Principal component analysis for yield and quality traits of sugar beet varieties treated by proline under saline stress

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Therefore, six sugar beet varieties were evaluated using different levels of proline to decrease the harmful effect of salinity in 2022/2023 and 2023/2024. Methods Proline treatments (control, 50, 100, and 200 ppm) were sprayed in the main plots, and six sugar beet varieties were arranged within subplots, such as Smart Seza, Smart Meyra, BTS 3880, BTS 3740, SV 2003, and Wombat Smart during the two seasons. Results The elite varieties recording high mean values for growth and yield traits were Smart Seza and Smart Mayra, whereas the best varieties reflecting high mean values for quality traits were SV2003 and Smart Seza. The root diameter (RD), root length (RL), top fresh weight (TW), root fresh weight / plant (RW), extractable sugar (EXT), sucrose (SU), and sugar yield (SY) showed a positive relationship with the root yield of sugar beets, whereas potassium (K), α-amino (N), sodium (Na), and sucrose loss to molasses (SLM) showed a negative relationship with sugar yield. Results revealed that out of 12, only three principal components (PCs) showed 95.6% variability among the studied traits. Maximum variation was found for PC1; therefore, the selection of important characteristics that increase sugar yield under PC1 may be desirable. Conclusions A 200 ppm proline concentration sprayed on sugar beet varieties can be recommended to achieve high yield and quality sugar traits. In general, Smart Seza and Smart Mayra were the best varieties with high mean values for growth, yield, and quality attributes. sugar beet yield quality proline regression correlation principal component and cluster heat map Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Sugar beet ( Beta vulgaris L.) has significant commercial value because of its high sucrose content. At the beginning of the nineteenth century, the only global source of sugar was sugar cane. Subsequently, sugar beets became one of the most important sources of sugar in the world. Because of its numerous industrial applications, sugar beet is thought to be a potentially profitable crop, second only to sugarcane [ 36 ]. People need this crop because it gives them high-quality energy and can be used to feed animals. Sugar beet is one of the main crops for obtaining sugar in Egypt. According to the Council of Sugar Crops (2023), sugar beet is currently regarded as the primary source of sugar production in Egypt, accounting for approximately 63.8% (1.71 million tons) of the country's total sugar production, but 27.3% (0.678 million tons) from sugarcane. Globally, saline stress is the main factor causing crop loss, with most major agricultural plants seeing average yield reductions of over 50% [ 6 , 36 , 41 ]. According to [ 38 ], soil salinity is a component of natural ecosystems in arid and semi-arid regions and is becoming an increasingly serious problem in agricultural soils worldwide [ 29 , 39 ]. One of the nations with the worst salinity issues is Egypt. For instance, low precipitation (less than 25 mm annually) has resulted in 33% of Egypt's cultivated land, which makes up only 4% of the country's total land area, becoming salinized [ 2 ]. During the early growth stages of sugar beet, the earth's electric conductivity (ECe) should not exceed three dSm − 1 . High salinity stress severely inhibits plant growth because of ion toxicity, an imbalance in mineral nutrition absorption, and reduced nutrient uptake, which causes physiological stress to plants. [ 44 ]. In saline soil and harsh soil conditions, we resort to helping the sugar beet by spraying with important amino acids that help plants in these conditions, such as spraying with proline. Proline is an important amino acid that is thought to function as an osmoregulator. It plays a crucial role in osmoregulatory mechanisms and enhances growth and physiological traits like yield, relative water content, and chlorophyll index. Additionally, it enhances water relations and sugar beet yield under water-stressed conditions. [ 19 ] who found that use of proline increased sugar beet yield and food quality, whereas 10 mM proline was more efficient. The yields of sugar and white sugar rose by 18% and 15%, respectively, at 10 mM proline. Proline's superior effect can be explained by its ability to improve sugar beets capacity to store and absorb nutrients like carbon and nitrogen, as well as act as a scavenger of free radicals and lessen the negative effects of drought stress [ 5 ]. They found that the application, when combined with Si + proline (2 mmol Si + 10 mmol proline), has been shown to help reduce drought stress damages in sugar beet plants. Principal component analysis (PCA) is a multivariate technique that examines a data table in which observations are defined by many quantitative dependent variables that are inter-correlated. Its objective is to extract important information from the table, represent it as a set of new orthogonal variables known as principal components, and use points on maps to illustrate the similarity patterns between the variables and the observations. Cross-validation methods can be used to assess the quality of the PCA model. To handle qualitative variables, PCA can be generalized as correspondence analysis (CA), and to manage heterogeneous sets of variables, it can be generalized as multiple factor analysis (MFA) [ 1 , 35 ]. Pearson’s correlation coefficients between the different traits demonstrate the existence of a relationship between these characteristics, whether positive, negative, or no relationship between them. A statistical method called regression enables scientists to investigate the presence and strength of this link. Several traits have a strong positive correlation with each other. These include root yield, top yield, root length, sugar yield, sucrose%, and recoverable sugar percentage were founded [ 16 , 37 ]. The objective of the current study was to evaluate differences between six sugar beet varieties sprayed with different proline concentrations for 12 traits that will be useful for selecting superior varieties under saline soil. In addition, the study determined the relationship between growth, yield, and quality traits of varieties using principal component analysis and cluster heat maps. 2. Material and Methods 2.1. Growth conditions and treatments A field study was conducted on a private farm in the Monshat Sinnuris region located in the western desert (29°17ˋ N; 30°53ˋ E), Al- Fayoum Governorate, Egypt. The study was conducted during the two seasons of 2022/2023 and 2022/2024 to estimate the responses of six imported sugar beet varieties under saline soil (Table 1 ). The experimental design was a randomized complete block design (RCBD) with a split plot for three replications. Proline treatments (control, 50, 100, and 200 ppm) were sprayed in the main plots, and six sugar beet varieties were arranged within subplots. Each experimental unit included five rows; 60 cm apart and 5 m long, and comprised an area of 15 m 2 . Experiments were performed on October 15th and 21st in the first and second seasons, respectively. The experimental soil samples were collected from one depth (0–30 cm) from the soil surface before cultivation to determine the physical and chemical properties of the soil according to [ 21 ], as given in Table 2 . Table 1 Name, company, and country of the examined monogerm sugar beet varieties No. Name Company Country 1 Smart Seza-KWS KWS Germany 2 Smart Meyra-KWS KWS Germany 3 BTS3880 Beta Seed UK 4 BTS 3740 Beta Seed UK 5 SV 2003 (Hopper Smart) SESVANDRHAVE Belgium 6 Wombat Smart SESVANDRHAVE Belgium *Source: Sugar Crops Research Institute, ARC, Giza, Egypt. Table 2 Physical and chemical soil properties at the experimental sites (Monshat Sinnuris) 2022/2023 season Particle size distribution % Texture class Moisture content (%) pH Sand Silt Clay F.C W.P A.W 21.9 39.9 38.2 Clay Loamy 14.2 5.2 9.6 7.12 EC dS/cm − 1 Soluble cations and anions (meq/l) Ca + 2 Mg + 2 Na + K + HCO 3 − SO 4 − 2 Cl − CO3- 5.13 8.95 13.61 17.88 10.92 3.18 20.61 25.41 2.14 2023/2024 season Particle size distribution % Texture class Moisture content (%) pH Sand Silt Clay F.C W.P A.W 23.6 29.9 46.5 Clay Loamy 13.2 5.5 7.7 7.84 EC dS/cm − 1 Soluble cations and anions (meq/l) Ca + 2 Mg + 2 Na + K + HCO 3 − SO 4 − 2 Cl − CO3- 5.17 9.53 14.24 16.44 11.51 3.98 19.83 24.98 2.97 The present study was executed to appreciate the employment of four concentrations of proline as important biochemical components of sugar beet varieties to enhance the growth of the plant under saline soil. All proline treatments were sprayed twice at four and eight leaf stages. During the study's two years, surface irrigation, fertilizer, and other agronomic practices were implemented as recommended. At harvest, a sample of 10 randomly chosen plants was taken from the three guarded central rows of each plot to estimate the following traits: 2.2. The studied traits 2.2.1 Growth traits 1. Root length (RL) (cm), 2. Root diameter (RD) (cm), 3. Root fresh weight/plant (RW) (kg), and 4. Top fresh weight / plant (TW) (kg). 2.2.2 Yield traits 1. Root yield (RY) (ton/fed): calculated from the root weight of the experimental unit. 2. Sugar yield (SY) (ton/fed) = extractable sugar % × root yield (ton/fed)/100 2.2.3 Quality traits 1. Impurities of juice, (K, Na and Alpha-Amino-N) concentrations were estimated as meq/100g beet, as described by [ 11 , 12 ]. 2. Sucrose percentage (SU) was polarimetrically determined on a lead acetate extract of fresh macerated root according to the method of [ 26 ]. 3. Sucrose loss to molasses percentage (SLM) = 0.14 (Na + K) + 0.25 (Alpha-amino nitrogen) + 0.50 [ 13 ]. 4. Extractable sugar percentage (EXT) = Sucrose % – SLM % − 0.6 [ 14 ]. 2.3. Statistical analysis Data collected for each season, the individual analysis of variance for every experiment (year), followed by the combined analysis of variance for different traits was performed on a plot mean basis. Data were statistically analyzed according to [ 20 ] using the GenStat V.19 computer software package. Bonfferroni test at a 5% level was used to compare the means according to [ 9 ]. Regression and principal component analyses were conducted using the Minitab 2021 computer software package. The cluster heat map was generated using the R computer software package. 3. Results and discussion The results of Bartlett's test showed homogenous error variance for most attributes in both years, allowing for a pooled analysis of variance over years. 3.3. Mean performance Highly significant differences among the studied varieties were detected for all studied traits in combined analysis. Differences between varieties may be due to their genetic makeup [ 18 ]. 3.1.1 Growth and yield traits The interaction of proline concentrations vs. varieties mean performances for the growth and yield studied traits are given in (Table 3 ). The interaction based on combined data indicated that, the higher proline concentrations had a better effect on the growth and yield characteristics of the varieties. For root length, Smart Mayra exhibited high significant root length at control (0 ppm), whereas Smart Mayra and BTS3880 exhibited high root length at 200-ppm concentrations. The BTS3880 variety showed a significant increase in the root length traits with increased proline concentrations from 0 to 200 ppm amounted to 13.23 cm. Concerning root diameter, the BTS3740 variety had a high value (8.80 cm) at 0 ppm, while Smart Seza recorded a highly significant value (14.54) at 200 ppm concentrations (Table 3 ). For the Smart Seza variety, rood diameter significantly increased from 8.53 to 14.54 cm with increasing proline concentrations from zero to 200 ppm. Regarding root fresh weight, Smart Seza variety showed high significant values at 0 ppm, 50 ppm and 100 ppm. On the other hand, the Smart Meyra was highly under high proline spray concentration. The root fresh weight trait of the Smart Meyra variety increased significantly by 105.4 kg with increasing proline concentrations (Table 3 ). In terms of root yield, Smart Seza and Smart Meyra varieties showed high significant root yields of 15.49 and 15.23 ton/fed, respectively, under control conditions, whereas the lowest yield was 13.58 ton/fed for the SV 2003 variety. Smart Seza and BTS3740 exhibited highly significant root yields with 200 ppm of proline concentration of 27.57 and 25.28 ton/fed, respectively, whereas the lowest root yield was 21.12 ton/fed for Wombat Smart (Table 3 ). A significant increase in the root yield trait was observed for the Smart Seza and amounted to 12.08 ton/fed accompanied by an increase in proline concentrations from zero to 200 ppm. 3.1.2 Sugar yield and quality traits The interaction of proline concentrations vs. variety for sugar yield and quality studied traits are given in (Table 4 ). These traits increased significantly as proline concentrations increased except for the sucrose loss to molasses trait, which decreased as proline concentrations increase. Concerning sugar yield, Wombat Smart showed the highest significant mean value (2.16 ton/fed) under control conditions, while the variety Smart Seza had the highest significant mean value (4.82 ton/fed) under 200 ppm concentrations. A significant increase in the sugar yield trait was observed being 2.69 ton/fed accompanied by an increase in proline concentrations from zero to 200 ppm for the Smart Seza (Table 4 ). The increase in sugar yield (2.28 ton/fed) as proline concentrations were raised from zero to 200 ppm can be referred to as an increase in root yield. Regarding sucrose percentage, Wombat Smart exhibited a high-value significant percentage (17.33%) under the control condition, whereas the variety SV2003 showed the highest mean sucrose concentration under 200 ppm concentrations. On the other hand, Smart Meyra showed the lowest significant percentages (15.62%) and (19.52%) under 0 and 200 ppm concentrations (Table 4 ). Regarding the response of sucrose % to increasing proline concentrations, sucrose % increased significantly by 3.9% when, proline concentrations were raised. In terms of extractable sugar %, Wombat Smart exhibited a high significant percentage (15.10%) under control (0 ppm), whereas SV2003 showed a high significant percentage (19.20%) under 200 ppm concentration. Spraying with 100 ppm of proline concentration decreased compared to 50 ppm under all varieties at extractable sugar trait. The SV2003 variety showed a significant increase in extractable sugar % traits of 4.03% accompanied by an increase in proline concentrations from 0 to 200 ppm (Table 4 ). Concerning sucrose loss to molasses percentage, SV2003 showed a low significance percentage (1.58%), whereas Smart Seza showed a high significance percentage (1.72%) under control (0 ppm) concentration. Wombat Smart showed a significant low-value percentage under 200 ppm proline concentrations, whereas the variety Smart Seza showed a high percentage. A significant decrease in sucrose loss to molasses trait amounted to 0.3% accompanying the increase of Proline concentrations from zero to 200 ppm, which was obtained for Smart Seza and SV2003. In general, the best varieties with high mean values for quality traits were SV2003 and Smart Seza. The variety BTS3740 had the highest mean of sugar yield (Table 3 ). 3.4. Correlation analysis The data shown in Fig. 1 Denotes the relationship between yield and quality traits of sugar beet plants grown under proline concentrations. According to Pearson's correlation coefficients, all traits showed highly significant among them. There is a significant positive correlation between a number of traits; sucrose percentage (SU), extractable sugar percentage (EXT), sugar yield (SY), root length (RL), root yield (RY), top fresh weight (TW), root fresh weight (RW), and root diameter (RD). This indicates that these parameters positively interacted with each other as they increased or decreased. However, root yield was highly positive and significantly correlated with sucrose percentage, extractable sugar percentage, sugar yield, root length, root yield, top fresh weight, root fresh weight, and root diameter [ 37 , 40 ]. Root yield with sugar yield recorded a high positive correlation value (0.97), whereas sucrose percentage with extractable sugar percentage recorded a perfect correlation (1.00), due to the sucrose % being included in the extractable sugar % calculation. On the other hand, sucrose percentage showed a negative correlation with sucrose loss for molasses % (SLM), potassium % (K), sodium % (Na) and α-amino % (N) parameters. This suggests that as the sucrose percentage increases, there may be a decrease in these traits and vice versa (Fig. 1). 3.5. Regression analysis The study employed linear regression analysis to identify the relationship between yield and the studied traits, reflecting the importance of the studied traits and their direction effect on yield. The linear regression coefficients for each attribute are displayed in Fig. 2 , along with the regression coefficient of determination (R 2 ). The visual dependence of the dependent variable, root yield (RY), on the main yield-related parameters, is shown in Fig. 2 . The highest coefficient of determination (93.5%) was found for sugar yield (SY), followed by root fresh weight (RW) (91.6%), root diameter (RD) (88.4%), and top fresh weight (TW) (84.7%). The lowest coefficient of determination (45.4%) was observed for sucrose. These traits exhibited a positive relationship with root yield [ 16 , 37 ] (Fig. 2 ). This explains that when these traits increase, the root yield of sugar beet increases, and vice versa. On the other hand, the regression coefficient of SY on related quality traits (Fig. 3 showed extractable sugar% had the highest coefficient of determination (70.9%), followed by sucrose percentage (SU) (69.8%). In contrast, potassium % (K), α-amino % (N), sodium % (Na), and sucrose loss to molasses (SLM) had a negative relationship with sugar yield. The coefficient of determination was found for potassium (K), α-amino % (N), sodium % (Na), and sucrose loss to molasses % (SLM) (63.0%), (61.4%), (55.3%), and (51.9%), respectively. This explains that when these traits decrease, the sugar yield of sugar beets increases and vice versa (Fig. 3 ). 3.6. Cluster heat map The cluster heat map (CHM) included in this study show the impact of various proline concentrations and varieties on the growth, productivity, and quality traits of sugar beet plants. The heat map and hierarchical clustering analysis simplify the identification of treatment groups with comparable impacts on the observed parameters. The results of the hierarchical clustering analysis showed that the 200 ppm concentration + four varieties (BTS3740, BTS3880, SV2003 and wombat Smart) were distinctly separated from the other treatments, demonstrating that these combinations had the least influence on the measured traits, especially potassium, α-amino nitrogen, sodium, and sucrose loss to molasses (Fig. 4 ). This suggests that spraying with high concentrations of proline affected these varieties through the studied traits. Among the treatments, 200-ppm concentration + two varieties (Smart Meryra and Smart Seza) gave the highest values for the measured parameters, except potassium, α-amino nitrogen, sodium, and Sucrose loss to molasses. This indicates that spraying with a high proline concentration has a stronger effect on Smart Meter and Smart Seza through the studied traits of sugar beet plants. Spray of proline concentrations on different varieties were clustered into two big groups, one of which contained 200 ppm + studied varieties and the other contained 0, 50, and 100 ppm + studied varieties. Concentrations of 50 and 100 ppm were clustered together, indicating that these treatments had similar effects on the measured parameters. Control concentration (0 ppm) + varieties showing the lowest impact on the measured parameters except potassium, α-amino nitrogen, sodium, and sucrose loss to molasses (Fig. 4 ). The two big groups were clustered into three subgroups: 200 ppm, 0 ppm and (50 and 100 ppm). The main differences between these three subgroups were the increase or decrease in potassium, α-amino nitrogen, sodium, and sucrose loss to molasses traits. In general, when proline concentration increased from zero to 200 ppm the studied traits increased except potassium, α-amino nitrogen, sodium, and sucrose loss to molasses. On the other hand, concentrations of 50 and 100 ppm caused a moderate effect on the studied traits. Zero concentration caused a decrease in root fresh weight /plant, sugar yield, root length, root diameter, extractable sugar %, top fresh weight/plant, sucrose % and root yield. Overall, the results of the heat map and hierarchical clustering analysis provide insightful information on how different treatments affect the growth, yield and quality of sugar beet plants, information that might be used to determine future farming methods [ 37 ] (Fig. 4 ). 3.7. Principal component analysis (PCA) A powerful multivariate method for determining independent principal components that have an impact on plant traits is principal component analysis independently through indirect selection for qualities that are effective. This approach helps breeders genetically to improve traits like yield that has low heritability, especially in early generations [ 8 ]. The PCA method is a good statistical tool used to reduce high-dimensional data to lower-dimensional data using different variables [ 42 ]. It facilitates the identification of similar variables and samples and includes information that can be compared [ 24 ]. Character and variety scores have values that are distributed symmetrically when the primary components of PC1, PC2 and PC3 were scaled. The three primary components accounted for characteristics in the assessment of diversity across sugar beet varieties using 12 traits, as shown by the total accumulated contribution rate of 95.6% (Table 5 ). According to the findings, the Eigen values and accumulated contribution rates for the first, second, and third principal components (PC1, PC2 and PC3) were 9.062 (75.5%), 1.441 (12%), and 0.964 (8%), respectively. The character in PC was chosen according to the highest value between the three PCs. The characters in the PC1 that displayed the highest variation were top fresh weight, root fresh weight, root yield, sodium, and sucrose% (Table 5 ). PC2 accounted for 12% of the variation, with potassium and sucrose loss to molasses as the main traits in this component, whereas PC3, which contains root length, root diameter, α-amino, extractable sugar, and sugar yield accounted for 8% [ 4 , 17 ]. Table 5 Eigen values, proportion of total variability between the studied traits and the first three principal components (PC). Trait PC1 PC2 PC3 Root length 0.27 -0.235 0.316 Root diameter 0.254 -0.151 -0.551 Top fresh weight/plant 0.324 0.025 -0.077 Root fresh weight /plant 0.321 0.100 -0.134 Root yield 0.315 -0.186 -0.191 Potassium 0.032 0.772 -0.346 Sodium 0.317 -0.025 0.226 α-amino 0.310 0.036 0.324 Sucrose loss to molasses 0.299 0.339 0.086 Extractable sugar % -0.32 -0.132 -0.166 Sucrose % -0.319 -0.125 -0.172 Sugar yield 0.253 -0.361 -0.443 Eigen value 9.062 1.441 0.964 Proportion of total variability % 75.5 12.0 8.0 Cumulative % 75.5 87.5 95.6 Variable indicators chosen using the PCA technique generally have the potential to help speed up the evaluation process, prevent resource waste, and enable scientific screening of high-quality varieties based on many parameters. As a result, the PCA method has been used in several crops [ 15 , 17 , 27 ]. Principle components analysis studies the interrelation among the characters and genotypes. The various sugar beet varieties were grouped using both cluster heat map (CHM) and principle component analysis (PCA) techniques, which may have something to do with their genetic makeup and geographic distribution [ 22 ]. The graph of the principal component is a useful tool for examining patterns of interaction between varieties and traits. Principal component analysis graph in (Fig. 5 ) was performed for 12 traits of sugar beet, and showed perfect, correlation between sucrose % and extractable sugar. There was a positive correlation between sugar yield, root length, root yield, and root diameter characteristics, as well as a positive correlation between α-amino nitrogen, sodium, top fresh weight, root fresh weight, and sucrose loss to molasses traits (acute angle). These traits with similar transcription will cluster together. On the other hand, a negative correlation was observed between potassium with sucrose %, extractable sugar, sugar yield, root length, and root yield. No correlation between potassium and (root yield and root diameter), and no correlation between sucrose % or extractable sugar content with top fresh weight and root fresh weight (straight angle) (Fig. 5 ). Sugar yield, root length, root yield, root diameter, α-amino nitrogen, sodium, top fresh weight, root fresh weight, and sucrose loss to molasses traits recorded high values for the variety Smart Seza. Furthermore, the variety Smart Meyra was found in the same sector and showed comparable traits to these characteristics. These variation points and features were grouped into a single sector, and their positive associations appeared in the angles connecting them (acute angles). However, the obtuse angles between potassium with sucrose, extractable sugar, Sugar yield, root length, and root yield indicate that an increase in potassium leads to a decrease in sucrose and extractable sugar of sugar beets, and these results are similar to those of [ 3 , 4 , 17 ]. 4. Discussion The majority of sugar beet plantings occur in Europe, with smaller numbers in Asia [ 25 ]. In Egypt, every type of sugar beet is imported from other countries. Although the nutritional and agronomic characteristics of these sugar beet types are less clear, they are characterized by high yields and high sucrose contents. Therefore, it is imperative to clarify the composite nutritional quality characteristics of these varieties and develop a comprehensive evaluation system for sugar beets in Egypt. This approach could provide a significant theoretical foundation for sugar beet, cultivation, production, and use. The growth, yield, and quality criteria of six different varieties of sugar beet were examined and studied in this study, and the results indicated that a given root length, root diameter, root fresh weight, and root yield were significantly increased by (11.22 cm), (4.29 cm), (89.22 kg), and (9.17 ton/fed), respectively, with increasing proline concentrations from 0 to 200 ppm (Table 3 ). In general, the best varieties with high mean values for growth and yield traits were Smart Seza and Smart Mayra, whereas the lowest mean values for growth and yield traits was Wombat Smart. All growth and yield studied traits were substantially increased as proline concentrations increased (Table 3 ). The sugar yield (ton/fed), sucrose%, and extractable sugar% showed significantly increased by (2.28), (3.90), and (4.19), respectively, with increasing proline concentrations from 0 to 200 ppm (Table, 4). Sucrose loss to molasses exhibited a significant decrease (0.29%) with increasing proline concentrations from 0 to 200 ppm. This result might be because increasing proline concentrations play a crucial role in osmoregulatory mechanisms and enhance growth and physiological traits like yield, relative water content, and chlorophyll index, which participate in increasing the values of the studied traits. Numerous indicators are available to evaluate sugar beet genotype characteristics, and differences in quality between varieties have been connected to many linked reasons [ 5 , 43 ]. In correlation analysis, the sucrose percentage (SU), extractable sugar percentage (EXT), sugar yield (SY), root length (RL), root yield (RY), top fresh weight (TW), root fresh weight (RW), and root diameter (RD) were positively associated with each other. The more these characteristics increase, the higher the root yield increases (Fig. 1). The regression coefficient of sugar yield on potassium (K), α-amino% (N), sodium% (Na), and sucrose loss to molasses% (SLM) showed a negative relationship [ 16 , 37 ]. This explains that when these traits decrease, the sugar yield of sugar beets increases (Fig. 3 ), when the sugar yield trait increases, the root yield of sugar beets increases, and vice versa (Fig. 2 ). Increases in root yield and quality may have contributed to the increase in sugar yield. Moreover, increased root length and diameter resulting from a higher proline concentration may have been related to improved cell elongation and cell division [ 7 , 37 ]. The various sugar beet varieties were grouped using both cluster heat map (CHM) and principle component analysis (PCA) techniques, which may be related to their genetic makeup and geographic distribution (Jia et al., 2015). Three subgroups, 200 ppm, 0 ppm, and (50 and 100 ppm) were, formed from the two larger groups. The primary variations among these three subgroups were attributed to variations in potassium, α-amino nitrogen, sodium, and sucrose loss to molasses characteristics. In contrast, 200 ppm had low values for these parameters, whereas the control had high values for these traits. However, 50 and 100 ppm exhibited moderate levels of these traits. Overall, the results of the hierarchical clustering analysis and heat map provide useful data about the effects of different treatments on sugar beet plant development, yield, and quality; this information may be utilized to establish future farming practices [ 37 ] (Fig. 4 ). The Eigen values and accumulated contribution rate for the first PC (PC1) were 9.062 (75.5%). The traits of top fresh weight, root fresh weight, root yield, sodium, and sucrose% accounted for the most variation expressed in the PC1 (Table 5 ). Generally, the scientific selection of high-quality varieties can be facilitated and resource waste can be avoided by using variable indicators chosen using the PCA technique based on several characteristics, and expedite the evaluation process. As a result, many crops have been using the PCA method [ 15 , 27 , 32 , 33 ]. Principle component analysis studies the interrelations among traits and genotypes. PCA showed that Smart Seza was the better variety in most traits, and Smart Meyra was a good variety with more characteristics, while Wombat Smart, SV2003, BTS3880, and BTS3740 were good in sucrose% and extractable sugar (Fig. 5 ). 5. Conclusion The results of this work, using a 200 ppm proline concentration combined with sugar beet varieties, can be recommended to achieve high yield and quality sugar traits. The results indicated that A given root length, root diameter, root fresh weight, and root yield increased significantly by (11.22 cm), (4.29 cm), (89.22 kg), and (9.17 ton/fed), respectively, with increasing proline concentrations from zero to 200 ppm (Table, 3). The sugar yield (ton/fed), sucrose %, and extractable sugar % significantly increased by (2.28), (3.90), and (4.19), respectively, with increasing proline concentrations from 0 to 200 ppm (Table, 4). Sucrose loss to molasses exhibited a significant decrease (0.29%) with increasing proline concentrations from zero to 200 ppm. In general, the best varieties that gave high mean values for growth, yield, and quality traits were Smart Seza and Smart Mayra, while the best variety that gave high mean value for quality traits only was SV2003. Correlation and linear regression analysis showed that sucrose percentage (SU), extractable sugar % (EXT), sugar yield (SY), root length (RL), root yield (RY), top fresh weight (TW), root fresh weight (RW), and root diameter (RD) were positively associated with each other. Cluster heat maps showed differences between varieties and proline concentrations based on increases or decreases in potassium, α-amino nitrogen, sodium, and sucrose loss to molasses traits. The results showed that the first principal component (PC1) had Eigen values and a proportion of total variability of 9.062 (75.5%). The parameters that accounted for the majority of the variance reflected in PC1 were top fresh weight, root fresh weight, root yield, sodium, and sucrose percentage. In the PC graph, the variety Smart Seza recorded high values for all studied traits, except for potassium, sucrose, and extractable sugar. These variation points and traits were grouped into a single sector, and their positive associations appeared in the angles connecting them (acute angles), this meaning when these traits increase, the sugar root yield and the sugar yield increase. Declarations Acknowledgments The authors gratefully acknowledge all staff and students for their contributions to the development of this research. Author contributions F.A. and K.S.—the first author and third author conceptualized the study and carried out data collection. R.A. wrote the main manuscript text and prepared figures and tables. Each author made a worthy contribution to the manuscript preparation. This manuscript was read and approved by all authors. Funding Information The authors did not receive support from any organization for the submitted work. Compliance with Ethical Standards Conflict of Interest; the authors declare that they have no conflict of interest. References Abdi H, Williams LJ. Principal component analysis. 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Statistical procedures for agricultural research. John wiley & sons. ‏ 1984. Jackson, ML. Soil Chemical Analysis, (2nd Indian Print) Prentice-Hall of India Pvt. Ltd. New Delhi. 1973; 38–336. Jia XF, Zhu SM, Wang Q, Long W, Zhang XL. Principal component analysis and cluster analysis of the elements in sugar beet roots of different geographical origins in Xinjiang. Modern Food Science and Technology. 2015;(7):302–308. https://doi.org/10.1088/1742-6596/1176/4/042021. Kapadia GJ, Rao GS. Anticancer effects of red beet pigments. Red beet biotechnology: food and pharmaceutical applications. Boston, MA: Springer US. 2012;125–154. Kucukbay FZ, Kuyumcu E, Karaca I, Ozdemir D. Determination of minerals and trace elements in soils and the relation with its concentrations in sugar beets. Asian Journal of Chemistry. 2010;22(5):3691–3704. Kumar R, Pathak AD. Recent trend of sugar beet in world. Souvenir-IISR-industry interface on research and development initiatives for sugar beet in India. 2013; 46–47. Le-Docte. Commercial determination of sugar beet in the beet root using the sacks. Le-Docte process. Int. Sugar. J. 1927;29:488–492 Li W, Gao HY, Chen HJ, Wu WJ, Fang. XJ. Evaluation of comprehensive quality of different varieties of bayberry based on principal components analysis. Journal of Chinese Institute of Food Science and Technology. 2017;17(6):161–171. https://doi.org/10.1088/1742-6596/1176/4/042021. Lu BF, Gui G, Zhou Y. Development and application of edible beets. Sugar Crops of China. 2008; 2:67–69. https://doi.org/10.1007/s12355-014-0353-y. Machado RMA, Serralheiro RP. Soil salinity: Effect on vegetable crop growth. A review. Agriculture Water Management . 2017;78(4):1-13. doi:10.1016/j.agwat.2017.04.012. Maheshwari RK, Parmar V, Joseph L. Latent therapeutic gains of beet root juice. World Journal of Pharmaceutical Research. 2013;2(4):804–820. Makhlouf BSI, El-Laboudy EHS, Abu-Ellail FFB. Effect of N-fixing bacteria on nitrogen fertilizer requirements for some sugar beet varieties. J. of Plant Production, Mansoura Univ. 2021;12(1):87–96. https://doi.org/10.21608/jpp.2021.152023. Mehareb E.M, El-Mansoub MMA.Genetic parameters and principal components analysis biplot for agronomical, insect and pathological traits in some sugarcane genotypes SVU- International Journal of Agricultural Science. 2020;2(2):60–77 https://doi: 10.21608/svuijas.2020.35913.1017 Mehareb EM, EL-Bakary HMY, Abo elenen. FouzFM. Comprehensive evaluation of sugar beet genotypes for yield and relative traits by multivariate analysis. SVU-International Journal of Agricultural Sciences, 2021;3(1):96–111. ‏ https://doi: 10.21608/svuijas.2021.58999.1072 Minitab LLC. Minitab. Retrieved from data, 2021. https://www.minitab.com/support/documentation. Mishra SP, Sarkar U, Taraphder S, Datta S, Swain D, Saikhom R, Panda S, Laishram M. Multivariate statistical data analysis-principal component analysis (PCA). International Journal of Livestock Research. 2017;7(5):60–78. https://doi.org/10.5455/ijlr.20170415115235. Mukherjee E, Gantait S. Genetic transformation in sugar beet (Beta vulgaris L.): technologies and applications. Sugar Tech. 2023;25(2):269–281. https://doi.org/10.1007/s12355-022-01176-6. Nassar MAA, El-Magharby SS, Ibrahim NS, Kandil EE, Abdelsalam NR. Productivity and quality variations in sugar beet induced by soil application of K-Humate and foliar application of biostimulants under salinity condition. Journal of Soil Science and Plant Nutrition. 2023;23(3):3872–3887. https://doi.org/10.1007/s42729-023-01307-2. Pathak H, Rao DLN. Carbon and nitrogen mineralization from added organic matter in saline and alkaline soils. Soil Biol. Biochem. 1998;30(6):695–702. https://doi.org/10.1016/S0038-0717(97)00208-3. Qadir M, Ghafoor A, Murtaza G. Amelioration strategies for saline soils: a review. Land Degrad. Dev. 2000;11(6):501–521. https://doi.org/10.1002/1099-145X(200011/12)11:6 3.0.CO;2-S. Singh D, Mall AK, Misra V, Kumar M, Pathak AD. Assessment of coefficient of variation, correlations between yield and yield attributes in sugar beet ( Beta vulgaris L.). Plant Arch. 2018;18:15–18. Ullah, A, Bano A, Khan, N. Climate change and salinity effects on crops and chemical communication between plants and plant growth-promoting microorganisms under stress. Frontiers in Sustainable Food Systems. 2021;5, 618092. ‏ Wold S, Esbensen K, Geladi P. Principal component analysis. Chemometrics and Intelligent Laboratory Systems. 1987;2(1–3):37–52. https://doi.org/10.1016/0169-7439(87)80084-9. Xiao Y, Juan DU, Yang XM, Pu XY, Zeng YW, Yang T, Yang JZ, Yang SM,. Chen ZY. Analysis of functional ingredients in barley grains from different regions between Southwest China and ICARDA. Southwest China Journal of Agricultural Sciences. 2017;30(8):1700-1706.https://doi.org/10.1155/2020/3836172. Yusuf M, Hasan SA, Ali B, Hayat S, Fariduddin Q, Ahmed A. Effect of salicylic acid on salinity induced changes in Brassica juncea. J. Integrative Plant Biol. 2007; 50:1096–1102. https://doi.org/10.1111/j.1744-7909.2008.00697.x. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5747758","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":397356282,"identity":"cbd72378-677d-4598-b8cc-650e73d81555","order_by":0,"name":"Farrag Farghal Boraiy Abu-Ellail","email":"","orcid":"","institution":"Sugar Crops Research Institute, Agricultural Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Farrag","middleName":"Farghal Boraiy","lastName":"Abu-Ellail","suffix":""},{"id":397356283,"identity":"e3be5e71-91e6-4ae9-b828-8720d108695c","order_by":1,"name":"Ramy Nabil Farez Abdelkawy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBADOX72BiBlYEG8FmPJngMgLRLEa0ncMCMBRBOhxbz9dJoE4x47YwPJ51c3/CiQYOBv707Aq0XmTO42CYZnyXLm0jllN3uADpM4c3YDXi0SDCAtB5iNLWfnpN3gAWoxkMgloIX/LUhLfeKGm2fSbv4hSosE2JbDiRtusB+7TZwtEm83WyQcOA4M5By22zIGEjyE/cKfu/HGhwPVwKg8/uzmmz82cvztvfi1AAGLRAKY5jEAk4SUgwDzBwjN/oAY1aNgFIyCUTACAQAdvEXmYflzKQAAAABJRU5ErkJggg==","orcid":"","institution":"Central Laboratory for Design and Statistical Analysis Research, Agricultural Research Center","correspondingAuthor":true,"prefix":"","firstName":"Ramy","middleName":"Nabil Farez","lastName":"Abdelkawy","suffix":""},{"id":397356284,"identity":"6f8e0002-8ce5-4b8a-9b91-f95cde52c7dd","order_by":2,"name":"Karam Abdel Sadek","email":"","orcid":"","institution":"Sugar Crops Research Institute, Agricultural Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Karam","middleName":"Abdel","lastName":"Sadek","suffix":""}],"badges":[],"createdAt":"2025-01-01 21:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5747758/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5747758/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73051586,"identity":"883d7ca7-466a-4d52-914b-caad03540a33","added_by":"auto","created_at":"2025-01-06 09:30:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":170212,"visible":true,"origin":"","legend":"\u003cp\u003ePearson’s correlation coefficients between the traits. White and dark red color indicates positive significant correlations and white and dark blue color indicates negative significant correlation. Where root fresh weight /plant (RW), sugar yield (SY), root length (RL), root diameter (RD), extractable sugar % (EXT), sucrose loss to molasses % (SLM), top fresh weight/plant (TW), sucrose % (SU), root yield (RY), potassium % (K), sodium % (Na) and α-amino % (N).\u003c/p\u003e\n\u003cp\u003e** Correlation is significant at the 0.01 level for all studied traits.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5747758/v1/e00bff393117bc354e1e324f.png"},{"id":73049763,"identity":"57ca3577-8181-4236-b093-8f694307a468","added_by":"auto","created_at":"2025-01-06 09:22:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":291178,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression of root yield (RY) according to root diameter (RD), root length (RL), top fresh weight (TW), root fresh weight /plant (RW), sugar yield (SY) and sucrose (SU).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5747758/v1/7c64b239b54b256f2a2195f4.png"},{"id":73049766,"identity":"037cd933-d9f6-489d-899e-b14fd51c4ebb","added_by":"auto","created_at":"2025-01-06 09:22:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":295590,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression of sugar yield (SY) on potassium % (K), α-amino % (N), sodium% (Na), sucrose loss to molasses% (SLM), sucrose % (SU) and extractable sugar% (EXT).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5747758/v1/286e7718ceaf946fa802b693.png"},{"id":73049767,"identity":"19e02c47-f2ca-42a9-bb0b-e942c25db38e","added_by":"auto","created_at":"2025-01-06 09:22:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":147833,"visible":true,"origin":"","legend":"\u003cp\u003eCluster heat map (CHM) analysis of sugar beet varieties response to four concentrations of proline, and their combinations. Clustered using Euclidean distance and complete linkage.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5747758/v1/a2cb9f516b33222d5e11ab3d.png"},{"id":73051590,"identity":"f78e9500-567d-4a07-bc6c-42cc4bbef6b0","added_by":"auto","created_at":"2025-01-06 09:30:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":68737,"visible":true,"origin":"","legend":"\u003cp\u003eprincipal component analysis of traits describing sugar beet varieties response to four proline concentrations, and their combinations.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5747758/v1/e1571ba2d92600460cfb8de8.png"},{"id":73306760,"identity":"d3cbb0a9-fc8f-4456-8d5d-b2d3939c25e5","added_by":"auto","created_at":"2025-01-08 17:16:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1644391,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5747758/v1/a4eb4d44-2306-44c8-8bb7-d24a4d4e1160.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Principal component analysis for yield and quality traits of sugar beet varieties treated by proline under saline stress","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSugar beet (\u003cem\u003eBeta vulgaris\u003c/em\u003e L.) has significant commercial value because of its high sucrose content. At the beginning of the nineteenth century, the only global source of sugar was sugar cane. Subsequently, sugar beets became one of the most important sources of sugar in the world. Because of its numerous industrial applications, sugar beet is thought to be a potentially profitable crop, second only to sugarcane [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. People need this crop because it gives them high-quality energy and can be used to feed animals. Sugar beet is one of the main crops for obtaining sugar in Egypt. According to the Council of Sugar Crops (2023), sugar beet is currently regarded as the primary source of sugar production in Egypt, accounting for approximately 63.8% (1.71\u0026nbsp;million tons) of the country's total sugar production, but 27.3% (0.678\u0026nbsp;million tons) from sugarcane.\u003c/p\u003e \u003cp\u003eGlobally, saline stress is the main factor causing crop loss, with most major agricultural plants seeing average yield reductions of over 50% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. According to [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], soil salinity is a component of natural ecosystems in arid and semi-arid regions and is becoming an increasingly serious problem in agricultural soils worldwide [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. One of the nations with the worst salinity issues is Egypt. For instance, low precipitation (less than 25 mm annually) has resulted in 33% of Egypt's cultivated land, which makes up only 4% of the country's total land area, becoming salinized [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. During the early growth stages of sugar beet, the earth's electric conductivity (ECe) should not exceed three dSm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. High salinity stress severely inhibits plant growth because of ion toxicity, an imbalance in mineral nutrition absorption, and reduced nutrient uptake, which causes physiological stress to plants. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn saline soil and harsh soil conditions, we resort to helping the sugar beet by spraying with important amino acids that help plants in these conditions, such as spraying with proline. Proline is an important amino acid that is thought to function as an osmoregulator. It plays a crucial role in osmoregulatory mechanisms and enhances growth and physiological traits like yield, relative water content, and chlorophyll index. Additionally, it enhances water relations and sugar beet yield under water-stressed conditions. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] who found that use of proline increased sugar beet yield and food quality, whereas 10 mM proline was more efficient. The yields of sugar and white sugar rose by 18% and 15%, respectively, at 10 mM proline. Proline's superior effect can be explained by its ability to improve sugar beets capacity to store and absorb nutrients like carbon and nitrogen, as well as act as a scavenger of free radicals and lessen the negative effects of drought stress [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. They found that the application, when combined with Si\u0026thinsp;+\u0026thinsp;proline (2 mmol Si\u0026thinsp;+\u0026thinsp;10 mmol proline), has been shown to help reduce drought stress damages in sugar beet plants.\u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) is a multivariate technique that examines a data table in which observations are defined by many quantitative dependent variables that are inter-correlated. Its objective is to extract important information from the table, represent it as a set of new orthogonal variables known as principal components, and use points on maps to illustrate the similarity patterns between the variables and the observations. Cross-validation methods can be used to assess the quality of the PCA model. To handle qualitative variables, PCA can be generalized as correspondence analysis (CA), and to manage heterogeneous sets of variables, it can be generalized as multiple factor analysis (MFA) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Pearson\u0026rsquo;s correlation coefficients between the different traits demonstrate the existence of a relationship between these characteristics, whether positive, negative, or no relationship between them. A statistical method called regression enables scientists to investigate the presence and strength of this link. Several traits have a strong positive correlation with each other. These include root yield, top yield, root length, sugar yield, sucrose%, and recoverable sugar percentage were founded [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe objective of the current study was to evaluate differences between six sugar beet varieties sprayed with different proline concentrations for 12 traits that will be useful for selecting superior varieties under saline soil. In addition, the study determined the relationship between growth, yield, and quality traits of varieties using principal component analysis and cluster heat maps.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Growth conditions and treatments\u003c/h2\u003e\n \u003cp\u003eA field study was conducted on a private farm in the Monshat Sinnuris region located in the western desert (29\u0026deg;17ˋ N; 30\u0026deg;53ˋ E), Al- Fayoum Governorate, Egypt. The study was conducted during the two seasons of 2022/2023 and 2022/2024 to estimate the responses of six imported sugar beet varieties under saline soil (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The experimental design was a randomized complete block design (RCBD) with a split plot for three replications. Proline treatments (control, 50, 100, and 200 ppm) were sprayed in the main plots, and six sugar beet varieties were arranged within subplots. Each experimental unit included five rows; 60 cm apart and 5 m long, and comprised an area of 15 m\u003csup\u003e2\u003c/sup\u003e. Experiments were performed on October 15th and 21st in the first and second seasons, respectively. The experimental soil samples were collected from one depth (0\u0026ndash;30 cm) from the soil surface before cultivation to determine the physical and chemical properties of the soil according to [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e], as given in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eName, company, and country of the examined monogerm sugar beet varieties\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompany\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmart Seza-KWS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKWS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmart Meyra-KWS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKWS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBTS3880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta Seed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBTS 3740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta Seed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSV 2003 (Hopper Smart)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSESVANDRHAVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWombat Smart\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSESVANDRHAVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*Source: Sugar Crops Research Institute, ARC, Giza, Egypt.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical and chemical soil properties at the experimental sites\u003c/strong\u003e (Monshat Sinnuris)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e2022/2023 season\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eParticle size distribution %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTexture class\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMoisture content (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSilt\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClay\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF.C\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eW.P\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA.W\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClay Loamy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEC dS/cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eSoluble cations and anions (meq/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCa\u003csup\u003e+\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMg\u003csup\u003e+\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCO3-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023/2024 season\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticle size distribution %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTexture class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMoisture content (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003epH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSand\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSilt\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eClay\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF.C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eW.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eA.W\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClay Loamy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEC dS/cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eSoluble cations and anions (meq/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCa\u003csup\u003e+\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMg\u003csup\u003e+\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCO3-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe present study was executed to appreciate the employment of four concentrations of proline as important biochemical components of sugar beet varieties to enhance the growth of the plant under saline soil. All proline treatments were sprayed twice at four and eight leaf stages. During the study\u0026apos;s two years, surface irrigation, fertilizer, and other agronomic practices were implemented as recommended. At harvest, a sample of 10 randomly chosen plants was taken from the three guarded central rows of each plot to estimate the following traits:\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. The studied traits\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Growth traits\u003c/h2\u003e\n \u003cp\u003e1. Root length (RL) (cm), 2. Root diameter (RD) (cm), 3. Root fresh weight/plant (RW) (kg), and 4. Top fresh weight / plant (TW) (kg).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2 Yield traits\u003c/h2\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e1. Root yield (RY) (ton/fed): calculated from the root weight of the experimental unit.\u003c/p\u003e\n\u003cp\u003e2. Sugar yield (SY) (ton/fed)\u0026thinsp;=\u0026thinsp;extractable sugar % \u0026times; root yield (ton/fed)/100\u003c/p\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2.3 Quality traits\u003c/h2\u003e\u003cspan\u003e\n \u003cp\u003e1. Impurities of juice, (K, Na and Alpha-Amino-N) concentrations were estimated as meq/100g beet, as described by [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e2. Sucrose percentage (SU) was polarimetrically determined on a lead acetate extract of fresh macerated root according to the method of [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e3. Sucrose loss to molasses percentage (SLM)\u0026thinsp;=\u0026thinsp;0.14 (Na\u0026thinsp;+\u0026thinsp;K)\u0026thinsp;+\u0026thinsp;0.25 (Alpha-amino nitrogen)\u0026thinsp;+\u0026thinsp;0.50 [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e4. Extractable sugar percentage (EXT)\u0026thinsp;=\u0026thinsp;Sucrose % \u0026ndash; SLM % \u0026minus;\u0026thinsp;0.6 [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n \u003c/span\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e\n \u003cp\u003eData collected for each season, the individual analysis of variance for every experiment (year), followed by the combined analysis of variance for different traits was performed on a plot mean basis. Data were statistically analyzed according to [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] using the GenStat V.19 computer software package. Bonfferroni test at a 5% level was used to compare the means according to [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Regression and principal component analyses were conducted using the Minitab 2021 computer software package. The cluster heat map was generated using the R computer software package.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eThe results of Bartlett\u0026apos;s test showed homogenous error variance for most attributes in both years, allowing for a pooled analysis of variance over years.\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Mean performance\u003c/h2\u003e\n \u003cp\u003eHighly significant differences among the studied varieties were detected for all studied traits in combined analysis. Differences between varieties may be due to their genetic makeup [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1 Growth and yield traits\u003c/h2\u003e\n \u003cp\u003eThe interaction of proline concentrations vs. varieties mean performances for the growth and yield studied traits are given in (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The interaction based on combined data indicated that, the higher proline concentrations had a better effect on the growth and yield characteristics of the varieties. For root length, Smart Mayra exhibited high significant root length at control (0 ppm), whereas Smart Mayra and BTS3880 exhibited high root length at 200-ppm concentrations. The BTS3880 variety showed a significant increase in the root length traits with increased proline concentrations from 0 to 200 ppm amounted to 13.23 cm. Concerning root diameter, the BTS3740 variety had a high value (8.80 cm) at 0 ppm, while Smart Seza recorded a highly significant value (14.54) at 200 ppm concentrations (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). For the Smart Seza variety, rood diameter significantly increased from 8.53 to 14.54 cm with increasing proline concentrations from zero to 200 ppm.\u003c/p\u003e\n \u003cp\u003eRegarding root fresh weight, Smart Seza variety showed high significant values at 0 ppm, 50 ppm and 100 ppm. On the other hand, the Smart Meyra was highly under high proline spray concentration. The root fresh weight trait of the Smart Meyra variety increased significantly by 105.4 kg with increasing proline concentrations (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In terms of root yield, Smart Seza and Smart Meyra varieties showed high significant root yields of 15.49 and 15.23 ton/fed, respectively, under control conditions, whereas the lowest yield was 13.58 ton/fed for the SV 2003 variety. Smart Seza and BTS3740 exhibited highly significant root yields with 200 ppm of proline concentration of 27.57 and 25.28 ton/fed, respectively, whereas the lowest root yield was 21.12 ton/fed for Wombat Smart (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). A significant increase in the root yield trait was observed for the Smart Seza and amounted to 12.08 ton/fed accompanied by an increase in proline concentrations from zero to 200 ppm.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2 \u003cstrong\u003eSugar yield and quality traits\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eThe interaction of proline concentrations vs. variety for sugar yield and quality studied traits are given in (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). These traits increased significantly as proline concentrations increased except for the sucrose loss to molasses trait, which decreased as proline concentrations increase.\u003c/p\u003e\n \u003cp\u003eConcerning sugar yield, Wombat Smart showed the highest significant mean value (2.16 ton/fed) under control conditions, while the variety Smart Seza had the highest significant mean value (4.82 ton/fed) under 200 ppm concentrations. A significant increase in the sugar yield trait was observed being 2.69 ton/fed accompanied by an increase in proline concentrations from zero to 200 ppm for the Smart Seza (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The increase in sugar yield (2.28 ton/fed) as proline concentrations were raised from zero to 200 ppm can be referred to as an increase in root yield.\u003c/p\u003e\n \u003cp\u003eRegarding sucrose percentage, Wombat Smart exhibited a high-value significant percentage (17.33%) under the control condition, whereas the variety SV2003 showed the highest mean sucrose concentration under 200 ppm concentrations. On the other hand, Smart Meyra showed the lowest significant percentages (15.62%) and (19.52%) under 0 and 200 ppm concentrations (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Regarding the response of sucrose % to increasing proline concentrations, sucrose % increased significantly by 3.9% when, proline concentrations were raised. In terms of extractable sugar %, Wombat Smart exhibited a high significant percentage (15.10%) under control (0 ppm), whereas SV2003 showed a high significant percentage (19.20%) under 200 ppm concentration. Spraying with 100 ppm of proline concentration decreased compared to 50 ppm under all varieties at extractable sugar trait. The SV2003 variety showed a significant increase in extractable sugar % traits of 4.03% accompanied by an increase in proline concentrations from 0 to 200 ppm (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eConcerning sucrose loss to molasses percentage, SV2003 showed a low significance percentage (1.58%), whereas Smart Seza showed a high significance percentage (1.72%) under control (0 ppm) concentration. Wombat Smart showed a significant low-value percentage under 200 ppm proline concentrations, whereas the variety Smart Seza showed a high percentage. A significant decrease in sucrose loss to molasses trait amounted to 0.3% accompanying the increase of Proline concentrations from zero to 200 ppm, which was obtained for Smart Seza and SV2003. In general, the best varieties with high mean values for quality traits were SV2003 and Smart Seza. The variety BTS3740 had the highest mean of sugar yield (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" 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\" style=\"width: 1076px; height: 461.815px;\" width=\"1076\" height=\"461.815\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Correlation analysis\u003c/h2\u003e\n \u003cp\u003eThe data shown in Fig.\u0026nbsp;1 Denotes the relationship between yield and quality traits of sugar beet plants grown under proline concentrations. According to Pearson\u0026apos;s correlation coefficients, all traits showed highly significant among them. There is a significant positive correlation between a number of traits; sucrose percentage (SU), extractable sugar percentage (EXT), sugar yield (SY), root length (RL), root yield (RY), top fresh weight (TW), root fresh weight (RW), and root diameter (RD). This indicates that these parameters positively interacted with each other as they increased or decreased. However, root yield was highly positive and significantly correlated with sucrose percentage, extractable sugar percentage, sugar yield, root length, root yield, top fresh weight, root fresh weight, and root diameter [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. Root yield with sugar yield recorded a high positive correlation value (0.97), whereas sucrose percentage with extractable sugar percentage recorded a perfect correlation (1.00), due to the sucrose % being included in the extractable sugar % calculation. On the other hand, sucrose percentage showed a negative correlation with sucrose loss for molasses % (SLM), potassium % (K), sodium % (Na) and \u0026alpha;-amino % (N) parameters. This suggests that as the sucrose percentage increases, there may be a decrease in these traits and vice versa (Fig. 1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Regression analysis\u003c/h2\u003e\n \u003cp\u003eThe study employed linear regression analysis to identify the relationship between yield and the studied traits, reflecting the importance of the studied traits and their direction effect on yield. The linear regression coefficients for each attribute are displayed in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, along with the regression coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e). The visual dependence of the dependent variable, root yield (RY), on the main yield-related parameters, is shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The highest coefficient of determination (93.5%) was found for sugar yield (SY), followed by root fresh weight (RW) (91.6%), root diameter (RD) (88.4%), and top fresh weight (TW) (84.7%). The lowest coefficient of determination (45.4%) was observed for sucrose. These traits exhibited a positive relationship with root yield [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). This explains that when these traits increase, the root yield of sugar beet increases, and vice versa. On the other hand, the regression coefficient of SY on related quality traits (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e showed extractable sugar% had the highest coefficient of determination (70.9%), followed by sucrose percentage (SU) (69.8%). In contrast, potassium % (K), \u0026alpha;-amino % (N), sodium % (Na), and sucrose loss to molasses (SLM) had a negative relationship with sugar yield. The coefficient of determination was found for potassium (K), \u0026alpha;-amino % (N), sodium % (Na), and sucrose loss to molasses % (SLM) (63.0%), (61.4%), (55.3%), and (51.9%), respectively. This explains that when these traits decrease, the sugar yield of sugar beets increases and vice versa (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. Cluster heat map\u003c/h2\u003e\n \u003cp\u003eThe cluster heat map (CHM) included in this study show the impact of various proline concentrations and varieties on the growth, productivity, and quality traits of sugar beet plants. The heat map and hierarchical clustering analysis simplify the identification of treatment groups with comparable impacts on the observed parameters. The results of the hierarchical clustering analysis showed that the 200 ppm concentration\u0026thinsp;+\u0026thinsp;four varieties (BTS3740, BTS3880, SV2003 and wombat Smart) were distinctly separated from the other treatments, demonstrating that these combinations had the least influence on the measured traits, especially potassium, \u0026alpha;-amino nitrogen, sodium, and sucrose loss to molasses (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). This suggests that spraying with high concentrations of proline affected these varieties through the studied traits. Among the treatments, 200-ppm concentration\u0026thinsp;+\u0026thinsp;two varieties (Smart Meryra and Smart Seza) gave the highest values for the measured parameters, except potassium, \u0026alpha;-amino nitrogen, sodium, and Sucrose loss to molasses. This indicates that spraying with a high proline concentration has a stronger effect on Smart Meter and Smart Seza through the studied traits of sugar beet plants.\u003c/p\u003e\n \u003cp\u003eSpray of proline concentrations on different varieties were clustered into two big groups, one of which contained 200 ppm\u0026thinsp;+\u0026thinsp;studied varieties and the other contained 0, 50, and 100 ppm\u0026thinsp;+\u0026thinsp;studied varieties. Concentrations of 50 and 100 ppm were clustered together, indicating that these treatments had similar effects on the measured parameters. Control concentration (0 ppm)\u0026thinsp;+\u0026thinsp;varieties showing the lowest impact on the measured parameters except potassium, \u0026alpha;-amino nitrogen, sodium, and sucrose loss to molasses (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The two big groups were clustered into three subgroups: 200 ppm, 0 ppm and (50 and 100 ppm). The main differences between these three subgroups were the increase or decrease in potassium, \u0026alpha;-amino nitrogen, sodium, and sucrose loss to molasses traits. In general, when proline concentration increased from zero to 200 ppm the studied traits increased except potassium, \u0026alpha;-amino nitrogen, sodium, and sucrose loss to molasses. On the other hand, concentrations of 50 and 100 ppm caused a moderate effect on the studied traits. Zero concentration caused a decrease in root fresh weight /plant, sugar yield, root length, root diameter, extractable sugar %, top fresh weight/plant, sucrose % and root yield. Overall, the results of the heat map and hierarchical clustering analysis provide insightful information on how different treatments affect the growth, yield and quality of sugar beet plants, information that might be used to determine future farming methods [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7. Principal component analysis (PCA)\u003c/h2\u003e\n \u003cp\u003eA powerful multivariate method for determining independent principal components that have an impact on plant traits is principal component analysis independently through indirect selection for qualities that are effective. This approach helps breeders genetically to improve traits like yield that has low heritability, especially in early generations [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. The PCA method is a good statistical tool used to reduce high-dimensional data to lower-dimensional data using different variables [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. It facilitates the identification of similar variables and samples and includes information that can be compared [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Character and variety scores have values that are distributed symmetrically when the primary components of PC1, PC2 and PC3 were scaled. The three primary components accounted for characteristics in the assessment of diversity across sugar beet varieties using 12 traits, as shown by the total accumulated contribution rate of 95.6% (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAccording to the findings, the Eigen values and accumulated contribution rates for the first, second, and third principal components (PC1, PC2 and PC3) were 9.062 (75.5%), 1.441 (12%), and 0.964 (8%), respectively. The character in PC was chosen according to the highest value between the three PCs. The characters in the PC1 that displayed the highest variation were top fresh weight, root fresh weight, root yield, sodium, and sucrose% (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). PC2 accounted for 12% of the variation, with potassium and sucrose loss to molasses as the main traits in this component, whereas PC3, which contains root length, root diameter, \u0026alpha;-amino, extractable sugar, and sugar yield accounted for 8% [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEigen values, proportion of total variability between the studied traits and the first three principal components (PC).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTop fresh weight/plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot fresh weight /plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot yield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026alpha;-amino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSucrose loss to molasses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtractable sugar %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSucrose %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSugar yield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEigen value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of total variability %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCumulative %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eVariable indicators chosen using the PCA technique generally have the potential to help speed up the evaluation process, prevent resource waste, and enable scientific screening of high-quality varieties based on many parameters. As a result, the PCA method has been used in several crops [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Principle components analysis studies the interrelation among the characters and genotypes. The various sugar beet varieties were grouped using both cluster heat map (CHM) and principle component analysis (PCA) techniques, which may have something to do with their genetic makeup and geographic distribution [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe graph of the principal component is a useful tool for examining patterns of interaction between varieties and traits. Principal component analysis graph in (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) was performed for 12 traits of sugar beet, and showed perfect, correlation between sucrose % and extractable sugar. There was a positive correlation between sugar yield, root length, root yield, and root diameter characteristics, as well as a positive correlation between \u0026alpha;-amino nitrogen, sodium, top fresh weight, root fresh weight, and sucrose loss to molasses traits (acute angle). These traits with similar transcription will cluster together. On the other hand, a negative correlation was observed between potassium with sucrose %, extractable sugar, sugar yield, root length, and root yield. No correlation between potassium and (root yield and root diameter), and no correlation between sucrose % or extractable sugar content with top fresh weight and root fresh weight (straight angle) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSugar yield, root length, root yield, root diameter, \u0026alpha;-amino nitrogen, sodium, top fresh weight, root fresh weight, and sucrose loss to molasses traits recorded high values for the variety Smart Seza. Furthermore, the variety Smart Meyra was found in the same sector and showed comparable traits to these characteristics. These variation points and features were grouped into a single sector, and their positive associations appeared in the angles connecting them (acute angles). However, the obtuse angles between potassium with sucrose, extractable sugar, Sugar yield, root length, and root yield indicate that an increase in potassium leads to a decrease in sucrose and extractable sugar of sugar beets, and these results are similar to those of [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe majority of sugar beet plantings occur in Europe, with smaller numbers in Asia [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In Egypt, every type of sugar beet is imported from other countries. Although the nutritional and agronomic characteristics of these sugar beet types are less clear, they are characterized by high yields and high sucrose contents. Therefore, it is imperative to clarify the composite nutritional quality characteristics of these varieties and develop a comprehensive evaluation system for sugar beets in Egypt. This approach could provide a significant theoretical foundation for sugar beet, cultivation, production, and use.\u003c/p\u003e \u003cp\u003eThe growth, yield, and quality criteria of six different varieties of sugar beet were examined and studied in this study, and the results indicated that a given root length, root diameter, root fresh weight, and root yield were significantly increased by (11.22 cm), (4.29 cm), (89.22 kg), and (9.17 ton/fed), respectively, with increasing proline concentrations from 0 to 200 ppm (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In general, the best varieties with high mean values for growth and yield traits were Smart Seza and Smart Mayra, whereas the lowest mean values for growth and yield traits was Wombat Smart. All growth and yield studied traits were substantially increased as proline concentrations increased (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The sugar yield (ton/fed), sucrose%, and extractable sugar% showed significantly increased by (2.28), (3.90), and (4.19), respectively, with increasing proline concentrations from 0 to 200 ppm (Table, 4). Sucrose loss to molasses exhibited a significant decrease (0.29%) with increasing proline concentrations from 0 to 200 ppm.\u003c/p\u003e \u003cp\u003eThis result might be because increasing proline concentrations play a crucial role in osmoregulatory mechanisms and enhance growth and physiological traits like yield, relative water content, and chlorophyll index, which participate in increasing the values of the studied traits. Numerous indicators are available to evaluate sugar beet genotype characteristics, and differences in quality between varieties have been connected to many linked reasons [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn correlation analysis, the sucrose percentage (SU), extractable sugar percentage (EXT), sugar yield (SY), root length (RL), root yield (RY), top fresh weight (TW), root fresh weight (RW), and root diameter (RD) were positively associated with each other. The more these characteristics increase, the higher the root yield increases (Fig.\u0026nbsp;1). The regression coefficient of sugar yield on potassium (K), α-amino% (N), sodium% (Na), and sucrose loss to molasses% (SLM) showed a negative relationship [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This explains that when these traits decrease, the sugar yield of sugar beets increases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), when the sugar yield trait increases, the root yield of sugar beets increases, and vice versa (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Increases in root yield and quality may have contributed to the increase in sugar yield. Moreover, increased root length and diameter resulting from a higher proline concentration may have been related to improved cell elongation and cell division [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe various sugar beet varieties were grouped using both cluster heat map (CHM) and principle component analysis (PCA) techniques, which may be related to their genetic makeup and geographic distribution (Jia et al., 2015). Three subgroups, 200 ppm, 0 ppm, and (50 and 100 ppm) were, formed from the two larger groups. The primary variations among these three subgroups were attributed to variations in potassium, α-amino nitrogen, sodium, and sucrose loss to molasses characteristics. In contrast, 200 ppm had low values for these parameters, whereas the control had high values for these traits. However, 50 and 100 ppm exhibited moderate levels of these traits. Overall, the results of the hierarchical clustering analysis and heat map provide useful data about the effects of different treatments on sugar beet plant development, yield, and quality; this information may be utilized to establish future farming practices [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The Eigen values and accumulated contribution rate for the first PC (PC1) were 9.062 (75.5%). The traits of top fresh weight, root fresh weight, root yield, sodium, and sucrose% accounted for the most variation expressed in the PC1 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Generally, the scientific selection of high-quality varieties can be facilitated and resource waste can be avoided by using variable indicators chosen using the PCA technique based on several characteristics, and expedite the evaluation process. As a result, many crops have been using the PCA method [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Principle component analysis studies the interrelations among traits and genotypes. PCA showed that Smart Seza was the better variety in most traits, and Smart Meyra was a good variety with more characteristics, while Wombat Smart, SV2003, BTS3880, and BTS3740 were good in sucrose% and extractable sugar (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe results of this work, using a 200 ppm proline concentration combined with sugar beet varieties, can be recommended to achieve high yield and quality sugar traits. The results indicated that A given root length, root diameter, root fresh weight, and root yield increased significantly by (11.22 cm), (4.29 cm), (89.22 kg), and (9.17 ton/fed), respectively, with increasing proline concentrations from zero to 200 ppm (Table, 3). The sugar yield (ton/fed), sucrose %, and extractable sugar % significantly increased by (2.28), (3.90), and (4.19), respectively, with increasing proline concentrations from 0 to 200 ppm (Table, 4). Sucrose loss to molasses exhibited a significant decrease (0.29%) with increasing proline concentrations from zero to 200 ppm. In general, the best varieties that gave high mean values for growth, yield, and quality traits were Smart Seza and Smart Mayra, while the best variety that gave high mean value for quality traits only was SV2003. Correlation and linear regression analysis showed that sucrose percentage (SU), extractable sugar % (EXT), sugar yield (SY), root length (RL), root yield (RY), top fresh weight (TW), root fresh weight (RW), and root diameter (RD) were positively associated with each other. Cluster heat maps showed differences between varieties and proline concentrations based on increases or decreases in potassium, α-amino nitrogen, sodium, and sucrose loss to molasses traits. The results showed that the first principal component (PC1) had Eigen values and a proportion of total variability of 9.062 (75.5%). The parameters that accounted for the majority of the variance reflected in PC1 were top fresh weight, root fresh weight, root yield, sodium, and sucrose percentage. In the PC graph, the variety Smart Seza recorded high values for all studied traits, except for potassium, sucrose, and extractable sugar. These variation points and traits were grouped into a single sector, and their positive associations appeared in the angles connecting them (acute angles), this meaning when these traits increase, the sugar root yield and the sugar yield increase.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge all staff and students for their contributions to the development of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF.A. and K.S.\u0026mdash;the first author and third author conceptualized the study and carried out data collection. R.A. wrote the main manuscript text and prepared figures and tables.\u003c/p\u003e\n\u003cp\u003eEach author made a worthy contribution to the manuscript preparation. This manuscript was read and approved by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConflict of Interest; the authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdi H, Williams LJ. Principal component analysis. Wiley interdisciplinary reviews: computational statistics. 2010;2(4):433\u0026ndash;459.\u003cspan dir=\"RTL\"\u003e\u0026rlm; \u003c/span\u003ehttps://doi.org/10.1002/wics.101.\u003c/li\u003e\n\u003cli\u003eAbu Ellail FFB, Hamam KA, Kheiralla KA, El-Hifny MZ Salinity tolerance in 280 genotypes of two-rows barley. Egypt. J. 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Investigation of the proline role in controlling traits related to sugar and root yield of sugar beet under water deficit conditions. Agric Water Manag. 2021;243:106448. https://doi.org/10.1016/j.agwat.2020.106448.\u003c/li\u003e\n\u003cli\u003eGomez KA, Gomez AA. Statistical procedures for agricultural research. John wiley \u0026amp; sons.\u003cspan dir=\"RTL\"\u003e\u0026rlm;\u003c/span\u003e 1984.\u003c/li\u003e\n\u003cli\u003eJackson, ML. Soil Chemical Analysis, (2nd Indian Print) Prentice-Hall of India Pvt. Ltd. New Delhi. 1973; 38\u0026ndash;336.\u003c/li\u003e\n\u003cli\u003eJia XF, Zhu SM, Wang Q, Long W, Zhang XL. Principal component analysis and cluster analysis of the elements in sugar beet roots of different geographical origins in Xinjiang. Modern Food Science and Technology. 2015;(7):302\u0026ndash;308. https://doi.org/10.1088/1742-6596/1176/4/042021.\u003c/li\u003e\n\u003cli\u003eKapadia GJ, Rao GS. Anticancer effects of red beet pigments. 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Climate change and salinity effects on crops and chemical communication between plants and plant growth-promoting microorganisms under stress. Frontiers in Sustainable Food Systems. 2021;5, 618092.\u003cspan dir=\"RTL\"\u003e\u0026rlm;\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eWold S, Esbensen K, Geladi P. Principal component analysis. Chemometrics and Intelligent Laboratory Systems. 1987;2(1\u0026ndash;3):37\u0026ndash;52. https://doi.org/10.1016/0169-7439(87)80084-9.\u003c/li\u003e\n\u003cli\u003eXiao Y, Juan DU, Yang XM, Pu XY, Zeng YW, Yang T, Yang JZ, Yang SM,. Chen ZY. Analysis of functional ingredients in barley grains from different regions between Southwest China and ICARDA. Southwest China Journal of Agricultural Sciences. 2017;30(8):1700-1706.https://doi.org/10.1155/2020/3836172.\u003c/li\u003e\n\u003cli\u003eYusuf M, Hasan SA, Ali B, Hayat S, Fariduddin Q, Ahmed A. Effect of salicylic acid on salinity induced changes in Brassica juncea. J. Integrative Plant Biol. 2007; 50:1096\u0026ndash;1102. https://doi.org/10.1111/j.1744-7909.2008.00697.x.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sugar beet, yield, quality, proline, regression, correlation, principal component, and cluster heat map","lastPublishedDoi":"10.21203/rs.3.rs-5747758/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5747758/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our study, saline soil stress negatively affects root and sugar yields. Therefore, six sugar beet varieties were evaluated using different levels of proline to decrease the harmful effect of salinity in 2022/2023 and 2023/2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProline treatments (control, 50, 100, and 200 ppm) were sprayed in the main plots, and six sugar beet varieties were arranged within subplots, such as Smart Seza, Smart Meyra, BTS 3880, BTS 3740, SV 2003, and Wombat Smart during the two seasons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe elite varieties recording high mean values for growth and yield traits were Smart Seza and Smart Mayra, whereas the best varieties reflecting high mean values for quality traits were SV2003 and Smart Seza. The root diameter (RD), root length (RL), top fresh weight (TW), root fresh weight / plant (RW), extractable sugar (EXT), sucrose (SU), and sugar yield (SY) showed a positive relationship with the root yield of sugar beets, whereas potassium (K), α-amino (N), sodium (Na), and sucrose loss to molasses (SLM) showed a negative relationship with sugar yield. Results revealed that out of 12, only three principal components (PCs) showed 95.6% variability among the studied traits. Maximum variation was found for PC1; therefore, the selection of important characteristics that increase sugar yield under PC1 may be desirable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA 200 ppm proline concentration sprayed on sugar beet varieties can be recommended to achieve high yield and quality sugar traits. In general, Smart Seza and Smart Mayra were the best varieties with high mean values for growth, yield, and quality attributes.\u003c/p\u003e","manuscriptTitle":"Principal component analysis for yield and quality traits of sugar beet varieties treated by proline under saline stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-06 09:22:35","doi":"10.21203/rs.3.rs-5747758/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe606565-2fb9-43bb-acf1-b375905e214f","owner":[],"postedDate":"January 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-08T17:08:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-06 09:22:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5747758","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5747758","identity":"rs-5747758","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}