality of Pink Lady apples was affected mainly by total precipitation from April to October,annual effective accumulated temperature, average diurnal temperature range from April to October, and annual daylight duration. The different fruit quality attributes were impacted distinctly by the various meteorological factors.

Our results showed that fruit mass was mainly affected by the annual total precipitation, the mean annual temperature,minimum temperature, and the mean temperature from April to October. SSC was affected by annual total precipitation,the mean annual temperature, mean temperature, and minimum temperature difference between day and night from April to October. These results are basically consistent with those of Weiet al. (1999, 2003) who reported that SSC was affected by average diurnal temperature, the maximum temperature in July, sunshine duration in June, and the maximum temperature in August and September (Zhuet al.2001), and temperature difference between day and night(Lakatoset al. 2012).

4.3. Optimum meteorological factors for high-quality Fuji apple

Ecological adaptability of apple fruit to varying environmental conditions (temperature, precipitation, air relative humidity,and soil type) were considered before starting to plant apple trees in some locations (Melke and Fetene 2014).The optimum meteorological factors for high-quality of Fuji apples were the mean annual temperature of 5.5–18.0°C,annual total precipitation of 602.0–1 121.0 mm, growth season (from April to October) mean temperature of 13.3–19.6°C, minimum temperature of 7.8–18.5°C, the maximum temperature of 19.5°C, temperature difference between day and night of 13.7°C, total precipitation of 227 mm, relative humidity of 57.5–84.0%, and sunshine percentage of 36.5–70.0%. Results from Qu and Zhou(2016a) showed the eight dominant climatic factors that can affect the potential distribution of Fuji apple in China. These factors are the mean annual temperature of 7.0–14.0°C,accumulated temperature of no less than 3 000–4 800°C,mean temperature of the coldest month ranging from −7.0 to 0.0°C, mean daily temperature range of 8.0–12.0°C during summer, annual precipitation of 400–800 mm, mean temperature of 20.0–26.0°C during the summer, and mean relative humidity 60.0–70.0% during summer. Zhuet al.(2001) proposed that the optimum meteorological factors for high-quality Fuji apples in Shaanxi Province were annual total precipitation of 500–650 mm, the minimum temperature of 14.0–17.0°C from 6 months to 9 months, the maximum temperature of 24.0–27.0°C from 8 to 9 months,relative humidity of 60–70 during summer, temperature difference between day and night of 10.0–12.0°C during the growing season, and sunshine percentage of ≥45.0%.Weiet al. (2003) reported the following values: annual total precipitation and precipitation in August, 547.0 and 133.0 mm, respectively; mean relative humidity in May, July,and September, were 65.0, 73.0, and 65.0%, respectively;mean temperatures in September and October of 13.0 and 12.0°C, respectively; mean diurnal temperature in September and October of 11.0 and 16.0°C, respectively;and annual mean and the maximum temperatures in July and October of 25.8 and 21.0°C, respectively; and minimum temperature in September of 12.9°C. The choice of meteorological factors and results in this study are basically the same as previous results in some ways such as the mean annual temperature, the annual total precipitation, and the temperature difference between day and night during the growing season but differed in choice of time for the minimum and maximum temperatures.

Apple qualities were affected by meteorological conditions, soil types, nutrient content of soil, and management practices. In the future, the effect of meteorological factors in different months and interaction of meteorological factors and soil nutrients on fruit qualities should be considered. Further studies should be conducted to confirm the results.

5. Conclusion

The model effect loading and weight of the impacts of meteorological factors on fruit quality were consistent with each other. The fruit qualities of Fuji apple were affected positively by the mean temperature, minimum temperature,maximum temperature, and sunshine percentage during the growing season (from April to October). However,these qualities were affected negatively by annual total precipitation and relative humidity from April to October.

The different fruit quality attributes were impacted distinctly by the different meteorological factors. The proposed optimum meteorological factors for high-quality Fuji apples were the mean annual temperature of 5.5–18.0°C, annual total precipitation of 602–1 121 mm, and,during the growth season (from April to October), the mean temperature of 13.3–19.6°C, minimum temperature of 7.8–18.5°C, maximum temperature of 19.5°C, temperature difference between day and night of 13.7°C, total precipitation of 227 mm, relative humidity of 57.5–84.0%,and sunshine percentage of 36.5–70.0%.

Acknowledgements

This work was supported by the Forest Scientific Research in the Public Interest, China (201404720), the earmarked fund for the China Agriculture Research System (CARS-27),and the Beijing Municipal Education Commission, China(CEFF-PXM2017_014207_000043).

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