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Article

Multivariate Analysis and Optimization Scheme of the Relationship between Leaf Nutrients and Fruit Quality in ‘Bingtang’ Sweet Orange Orchards

by
Sheng Cao
1,2,
Bin Zeng
1,2,
Xuan Zhou
3,
Sufeng Deng
1,2,
Wen Zhang
1,2,
Sainan Luo
1,2,
Mengyun Ouyang
4,* and
Shuizhi Yang
1,2,*
1
Hunan Horticultural Research Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, China
2
Yuelushan Laboratory, Pomology Variety Innovation Center, Changsha 410128, China
3
Hunan Soil and Fertilizer Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, China
4
Hunan Nuclear Agricultural Science and Space Breeding Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, China
*
Authors to whom correspondence should be addressed.
Horticulturae 2024, 10(9), 976; https://doi.org/10.3390/horticulturae10090976
Submission received: 15 August 2024 / Revised: 4 September 2024 / Accepted: 12 September 2024 / Published: 14 September 2024
(This article belongs to the Section Fruit Production Systems)
Browse Figures
Versions Notes

Abstract

:
Citrus trees require a balanced and adequate supply of macronutrient and micronutrient elements for high yield and fruit quality. Foliar nutrient analysis has been widely used in fruit-tree nutrient diagnosis and fertilization calculation. However, there is no information on ways to produce optimal fruit quality in sweet oranges. In the present study, fruit and leaf samples were collected from 120 ‘Bingtang’ sweet orange [Citrus sinensis (L.) Osbeck] orchards during four consecutive years (2019–2022). Parameters of leaf nutrition and fruit quality were analyzed based on these samples. Diagnostic results based on leaf classification standards indicated that the most deficient elements were Ca, Mg, and B, followed by N and Zn. Fruit quality, determined by single fruit weight (SFW), fruit shape index (FSI), total soluble solids (TSS), titratable acidity (TA), vitamin C (Vc), and maturation index (MI = TSS/TA) during fruit maturation, exhibited inconsistent responses to leaf mineral nutrition concentrations. The leaf-nutrient optimum values for high quality of the ‘Bingtang’ sweet orange fruit were ranges of 2.41–4.92% N, 0.10–0.28% P, 1.30–2.11% K, 2.99% Ca, 0.26–0.41% Mg, 340–640 mg/kg S, 89.65–127.46 mg/kg Fe, 13.48–51.93 mg/kg Mn, 2.60–13.84 mg/kg Cu, 15.59–51.48 mg/kg Zn, and 53.95 mg/kg for B. These results suggest the leaf-nutrient optimum values for diagnosis can be used not only to identify the nutrient constraints but also to provide guidance for the establishment of fertilization regimes in citrus cultivation.

1. Introduction

‘Bingtang’ sweet orange [C. sinensis (L.) Osbeck], a high-quality variety grown in Hunan Province, China, is a bud mutation of the common sweet orange and is characterized by a low acid content. Yongxing County is the main producing area of ‘Bingtang’ in Hunan. The existing 4330 ha cultivation area in Yongxing is considered the major production area of high-quality sweet oranges in China [1]. However, nutrient imbalances or deficiencies have been identified in citrus orchards in recent years due to poor soil fertility and long-term unreasonable fertilization [2,3]. In addition, excessive use of chemical fertilizers not only causes soil nutrient imbalances and rapid decline in fertility but also increases the loss of soil nutrients and the risk of environmental pollution [4,5,6]. Therefore, it is urgent to improve the efficient utilization of crop nutrients and reduce fertilizer loss in citrus orchards.
In fruit crops, soil analysis alone is not a satisfactory guide for fertilization recommendation. A citrus grower cannot rely on soil analysis alone to formulate a fertilizer program or diagnose a nutritional problem in a grove, mainly because of the difficulty in determining accurately enough the nutrient availability in root zones, where deep-rooted plants take up most nutrients [7,8,9]. The mineral-element content in leaves reflects the nutritional status of the fruit trees. Leaf mineral nutrient analysis has been widely used as an early diagnostic method to avoid irreversible losses of nutrients [10,11]. Much research has been carried out in the past to develop and improve leaf analysis for identifying nutritional constraints and, subsequently, the fertilizer recommendation in fruit trees [12,13].
The delayed response of fruit trees to fertilizer applications compared with annual crops makes it difficult to determine their nutritional status through soil analysis immediately after application. The plant’s nutritional status is, oftentimes, better reflected by the element concentration in the leaves than the other organs. Foliar analysis shows actual uptake. It is considered to indicate the current nutritional status of fruit trees as an alternative to soil analysis [14,15]. The critical values or optimum nutrient concentrations in foliar analysis play an important role in citrus cultivation. In particular, earlier analysis in the growing season could allow for sufficient time to correct any deficiencies before harvest. In earlier studies, Cao et al. [1] used the partial least squares regression analysis method to determine the precise relationship between soil nutrients and ‘Bingtang’ sweet orange quality, and then, the recommended values of soil nutrient content for the optimal fruit quality were obtained. In addition, Li et al. [16], Guo et al. [17], and Zhang et al. [18] applied the canonical correlation analysis to screen for the main soil nutrient factors affecting the fruit quality index of grape, kiwifruit, and apple, respectively, and established the regression equation between them. These studies found that different tree species have different absorption and utilization abilities for mineral nutrients, and the effects of mineral nutrient elements on the quality indexes of different fruits also varied. Thus, it is important to study the relationship between the mineral-element content in leaves and fruit quality. However, the critical values of the mineral composition of sweet orange leaves have not been published in detail. For scientific fertilizer recommendation, the correct diagnosis of plant nutrient deficiency is important and should be an integrative approach to crop production [19,20,21,22].
Herein, in this work, the ‘Bingtang’ sweet orange was used as the testing material in order to monitor the status of leaf-nutrient abundance and deficiency in citrus orchards and to identify the leaf-nutrient limiting factors affecting the improvement of fruit quality. Through the determination of related parameters based on these samples, multivariate statistical analysis methods were used to quantitatively formulate the leaf nutritional content requirement scheme of high-quality fruit. The findings are expected to improve the understanding of citrus-orchard nutrition in Hunan Province and provide important implications for future orchard fertilization management.

2. Materials and Methods

2.1. Experimental Sites

In this study, 120 representative citrus orchards were involved in Yongxing (25°58′–26°29′ N, 112°43′–113°35′ E) during the 2019–2022 growing season, which is in the hilly region of Southern Hunan, China. The orchard has a subtropical continental humid monsoon climate, characterized by an average total annual precipitation of 1370.6 mm and mean annual sunshine hours of 1625.2 h, as well as an average annual temperature of 17.6 °C (Table 1). The dominant soil type is red soil, which is primarily derived from Quaternary red clay, and it has a silty loam texture and is characterized as an Ultisol based on the USDA soil taxonomy [23]. All studied citrus orchards were managed according to the ordinary practices used by growers in the region. Chemical fertilizers were mainly applied during citrus cultivation. In mid-July of every year, complex fertilizer (N:P2O5:K2O = 15:15:15, m/m/m, 750 kg/ha) was spread along vertical edges beneath the tree canopy. The basic physicochemical properties of citrus-orchard soil are described in Table S1. The geographical locations of the sampling sites are shown in Figure 1.

2.2. Plant Materials

The cultivar of citrus studied was ‘Bingtang’ sweet orange grafted onto trifoliate orange [Poncirus trifoliata (L.) Raf.], with an average plant age of about 15–20 years old. The cultivation density of citrus (broad row 350 ± 15 cm; narrow row 250 ± 15 cm; the distance between two plants 300 ± 15 cm) ranged from 950 to 1350 plants per hectare. Leaf and fruit samples were collected during the harvest season in late November to early December. Overall, a total of 120 leaf samples and 120 fruit samples were obtained from 2019 to 2022. To reduce experimental errors, the two trees at the beginning and the end of each row were avoided. Leaves and fruits were randomly sampled from five similar trees at each citrus orchard (600 trees in total). Each composite leaf sample consisted of about 100 leaves taken from the north, east, south, and west directions and the same height as the tree’s periphery. Leaves were collected from non-fruiting spring vegetative shoots (nine months old) of the same variety and rootstock. After being washed with running tap water and three times with deionized water, the leaves were oven-dried at 70 °C until constant weight was achieved and then ground into a fine powder using a centrifugal mill (0.425 mm sieve). During the fruit sampling period, the ripening period was about the same in the various orchards. Fruit samples (one composite sample per orchard) were collected from the same plants chosen for collecting leaf samples and each sample consisted of at least 10 fruits. The harvested fruits were immediately sent to the laboratory for fruit quality parameter analysis.

2.3. Leaf Minerals and Fruit Quality Parameter Analysis

Analysis of the leaf mineral elements was performed according to the method described by Bao [24]. Leaf N, P, and K concentrations were measured after the samples were digested with H2SO4-H2O2. The leaf N concentration was determined by the Kjeldahl method, whereas leaf P and K concentrations were assayed by vanadomolybdate and flame spectrophotometry, respectively. After digestion with HNO3-HClO4, the leaf Ca, Mg, Fe, Mn, Cu, and Zn concentrations were determined by atomic absorption spectrophotometry, and the leaf S concentration was measured using X-ray fluorescence spectrometry (Zetium, Malvern Panalytical, Almelo, The Netherlands). The leaf B concentration was extracted with 0.1 M HCl and determined using the curcumin colorimetric method. The contents of N, P, K, Ca, and Mg were expressed as %, and the contents of S, Fe, Mn, Cu, Zn, and B were expressed as mg/kg. The nutrient concentrations of the leaves were expressed as dry weights (DW). Three technical replicates were analyzed for each sample.
Single fruit weight (SFW) was determined using an electronic balance with an accuracy of 0.01 (Yongzhou Weighing Apparatus, Jinhua, China) and expressed in grams. The length and diameter of the fruits were assessed using a digital Vernier caliper (Harbin Measuring & Cutting Tool Group Co., Ltd., Harbin, China, precision ± 0.02 mm), and the length/diameter ratio was used as the fruit shape index (FSI). Total soluble solids content (TSS, °Brix) was determined using a digital refractometer (model: Pocket PAL-1, Atago Inc., Tokyo, Japan) and expressed as a percent (%). Titratable acid (TA) was measured using NaOH (0.10 N) until a pH of 8.20, using an automatic potentiometric titrator (TitroLine 6000, SI Analytics GmbH, Mainz, Germany), and was expressed as % (w/v) citric acid. The concentration of vitamin C (Vc) was determined by 2,6-dichlorindophenol sodium titration and expressed in mg/100 g [25]. The maturation index (MI) was calculated using the TSS/TA ratio. All analyses were performed in triplicate.

2.4. Statistical Analysis

The results were the means ± standard deviation (SD) of three replicates. The leaf-nutrient index (U) consists of the following total nutrient contents: N (x1), P (x2), K (x3), Ca (x4), Mg (x5), S (x6), Fe (x7), Mn (x8), Cu (x9), Zn (x10), and B (x11). The fruit quality index (V) consists of SFW (y1), FSI (y2), TSS (y3), TA (y4), Vc (y5), and MI (y6). Statistical analysis was performed using SPSS (version 22.0; SPSS, Inc., Chicago, IL, USA) and R (version 3.6; University of Auckland, New Zealand) for the Pearson test, canonical correlation analysis (CCA), partial least-squares regression (PLSR), and model establishment of the relationship between fruit quality attributes and leaf factors. LINGO software (version 11.0; Lindo System Inc., Washington, DC, USA) was used to calculate the optimization scheme for the leaf nutrients.

3. Results

3.1. Nutritional Status of Citrus Orchard

3.1.1. Leaf-Nutrient Status

The analysis of leaf-nutrient-related indicators can provide data support for nutrient diagnostics in citrus orchards. According to the classification standard of leaf-nutrient diagnosis [26], the spatial distribution of leaf-nutrient content in citrus orchards varied significantly (Table 2). Among these leaf samples tested, the average concentrations of total Ca and Mg were 1.00% and 0.26%, with 75.63% and 70.12% of the leaves were severely deficient in Ca and Mg, respectively. Meanwhile, the proportion of total N content below 2.50% was 66.67%. Whereas the average content of total P in the citrus leaf was 0.28%, and the proportion from 0.15 to 0.18% of citrus orchards was only 9.72%. A total K content in the optimum range from 1.00–2.00% was found in 73.61% of the citrus orchards. The average content of total S in the leaf samples was 340 mg/kg, and the proportions of high and low S content were 25.00% and 26.39%, respectively. Specifically, the average contents of total Fe, total Mn, and total Cu were 109.65, 51.93, and 13.84 mg/kg, respectively. Furthermore, in approximately 45.83% of citrus orchards, the content of total Fe was shown to be at the optimum level, and more than 69.44% and 37.50% of the total Mn and Cu contents were at a high level, respectively. In addition, the average contents of total Zn and total B were 19.59 and 21.58 mg/kg, respectively, which were lower than the optimum levels of 20.00–30.00 mg/kg and 30.00–100.00 mg/kg, respectively. Notably, serious Zn deficiency in leaves was found in 63.89% of the orchards in the study area, and nearly 87.50% of the citrus orchards were B deficient. In total, the coefficients of variation (CV) of Mn, Cu, Zn, and B were particularly elevated, ranging from 44.01% to 73.61%, meaning that the contents of the four elements varied highly from different citrus orchards.

3.1.2. Fruit Quality Characteristics

The quality of fruit mainly includes the appearance and internal quality, shown in Table 3. From the ‘Bingtang’ sweet orange fruit quality measurements, the values of the single fruit weight and the fruit shape index were 143.14 g and 0.94, respectively. In terms of fruit flavor, the mean contents of TSS, TA, Vc, and MI were 12.69%, 0.62%, 62.33 mg/100 g, and 26.52, respectively. Meanwhile, there were significant differences in TA, Vc, and MI among the fruit samples, and small differences were also found for SFW, FSI, and TSS.

3.2. Relationship between Fruit Quality and Leaf Nutrients

3.2.1. Pearson Correlation Coefficients between Fruit Quality and Leaf Nutrients

Pearson correlation analysis (r) was used to examine the relationships between some fruit quality attributes and leaf-nutrient concentrations. The r values ranged from weak to moderate and are shown in Figure 2. The relationships between fruit quality and leaf nutrients are complex and can be synergistic or antagonistic. Fruit SFW was strongly positively correlated with leaf N (r = 0.30 *), K (r = 0.46 **), S (r = 0.33 **), and Mn (r = 0.45 **). Fruit FSI was strongly positively related to leaf K (r = 0.49 **) and Cu (r = 0.55 **). Meanwhile, the TSS of fruit juice was significantly correlated with leaf K (r = 0.30 *), S (r = 0.31 **), Fe (r = 0.28 *), and Zn (r = 0.36 **). In addition to leaf Ca (r = 0.33 **) and Mg (r = 0.52 **), the TA content of fruit juice was negatively related to leaf K (r = −0.27 *), S (r = −0.27 *), Fe (r = −0.29 *), and Zn (r = −0.31 *). Furthermore, the Vc content of fruit juice was positively correlated with leaf K (r = 0.40 **) and Cu (r = 0.27 *), and MI was strongly negatively correlated with leaf Mg content (r = −0.45 **).

3.2.2. Canonical Correlation Coefficients between Fruit Quality and Leaf Nutrients

To further clarify the multiple independent variables’ interaction relationship between leaf mineral content and fruit quality attributes in citrus orchards, CCA was used to reflect the overall correlation between the two groups of indexes.

Chi-Square Test for CCA

It can be seen from Table 4 that, among the five groups of correlation coefficient matrices, only the first group achieved a very significant correlation after the chi-square test of the typical correlation coefficient between leaf nutrients and fruit quality factors across different groups. The correlation coefficient of U and V in the canonical variable was 0.814 (p = 0.0001), which reached the extremely significant level of p < 0.01, indicating that the (U, V) of the canonical variable matrix was statistically significant. Moreover, the correlation information contained in the first linear function combination accounted for 86.30% of the difference between the two groups of variables, indicating that the model could be used to analyze certain nutrients in nutrient diagnosis for ‘Bingtang’ sweet orange quality.

CCA between Leaf-Nutrient Concentrations and Fruit Quality Attributes

Based on the results of the significance test, the canonical correlation coefficient, the standardized fitting equation of the leaf-nutrient index (U) and the fruit quality (V) were calculated as follows:
U = 0.350x1 − 0.087x2 + 0.443x3 − 0.121x4 − 0.032x5 + 0.363x6 + 0.346x7 + 0.041x8 + 0.345x9 − 0.320x10 − 0.077x11
V = 0.487y1 + 0.495y2 + 0.183y3 − 0.756y4 − 0.025y5 − 0.436y6
In general, a significant correlation between variables was established when the dimension coefficient was greater than 0.3. The correlation coefficients of leaf nutrient U with total values for N (x1), K (x3), S (x6), Fe (x7), Mn (x8), Cu (x9), Zn (x10), and B (x11) were higher, with correlation coefficients of 0.453, 0.840, 0.581, 0.457, 0.467, 0.522, 0.418, and 0.301, respectively. In contrast, higher correlation coefficients of fruit quality (V) with SFW (y1), FSI (y2), TSS (y3), TA (y4), and Vc (y5) were also found, with correlation coefficients of 0.669, 0.779, 0.345, −0.430, and 0.478, respectively. The linear combination showed that the nutrient concentrations of N, K, S, Fe, Mn, Cu, Zn, and B in the leaves were positively correlated with the SFW, FSI, TSS, and Vc content, the exception being the TA content in fruit quality (Figure 3).

3.2.3. Screening for Leaf-Nutrient Factors Affecting ‘Bingtang’ Sweet Orange Quality

Based on the CCA results, the PLSR method was successfully applied to set up the optimal regression equation between leaf nutrients and fruit quality (Table 5). SFW was positively correlated with leaf N, K, Ca, S, and Mn contents, but was negatively correlated with leaf Zn content. Meanwhile, the leaf N, K, Mg, S, Fe, Cu, and Zn contents had a great impact on FSI. Except for the negative correlation with the content of total Ca and total Zn in leaves, the influencing factors of other leaf nutrients were positively correlated with FSI. TSS was positively correlated with leaf Zn, but was negatively correlated with leaf S. The TA content was positively correlated with the contents of total Ca and total Mg in leaves, but negatively correlated with the contents of total Fe and total Zn in leaves. The factors influencing the Vc content of the fruit were observed for leaf K, Mn, and Cu contents. MI was mainly affected by the content of total Mg and total Fe in the leaves. The results showed that, for the overall quality of the ‘Bingtang’ sweet orange, it was not the case that the leaf-nutrient index values were better the larger or smaller they were but that there may be some suitable range (or value) that ensures the optimal overall fruit quality.

3.3. Optimization Scheme for Leaf-Nutrient Content in ‘Bingtang’ Sweet Orange

To illustrate the effects of leaf-nutrient content corresponding to high-quality fruit, linear programming equations were further performed using the regression analysis in Table 5. 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 SFW (y1), FSI (y2), TSS (y3), TA (y4), Vc (y5), and MI (y6) were 143.14 g, 0.94, 12.69%, 0.62%, 62.33 mg/100 g, and 26.52, respectively. Based on the field investigation, most of the standards were calculated from a leaf sample analysis of the higher fruit quality tree group, meaning that the nutrient concentration of the higher fruit quality tree leaves was suitable for the critical range or concentration. The restraint ranges of the leaf-nutrient factors were the average and maximum values from 120 samples of citrus orchards. For example, the linear programming equation for the maximum SFW is as follows:
ymax1 = −66.157 + 3.253x1 + 5.421x3 + 5.094x4 + 85.608x6 + 0.145x8 − 1.800x10
ymax2 = 0.722 + 0.003x1 + 0.005x3 − 0.007x4 + 0.062x5 + 0.126x6 + 0.001x7 + 0.001x9 − 0.003x10 ≥ 0.94
ymax3 = 11.067 − 2.539x6 + 0.039x10 ≥ 12.69
ymin4 = 0.284 + 0.024x4 + 0.637x5 − 0.001x7 − 0.004x10 ≤ 0.62
ymax5 = 40.109 + 2.092x3 − 0.058x8 + 0.064x9 ≥ 62.33
ymax6 = 45.042 − 42.159x5 + 0.070x7 ≥ 26.52
where x1–x11 denote the total nutrient concentrations of N, P, K, Ca, Mg, S, Fe, Mn, Cu, Zn, and B in the leaves, respectively, and 2.41 ≤ x1 ≤ 4.92, 0.28 ≤ x2 ≤ 0.47, 1.30 ≤ x3 ≤ 2.11, 1.00 ≤ x4 ≤ 2.99, 0.26 ≤ x5 ≤ 0.41, 340 ≤ x6 ≤ 640, 109.65 ≤ x7 ≤ 170.10, 51.93 ≤ x8 ≤ 109.69, 13.84 ≤ x9 ≤ 36.11, 19.59 ≤ x10 ≤ 51.48, and 21.58 ≤ x11 ≤ 53.95.
Using the same method, linear programming equations for the maximum FSI (ymax2), TSS (ymax3), Vc (ymax5), MI (ymax6), and minimum TA (ymin4) content were successfully established. The range of leaf-nutrient factors for the optimum fruit quality of citrus orchards was obtained by data calculation (Table 6). The optimum leaf total nutrient concentrations for high-quality fruit of ‘Bingtang’ sweet orange were obtained for 2.41–4.92% N, 0.10–0.28% P, 1.30–2.11% K, 0.26–0.41% Mg, 340–640 mg/kg S, 89.65–127.46 mg/kg Fe, 13.48–51.93 mg/kg Mn, 2.60–13.84 mg/kg Cu, and 15.59–51.48 mg/kg Zn, and the limit values of Ca and B contents were obtained, with 2.99% and 53.95 mg/kg, respectively. Subsequently, the SFW, FSI, TSS, TA, Vc, and MI of ‘Bingtang’ sweet orange quality indicators can reach 206.12 g, 1.01, 15.45%, 0.40%, 92.76 mg/100 g, and 37.81, respectively.

4. Discussion

In fruit trees, leaves are the most sensitive vegetative organ to mineral elements, which can reflect nutrient accumulation and redistribution throughout the growing plant. Leaf analysis integrates all the factors that might influence nutrient availability and uptake. Dominguez et al. [27] concluded that the most adequate moment for nutritional diagnosis would correspond to the period in which nutrient concentrations remain highly stable for some time in the tissue analyzed, allowing for a reliable comparison with their references. Fortunately, previous research provides a guide [1,9,16]. Samples were taken during the fruit maturation stage, when ‘Bingtang’ sweet orange quality tends to be stable, and the leaf nutrients do not fluctuate much. Based on the field investigation, most of the ‘Bingtang’ sweet orange orchards appeared to have nutrient deficiency symptoms. Through the determination and analysis of the mineral nutrient content in the leaves of spring vegetative shoots, the status of nutrient deficiency in orchard leaves was clarified, and the nutrient-limiting factors in the tree body were also identified [7,28]. In this study, most of the average nutrient concentrations of the leaves from the 120 citrus orchards differed significantly among the three grades. According to the leaf-nutrient grade standards established by Lu [26], more than 65% of ‘Bingtang’ sweet orange orchards were found to have excess levels of total P, possibly because orchard farmers usually have overused complex fertilizers (N-P-K = 15-15-15) for many years. The phenomenon of Ca and Mg deficiency was common in the acidic soil of Southern China, which may be related to strong soil weathering and leaching in subtropical climates. When the soil pH decreased, the positive charge in the soil increased. And the adsorption of Ca2+ and Mg2+ decreased significantly, further affecting the nutrient uptake of fruit-tree roots [29,30]. The concentrations of Mn, Cu, and Zn in the leaves had a high-level spatial variation and may be related to the extensive use of Bordeaux mixture and mancozeb in the control of citrus diseases and insect pests in some orchards. In addition, little attention has been paid to Zn fertilization by local farmers in orchard production. According to our investigation, ignoring the foliar application of B fertilizer in citrus orchards was also crucial for B deficiency in trees. In addition, we found that the coefficients of variation (CVs) for SFW, FSI, and TSS were less than 12%, meaning that fruit quality from different orchards was relatively identical [9,31]. In contrast, the CVs of the TA showed a high level of variation, possibly because of high soil acidity and nutrient imbalances (Table S1). In fact, soil acidification in citrus orchards was widespread in the main citrus-producing areas of Southern China [12,32,33].
Recently, many researchers have used the mineral nutrition status of leaves to study the relationship between different elements and the quality and yield of various fruits [2,12,34]. We could use these research results to establish criteria for diagnosing leaf nutrition constraints for high-yield and high-quality citrus fertilization and, thus, further improve the level of citrus cultivation. Previous studies have indicated that the fruit quality index is affected by many leaf-nutrient factors [9]. As well, there are not only synergistic and antagonistic effects among different elements but also supplementary and substitution relationships between leaf-nutrient elements and fruit quality factors [35]. Thus, it is necessary to combine fruit quality diagnosis with leaf-nutrient diagnosis to accurately identify the production issue in the fertilization management of orchards. In this study, a correlation analysis of 11 leaf-nutrient indexes and 6 fruit quality parameters in the ‘Bingtang’ sweet orange orchards was carried out. Simple bivariate correlation analysis results showed that the correlation between the two indicator groups is not strong. When calculating the canonical correlation coefficients, the canonical variable was 0.814. At the same time, we observed that leaf N, K, S, Fe, Mn, Cu, Zn, and B displayed positive relations with SFW, FSI, TSS, and Vc but were negatively related to the TA content in fruit quality. Obviously, there was a big difference between the leaf-nutrition factors that affect fruit quality factors screened by canonical correlation analysis and the leaf-nutrition factors selected according to the correlation coefficient of a single factor, which indicates that it is not comprehensive enough to use simple correlation analysis in the study of the relationship between leaf nutrition and fruit quality. There may be multicollinearity among leaf nutrients. Since canonical correlation analysis can better address the problem of multicollinearity, this method was chosen for critical leaf-nutrient factors that affect fruit quality [1,16,17].
Macronutrients and micronutrients are indispensable nutrient elements for fruit growth and quality improvement [36,37]. Notably, the lack or excess of any mineral element may cause an imbalance in fruit nutrients and physiological diseases affecting the growth of citrus trees [38,39,40]. Fertilization imbalances cause waste, pollute the environment, and cause toxicity to fruit trees, resulting in a decline in fruit quality [3,4]. The knowledge of the desirable level for each nutrient in the leaf allows one to define its excess or deficiency in the plant and design a correct fertilization program. In recent studies, fertilizer recommendations for sweet oranges are based on calibrated leaf-nutrient concentrations and fruit quality parameter tests [41]. Li et al. [16] screened for major soil-nutrient deficiencies affecting berry quality and defined the corresponding soil-nutrient indicator content. Gui et al. [11] investigated the floral nutrients analysis in Satsuma mandarin and obtained the critical concentrations of K, Ca, Cu, Fe, Mn, and B in flowers, which are 2.08–2.10%, 0.53–0.56%, 13.01–13.80 mg/kg, 65.80–69.90 mg/kg, 16.55 mg/kg, and 30.64–33.20 mg/kg, respectively. In the present study, the optimum leaf mineral nutrient content on fruit quality was determined by partial least-squares regression and linear programming in ‘Bingtang’ sweet orange during fruit maturation. The results showed that the optimum concentrations of N, P, K, Ca, Mg, S, Fe, Mn, Cu, Zn, and B in leaves are 2.41–4.92%, 0.10–0.28%, 1.30–2.11%, 2.99%, 0.26–0.41%, 340–640 mg/kg, 89.65–127.46 mg/kg, 13.48–51.93 mg/kg, 2.60–13.84 mg/kg, 15.59–51.48 mg/kg, and 53.95 mg/kg, respectively. The leaf-nutrient optimization results (Table 6) calculated by the theory were compared with the measured value of leaf nutrients in the orchard investigated at present (Table 2), and the problem of nutrient management in the orchard was analyzed. It is noteworthy that, when the quality of the citrus fruit is optimal, the leaf Ca and B contents all took the maximum value, indicating that increasing the content of these nutrient indicators could directly affect the improvement of fruit quality. Interestingly, Hu et al. [7] established the diagnosis standards and norms of citrus-leaf macronutrient and micronutrient analysis. There are some variations in standards. The macronutrient (N, P, K, and Ca) concentration range was relatively wider than the recommended standards or norms, whereas the micronutrients (Fe, Mn, Cu, Zn, and B) were relatively narrower, possibly because of differences in soil fertility and fruit-tree nutritional properties. Therefore, further studies of field verification and adjustment should be carried out based on the nutritional management level of orchards in different areas.

5. Conclusions

We monitored the ‘Bingtang’ sweet orange fruit tree vegetative growth and fruit quality parameters during fruit maturation. In general, the contents of N, Ca, Mg, B, and Zn in the leaves were especially lacking. The CVs of TA, Vc, and MI in the fruits were particularly elevated. The results of the present study demonstrate that fruit quality attributes could be influenced by leaf mineral nutrients. In terms of the Pearson correlation coefficient and canonical correlation coefficients, it was found that SFW, FSI, TSS, Vc, and MI were positively correlated with most leaf-nutrient indicators, while TA was inversely correlated. Multivariate statistical analysis results showed that, when the leaf-nutrient indicators were N, 2.41–4.92%; P, 0.10–0.28%; K, 1.30–2.11%; Ca, 2.99%; Mg, 0.26–0.41%; S, 340–640 mg/kg; Fe, 89.65–127.46 mg/kg; Mn, 13.48–51.93 mg/kg; Cu, 2.60–13.84 mg/kg; Zn, 15.59–51.48 mg/kg; B, 53.95 mg/kg, the ‘Bingtang’ sweet orange quality indicators could reach the optimal values of SFW, 206.12 g; FSI, 1.01; TSS, 15.45%; TA, 0.40%; Vc, 92.76 mg/100 g; MI, 37.81.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/horticulturae10090976/s1, Table S1: Basic physicochemical properties of citrus orchards soil (n = 120).

Author Contributions

Methodology, B.Z., S.D., and M.O.; Software, S.C.; Validation, B.Z., S.D., and S.L.; Formal analysis, S.C.; Investigation, X.Z. and W.Z.; Resources, B.Z., S.D., and M.O.; Data curation, M.O.; Writing–original draft preparation, S.C. and M.O.; Writing–review and editing, S.C. and S.Y.; Visualization, S.L.; Supervision, X.Z., W.Z., and S.Y.; Project administration, S.Y.; Funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the earmarked fund for the Hunan Agriculture Research System (grant No. HARS-09) to S.Y., the Agricultural Science and Technology Innovation Project of Hunan Province (grant No. 2023CX23) to B.Z., and the Key Research and Development Project of Hunan Province (grant No. 2023NK2028) to X.Z.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Horticulturae 10 00976 g001
Figure 1. Location of sampling points in citrus orchards.
Figure 1. Location of sampling points in citrus orchards.
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Figure 2. Pearson correlation coefficients between fruit quality and leaf nutrients; * and ** indicate significant differences at p < 0.05 and p < 0.01, respectively. Abbreviations: SFW, single fruit weight; FSI, fruit shape index; TSS, total soluble solids; TA, titratable acidity; Vc, vitamin C; MI, TSS/TA.
Figure 2. Pearson correlation coefficients between fruit quality and leaf nutrients; * and ** indicate significant differences at p < 0.05 and p < 0.01, respectively. Abbreviations: SFW, single fruit weight; FSI, fruit shape index; TSS, total soluble solids; TA, titratable acidity; Vc, vitamin C; MI, TSS/TA.
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Figure 3. Correlation coefficient structure equation model between leaf nutrients and fruit quality for ‘Bingtang’ sweet orange. Abbreviations: SFW, single fruit weight; FSI, fruit shape index; TSS, total soluble solids; TA, titratable acidity; Vc, vitamin C; MI, TSS/TA. Significance levels are ** p < 0.01.
Figure 3. Correlation coefficient structure equation model between leaf nutrients and fruit quality for ‘Bingtang’ sweet orange. Abbreviations: SFW, single fruit weight; FSI, fruit shape index; TSS, total soluble solids; TA, titratable acidity; Vc, vitamin C; MI, TSS/TA. Significance levels are ** p < 0.01.
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Table 1. Basic situation of citrus orchards (n = 120).
Table 1. Basic situation of citrus orchards (n = 120).
Sub-RegionsSampling Time (y)Longitude (E)Latitude (N)Altitude (m)Annual Precipitation (mm)Annual Temperature (°C)
Bai Lin Town (n = 12)2019113°28′–113°33′26°34′–26°39′164.13–236.201480.217.6
Bian Jiang Town (n = 8)2019112°58′–113°12′26°01′–26°16′121.29–191.691417.017.6
Da Bujiang Town (n = 15)2019113°23′–113°33′26°09′–26°16′278.96–652.281500.017.5
Gao Tingsi Town (n = 10)2019112°50′–112°57′26°01′–26°06′125.09–187.961300.017.5
Huang Ni Town (n = 8)2020113°05′–113°15′26°08′–26°15′163.77–185.981300.017.5
Li Yutang Town (n = 9)2020113°14′–113°26′26°06′–26°17′218.04–495.361310.017.5
Long Xingshi Town (n = 14)2020113°18′–113°28′26°14′–26°21′207.41–451.011450.018.1
Ma Tian Town (n = 10)2021112°49′–113°00′26°04′–26°10′139.71–285.171300.017.5
Tai He Town (n = 6)2021113°11′–113°19′26°12′–26°20′156.08–162.221480.217.6
Jin Gui Town (n = 16)2021113°04′–113°13′26°14′–26°20′120.99–288.951250.017.5
Yang Tang Town (n = 8)2022112°49′–112°55′25°58′–26°04′181.08–206.071460.017.5
Yue Lai Town (n = 4)2022112°46′–112°50′26°05′–26°11′240.43–244.451200.017.4
Note: n represents the number of citrus orchards sampled.
Table 2. Concentrations and distribution of nutrients in ‘Bingtang’ sweet orange leaves (n = 120).
Table 2. Concentrations and distribution of nutrients in ‘Bingtang’ sweet orange leaves (n = 120).
NutrientRangeMean ± SDCV (%)Optimum RangeDeficiencyOptimumExcess
Samples (%)Samples (%)Samples (%)
N (%)1.33–4.922.41 ± 0.4418.162.50–3.3066.6731.941.39
P (%)0.10–0.470.28 ± 0.1036.600.15–0.1822.229.7268.06
K (%)0.47–2.111.30 ± 0.3930.031.00–2.0020.8373.615.56
Ca (%)0.51–2.991.00 ± 0.1931.902.00–5.0075.6324.370
Mg (%)0.11–0.410.26 ± 0.0231.380.30–0.5070.1229.880
S (mg/kg)180–640340 ± 11.0532.93250–40026.3948.6125.00
Fe (mg/kg)20.48–170.10109.65 ± 34.3231.3075.00–120.0020.8345.8333.33
Mn (mg/kg)13.48–109.6951.93 ± 38.2373.6120.00–50.004.1726.3969.44
Cu (mg/kg)2.60–36.1113.84 ± 7.8957.014.00–10.0031.9430.5637.50
Zn (mg/kg)12.05–51.4819.59 ± 11.1256.7720.00–30.0063.8920.8315.28
B (mg/kg)5.48–53.9521.58 ± 9.5044.0130.00–100.0087.5012.500
Optimum ranges were referred to the classification standard of ‘Bingtang’ sweet orange leaves [26]. Data shown are the average of three replicates ± standard deviation (SD). CV, coefficient of variation.
Table 3. Fruit quality characteristics of ‘Bingtang’ sweet orange (n = 120).
Table 3. Fruit quality characteristics of ‘Bingtang’ sweet orange (n = 120).
ItemSFW (g)FSITSS (%)TA (%)Vc (mg/100 g)MI
Range82.00–302.000.78–1.269.40–15.430.41–1.1937.75–154.079.63–102.56
Mean ± SD143.14 ± 12.470.94 ± 0.0912.69 ± 1.520.62 ± 0.2662.33 ± 18.0226.52 ± 6.30
CV (%)8.669.5711.9831.6228.9023.49
Data shown are the average of three replicates ±SD. Abbreviations: SFW, single fruit weight; FSI, fruit shape index; TSS, total soluble solids; TA, titratable acidity; Vc, vitamin C; MI, TSS/TA.
Table 4. Significance in the chi-square test between fruit quality and leaf nutrients.
Table 4. Significance in the chi-square test between fruit quality and leaf nutrients.
Typical VectorCanonical Correlation Coefficient λiEigenvalue λi2Wilk’sChi-Square ValueFreedom DegreeSignificant LevelCumulative Contribution Ratio λi/∑λi2
10.814 **0.6630.080156.651660.00010.863
20.5710.3260.43052.268360.05900.124
30.4630.2150.63827.818240.26780.010
40.3720.1390.81312.841140.53910.002
50.2370.0560.9443.59360.73160.001
Note: ** indicate significant differences at p < 0.01.
Table 5. Regression equation of major leaf-nutrient factors affecting fruit quality.
Table 5. Regression equation of major leaf-nutrient factors affecting fruit quality.
Fruit QualityLeaf-Nutrient FactorRegression EquationF-Value
SFW (y1)x1, x3, x4, x6, x8, x10y1 = −66.157 + 3.253x1 + 5.421x3 + 5.094x4 + 85.608x6 + 0.145x8 − 1.800x109.327 **
FSI (y2)x1, x3, x4, x5, x6, x7, x9, x10y2 = 0.722 + 0.003x1 + 0.005x3 − 0.007x4 + 0.062x5 + 0.126x6 + 0.001x7 + 0.001x9 − 0.003x108.515 **
TSS (y3)x6, x10y3 = 11.067 − 2.539x6 + 0.039x106.601 **
TA (y4)x4, x5, x7, x10y4 = 0.284 + 0.024x4 + 0.637x5 − 0.001x7 − 0.004x1011.990 **
Vc (y5)x3, x8, x9y5 = 40.109 + 2.092x3 − 0.058x8 + 0.064x98.529 **
MI (y6)x5, x7y6 = 45.042 − 42.159x5 + 0.070x710.470 **
Abbreviations: SFW, single fruit weight; FSI, fruit shape index; TSS, total soluble solids; TA, titratable acidity; Vc, vitamin C; MI, TSS/TA. x1–x11, the nutrient concentrations of N, P, K, Ca, Mg, S, Fe, Mn, Cu, Zn, and B in the leaves. ** indicate significant differences at p < 0.01.
Table 6. Proposed optimum leaf mineral element for high-quality ‘Bingtang’ sweet orange.
Table 6. Proposed optimum leaf mineral element for high-quality ‘Bingtang’ sweet orange.
Leaf-Nutrient FactorSFW (g)FSITSS (%)TA (%)Vc (mg/100 g)MIOptimum Range
N (%)4.924.922.412.412.412.412.41–4.92
P (%)0.280.100.280.280.100.100.10–0.28
K (%)2.112.111.301.302.111.301.30–2.11
Ca (%)2.992.992.992.992.992.992.99
Mg (%)0.260.390.260.410.260.260.26–0.41
S (mg/kg)640640340340340340340–640
Fe (mg/kg)127.46127.4689.65127.4689.65127.4689.65–127.46
Mn (mg/kg)51.9313.4851.9351.9351.9351.9313.48–51.93
Cu (mg/kg)2.6013.8413.8413.8413.842.602.60–13.84
Zn (mg/kg)15.5915.5951.4815.5951.4851.4815.59–51.48
B (mg/kg)53.9553.9553.9553.9553.9553.9553.95
Objective value206.121.0115.450.4092.7637.81——
Abbreviations: SFW, single fruit weight; FSI, fruit shape index; TSS, total soluble solids; TA, titratable acidity; Vc, vitamin C; MI, TSS/TA.
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Cao, S.; Zeng, B.; Zhou, X.; Deng, S.; Zhang, W.; Luo, S.; Ouyang, M.; Yang, S. Multivariate Analysis and Optimization Scheme of the Relationship between Leaf Nutrients and Fruit Quality in ‘Bingtang’ Sweet Orange Orchards. Horticulturae 2024, 10, 976. https://doi.org/10.3390/horticulturae10090976

AMA Style

Cao S, Zeng B, Zhou X, Deng S, Zhang W, Luo S, Ouyang M, Yang S. Multivariate Analysis and Optimization Scheme of the Relationship between Leaf Nutrients and Fruit Quality in ‘Bingtang’ Sweet Orange Orchards. Horticulturae. 2024; 10(9):976. https://doi.org/10.3390/horticulturae10090976

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Cao, Sheng, Bin Zeng, Xuan Zhou, Sufeng Deng, Wen Zhang, Sainan Luo, Mengyun Ouyang, and Shuizhi Yang. 2024. "Multivariate Analysis and Optimization Scheme of the Relationship between Leaf Nutrients and Fruit Quality in ‘Bingtang’ Sweet Orange Orchards" Horticulturae 10, no. 9: 976. https://doi.org/10.3390/horticulturae10090976

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