Multivariate analysis unveils antioxidant-nutrient trade-offs in Maize Hybrids: A hierarchical framework for acid soil tolerance evaluation

Multivariate analysis unveils antioxidant-nutrient trade-offs in Maize Hybrids: A hierarchical framework for acid soil tolerance evaluation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multivariate analysis unveils antioxidant-nutrient trade-offs in Maize Hybrids: A hierarchical framework for acid soil tolerance evaluation Xinghua Chen, Yuxin Xia, Liuqing Chen, Xiaoqi Yin, Suren Deng, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6021236/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 May, 2025 Read the published version in Plant and Soil → Version 1 posted 5 You are reading this latest preprint version Abstract Background and Aims Acid soils, characterized by nutrient deficiencies and metal ion toxicity, severely limit maize yields. Cultivating acid soil tolerant maize represents a promising strategy to address these edaphic constraints. Methods Through controlled pot experiments, 50 maize hybrids were subjected to acidic soil stress (AS) and optimal soil conditions (CK), evaluating 15 morpho-physiological traits at the V5 stage. Multivariate statistical approaches were employed to identify critical tolerance indicators, with subsequent field validation conducted on four selected genotypes. Results Acidic soil stress induced significant alterations across all measured parameters compared to control conditions and revealed substantial genotypic variation in stress responses. Cluster analysis classified the 50 hybrids into five distinct tolerance categories, with two predominant adaptation strategies. Antioxidant-dependent resistance characterized by elevated peroxidase (POD), ascorbate peroxidase (APX), and catalase (CAT) activities. This strategy prioritized oxidative defense at the expense of biomass production (acid-sensitive varieties). Nutrient optimization strategy demonstrated by superior nitrogen and phosphorus acquisition efficiencies, enabling sustained growth under stress conditions (acid-tolerant varieties). Stepwise regression identified six critical evaluation parameters: plant height, fresh weight, stem diameter, leaf area, total nitrogen and phosphorus accumulation, complemented by antioxidant enzyme profiles and reactive oxygen species levels. Conclusion This study establishes a comprehensive evaluation framework incorporating 11 validated indicators for screening adaptive maize varieties in acid soil conditions. Field validation confirmed the accuracy of multivariate analysis in selecting acid soil tolerant varieties. Acid soil Maize Acid tolerance mechanism Antioxidant enzymes Comprehensive evaluation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Soil acidity is one of the major causes of land degradation, affecting approximately 50% of arable land worldwide (Kochian 1995 ). Soil naturally acidifies were due to the leaching of bicarbonate and organic anions. However, the extensive use of nitrogen (N) fertilizers and the removal of base cations through agricultural activities have accelerated soil acidification (Zhu et al. 2018 ). Between 1980 and 2010, the average pH of the topsoil in major agricultural regions of China decreased by 0.5 units (Guo et al. 2010 ). In China, acid soils are predominantly found in the south of Yangtze River, with approximately 47% of cultivated soils having a pH lower than 6.5, and about 15% of soils having a pH below 5.5 (Zhu et al. 2024 ). If the overuse of N fertilizers continues, cereal yield losses in China are projected to range from 4–24% between 2010 and 2050 due to further declines in soil pH (Zhu et al. 2020 ). Soil acidification significantly limits crop growth and yields through multiple factors. When soils with pH falls below 5.5, the levels of toxic metals, including aluminum (Al 3+ ), iron (Fe 3+ ) and manganese (Mn 2+ ), are released, which damage roots and reduce photosynthetic efficiency in leaves (Gupta et al. 2013 ; Tandzi et al. 2018 ). Additionally, soil acidity often leads to low nutrient availability, including N, phosphorus (P), potassium (K + ), calcium (Ca 2+ ), magnesium (Mg 2+ ) and molybdenum (Mo 2+ ), further hindering crop growth and production (Zhao et al. 2014 ; Tandzi et al. 2018 ). While the application of lime or organic fertilizers can mitigate the effects of soil acidity and enhance cop yields, these solutions may not be affordable for the small-scale farmers and can have negative environmental effects (Tang et al. 2013 ). In contrast, the use of acid-tolerant crop varieties presents a more sustainable and cost-effective alternative for improving yield in acid soils. Maize ( Zea mays L.) is one of the most important crops globally but is highly sensitive to soil acidity. Studies have shown that soil acidity leads to significant yield reductions, with average decline of 44.6% in Latin America and Asia and 37.3% in Cameroon (Tandzi et al. 2018 ). However, notable variations in yield exist among maize genotypes, with acid-tolerant varieties maintaining stable yields under acid soil conditions (Devkota et al. 2019 ). Developing acid-tolerant maize varieties has thus become a key breeding objective, with grain yield and yield component traits serving as the primary target (Tandzi et al. 2018 ). However, evaluating these traits is time-consuming and costly, highlighting the need for efficient secondary indicators to facilitate the rapid and cost-effective screening of acid-tolerant varieties to enhance productivity under acidic soil stress. While morphological, physiological and biochemical traits have been widely used to screen for drought, flooding, and salinity-tolerant (Navakode et al. 2008 , Cançado et al. 2009 ; Alvim et al. 2012 ), comparatively fewer indicators have been employed for acidic soil tolerance. Studies on acid-tolerant crops have primarily focused on root morphological changes under Al 3+ toxicity (Maron et al. 2013 ; Zhang et al. 2018 ; Furlan et al. 2020 ). However, acid stress also inhibits nutrient uptake, reduces shoot growth, decreases photosynthetic capacity, and disrupts the antioxidant system, ultimately affecting plant height, biomass, and yield (Ali et al. 2008 ; Giannakoula et al. 2010 ; Dai et al. 2011 ; Qian et al. 2013 ). Moreover, excessive reactive oxygen species (ROS) generated under acidic stress cause cellular damage (Yamamoto et al. 2003 ). To counteract ROS, plants enhance antioxidant enzymes such as superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX) to mitigate oxidative stress (Hu et al. 2022 ). These findings underscore the need for integrating morphological and physiological indicators to comprehensively evaluate tolerance of acidic soil stress in crops. Multivariate analysis has been increasingly used to assess crop resistance to abiotic stress. For example, Chen (2012) evaluated drought tolerance in Chinese winter bread wheat using 14 morphological, yield-related, and physiological traits with membership function value (MFV). Subsequent studies applied similar methods, integrating MFVs and principal component analysis (PCA) to reduce redundancy among tightly correlated indicators (Zou et al. 2020 ; Khan et al. 2024 ). Building on these approaches, this study screened 15 indices to evaluate acid soil tolerance in maize hybrids under greenhouse conditions. Using a combination of multivariate analysis techniques, we developed optimal regression equations and identified key indicators for assessing acid soil resistance in maize. The selected tolerant varieties of acidic soil stress were subsequently validated under field conditions, establishing a practical and cost-effective framework for breeding resilient maize varieties. Materials and methods Plant materials and growth conditions A total of 50 hybrid maize varieties, purchased from local market, were used in this study (Supplemental Table S1 ). Acid soils were collected from 0–20 cm layer of red soils at Heshengqiao, Xianning City, Hubei Province (29.87° N, 114.29° E). The basic chemical properties of the soils were as follows: pH 5.12, 0.53 g/kg total N, 1.34 mg/kg available phosphorus, 119.95 mg/kg available potassium, 2.82g/kg total carbon, 502.31mg/kg exchangeable calcium, 45.68mg/kg exchangeable magnesium, and 317.72 mg/kg aluminum (Al). To create control soils, 2.0 g calcium hydroxide per kilogram of soil was added and thoroughly mixed, resulting in a pH of 6.69, with the following chemical properties: 0.55 g/kg total N, 1.49 mg/kg available phosphorus, 120.89 mg/kg available potassium, 3.11 g/kg total carbon, 645.23 mg/kg exchangeable calcium, 122.57 mg/kg exchangeable magnesium, and 43.97 mg/kg Al. Pot experiments were conducted with each pot containing 3.2 kg of air-dried, finely ground acid soil (AS) or control soils (CK). After watering and allowing the soil to mature for 7 days, eight seeds were sown in each pot. Ten days after sowing, three seedlings were retained per pot. The experiments were carried out in a greenhouse at Huazhong Agricultural University, Wuhan, Hubei Province (30.53° N, 114.36° E). Measurement of morphological indexes Maize plants were sampled at the V5 stage (30 days after sowing) to measure various physiological indexes. The plant height (PH) was measured from the base of the plant to the tip of the longest leaf. The stem diameter (SD)was measured at the thickest part of the stem using a digital caliper. Chlorophyll content was assessed using a chlorophyll meter (SPAD-502, MINOLTA, Japan), and the SPAD value was recorded. The fresh weight (FW) and dry weight (DW) of shoots were determined using a microbalance with a precision of 1 mg. The LA was calculated by measuring the longest and widest parts of each leaf, using the following formula: $$\:LA=0.75*\:\sum\:_{i=1}^{m}\sum\:_{j=1}^{n}\left({L}_{ij}*{W}_{ij}\right)/m$$ Where 0.75 is the empirical correction coefficient for maize LA; M is the number of plants measured; N is the total number of leaves for the i-th plant; L ij and W ij are the maximum leaf length and leaf width of the j-th leaf of the i-th plant, respectively. Determination of the antioxidant enzyme activities and malondialdehyde content Approximately 0.1 g of fresh leaves were ground with liquid nitrogen and homogenized in 1.5 mL of 50 mM phosphate buffer solution (pH 7.8). The mixture was centrifuged, and the supernatants were used to measure antioxidant enzyme activities and malondialdehyde (MDA) content. Superoxide dismutase (SOD) activity was measured using the method described by Giannopolitis and Ries ( 1977 ). The absorbance was measured at 560 nm, and one unit (U) of SOD activity was defined as the reduction of NBT by 50% per gram of fresh tissue per minute. Peroxidase (POD) activity was measured following the spectrophotometric method described by Cakmak and Marschner ( 1992 ). The absorbance change at 470 nm was monitored over a 3-minute period, with measurements taken every 30 seconds. One unit (U) of POD activity was defined as 0.01 increase in absorbance per gram of fresh tissue per minute. Ascorbate peroxidase (APX) activity was determined by measuring the change in absorbance at 290 nm. One unit (U) of APX activity was defined as the decomposition of 1 µM H 2 O 2 per gram of tissue per minute (Nakano and Asada 1987 ). Catalase (CAT) activity was determined using a commercial kit (Suzhou Greys Biotech Co., Ltd, G0106W) following the manufacturer's instructions. Briefly, 10 µL of supernatant was incubated with the kit reagents at 25 ℃ for 5 minutes, and the absorbance was measured at 510 nm. One unit (U) of CAT activity was defined as the decomposition of 1 µM H 2 O 2 per gram of tissue per minute at 25 ℃. MDA content was measured using the thiobarbituric acid method (Heath and Packer 1968 ). The absorbance of supernatant was measured at 532 nm, 600 nm, and 450 nm, respectively. Measurement of the reactive oxygen species Hydrogen peroxide (H 2 O 2 ) and superoxide (O 2 − ) content were measured according to the method outlined by (Wang et al. 2023 ). Briefly, 250 µL of the supernatant, prepared as for the SOD assay, was mixed with 250 µL of 50 mM PBS (pH 7.8) and 500 µL of 1 mM hydroxylamine hydrochloride (dissolved in 50 mM PBS, pH 7.8), and incubated for 1 hour at 25°C. Subsequently, 500 µL of 17 mM sulfanilamide (dissolved in 3 M acetic acid) and 500 µL of 7 mM α-naphthylamine (soluble in 99.5% acetic acid) were added, and the mixture was incubated for 20 minutes at 25°C. The absorbance of the reaction mixture was measured at 540 nm. For the H 2 O 2 assay, approximately 0.1 g of fresh maize leaf tissue was grounded into fine powder with liquid N, then homogenized in 1.5 mL of 0.1% trichloroacetic acid (TCA). The homogenate was centrifuged at 8000 g for 10 minutes at 4°C. A 0.5 mL aliquot of the supernatant was mixed with 0.5 mL of 10 mM phosphate-buffered saline (PBS, pH 7.0) and 1.0 mL of 1 M potassium iodide solution, and incubated for 1 h at 28°C. The absorbance of the mixture was measured at 390 nm. Total nitrogen, phosphorus and potassium accumulation Shoots from each seedling were sampled and dried to constant weight at 80 ℃. A 25 mg sample of dried shoots was digested with concentrated sulfuric acid (98%), followed by the addition of 5–6 drops of 30% H 2 O 2 every 30 min until the solution turned colorless. The colorless solution was then boiled for 1 hour, cooled, and diluted to the appropriate volume for nutrient analysis. Total N was measured using an AA3 flow analyzer. The N in the digestion solution reacted with sodium salicylate and sodium hypochlorite in the presence of a nitroprusside catalyst to form a blue complex, quantified by absorbance at 600 nm. Total phosphorus was determined by mixing 200 µL of the digested solution with 2 drops of 2,4-dinitrophenol, adjusting the pH, and adding molybdenum-antimony reagent. After incubation at 25°C for 30 minutes, absorbance was measured at 700 nm. For total potassium, the digested solution was diluted twofold and analyzed by flame photometry. Nutrient accumulation per seedling was calculated using the following formulas: Total nutrient accumulation = nutrient concentration (%) \(\:*\) dry weight Maize yield and yield components in the field trial A field trial was conducted on April 20, 2024, in the Caidian district of Wuhan, Hubei province, China (30.6°N, 114.0°E), using four maize varieties: MY908, QKY900, SD188, and FNY6. The soil type was yellow-brown soil, and compound fertilizer (NPK, 15-15-15) was applied at 450 kg/hm 2 , following local farming practices. Additionally, 600 kg/hm 2 of soil conditioner (pH 8–11) was added in the control treatment (CK). Soil properties after fertilization are detailed in supplemental Table S2 . Each variety was planted in plots of 40 m 2 with spacing of 25 cm between plants and 50 cm between rows, resulting in a planting density of 75000 plants/hm². Nine plants per replicate were sampled to measure theoretical yield and yield components, including ear length, ear diameter, kernel number per ear, and 100 kernels weight. The theoretical yield (kg/hm 2 ) was calculated by the following formula: Yield (kg/hm 2 ) = (kernel number per ear * 100 kernels weight /100) *planting density. Acid soil tolerance analysis Equation (1) illustrates the calculation of acid soil tolerance index (ATI) in accordance with Talebi ( 2009 ). Acid soil tolerance index (ATI) = ( \(\:AS\times\:CK\) ) / ( \(\:\stackrel{-}{CK}\) ) 2 (1) Where \(\:AS\) index under acid soil conditions; \(\:\:CK\:\) : index under control soil conditions; \(\:\stackrel{-}{CK}\:\) : mean index under control soil conditions. The membership function value (µ) of each index of different maize varieties was calculated using Eq. (2). Therefore, each index has its unique membership function value (Chen et al. 2012 ), and Eq. (3) calculates the mean membership function value (MMFV) for each maize variety. µ(X ij ) = (X ij \(\:-\) X min )/ (X max \(\:-\) X min ) (2) Where X ij was the ATI value of ith variety in the jth indicator; X min and X max denoted the minimum ATI value and maximum ATI value for the jth indicator among all varieties. Using the formulas from Zou et al. ( 2020 ), we calculated the overall acid soil resistance ability ( D -value) for each maize variety as follows: $$\:\text{V}j=\sqrt{{\varSigma\:}_{\text{i}=1}^{n}{\left({x}_{ij}-{\stackrel{-}{x}}_{j}\right)}^{2}}/\:{\stackrel{-}{x}}_{j}$$ 4 W j =V j / \(\:\sum\:_{j=1}^{n}Vj\) (5) D = \(\:\sum\:_{j=1}^{n}[\) μ(X j )*W j ] (6) Where V j , and X̅ j represented the standard deviation coefficient and the average value for the jth indicator across all varieties. W represented the weight of the jth indicator in all indicators. Results Growth and physiological responses of hybrid maize in acid soil To investigate the acidic soil stress characteristics of different maize varieties, we evaluated the morphological traits (PH, SPAD, SD, FW, LA), nutrient accumulation (TN, TP, TK), antioxidant enzyme activities (SOD, POD, APX, CAT), MDA and ROS (O 2 − and H 2 O 2 ) levels in 50 hybrid maize varieties under AS and CK (Figures S1 -S7, Table S3 ). A two-way ANOVA analysis revealed that varieties, treatment, and their interaction significantly influenced all 15 indicators during acidic soil stress ( P < 0.001; Table 1 ). The coefficient of variation (CV%) was calculated to assess the dispersion of biological traits under different soil treatments. CV% values ranged from 8.52–66.02%, with APX showing the highest variability (54.37% and 66.02%) and SPAD the lowest CV% (8.52% and 9.13%) under AS and CK conditions, respectively (Table 1 ). Growth and nutrient indices (PH, SD, FW, LA, TN, TP, and TK) were lower under AS than CK, resulting in the acid soil tolerance index (ATI) values below 1.0. Conversely, antioxidant enzyme activities (SOD, POD, APX, CAT) MDA and ROS were higher under AS, with ATI values exceeding 1.0 (Table 1 ). These results suggest a high degree of inter-varietal variation and can be used for screening acid soil tolerant varieties. Table 1 Statistical analysis of main indexes in maize under acid soil stress Index Treatment Mean CV% Mean ATI Treatment (T) Varieties (V) T*V PH AS 58.13 14.5 0.92 *** *** *** (cm) CK 63.86 10.71 SD AS 3.21 14.92 0.87 *** *** *** (cm) CK 3.73 13.02 FW AS 2.64 36.97 0.84 *** *** *** (g/plant) CK 3.31 27.32 LA AS 168.63 39.64 0.88 *** *** *** (cm 2 ) CK 197.06 34.37 SPAD AS 30.45 8.52 0.98 *** *** *** value CK 31.05 9.13 TN AS 310.54 32.38 0.76 *** *** *** (mg/plant) CK 418.22 27.7 TP AS 53.37 47.95 0.89 *** *** *** (mg/plant) CK 61.69 40.42 TK AS 286.38 36.74 0.96 *** *** *** (mg/plant) CK 311.23 32.56 SOD AS 504.78 13.62 1.05 *** *** *** (U) CK 487.27 18.54 POD AS 1995.64 23.54 1.27 *** *** *** (U) CK 1614.74 23.68 APX AS 103.48 54.37 2.63 *** *** *** (U) CK 42.87 66.02 CAT AS 493.17 30.34 1.80 *** *** *** (U) CK 278.86 33.1 MDA AS 13.79 17.06 1.18 *** *** *** (nmol/g FW) CK 11.79 17.26 O 2 − AS 14 25.74 1.02 *** *** *** (nmol/g FW) CK 13.65 23.32 H 2 O 2 AS 0.56 49.54 1.37 *** *** *** (µmol/g FW) CK 0.42 39.68 AS, acid soil; CK, control soil. PH, plant height; SD, stem diameter; FW, fresh weight; LA, leaf area; TN, total nitrogen accumulation; TP, total phosphorus accumulation; TK, total potassium accumulation; SOD, superoxide dismutase; POD, peroxidase; APX, ascorbate peroxidase; CAT, catalase; MDA, malondialdehyde; O 2 − , superoxide anion and H 2 O 2 , hydrogen peroxide. CV%, coefficient of variation. ATI, acidic stress tolerance index. Asterisks indicate statistically significant differences of each index: *** P < 0.001 (two-way ANOVA). Evaluation of maize acid soil tolerances by mean membership function values (MMFV) and comprehensive assessment values ( D ) To address the limitations of using a single physiological index for assessing acid soil tolerance, we calculated the mean membership function value (MMFV) for each variety. MMFV rankings were used to compare acid soil tolerance among maize cultivars (Table 2 ). A higher MMFV indicates greater acid soil tolerance, with MY908 exhibiting the highest MMFV (0.58), indicating strong acid soil tolerance, while XY045 had the lowest MMFV (0.17), indicating sensitivity to acid soils. To provide a more robust evaluation of acid soil tolerance, a comprehensive assessment value ( D ) was calculated using formula (6). The D values, which also rank acid soil resistance, displayed a wider range (0.13–0.60) compared to MMFV. While the rankings based on MMFV and D values were consistent for most varieties, discrepancies were observed for some. For instance, LP243 and SR818 had high MMFVs (0.50) but relatively low D values (< 0.40). Similarly, XY10 and SR565 showed MMFVs exceeding 0.40, yet their D values were only 0.28 (Table 2 ). These results highlight the importance of combining MMFV and D values for a more comprehensive evaluation of maize acid soil tolerance, ensuring a nuanced understanding of cultivar performance under acid soil conditions. Table 2 The mean membership function value (MMFV) and comprehensive evaluation value ( D ) of maize varieties under acidic soil stress Varieties MMFV Rank D Rank Varieties MMFV Rank D Rank MY908 0.58 1 0.60 1 LY16 0.38 26 0.36 16 RHY288 0.57 2 0.52 3 LP218 0.38 27 0.27 39 RHY3 0.56 3 0.54 2 YH868 0.38 28 0.32 28 KY8 0.52 4 0.52 4 JF86 0.37 29 0.29 33 LP243 0.50 5 0.37 14 SY29 0.37 30 0.35 21 SR818 0.50 6 0.35 18 ZY303 0.36 31 0.33 25 LY128 0.48 7 0.48 5 HY1702 0.36 32 0.28 34 ND372 0.48 8 0.46 6 EHD1 0.34 33 0.32 26 ZB1 0.47 9 0.34 22 MD26 0.34 34 0.30 30 JY666 0.47 10 0.41 9 KN20 0.33 35 0.25 42 QKY900 0.46 11 0.44 7 DD1311 0.33 36 0.28 37 FY18 0.46 12 0.36 15 SD188 0.33 37 0.31 29 DK653 0.44 13 0.35 19 JD7 0.32 38 0.32 27 XY335 0.44 14 0.41 8 JB288 0.31 39 0.35 17 XND9 0.44 15 0.34 23 TL1 0.31 40 0.27 38 WY3 0.43 16 0.41 10 HW267 0.30 41 0.24 43 KNY108 0.43 17 0.33 24 DD816 0.30 42 0.26 40 CY10 0.42 18 0.37 12 DH605 0.27 43 0.22 45 XY10 0.42 19 0.28 35 TR1818 0.27 44 0.25 41 LC839 0.42 20 0.37 13 BN5 0.27 45 0.23 44 DH682 0.41 21 0.39 11 ZD958 0.26 46 0.19 46 SR565 0.40 22 0.28 36 LD9088 0.24 47 0.19 47 FL21 0.39 23 0.35 20 DD6531 0.24 48 0.17 48 GY14022 0.38 24 0.29 32 FNY6 0.21 49 0.14 49 BJ186 0.38 25 0.29 31 XY045 0.17 50 0.13 50 Identification of principal components for acidic soil stress through PCA Principal component analysis (PCA) was performed based on the ATIs of 15 individual indicators. Five principal components (PCs) with eigenvalues exceeding 1.0 were identified ( F1 to F5 ), collectively explaining 74.36% of the total variation (Table S4). The PC1 was predominantly associated with growth and nutritional indicators, including FW, SD, PH, TN, TP, and TK (Fig. 1 ). The PC2 was primarily correlated with H 2 O 2 accumulation under acidic soil stress (Table 1 ). PC3 was strongly linked to ROS scavenging processes (H 2 O 2 , O 2 − ), and antioxidant enzymes such as CAT, APX, POD, and SOD. PC4 highlighted the significance of SPAD in regulating chlorophyll content, while PC5 was associated with MDA, reflecting membrane lipid peroxidation level in plant (Fig. 1 ). Using the weights of each principal component, scores ( F1 to F5 ) were calculated for each maize variety, resulting in a composite score ( F value) to rank acid soil resistance. Varieties such as RHY288, KY8, MY908, LP243, QKY900, RHY3, MY908, LY128, and SR818 had F values exceeding 1.0, indicating strong acid soil tolerance. In contrast, varieties XY045, LD9088, SD188, and FNY6 had F values below − 1.0, reflecting sensitivity to acid soil conditions (Fig. 2 ). Correlation analysis and key indicator identification for acid soil resistance assessment A Pearson’s correlation analysis was performed to explore relationships among individual indicators, D values, and F values. Growth indicators (PH, SD, FW, LA) showed strong positive correlations with nutritional indicators (TN, TP, TK), particularly between FW and TN, TP, and TK (r = 0.88, 0.76 and 0.87, respectively) (Fig. 3 ). Conversely, negative correlations were observed between growth and nutritional indicators and antioxidant enzyme activities (POD, APX, CAT). Specifically, POD activity negatively correlated with DW, FW, TP, and TK, while APX and CAT activities showed significant negative correlations with FW and TK, respectively. Notably, ROS content did not significantly correlate with growth or nutritional indicators (Fig. 3 ). The correlation between D value and F value was high (r = 0.79), indicating strong consistency in acidic soil stress evaluations by these methods. Growth and nutritional indicators demonstrated the strongest positive correlations with D and F values, especially FW and TN (r > 0.70), highlighting their importance in assessing maize acidic soil stress. However, while ROS content was significantly positively correlated with D value, POD and CAT activities were significantly negatively correlated with the F value, suggesting some methodological differences (Fig. 3 ). To identify key indicators for acid resistance, stepwise regression analysis was conducted using D and F values as dependent variables and the 15 individual indicators as independent variables. Regression models revealed that 11 individual indicators (PH, SD, FW, LA, TN, TP, POD, APX, CAT, H 2 O 2 , and O 2 − ) significantly influenced D and F values (R 2 > 0.95, P < 0.001) (Table 3 ). These indicators were identified as the critical traits for evaluating maize tolerance to acidic soil stress. Table 3 Stepwise regression analysis of acid-tolerant maize with combined D value and F value Stress Models R 2 F P > F Acid Stress D = -0.10 + 0.11*X3 + 0.03*X4 + 0.14*X5 + 0.02*X10 + 0.01*X11 + 0.02*X12 + 0.07*X14 + 0.03*X15 0.956 111.42 0.001 F = -3.31 + 0.98*X1 + 1.01*X2 + 0.38*X3 + 0.24*X4 + 1.14*X5 + 0.20*X6–0.12*X12 + 0.12*X15 0.987 392.40 0.001 X1, X2, X3, X4, X5, X6, X10, X11, X12, X14, and X15 represented plant height, stem diameter, fresh weight, leaf area, total nitrogen accumulation, total phosphorus accumulation, peroxidase, ascorbate peroxidase, catalase, superoxide anion and hydrogen peroxide, respectively. Clustering analysis of maize varieties based on D and F values Clustering analysis grouped the 50 hybrid maize varieties into five distinct categories based on their comprehensive D and F values (Fig. 4 ). These categories reflected varying levels of acid soil tolerance: highly acid-tolerance (HAT), acid-tolerance (AT), moderate acid-tolerance (MAT), acid-sensitive (AS), and highly acid-sensitive (HAS). Group I, comprising varieties such as MY908, RHY3, QKY900, LP243, KY8, and RHY288, displayed the highest D and F values, making it the most acid-tolerant group. In contrast, Group V, including varieties like FNY6, SD188, LD9088, and XY045, exhibited the lowest D and F values, classifying them as highly acid- sensitive. Validation of acid soil tolerance varieties under field conditions To assess the yield and its components in maize varieties with different acid soil tolerance, two HAT varieties, MY908 and QKY900, and two HAS varieties, SD188 and FNY6, were grown under field conditions. The ear length, ear diameter, kernel number per ear, 100-kernel weight, and final yield of MY908 and QKY900 were similar under both AS and normal conditions. This stability indicated that these two varieties maintained their performance and were acid-tolerant in the field. In contrast, acid soil stress significantly reduced the yields of FNY6 and SD188 by 9.98% and 21.84%, respectively (Table 4 ). The primary cause of yield reduction in HAS varieties was marked decrease in kernels number per ear under AS conditions (Table 4 ). These findings confirm that HAT varieties like MY908 and QKY900 are suitable for cultivation in acid soils, whereas HAS varieties such as SD188 and FNY6 are more susceptible to yield losses under such conditions. Table 4 Effect of acidic soil stress on yield components and theoretical yield of maize Varieties Treatment Ear length (cm) Ear diameter (cm) Kernel number per ear 100 kernels weight (g) Yield (t/ha) MY908 AS 16.67 ± 0.58 17.17 ± 0.29 543.33 ± 62.43 28.00 ± 1.45 11.37 ± 0.71 CK 18.33 ± 1.53 16.83 ± 0.29 551.33 ± 29.96 26.57 ± 0.95 10.98 ± 0.71 QKY900 AS 17.50 ± 0.50 16.67 ± 0.29 577.33 ± 72.04 22.97 ± 0.51 9.96 ± 1.43 CK 17.83 ± 0.76 16.43 ± 0.21 572.33 ± 58.59 22.60 ± 0.35 10.34 ± 1.05 Varieties (V) ns ns ns ns ns Treatment (T) ns ns ns ns ns V*T ns ns ns ns ns FNY6 AS 18.00 ± 1.00 18.67 ± 0.58 526.00 ± 40.84 20.60 ± 0.35 8.12 ± 0.55 CK 16.8 ± 0.72 16.83 ± 0.29 583.33 ± 40.41 20.63 ± 0.75 9.02 ± 0.53 SD188 AS 16.00 ± 0.01 16.77 ± 0.40 466.00 ± 64.16 22.90 ± 0.60 7.98 ± 0.90 CK 17.07 ± 0.12 16.43 ± 0.21 577.33 ± 46.14 23.60 ± 0.61 10.21 ± 0.77 Varieties (V) ns ** ns *** ns Treatment (T) ns ** * ns * V*T * * ns ns ns Asterisks indicate statistically significant differences: *, P < 0.05; **, P ≤ 0.01; ***, P ≤ 0.001 (two-way ANOVA). Data are means ± standard deviation (n = 3). Discussion The growth and physiological responses of hybrid maize varieties to acid soil Aluminum tolerance is one of the most reliable indicators for assessing plant resistance to strongly acid stress (pH < 4.5) (Ma et al. 2004 ; Alvim et al. 2012 ). However, in addition to Al toxicity, acidic soils were always accompanied by a lack of essential nutrients, especially phosphorus nutrients and alkaline ions such as calcium, magnesium and potassium (Zhao et al. 2014 ). Therefore, the growth inhibition of crops under acidic soil conditions was a comprehensive effect of aluminum toxicity and nutrient deficiency. Particularly in moderate and weakly acidic soils (5.0 < pH < 6.5), nutrient deficiency could be the major problem to restrict crop growth. In this study, we comprehensively analyzed the growth phenotypes and physiological and biochemical responses of maize under acid soil conditions, providing a faster and more efficient method for screening acid-tolerant varieties. The results demonstrated that acidic soil stress significantly inhibited maize growth and nutrient uptake, reducing PH, SD, FW, LA, TN, TP, and TK. The combined effects of Al toxicity and nutrient deficiency elevated ROS levels in maize. Consistently, ROS and MDA contents in maize leaves were significantly higher under acid soil conditions than in control soils, indicating oxidative stress. Elevated ROS levels were accompanied by significantly increased antioxidant enzyme activities, including SOD, POD, APX, and CAT. Notably, clustering analysis identified MY908, RHY3, QKY900, LP243, KY8, and RHY288 as the most acid-tolerant varieties, based on their high D and F values. These findings provide valuable insights for selecting acid-tolerant maize varieties, which are crucial for improving maize productivity in acidic soil stress conditions. Comprehensive evaluation of maize varieties’ tolerance to acid soil To quantitatively assess the stress resistance of crops, other researchers have proposed the tolerance coefficient, a mathematical indicator that relates plant performance under stress and non-stress conditions (Talebi 2009 ; Jafari et al. 2012 ; Grzesiak et al. 2018 ). This coefficient effectively reflects plants responses to abiotic stress and is widely used in tolerance studies. However, crop tolerance to abiotic stress is a complex trait influenced by both genetic and environmental factors. As such, a single tolerance coefficient may not fully capture the comprehensive stress tolerance of crops. To address this limitation, various multivariate analysis methods have been developed to assess tolerance to abiotic stresses, such as drought, Al toxicity, waterlogging stress, and salt damage (Aghaie et al. 2018 ; Khan et al. 2024 ; Wang et al. 2023 ; Zhang et al. 2020 ). In this study, we ranked maize varieties based on their acid soil tolerance using two methods, MMFV and D value. Both methods yielded similar rankings, as they were both derived from the normalized acid soil tolerance index (ATI) (Table 2 ). However, few varieties showed different rankings between MMFV and D value, despite a high positive correlation (r = 0.93). This discrepancy arose due to distinct calculation methods, MMFV represents the mean membership value across all indices, while the D value incorporates both the membership function value and the weight of each index (Zou et al. 2020 ; Wang et al. 2023 ). Thus, the D value is better suited for evaluating crop resistance, especially when the standard deviation across indicators is large. To further refine our evaluation, we used PCA to reduce dimensionality and address potential redundancy between indicators. PCA grouped the 15 indicators into five principal components (Fig. 1 ). Principal component 1 (PC1), which primarily reflects growth and nutritional traits, accounted for over one-third of the total variation, highlighting the significance of these indices in evaluating maize tolerance to acid soil. A composite score ( F value) for each variety was then calculated based on the contribution of these principal components (Fig. 2 ). This integrated evaluation, combining both D and F values, offers a more comprehensive assessment of crop performance under abiotic stress (Chen et al. 2012 ; Wang et al. 2017 ; Khan et al. 2024 ). Cluster analysis, based on D and F values, was employed to classify the maize varieties into distinct resistance groups, as done in previous studies (Zhang et al. 2020 ; Zou et al. 2020 ; Sun et al. 2021 ). The 50 maize varieties were grouped into 5 clusters based on their overall acid soil resistance. The varieties MY908, RHY3, QKY900, LP243, KY8, and RHY288 were identified as HAT, while FNY6, SD188, LD9088, and XY045 were classified as HAS. Differential responses of tolerant and sensitive maize varieties to acidic soil To identify key indicators for screening acid tolerance maize genotypes, stepwise regression equations based on D and F values were established. Both equations identified eight key indicators, totaling 11 variables, including six morphological and nutritional traits (PH, FW, SD, LA, TN, TP), three antioxidant enzymes (POD, APX, CAT), and ROS content (O 2 − , H 2 O 2 ), for evaluating acid soil tolerance in maize (Table 3 ). Several of these indicators have previously been recognized as critical for assessing plant resilience to acid stress (Poschenrieder et al. 1995 ; Zhang et al. 2020 ; Zhu and Shen 2023 ). The variables PH, FW, SD, LA, TN, and TP were positively correlated with each other and with D and F values (Fig. 3 ). Consistent with these correlations, the HAT varieties exhibited minimal differences in PH, FW, SD, LA, and TP between AS and CK conditions, indicating that their growth was not significantly affected by acid soil stress (Figures S8-S9). In contrast, the growth of HAS varieties was significantly inhibited by acidic soil stress, with reduced values of PH, FW, SD, LA TN, and TP under AS compared to CK conditions (Figures S8-S9). There was no significant difference in H 2 O 2 content, or POD, APX, and CAT activities between HAT and HAS varieties, as all remained at low levels (Figures S10-S11). However, acidic soil stress significantly induced H 2 O 2 content and the activities of these antioxidant enzyme in both HAT and HAS varieties (Figures S10-S11). While H 2 O 2 levels were similar between the two groups, the average activities of APX, CAT, and POD were higher in HAS varieties, with POD activity reaching statistically significant levels, suggesting that HAS varieties exerted a stronger antioxidant response under acidic soil stress (Figures S10-S11). The accumulation of ROS in plant cells under AS conditions, induced by both toxic metal ions and nutrient deficiencies, acts as a signaling mechanism to trigger plant defense responses but also disrupts cellular structures and metabolic processes (Berni et al. 2019 ; Nieves-Cordones et al. 2019 ). To restore oxidative balance, plants express antioxidant enzymes to scavenge ROS and enhance resistance to acidic soil stress (Castro et al. 2021 ). However, the energy required for this antioxidant response may detract from resources available for growth. Conversely, plants that allocate more energy to nutrient absorption rather than antioxidant enzyme production are better able to promote growth and adapt to acidic soils (Fig. 5 ). As breeding goals prioritize higher biomass and yield, maize genotypes that allocate more metabolic energy to nutrient absorption are classified as tolerant varieties, while those that devote more energy to antioxidant enzymes production are considered sensitive varieties. Conclusion In this study, PH, FW, SD, LA, TN, TP, POD, APX, CAT, O 2 − and H 2 O 2 contents were distinguished as the key parameters for screening acid-tolerant maize varieties by cluster analysis. Furthermore, a comprehensive multivariate analysis revealed two distinct adaptation strategies in maize under acidic soil stress: 1) Antioxidant-dependent resistance characterized by enhanced antioxidant enzyme activity at the expense of growth suppression (HAS varieties); 2) Nutrient optimization strategy demonstrated by superior nutrient acquisition efficiency to sustain growth under stress conditions (HAT varieties). Finally, a field trial confirmed the accuracy of the comprehensive analysis in screening acid-tolerant varieties. The present study provides a comprehensive assessment of acid soil tolerance in maize and provides insights to improve yield in acidic soils through the use of suitable germplasm. Declarations Acknowledgements We thank Limei Zhang and Huan He at the public laboratory platform of the College of Resources and Environment, Huazhong Agricultural University, for their help measuring the N, P, and K concentrations in plant tissues. Author contributions XC, YX, XY and SD perform physiological experiments and analyzed the data. LC performed the field experiment and analyzed the data. XC and CW wrote the manuscript. CW, SD, LS, FX, GD and QZ supervised the experiments. CW, XC, FX, GD and QZ designed the project. CW agrees to serve as the author responsible for contact and ensures communication. All authors read and approved the final manuscript. Funding This work was supported by the National Key Research and Development Program of China (2022YFD1900700), the Opening Project of Guangdong Provincial Key Laboratory of Quality & Safety Risk Assessment for Agro-products (SZKF202201), the National Natural Science Foundation of China (3207266) and the Fundamental Research Funds for the Central University of China (2662024ZHPY001). Data availability All data generated or analyzed during the present study can be found within the manuscript and its supporting materials. Declaration of competing interests The authors declare that they have no conflicts of interest. Supporting information (SI) Additional Supporting Information may be found in the online version of this article. References Aghaie P, Hosseini Tafreshi SA, Ebrahimi MA, Haerinasab M (2018) Tolerance evaluation and clustering of fourteen tomato cultivars grown under mild and severe drought conditions. Sci Hortic 232: 1-12. https://doi.org/10.1016/j.scienta.2017.12.041. Ali B, Hasan SA, Hayat S, Hayat Q, Yadav S, Fariduddin Q, Ahmad A (2008) A role for brassinosteroids in the amelioration of aluminium stress through antioxidant system in mung bean ( Vigna radiata L. Wilczek). Environ Exp Bot 62: 153-159. https://doi.org/10.1016/j.envexpbot.2007.07.014. Alvim MN, Ramos FT, Oliveira DC, Isaias RMS, França MGC (2012) Aluminium localization and toxicity symptoms related to root growth inhibition in rice ( Oryza sativa L.) seedlings. J Biosci 37: 1079-1088. https://doi.org/10.1007/s12038-012-9275-6. Berni R, Luyckx M, Xu X, Legay S, Sergeant K, Hausman J-F, Lutts S, Cai G, Guerriero G (2019) Reactive oxygen species and heavy metal stress in plants: Impact on the cell wall and secondary metabolism. Environ Exp Bot 161: 98-106. https://doi.org/10.1016/j.envexpbot.2018.10.017. Cakmak I, Marschner H (1992) Magnesium deficiency and high light intensity enhance activities of superoxide dismutase, ascorbate peroxidase, and glutathione reductase in bean leaves 1. Plant Physiol 98: 1222-1227. https://doi.org/10.1104/pp.98.4.1222. Cançado GMA, Ribeiro AP, Piñeros MA, Miyata LY, Alvarenga ÂA, Villa F, Pasqual M, Purgatto E (2009) Evaluation of aluminium tolerance in grapevine rootstocks. Vitis 48: 167-173. Castro B, Citterico M, Kimura S, Stevens DM, Wrzaczek M, Coaker GL (2021) Stress-induced reactive oxygen species compartmentalization, perception and signalling. Nat Plants 7: 403 - 412. https://doi.org/10.1038/s41477-021-00887-0. Chen X, Min D, Yasir TA, Hu Y (2012) Evaluation of 14 morphological, yield-related and physiological traits as indicators of drought tolerance in Chinese winter bread wheat revealed by analysis of the membership function value of drought tolerance (MFVD). Field Crop Res 137: 195-201. https://doi.org/10.1016/j.fcr.2012.09.008. Dai H, Shan W, Zhao J, Zhang G, Li C, Wu F (2011) Difference in response to aluminum stress among Tibetan wild barley genotypes. J Plant Nutr Soil Sci 174: 952-960. https://doi.org/10.1002/jpln.201100148. Devkota S, Panthi S, Shrestha J (2019) Evaluation of maize varieties in acid soil condition. Agrica 8(2): 128-133. https://doi.org/10.5958/2394-448X.2019.00018.X. Furlan F, Borgo L, Rabêlo FHS, Rossi ML, Linhares FS, Martinelli AP, Azevedo RA, Lavres J (2020) Aluminum-induced toxicity in Urochloa brizantha genotypes: A first glance into root Al-apoplastic and -symplastic compartmentation, Al-translocation and antioxidant performance. Chemosphere 243: 125362. https://doi.org/10.1016/j.chemosphere.2019.125362. Giannakoula A, Moustakas M, Syros T, Yupsanis T (2010) Aluminum stress induces up-regulation of an efficient antioxidant system in the Al-tolerant maize line but not in the Al-sensitive line. Environ Exp Bot 67: 487-494. https://doi.org/10.1016/j.envexpbot.2009.07.010. Giannopolitis CN, Ries SK (1977) Superoxide dismutases: I. Occurrence in higher plants. Plant Physiol 59 2: 309-314. Grzesiak S, Hordyńska N, Szczyrek P, Grzesiak MT, Noga A, Szechyńska-Hebda M (2018) Variation among wheat ( Triticum easativum L.) genotypes in response to the drought stress: I – selection approaches. J Plant Interact 14: 30 - 44. https://doi.org/10.1080/17429145.2018.1550817. Guo JH, Liu X, Zhang Y, Shen J, Han W, Zhang WF, Christie P, Goulding KWT, Vitousek PM, Zhang F (2010) Significant acidification in major Chinese croplands. Science 327: 1008 - 1010. https://doi.org/10.1126/science.1182570. Gupta N, Gaurav SS, Kumar A (2013) Molecular basis of aluminium toxicity in plants: A review. Am J Plant Sci 4: 21-37. https://doi.org/10.4236/AJPS.2013.412A3004. Heath RL, Packer L (1968) Photoperoxidation in isolated chloroplasts. I. Kinetics and stoichiometry of fatty acid peroxidation. Arch Biochem Biophys 125 1: 189-198. Hu D, Li R, Dong S, Zhang J, Zhao B, Ren B, Ren H, Yao H, Wang Z, Liu P (2022) Maize ( Zea mays L.) responses to salt stress in terms of root anatomy, respiration and antioxidative enzyme activity. BMC Plant Biol 22: 602. https://doi.org/10.1186/s12870-022-03972-4. Jafari A, Paknejad F, Jami AL-Ahmadi M (2012) Evaluation of selection indices for drought tolerance of corn ( Zea mays L.) hybrids. Int J Plant Prod 3: 33-38. https://doi.org/10.22069/ijpp.2012.661. Khan R, Gao F, Khan K, Shah MA, Ahmad H, Fan ZP, Zhou XB (2024) Evaluation of maize varieties via multivariate analysis: Roles of ionome, antioxidants, and autophagy in salt tolerance. Plant Physiol 196: 195-209. https://doi.org/10.1093/plphys/kiae335. Kochian LV (1995) Cellular mechanisms of aluminum toxicity and resistance in plants. Annu Rev Plant Biol 46: 237-260. doi: 10.1146/annurev.pp.46.060195.001321. Ma JF, Nagao S, Sato K, Ito H, Furukawa J, Takeda K (2004) Molecular mapping of a gene responsible for Al-activated secretion of citrate in barley. J Exp Bot 55: 1335-1341. https://doi.org/10.1093/jxb/erh152. Maron LG, Guimarães CT, Kirst M, Albert PS, Birchler JA, Bradbury PJ, Buckler ES, Coluccio AE, Danilova TV, Kudrna D, Magalhaes JV, Piñeros MA, Schatz MC, Wing RA, Kochian LV (2013) Aluminum tolerance in maize is associated with higher MATE1 gene copy number. Proc Natl Acad Sci 110: 5241-5246. https://doi.org/10.1073/pnas.1220766110. Nakano Y, Asada K (1987) Purification of ascorbate peroxidase in spinach chloroplasts; its inactivation in ascorbate-depleted medium and reactivation by monodehydroascorbate radical. Plant Cell Physiol 28: 131-140. https://doi.org/10.1094/Phyto-77-112. Navakode S, Weidner A, Lohwasser U, Röder MS, Börner A (2008) Molecular mapping of quantitative trait loci (QTLs) controlling aluminium tolerance in bread wheat. Euphytica 166: 283-290. https://doi.org/10.1007/s10681-008-9845-8. Nieves‐Cordones M, López-Delacalle M, Ródenas R, Martínez V, Rubio F, Rivero RM (2019) Critical responses to nutrient deprivation: A comprehensive review on the role of ROS and RNS. Environ Exp Bot 161: 74-85. https://doi.org/10.1016/j.envexpbot.2018.10.039. Poschenrieder C, Llugany M, Barceló J (1995) Short‐term effects of pH and aluminium on mineral nutrition in maize varieties differing in proton and aluminium tolerance. J Plant Nutr 18: 1495-1507. https://doi.org/10.1080/01904169509364998. Qian P, Sun R, Ali B, Gill RA, Xu L, Zhou W (2013) Effects of hydrogen sulfide on growth, Antioxidative capacity, and ultrastructural changes in oilseed rape seedlings under aluminum toxicity. J Plant Growth Regul 33: 526-538. https://doi.org/10.1007/s00344-013-9402-0. Sun F, Chen Q, Chen Q, Jiang M, Gao W, Qu Y (2021) Screening of key drought tolerance indices for cotton at the flowering and boll setting stage using the dimension deduction method. Front Plant Sci 12: 619926. https://doi.org/10.3389/fpls.2021.619926. Talebi R (2009) Effective selection criteria for assessing drought stress tolerance in durum wheat ( Triticum durum Desf.). Gen Appl Plant Physiol35: 64-74. Tandzi LN, Mutengwa CS, Ngonkeu ELM, Gracen VE (2018) Breeding maize for tolerance to acidic soils: A review. Agronomy 8: 84. https://doi.org/10.3390/agronomy8060084. Tang C, Weligama C, Sale P (2013) Subsurface Soil Acidification in Farming Systems: Its Possible Causes and Management Options. In: J Xu, DL Sparks (eds) Molecular Environmental Soil Science. Dordrecht, Springer Netherlands , pp 389-412. Wang GY, Ahmad S, Wang Y, Wang BW, Huang JH, Jahan MS, Zhou XB, Shi CQ (2023) Multivariate analysis compares and evaluates drought and flooding tolerances of maize germplasm. Plant Physiol 193: 339-355. https://doi.org/10.1093/plphys/kiad317. Wang Y, Xing G, Qin G, Fu B, Bo W (2017) Evaluation of drought resistance in Iris germanica L. based on subordination function and principal component analysis. Emir J Food Agric 29: 770-778. https://doi.org/10.9755/ejfa.2017.v29.i10.1260. Yamamoto Y, Kobayashi Y, Devi SR, Rikiishi S, Matsumoto H (2003) Oxidative stress triggered by aluminum in plant roots. Plant Soil 255: 239-243. https://doi.org/10.1023/A:1026127803156. Zhang M, Lu X, Li C, Zhang B, Zhang C, Zhang X-s, Ding Z (2018) Auxin efflux carrier ZmPGP1 mediates root growth inhibition under aluminum stress. Plant Physiol 177: 819-832. https://doi.org/10.1104/pp.17.01379. Zhang Y, Xu S, Mo L (2020) Screening of grape genotypes for aluminum tolerance and evaluation on selecting indices. Soil Sci Plant Nutr 66: 560-568. https://doi.org/10.1080/00380768.2020.1779572. Zhao XQ, Chen RF, Shen RF (2014) Coadaptation of plants to multiple stresses in acidic soils. Soil Science 179: 503-513. https://doi.org/10.1097/ss.0000000000000086. Zhu Q, de Vries W, Liu X, Hao T, Zeng M, Shen J, Zhang F (2018) Enhanced acidification in Chinese croplands as derived from element budgets in the period 1980-2010. Sci Total Environ 618: 1497-1505. https://doi.org/10.1016/j.scitotenv.2017.09.289. Zhu Q, Liu X, Hao T, Zeng M, Shen J, Zhang F, de Vries W (2020) Cropland acidification increases risk of yield losses and food insecurity in China. Environ Pollut 256: 113145. https://doi.org/10.1016/j.envpol.2019.113145. Zhu X, Ros GH, Xu M, Xu D, Cai Z, Sun N, Duan Y, Vries WD (2024) The contribution of natural and anthropogenic causes to soil acidification rates under different fertilization practices and site conditions in southern China. Sci Total Environ 934: 172986. https://doi.org/10.1016/j.scitotenv.2024.172986. Zhu XF, Shen RF (2023) Towards sustainable use of acidic soils: Deciphering aluminum-resistant mechanisms in plants. Fundam Res4: 1533-1541. https://doi.org/10.1016/j.fmre.2023.03.004. Zou J, Hu W, Li Y, He J, Zhu H, Zhou Z (2020) Screening of drought resistance indices and evaluation of drought resistance in cotton ( Gossypium hirsutum L.). J Integr Agr 19: 495-508. https://doi.org/10.1016/s2095-3119(19)62696-1. Supplementary Files PLSOD2500508highlighted.docx SupplementalTable.docx Supplementaryfigures.pdf Cite Share Download PDF Status: Published Journal Publication published 13 May, 2025 Read the published version in Plant and Soil → Version 1 posted Reviewers agreed at journal 03 Apr, 2025 Reviewers invited by journal 03 Apr, 2025 Editor assigned by journal 02 Apr, 2025 First submitted to journal 01 Apr, 2025 Editorial decision: Minor revisions 15 Mar, 2025 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-6021236","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":438159390,"identity":"fb715954-c80c-4b18-a749-329398fd4bda","order_by":0,"name":"Xinghua Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xinghua","middleName":"","lastName":"Chen","suffix":""},{"id":438159391,"identity":"199a8070-7228-41e0-8027-1c2eb20865e8","order_by":1,"name":"Yuxin Xia","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Xia","suffix":""},{"id":438159392,"identity":"34f1a7c4-ace7-4a29-b1c8-18a3cd8dff63","order_by":2,"name":"Liuqing Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Liuqing","middleName":"","lastName":"Chen","suffix":""},{"id":438159393,"identity":"8d6d23cf-6dcf-4079-a60d-0c4fc011e070","order_by":3,"name":"Xiaoqi Yin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqi","middleName":"","lastName":"Yin","suffix":""},{"id":438159394,"identity":"1f8632f5-d8c6-4ae1-b128-4e79f8f2204f","order_by":4,"name":"Suren Deng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Suren","middleName":"","lastName":"Deng","suffix":""},{"id":438159395,"identity":"975e4d9c-1207-4a88-99fb-e1ecbae33021","order_by":5,"name":"Munyaneza Venuste","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Munyaneza","middleName":"","lastName":"Venuste","suffix":""},{"id":438159396,"identity":"8e2dde42-d086-446b-aaa2-0e4d41e0f2f6","order_by":6,"name":"Lei Shi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Shi","suffix":""},{"id":438159397,"identity":"209c35e6-d405-4041-a9f0-9386aa39a411","order_by":7,"name":"Fangsen Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fangsen","middleName":"","lastName":"Xu","suffix":""},{"id":438159398,"identity":"4314cb89-b452-4c76-abd6-db2e6d9befbe","order_by":8,"name":"Qiang Zhu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Zhu","suffix":""},{"id":438159399,"identity":"81d74b3e-5329-440e-ac6e-1023fe944bcc","order_by":9,"name":"Guangda Ding","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Guangda","middleName":"","lastName":"Ding","suffix":""},{"id":438159400,"identity":"cdf5648c-cea2-4902-a5d3-72c4809bf423","order_by":10,"name":"Chuang Wang","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-7663-7433","institution":"Huazhong Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Chuang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-02-13 08:44:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6021236/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6021236/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11104-025-07525-0","type":"published","date":"2025-05-13T15:57:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80025073,"identity":"1f6f2b54-e1d5-458c-83e8-2f48e1ca598d","added_by":"auto","created_at":"2025-04-07 06:05:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67307,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeat map of weight for the top five principal components (PCs) of 15 individual indicators in maize under acid soil stress.\u003c/strong\u003e PH, plant height; SD, stem diameter; FW, fresh weight; LA, leaf area; TN, total nitrogen accumulation, TP, total phosphorus accumulation; TK, total potassium accumulation; SOD, superoxide dismutase; POD, peroxidase; APX, ascorbate peroxidase; CAT, catalase; MDA, malondialdehyde; O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, superoxide anion and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, hydrogen peroxide.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6021236/v1/5f1fc060637cb0379b796d8a.jpg"},{"id":80025065,"identity":"ac08e710-bf6c-4f7c-a140-2dcd6133cbe9","added_by":"auto","created_at":"2025-04-07 06:05:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":108845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe score of principal components for all the varieties under acid soil stress.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6021236/v1/e18c6fa19cc627b7817778e7.jpg"},{"id":80025066,"identity":"4b243650-bd4a-4987-92dd-b46d6a7bdec8","added_by":"auto","created_at":"2025-04-07 06:05:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87900,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis between all the traits, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e values. \u003c/strong\u003ePH, plant height; SD, stem diameter; FW, fresh weight; LA, leaf area; TN, total nitrogen accumulation, TP, total phosphorus accumulation; TK, total potassium accumulation; SOD, superoxide dismutase; POD, peroxidase; APX, ascorbate peroxidase; CAT, catalase; MDA, malondialdehyde; O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, superoxide anion and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, hydrogen peroxide. Asterisks indicate statistically significant differences: *, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e ≤ 0.01; ***, \u003cem\u003eP\u003c/em\u003e ≤ 0.001 (Pearson correlation analysis).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6021236/v1/c477878d2392f255d5eb62c9.jpg"},{"id":80025067,"identity":"c7c5e018-5608-4f1f-a5a4-083d1aafdc77","added_by":"auto","created_at":"2025-04-07 06:05:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43335,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCluster analysis of 50 hybrid maize varieties with combined \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e values. \u003c/strong\u003eThe maize varieties were grouped into five types, ranging from highly acid-tolerance (I), acid-tolerance (II), moderate acid-tolerance (III), acid-sensitive (IV), and highly acid-sensitive (V).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6021236/v1/01af950ee8613ef055e3480d.jpg"},{"id":80025068,"identity":"d570c8dd-156f-4889-973f-85dfc287550c","added_by":"auto","created_at":"2025-04-07 06:05:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":61985,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhysiological response model for acid-tolerant and acid-sensitive maize under acid soil conditions.\u003c/strong\u003e Acid-sensitive varieties used more energy to expressed antioxidant enzymes, which promoted the survival of the plants under acid soil conditions. Conversely, acid-tolerant varieties allocate more energy to nutrient absorption and promote growth to adapt to acid soils. Arrows and T lines indicate enhancement and inhibition, respectively. PH, plant height; SD, stem diameter; FW, fresh weight; LA, leaf area; TN, total nitrogen accumulation, TP, total phosphorus accumulation; TK, total potassium accumulation; POD, peroxidase; APX, ascorbate peroxidase; CAT, catalase; O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, superoxide anion and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, hydrogen peroxide.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6021236/v1/7442e234a53cbbd9b085cd28.jpg"},{"id":83067812,"identity":"1f46cbab-8456-4deb-ba77-287fda282d90","added_by":"auto","created_at":"2025-05-19 16:06:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2079025,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6021236/v1/12f51d02-c73d-4c0b-a014-b8946b72bdb6.pdf"},{"id":80025094,"identity":"584910ec-5596-4250-8db3-3ef2b0023157","added_by":"auto","created_at":"2025-04-07 06:06:47","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":103063,"visible":true,"origin":"","legend":"","description":"","filename":"PLSOD2500508highlighted.docx","url":"https://assets-eu.researchsquare.com/files/rs-6021236/v1/863b7b85573d2f4ea8073999.docx"},{"id":80025076,"identity":"f218f581-cc5b-4aa3-bdf3-1e07837b1ed9","added_by":"auto","created_at":"2025-04-07 06:06:05","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":28955,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-6021236/v1/9335d1a7c1fbba90fb9616f3.docx"},{"id":80025074,"identity":"ade3540f-3117-41c0-9e41-bafa60964551","added_by":"auto","created_at":"2025-04-07 06:05:57","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":302316,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6021236/v1/c85d4d4167b2e44a8ee5e244.pdf"}],"financialInterests":"","formattedTitle":"Multivariate analysis unveils antioxidant-nutrient trade-offs in Maize Hybrids: A hierarchical framework for acid soil tolerance evaluation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoil acidity is one of the major causes of land degradation, affecting approximately 50% of arable land worldwide (Kochian \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Soil naturally acidifies were due to the leaching of bicarbonate and organic anions. However, the extensive use of nitrogen (N) fertilizers and the removal of base cations through agricultural activities have accelerated soil acidification (Zhu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Between 1980 and 2010, the average pH of the topsoil in major agricultural regions of China decreased by 0.5 units (Guo et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In China, acid soils are predominantly found in the south of Yangtze River, with approximately 47% of cultivated soils having a pH lower than 6.5, and about 15% of soils having a pH below 5.5 (Zhu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). If the overuse of N fertilizers continues, cereal yield losses in China are projected to range from 4\u0026ndash;24% between 2010 and 2050 due to further declines in soil pH (Zhu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil acidification significantly limits crop growth and yields through multiple factors. When soils with pH falls below 5.5, the levels of toxic metals, including aluminum (Al\u003csup\u003e3+\u003c/sup\u003e), iron (Fe\u003csup\u003e3+\u003c/sup\u003e) and manganese (Mn\u003csup\u003e2+\u003c/sup\u003e), are released, which damage roots and reduce photosynthetic efficiency in leaves (Gupta et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tandzi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, soil acidity often leads to low nutrient availability, including N, phosphorus (P), potassium (K\u003csup\u003e+\u003c/sup\u003e), calcium (Ca\u003csup\u003e2+\u003c/sup\u003e), magnesium (Mg\u003csup\u003e2+\u003c/sup\u003e) and molybdenum (Mo\u003csup\u003e2+\u003c/sup\u003e), further hindering crop growth and production (Zhao et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tandzi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While the application of lime or organic fertilizers can mitigate the effects of soil acidity and enhance cop yields, these solutions may not be affordable for the small-scale farmers and can have negative environmental effects (Tang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In contrast, the use of acid-tolerant crop varieties presents a more sustainable and cost-effective alternative for improving yield in acid soils.\u003c/p\u003e \u003cp\u003eMaize (\u003cem\u003eZea mays\u003c/em\u003e L.) is one of the most important crops globally but is highly sensitive to soil acidity. Studies have shown that soil acidity leads to significant yield reductions, with average decline of 44.6% in Latin America and Asia and 37.3% in Cameroon (Tandzi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, notable variations in yield exist among maize genotypes, with acid-tolerant varieties maintaining stable yields under acid soil conditions (Devkota et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Developing acid-tolerant maize varieties has thus become a key breeding objective, with grain yield and yield component traits serving as the primary target (Tandzi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, evaluating these traits is time-consuming and costly, highlighting the need for efficient secondary indicators to facilitate the rapid and cost-effective screening of acid-tolerant varieties to enhance productivity under acidic soil stress.\u003c/p\u003e \u003cp\u003eWhile morphological, physiological and biochemical traits have been widely used to screen for drought, flooding, and salinity-tolerant (Navakode et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Can\u0026ccedil;ado et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Alvim et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), comparatively fewer indicators have been employed for acidic soil tolerance. Studies on acid-tolerant crops have primarily focused on root morphological changes under Al\u003csup\u003e3+\u003c/sup\u003e toxicity (Maron et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Furlan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, acid stress also inhibits nutrient uptake, reduces shoot growth, decreases photosynthetic capacity, and disrupts the antioxidant system, ultimately affecting plant height, biomass, and yield (Ali et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Giannakoula et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Dai et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Qian et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Moreover, excessive reactive oxygen species (ROS) generated under acidic stress cause cellular damage (Yamamoto et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). To counteract ROS, plants enhance antioxidant enzymes such as superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX) to mitigate oxidative stress (Hu et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These findings underscore the need for integrating morphological and physiological indicators to comprehensively evaluate tolerance of acidic soil stress in crops. Multivariate analysis has been increasingly used to assess crop resistance to abiotic stress. For example, Chen (2012) evaluated drought tolerance in Chinese winter bread wheat using 14 morphological, yield-related, and physiological traits with membership function value (MFV). Subsequent studies applied similar methods, integrating MFVs and principal component analysis (PCA) to reduce redundancy among tightly correlated indicators (Zou et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Building on these approaches, this study screened 15 indices to evaluate acid soil tolerance in maize hybrids under greenhouse conditions. Using a combination of multivariate analysis techniques, we developed optimal regression equations and identified key indicators for assessing acid soil resistance in maize. The selected tolerant varieties of acidic soil stress were subsequently validated under field conditions, establishing a practical and cost-effective framework for breeding resilient maize varieties.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials and growth conditions\u003c/h2\u003e \u003cp\u003eA total of 50 hybrid maize varieties, purchased from local market, were used in this study (Supplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Acid soils were collected from 0\u0026ndash;20 cm layer of red soils at Heshengqiao, Xianning City, Hubei Province (29.87\u0026deg; N, 114.29\u0026deg; E). The basic chemical properties of the soils were as follows: pH 5.12, 0.53 g/kg total N, 1.34 mg/kg available phosphorus, 119.95 mg/kg available potassium, 2.82g/kg total carbon, 502.31mg/kg exchangeable calcium, 45.68mg/kg exchangeable magnesium, and 317.72 mg/kg aluminum (Al). To create control soils, 2.0 g calcium hydroxide per kilogram of soil was added and thoroughly mixed, resulting in a pH of 6.69, with the following chemical properties: 0.55 g/kg total N, 1.49 mg/kg available phosphorus, 120.89 mg/kg available potassium, 3.11 g/kg total carbon, 645.23 mg/kg exchangeable calcium, 122.57 mg/kg exchangeable magnesium, and 43.97 mg/kg Al. Pot experiments were conducted with each pot containing 3.2 kg of air-dried, finely ground acid soil (AS) or control soils (CK). After watering and allowing the soil to mature for 7 days, eight seeds were sown in each pot. Ten days after sowing, three seedlings were retained per pot. The experiments were carried out in a greenhouse at Huazhong Agricultural University, Wuhan, Hubei Province (30.53\u0026deg; N, 114.36\u0026deg; E).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement of morphological indexes\u003c/h3\u003e\n\u003cp\u003eMaize plants were sampled at the V5 stage (30 days after sowing) to measure various physiological indexes. The plant height (PH) was measured from the base of the plant to the tip of the longest leaf. The stem diameter (SD)was measured at the thickest part of the stem using a digital caliper. Chlorophyll content was assessed using a chlorophyll meter (SPAD-502, MINOLTA, Japan), and the SPAD value was recorded. The fresh weight (FW) and dry weight (DW) of shoots were determined using a microbalance with a precision of 1 mg. The LA was calculated by measuring the longest and widest parts of each leaf, using the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:LA=0.75*\\:\\sum\\:_{i=1}^{m}\\sum\\:_{j=1}^{n}\\left({L}_{ij}*{W}_{ij}\\right)/m$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere 0.75 is the empirical correction coefficient for maize LA; M is the number of plants measured; N is the total number of leaves for the i-th plant; \u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e are the maximum leaf length and leaf width of the j-th leaf of the i-th plant, respectively.\u003c/p\u003e\n\u003ch3\u003eDetermination of the antioxidant enzyme activities and malondialdehyde content\u003c/h3\u003e\n\u003cp\u003eApproximately 0.1 g of fresh leaves were ground with liquid nitrogen and homogenized in 1.5 mL of 50 mM phosphate buffer solution (pH 7.8). The mixture was centrifuged, and the supernatants were used to measure antioxidant enzyme activities and malondialdehyde (MDA) content.\u003c/p\u003e \u003cp\u003eSuperoxide dismutase (SOD) activity was measured using the method described by Giannopolitis and Ries (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). The absorbance was measured at 560 nm, and one unit (U) of SOD activity was defined as the reduction of NBT by 50% per gram of fresh tissue per minute. Peroxidase (POD) activity was measured following the spectrophotometric method described by Cakmak and Marschner (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The absorbance change at 470 nm was monitored over a 3-minute period, with measurements taken every 30 seconds. One unit (U) of POD activity was defined as 0.01 increase in absorbance per gram of fresh tissue per minute. Ascorbate peroxidase (APX) activity was determined by measuring the change in absorbance at 290 nm. One unit (U) of APX activity was defined as the decomposition of 1 \u0026micro;M H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e per gram of tissue per minute (Nakano and Asada \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Catalase (CAT) activity was determined using a commercial kit (Suzhou Greys Biotech Co., Ltd, G0106W) following the manufacturer's instructions. Briefly, 10 \u0026micro;L of supernatant was incubated with the kit reagents at 25 ℃ for 5 minutes, and the absorbance was measured at 510 nm. One unit (U) of CAT activity was defined as the decomposition of 1 \u0026micro;M H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e per gram of tissue per minute at 25 ℃. MDA content was measured using the thiobarbituric acid method (Heath and Packer \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1968\u003c/span\u003e). The absorbance of supernatant was measured at 532 nm, 600 nm, and 450 nm, respectively.\u003c/p\u003e\n\u003ch3\u003eMeasurement of the reactive oxygen species\u003c/h3\u003e\n\u003cp\u003eHydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) and superoxide (O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) content were measured according to the method outlined by (Wang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Briefly, 250 \u0026micro;L of the supernatant, prepared as for the SOD assay, was mixed with 250 \u0026micro;L of 50 mM PBS (pH 7.8) and 500 \u0026micro;L of 1 mM hydroxylamine hydrochloride (dissolved in 50 mM PBS, pH 7.8), and incubated for 1 hour at 25\u0026deg;C. Subsequently, 500 \u0026micro;L of 17 mM sulfanilamide (dissolved in 3 M acetic acid) and 500 \u0026micro;L of 7 mM α-naphthylamine (soluble in 99.5% acetic acid) were added, and the mixture was incubated for 20 minutes at 25\u0026deg;C. The absorbance of the reaction mixture was measured at 540 nm. For the H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e assay, approximately 0.1 g of fresh maize leaf tissue was grounded into fine powder with liquid N, then homogenized in 1.5 mL of 0.1% trichloroacetic acid (TCA). The homogenate was centrifuged at 8000 g for 10 minutes at 4\u0026deg;C. A 0.5 mL aliquot of the supernatant was mixed with 0.5 mL of 10 mM phosphate-buffered saline (PBS, pH 7.0) and 1.0 mL of 1 M potassium iodide solution, and incubated for 1 h at 28\u0026deg;C. The absorbance of the mixture was measured at 390 nm.\u003c/p\u003e\n\u003ch3\u003eTotal nitrogen, phosphorus and potassium accumulation\u003c/h3\u003e\n\u003cp\u003eShoots from each seedling were sampled and dried to constant weight at 80 ℃. A 25 mg sample of dried shoots was digested with concentrated sulfuric acid (98%), followed by the addition of 5\u0026ndash;6 drops of 30% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e every 30 min until the solution turned colorless. The colorless solution was then boiled for 1 hour, cooled, and diluted to the appropriate volume for nutrient analysis. Total N was measured using an AA3 flow analyzer. The N in the digestion solution reacted with sodium salicylate and sodium hypochlorite in the presence of a nitroprusside catalyst to form a blue complex, quantified by absorbance at 600 nm. Total phosphorus was determined by mixing 200 \u0026micro;L of the digested solution with 2 drops of 2,4-dinitrophenol, adjusting the pH, and adding molybdenum-antimony reagent. After incubation at 25\u0026deg;C for 30 minutes, absorbance was measured at 700 nm. For total potassium, the digested solution was diluted twofold and analyzed by flame photometry. Nutrient accumulation per seedling was calculated using the following formulas:\u003c/p\u003e \u003cp\u003eTotal nutrient accumulation\u0026thinsp;=\u0026thinsp;nutrient concentration (%)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:*\\)\u003c/span\u003e\u003c/span\u003edry weight\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMaize yield and yield components in the field trial\u003c/h2\u003e \u003cp\u003eA field trial was conducted on April 20, 2024, in the Caidian district of Wuhan, Hubei province, China (30.6\u0026deg;N, 114.0\u0026deg;E), using four maize varieties: MY908, QKY900, SD188, and FNY6. The soil type was yellow-brown soil, and compound fertilizer (NPK, 15-15-15) was applied at 450 kg/hm\u003csup\u003e2\u003c/sup\u003e, following local farming practices. Additionally, 600 kg/hm\u003csup\u003e2\u003c/sup\u003e of soil conditioner (pH 8\u0026ndash;11) was added in the control treatment (CK). Soil properties after fertilization are detailed in supplemental Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. Each variety was planted in plots of 40 m\u003csup\u003e2\u003c/sup\u003e with spacing of 25 cm between plants and 50 cm between rows, resulting in a planting density of 75000 plants/hm\u0026sup2;. Nine plants per replicate were sampled to measure theoretical yield and yield components, including ear length, ear diameter, kernel number per ear, and 100 kernels weight. The theoretical yield (kg/hm\u003csup\u003e2\u003c/sup\u003e) was calculated by the following formula:\u003c/p\u003e \u003cp\u003eYield (kg/hm\u003csup\u003e2\u003c/sup\u003e) = (kernel number per ear * 100 kernels weight /100) *planting density.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAcid soil tolerance analysis\u003c/h3\u003e\n\u003cp\u003eEquation (1) illustrates the calculation of acid soil tolerance index (ATI) in accordance with Talebi (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAcid soil tolerance index (ATI) = (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:AS\\times\\:CK\\)\u003c/span\u003e\u003c/span\u003e) / (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{CK}\\)\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e2\u003c/sup\u003e (1)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:AS\\)\u003c/span\u003e\u003c/span\u003e index under acid soil conditions;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:CK\\:\\)\u003c/span\u003e\u003c/span\u003e: index under control soil conditions; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{CK}\\:\\)\u003c/span\u003e\u003c/span\u003e: mean index under control soil conditions.\u003c/p\u003e \u003cp\u003eThe membership function value (\u0026micro;) of each index of different maize varieties was calculated using Eq.\u0026nbsp;(2). Therefore, each index has its unique membership function value (Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and Eq.\u0026nbsp;(3) calculates the mean membership function value (MMFV) for each maize variety.\u003c/p\u003e \u003cp\u003e\u0026micro;(X\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e) = (X\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e X\u003csub\u003emin\u003c/sub\u003e)/ (X\u003csub\u003emax\u003c/sub\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e X\u003csub\u003emin\u003c/sub\u003e) (2)\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"346\" height=\"33\"\u003e\u003c/p\u003e \u003cp\u003eWhere X\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e was the ATI value of ith variety in the jth indicator; X\u003csub\u003emin\u003c/sub\u003e and X\u003csub\u003emax\u003c/sub\u003e denoted the minimum ATI value and maximum ATI value for the jth indicator among all varieties.\u003c/p\u003e \u003cp\u003eUsing the formulas from Zou et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we calculated the overall acid soil resistance ability (\u003cem\u003eD\u003c/em\u003e-value) for each maize variety as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{V}j=\\sqrt{{\\varSigma\\:}_{\\text{i}=1}^{n}{\\left({x}_{ij}-{\\stackrel{-}{x}}_{j}\\right)}^{2}}/\\:{\\stackrel{-}{x}}_{j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eW\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e=V\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{j=1}^{n}Vj\\)\u003c/span\u003e\u003c/span\u003e (5)\u003c/p\u003e \u003cp\u003e \u003cem\u003eD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{j=1}^{n}[\\)\u003c/span\u003e\u003c/span\u003eμ(X\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e)*W\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e] (6)\u003c/p\u003e \u003cp\u003eWhere V\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e, and X̅\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e represented the standard deviation coefficient and the average value for the jth indicator across all varieties. W represented the weight of the jth indicator in all indicators.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGrowth and physiological responses of hybrid maize in acid soil\u003c/h2\u003e \u003cp\u003eTo investigate the acidic soil stress characteristics of different maize varieties, we evaluated the morphological traits (PH, SPAD, SD, FW, LA), nutrient accumulation (TN, TP, TK), antioxidant enzyme activities (SOD, POD, APX, CAT), MDA and ROS (O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) levels in 50 hybrid maize varieties under AS and CK (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S7, Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). A two-way ANOVA analysis revealed that varieties, treatment, and their interaction significantly influenced all 15 indicators during acidic soil stress (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The coefficient of variation (CV%) was calculated to assess the dispersion of biological traits under different soil treatments. CV% values ranged from 8.52\u0026ndash;66.02%, with APX showing the highest variability (54.37% and 66.02%) and SPAD the lowest CV% (8.52% and 9.13%) under AS and CK conditions, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Growth and nutrient indices (PH, SD, FW, LA, TN, TP, and TK) were lower under AS than CK, resulting in the acid soil tolerance index (ATI) values below 1.0. Conversely, antioxidant enzyme activities (SOD, POD, APX, CAT) MDA and ROS were higher under AS, with ATI values exceeding 1.0 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These results suggest a high degree of inter-varietal variation and can be used for screening acid soil tolerant varieties.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical analysis of main indexes in maize under acid soil stress\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCV%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean ATI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTreatment (T)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVarieties (V)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT*V\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(g/plant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(mg/plant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e418.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(mg/plant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(mg/plant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e311.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e504.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(U)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e487.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1995.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(U)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1614.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(U)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e493.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(U)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(nmol/g FW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(nmol/g FW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(\u0026micro;mol/g FW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAS, acid soil; CK, control soil. PH, plant height; SD, stem diameter; FW, fresh weight; LA, leaf area; TN, total nitrogen accumulation; TP, total phosphorus accumulation; TK, total potassium accumulation; SOD, superoxide dismutase; POD, peroxidase; APX, ascorbate peroxidase; CAT, catalase; MDA, malondialdehyde; O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, superoxide anion and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, hydrogen peroxide. CV%, coefficient of variation. ATI, acidic stress tolerance index. Asterisks indicate statistically significant differences of each index: ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (two-way ANOVA).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEvaluation of maize acid soil tolerances by mean membership function values (MMFV) and comprehensive assessment values (\u003c/b\u003e \u003cb\u003eD\u003c/b\u003e \u003cb\u003e)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo address the limitations of using a single physiological index for assessing acid soil tolerance, we calculated the mean membership function value (MMFV) for each variety. MMFV rankings were used to compare acid soil tolerance among maize cultivars (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A higher MMFV indicates greater acid soil tolerance, with MY908 exhibiting the highest MMFV (0.58), indicating strong acid soil tolerance, while XY045 had the lowest MMFV (0.17), indicating sensitivity to acid soils. To provide a more robust evaluation of acid soil tolerance, a comprehensive assessment value (\u003cem\u003eD\u003c/em\u003e) was calculated using formula (6). The \u003cem\u003eD\u003c/em\u003e values, which also rank acid soil resistance, displayed a wider range (0.13\u0026ndash;0.60) compared to MMFV. While the rankings based on MMFV and \u003cem\u003eD\u003c/em\u003e values were consistent for most varieties, discrepancies were observed for some. For instance, LP243 and SR818 had high MMFVs (0.50) but relatively low \u003cem\u003eD\u003c/em\u003e values (\u0026lt;\u0026thinsp;0.40). Similarly, XY10 and SR565 showed MMFVs exceeding 0.40, yet their \u003cem\u003eD\u003c/em\u003e values were only 0.28 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results highlight the importance of combining MMFV and \u003cem\u003eD\u003c/em\u003e values for a more comprehensive evaluation of maize acid soil tolerance, ensuring a nuanced understanding of cultivar performance under acid soil conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe mean membership function value (MMFV) and comprehensive evaluation value (\u003cem\u003eD\u003c/em\u003e) of maize varieties under acidic soil stress\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVarieties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMFV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVarieties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMMFV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMY908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLY16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRHY288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLP218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRHY3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYH868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKY8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJF86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLP243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSY29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSR818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZY303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLY128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHY1702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eND372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEHD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMD26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJY666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKN20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQKY900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDD1311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFY18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDK653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJD7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXY335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJB288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXND9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWY3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHW267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKNY108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDD816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCY10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDH605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXY10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTR1818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDH682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZD958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSR565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLD9088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFL21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDD6531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGY14022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFNY6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBJ186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eXY045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of principal components for acidic soil stress through PCA\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) was performed based on the ATIs of 15 individual indicators. Five principal components (PCs) with eigenvalues exceeding 1.0 were identified (\u003cem\u003eF1\u003c/em\u003e to \u003cem\u003eF5\u003c/em\u003e), collectively explaining 74.36% of the total variation (Table S4). The PC1 was predominantly associated with growth and nutritional indicators, including FW, SD, PH, TN, TP, and TK (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The PC2 was primarily correlated with H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e accumulation under acidic soil stress (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). PC3 was strongly linked to ROS scavenging processes (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), and antioxidant enzymes such as CAT, APX, POD, and SOD. PC4 highlighted the significance of SPAD in regulating chlorophyll content, while PC5 was associated with MDA, reflecting membrane lipid peroxidation level in plant (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Using the weights of each principal component, scores (\u003cem\u003eF1\u003c/em\u003e to \u003cem\u003eF5\u003c/em\u003e) were calculated for each maize variety, resulting in a composite score (\u003cem\u003eF\u003c/em\u003e value) to rank acid soil resistance. Varieties such as RHY288, KY8, MY908, LP243, QKY900, RHY3, MY908, LY128, and SR818 had \u003cem\u003eF\u003c/em\u003e values exceeding 1.0, indicating strong acid soil tolerance. In contrast, varieties XY045, LD9088, SD188, and FNY6 had \u003cem\u003eF\u003c/em\u003e values below \u0026minus;\u0026thinsp;1.0, reflecting sensitivity to acid soil conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis and key indicator identification for acid soil resistance assessment\u003c/h2\u003e \u003cp\u003eA Pearson\u0026rsquo;s correlation analysis was performed to explore relationships among individual indicators, \u003cem\u003eD\u003c/em\u003e values, and \u003cem\u003eF\u003c/em\u003e values. Growth indicators (PH, SD, FW, LA) showed strong positive correlations with nutritional indicators (TN, TP, TK), particularly between FW and TN, TP, and TK (r\u0026thinsp;=\u0026thinsp;0.88, 0.76 and 0.87, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Conversely, negative correlations were observed between growth and nutritional indicators and antioxidant enzyme activities (POD, APX, CAT). Specifically, POD activity negatively correlated with DW, FW, TP, and TK, while APX and CAT activities showed significant negative correlations with FW and TK, respectively. Notably, ROS content did not significantly correlate with growth or nutritional indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe correlation between \u003cem\u003eD\u003c/em\u003e value and \u003cem\u003eF\u003c/em\u003e value was high (r\u0026thinsp;=\u0026thinsp;0.79), indicating strong consistency in acidic soil stress evaluations by these methods. Growth and nutritional indicators demonstrated the strongest positive correlations with \u003cem\u003eD\u003c/em\u003e and \u003cem\u003eF\u003c/em\u003e values, especially FW and TN (r\u0026thinsp;\u0026gt;\u0026thinsp;0.70), highlighting their importance in assessing maize acidic soil stress. However, while ROS content was significantly positively correlated with \u003cem\u003eD\u003c/em\u003e value, POD and CAT activities were significantly negatively correlated with the \u003cem\u003eF\u003c/em\u003e value, suggesting some methodological differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo identify key indicators for acid resistance, stepwise regression analysis was conducted using \u003cem\u003eD\u003c/em\u003e and \u003cem\u003eF\u003c/em\u003e values as dependent variables and the 15 individual indicators as independent variables. Regression models revealed that 11 individual indicators (PH, SD, FW, LA, TN, TP, POD, APX, CAT, H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) significantly influenced \u003cem\u003eD\u003c/em\u003e and \u003cem\u003eF\u003c/em\u003e values (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.95, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These indicators were identified as the critical traits for evaluating maize tolerance to acidic soil stress.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStepwise regression analysis of acid-tolerant maize with combined \u003cem\u003eD\u003c/em\u003e value and \u003cem\u003eF\u003c/em\u003e value\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAcid Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eD\u003c/em\u003e = -0.10\u0026thinsp;+\u0026thinsp;0.11*X3\u0026thinsp;+\u0026thinsp;0.03*X4\u0026thinsp;+\u0026thinsp;0.14*X5\u0026thinsp;+\u0026thinsp;0.02*X10\u0026thinsp;+\u0026thinsp;0.01*X11\u0026thinsp;+\u0026thinsp;0.02*X12\u0026thinsp;+\u0026thinsp;0.07*X14\u0026thinsp;+\u0026thinsp;0.03*X15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e = -3.31\u0026thinsp;+\u0026thinsp;0.98*X1\u0026thinsp;+\u0026thinsp;1.01*X2\u0026thinsp;+\u0026thinsp;0.38*X3\u0026thinsp;+\u0026thinsp;0.24*X4\u0026thinsp;+\u0026thinsp;1.14*X5\u0026thinsp;+\u0026thinsp;0.20*X6\u0026ndash;0.12*X12\u0026thinsp;+\u0026thinsp;0.12*X15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e392.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eX1, X2, X3, X4, X5, X6, X10, X11, X12, X14, and X15 represented plant height, stem diameter, fresh weight, leaf area, total nitrogen accumulation, total phosphorus accumulation, peroxidase, ascorbate peroxidase, catalase, superoxide anion and hydrogen peroxide, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eClustering analysis of maize varieties based on\u003c/b\u003e \u003cb\u003eD\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eF\u003c/b\u003e \u003cb\u003evalues\u003c/b\u003e\u003c/p\u003e \u003cp\u003eClustering analysis grouped the 50 hybrid maize varieties into five distinct categories based on their comprehensive \u003cem\u003eD\u003c/em\u003e and \u003cem\u003eF\u003c/em\u003e values (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These categories reflected varying levels of acid soil tolerance: highly acid-tolerance (HAT), acid-tolerance (AT), moderate acid-tolerance (MAT), acid-sensitive (AS), and highly acid-sensitive (HAS). Group I, comprising varieties such as MY908, RHY3, QKY900, LP243, KY8, and RHY288, displayed the highest \u003cem\u003eD\u003c/em\u003e and \u003cem\u003eF\u003c/em\u003e values, making it the most acid-tolerant group. In contrast, Group V, including varieties like FNY6, SD188, LD9088, and XY045, exhibited the lowest \u003cem\u003eD\u003c/em\u003e and \u003cem\u003eF\u003c/em\u003e values, classifying them as highly acid- sensitive.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eValidation of acid soil tolerance varieties under field conditions\u003c/h2\u003e \u003cp\u003eTo assess the yield and its components in maize varieties with different acid soil tolerance, two HAT varieties, MY908 and QKY900, and two HAS varieties, SD188 and FNY6, were grown under field conditions. The ear length, ear diameter, kernel number per ear, 100-kernel weight, and final yield of MY908 and QKY900 were similar under both AS and normal conditions. This stability indicated that these two varieties maintained their performance and were acid-tolerant in the field. In contrast, acid soil stress significantly reduced the yields of FNY6 and SD188 by 9.98% and 21.84%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The primary cause of yield reduction in HAS varieties was marked decrease in kernels number per ear under AS conditions (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings confirm that HAT varieties like MY908 and QKY900 are suitable for cultivation in acid soils, whereas HAS varieties such as SD188 and FNY6 are more susceptible to yield losses under such conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of acidic soil stress on yield components and theoretical yield of maize\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVarieties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEar length (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEar diameter (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKernel number per ear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100 kernels weight (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYield (t/ha)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMY908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e543.33\u0026thinsp;\u0026plusmn;\u0026thinsp;62.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e551.33\u0026thinsp;\u0026plusmn;\u0026thinsp;29.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQKY900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e577.33\u0026thinsp;\u0026plusmn;\u0026thinsp;72.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e572.33\u0026thinsp;\u0026plusmn;\u0026thinsp;58.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVarieties (V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTreatment (T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eV*T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFNY6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e526.00\u0026thinsp;\u0026plusmn;\u0026thinsp;40.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e583.33\u0026thinsp;\u0026plusmn;\u0026thinsp;40.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSD188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e466.00\u0026thinsp;\u0026plusmn;\u0026thinsp;64.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e577.33\u0026thinsp;\u0026plusmn;\u0026thinsp;46.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVarieties (V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTreatment (T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eV*T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAsterisks indicate statistically significant differences: *, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01; ***, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001 (two-way ANOVA). Data are means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (n\u0026thinsp;=\u0026thinsp;3).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eThe growth and physiological responses of hybrid maize varieties to acid soil\u003c/h2\u003e \u003cp\u003eAluminum tolerance is one of the most reliable indicators for assessing plant resistance to strongly acid stress (pH\u0026thinsp;\u0026lt;\u0026thinsp;4.5) (Ma et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Alvim et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, in addition to Al toxicity, acidic soils were always accompanied by a lack of essential nutrients, especially phosphorus nutrients and alkaline ions such as calcium, magnesium and potassium (Zhao et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, the growth inhibition of crops under acidic soil conditions was a comprehensive effect of aluminum toxicity and nutrient deficiency. Particularly in moderate and weakly acidic soils (5.0\u0026thinsp;\u0026lt;\u0026thinsp;pH\u0026thinsp;\u0026lt;\u0026thinsp;6.5), nutrient deficiency could be the major problem to restrict crop growth. In this study, we comprehensively analyzed the growth phenotypes and physiological and biochemical responses of maize under acid soil conditions, providing a faster and more efficient method for screening acid-tolerant varieties. The results demonstrated that acidic soil stress significantly inhibited maize growth and nutrient uptake, reducing PH, SD, FW, LA, TN, TP, and TK. The combined effects of Al toxicity and nutrient deficiency elevated ROS levels in maize. Consistently, ROS and MDA contents in maize leaves were significantly higher under acid soil conditions than in control soils, indicating oxidative stress. Elevated ROS levels were accompanied by significantly increased antioxidant enzyme activities, including SOD, POD, APX, and CAT. Notably, clustering analysis identified MY908, RHY3, QKY900, LP243, KY8, and RHY288 as the most acid-tolerant varieties, based on their high \u003cem\u003eD\u003c/em\u003e and \u003cem\u003eF\u003c/em\u003e values. These findings provide valuable insights for selecting acid-tolerant maize varieties, which are crucial for improving maize productivity in acidic soil stress conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eComprehensive evaluation of maize varieties\u0026rsquo; tolerance to acid soil\u003c/h2\u003e \u003cp\u003eTo quantitatively assess the stress resistance of crops, other researchers have proposed the tolerance coefficient, a mathematical indicator that relates plant performance under stress and non-stress conditions (Talebi \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Jafari et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Grzesiak et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This coefficient effectively reflects plants responses to abiotic stress and is widely used in tolerance studies. However, crop tolerance to abiotic stress is a complex trait influenced by both genetic and environmental factors. As such, a single tolerance coefficient may not fully capture the comprehensive stress tolerance of crops. To address this limitation, various multivariate analysis methods have been developed to assess tolerance to abiotic stresses, such as drought, Al toxicity, waterlogging stress, and salt damage (Aghaie et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we ranked maize varieties based on their acid soil tolerance using two methods, MMFV and \u003cem\u003eD\u003c/em\u003e value. Both methods yielded similar rankings, as they were both derived from the normalized acid soil tolerance index (ATI) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, few varieties showed different rankings between MMFV and \u003cem\u003eD\u003c/em\u003e value, despite a high positive correlation (r\u0026thinsp;=\u0026thinsp;0.93). This discrepancy arose due to distinct calculation methods, MMFV represents the mean membership value across all indices, while the \u003cem\u003eD\u003c/em\u003e value incorporates both the membership function value and the weight of each index (Zou et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, the \u003cem\u003eD\u003c/em\u003e value is better suited for evaluating crop resistance, especially when the standard deviation across indicators is large. To further refine our evaluation, we used PCA to reduce dimensionality and address potential redundancy between indicators. PCA grouped the 15 indicators into five principal components (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Principal component 1 (PC1), which primarily reflects growth and nutritional traits, accounted for over one-third of the total variation, highlighting the significance of these indices in evaluating maize tolerance to acid soil. A composite score (\u003cem\u003eF\u003c/em\u003e value) for each variety was then calculated based on the contribution of these principal components (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This integrated evaluation, combining both \u003cem\u003eD\u003c/em\u003e and \u003cem\u003eF\u003c/em\u003e values, offers a more comprehensive assessment of crop performance under abiotic stress (Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Cluster analysis, based on \u003cem\u003eD\u003c/em\u003e and \u003cem\u003eF\u003c/em\u003e values, was employed to classify the maize varieties into distinct resistance groups, as done in previous studies (Zhang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zou et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The 50 maize varieties were grouped into 5 clusters based on their overall acid soil resistance. The varieties MY908, RHY3, QKY900, LP243, KY8, and RHY288 were identified as HAT, while FNY6, SD188, LD9088, and XY045 were classified as HAS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDifferential responses of tolerant and sensitive maize varieties to acidic soil\u003c/h2\u003e \u003cp\u003eTo identify key indicators for screening acid tolerance maize genotypes, stepwise regression equations based on \u003cem\u003eD\u003c/em\u003e and \u003cem\u003eF\u003c/em\u003e values were established. Both equations identified eight key indicators, totaling 11 variables, including six morphological and nutritional traits (PH, FW, SD, LA, TN, TP), three antioxidant enzymes (POD, APX, CAT), and ROS content (O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e), for evaluating acid soil tolerance in maize (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Several of these indicators have previously been recognized as critical for assessing plant resilience to acid stress (Poschenrieder et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhu and Shen \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The variables PH, FW, SD, LA, TN, and TP were positively correlated with each other and with \u003cem\u003eD\u003c/em\u003e and \u003cem\u003eF\u003c/em\u003e values (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Consistent with these correlations, the HAT varieties exhibited minimal differences in PH, FW, SD, LA, and TP between AS and CK conditions, indicating that their growth was not significantly affected by acid soil stress (Figures S8-S9). In contrast, the growth of HAS varieties was significantly inhibited by acidic soil stress, with reduced values of PH, FW, SD, LA TN, and TP under AS compared to CK conditions (Figures S8-S9). There was no significant difference in H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e content, or POD, APX, and CAT activities between HAT and HAS varieties, as all remained at low levels (Figures S10-S11). However, acidic soil stress significantly induced H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e content and the activities of these antioxidant enzyme in both HAT and HAS varieties (Figures S10-S11). While H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e levels were similar between the two groups, the average activities of APX, CAT, and POD were higher in HAS varieties, with POD activity reaching statistically significant levels, suggesting that HAS varieties exerted a stronger antioxidant response under acidic soil stress (Figures S10-S11).\u003c/p\u003e \u003cp\u003eThe accumulation of ROS in plant cells under AS conditions, induced by both toxic metal ions and nutrient deficiencies, acts as a signaling mechanism to trigger plant defense responses but also disrupts cellular structures and metabolic processes (Berni et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nieves-Cordones et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To restore oxidative balance, plants express antioxidant enzymes to scavenge ROS and enhance resistance to acidic soil stress (Castro et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the energy required for this antioxidant response may detract from resources available for growth. Conversely, plants that allocate more energy to nutrient absorption rather than antioxidant enzyme production are better able to promote growth and adapt to acidic soils (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e5\u003c/span\u003e). As breeding goals prioritize higher biomass and yield, maize genotypes that allocate more metabolic energy to nutrient absorption are classified as tolerant varieties, while those that devote more energy to antioxidant enzymes production are considered sensitive varieties.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, PH, FW, SD, LA, TN, TP, POD, APX, CAT, O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e contents were distinguished as the key parameters for screening acid-tolerant maize varieties by cluster analysis. Furthermore, a comprehensive multivariate analysis revealed two distinct adaptation strategies in maize under acidic soil stress: 1) Antioxidant-dependent resistance characterized by enhanced antioxidant enzyme activity at the expense of growth suppression (HAS varieties); 2) Nutrient optimization strategy demonstrated by superior nutrient acquisition efficiency to sustain growth under stress conditions (HAT varieties). Finally, a field trial confirmed the accuracy of the comprehensive analysis in screening acid-tolerant varieties. The present study provides a comprehensive assessment of acid soil tolerance in maize and provides insights to improve yield in acidic soils through the use of suitable germplasm.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe thank Limei Zhang and Huan He at the public laboratory platform of the College of Resources and Environment, Huazhong Agricultural University, for their help measuring the N, P, and K concentrations in plant tissues.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eXC, YX, XY and SD perform physiological experiments and analyzed the data. LC performed the field experiment and analyzed the data. XC and CW wrote the manuscript. CW, SD, LS, FX, GD and QZ supervised the experiments. CW, XC, FX, GD and QZ designed the project. CW agrees to serve as the author responsible for contact and ensures communication. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Research and Development Program of China (2022YFD1900700), the Opening Project of Guangdong Provincial Key Laboratory of Quality \u0026amp; Safety Risk Assessment for Agro-products (SZKF202201), the National Natural Science Foundation of China (3207266) and the Fundamental Research Funds for the Central University of China\u0026nbsp;(2662024ZHPY001).\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during the present study can be found within the manuscript and its supporting materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eSupporting information (SI)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditional Supporting Information may be found in the online version of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAghaie P, Hosseini Tafreshi SA, Ebrahimi MA, Haerinasab M (2018) Tolerance evaluation and clustering of fourteen tomato cultivars grown under mild and severe drought conditions. Sci Hortic 232: 1-12. https://doi.org/10.1016/j.scienta.2017.12.041.\u003c/li\u003e\n\u003cli\u003eAli B, Hasan SA, Hayat S, Hayat Q, Yadav S, Fariduddin Q, Ahmad A (2008) A role for brassinosteroids in the amelioration of aluminium stress through antioxidant system in mung bean (\u003cem\u003eVigna radiata\u003c/em\u003e L. Wilczek). Environ Exp Bot 62: 153-159. https://doi.org/10.1016/j.envexpbot.2007.07.014.\u003c/li\u003e\n\u003cli\u003eAlvim MN, Ramos FT, Oliveira DC, Isaias RMS, Fran\u0026ccedil;a MGC (2012) Aluminium localization and toxicity symptoms related to root growth inhibition in rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) seedlings. J Biosci 37: 1079-1088. https://doi.org/10.1007/s12038-012-9275-6.\u003c/li\u003e\n\u003cli\u003eBerni R, Luyckx M, Xu X, Legay S, Sergeant K, Hausman J-F, Lutts S, Cai G, Guerriero G (2019) Reactive oxygen species and heavy metal stress in plants: Impact on the cell wall and secondary metabolism. Environ Exp Bot 161: 98-106. https://doi.org/10.1016/j.envexpbot.2018.10.017.\u003c/li\u003e\n\u003cli\u003eCakmak I, Marschner H (1992) Magnesium deficiency and high light intensity enhance activities of superoxide dismutase, ascorbate peroxidase, and glutathione reductase in bean leaves 1. Plant Physiol 98: 1222-1227. https://doi.org/10.1104/pp.98.4.1222.\u003c/li\u003e\n\u003cli\u003eCan\u0026ccedil;ado GMA, Ribeiro AP, Pi\u0026ntilde;eros MA, Miyata LY, Alvarenga \u0026Acirc;A, Villa F, Pasqual M, Purgatto E (2009) Evaluation of aluminium tolerance in grapevine rootstocks. Vitis 48: 167-173.\u003c/li\u003e\n\u003cli\u003eCastro B, Citterico M, Kimura S, Stevens DM, Wrzaczek M, Coaker GL (2021) Stress-induced reactive oxygen species compartmentalization, perception and signalling. Nat Plants 7: 403 - 412. https://doi.org/10.1038/s41477-021-00887-0.\u003c/li\u003e\n\u003cli\u003eChen X, Min D, Yasir TA, Hu Y (2012) Evaluation of 14 morphological, yield-related and physiological traits as indicators of drought tolerance in Chinese winter bread wheat revealed by analysis of the membership function value of drought tolerance (MFVD). Field Crop Res 137: 195-201. https://doi.org/10.1016/j.fcr.2012.09.008.\u003c/li\u003e\n\u003cli\u003eDai H, Shan W, Zhao J, Zhang G, Li C, Wu F (2011) Difference in response to aluminum stress among Tibetan wild barley genotypes. J Plant Nutr Soil Sci 174: 952-960. https://doi.org/10.1002/jpln.201100148.\u003c/li\u003e\n\u003cli\u003eDevkota S, Panthi S, Shrestha J (2019) Evaluation of maize varieties in acid soil condition. Agrica 8(2): 128-133. https://doi.org/10.5958/2394-448X.2019.00018.X.\u003c/li\u003e\n\u003cli\u003eFurlan F, Borgo L, Rab\u0026ecirc;lo FHS, Rossi ML, Linhares FS, Martinelli AP, Azevedo RA, Lavres J (2020) Aluminum-induced toxicity in Urochloa brizantha genotypes: A first glance into root Al-apoplastic and -symplastic compartmentation, Al-translocation and antioxidant performance. Chemosphere 243: 125362. https://doi.org/10.1016/j.chemosphere.2019.125362.\u003c/li\u003e\n\u003cli\u003eGiannakoula A, Moustakas M, Syros T, Yupsanis T (2010) Aluminum stress induces up-regulation of an efficient antioxidant system in the Al-tolerant maize line but not in the Al-sensitive line. Environ Exp Bot 67: 487-494. https://doi.org/10.1016/j.envexpbot.2009.07.010.\u003c/li\u003e\n\u003cli\u003eGiannopolitis CN, Ries SK (1977) Superoxide dismutases: I. Occurrence in higher plants. Plant Physiol 59 2: 309-314.\u003c/li\u003e\n\u003cli\u003eGrzesiak S, Hordyńska N, Szczyrek P, Grzesiak MT, Noga A, Szechyńska-Hebda M (2018) Variation among wheat (\u003cem\u003eTriticum easativum\u003c/em\u003e L.) genotypes in response to the drought stress: I \u0026ndash; selection approaches. J Plant Interact 14: 30 - 44. https://doi.org/10.1080/17429145.2018.1550817.\u003c/li\u003e\n\u003cli\u003eGuo JH, Liu X, Zhang Y, Shen J, Han W, Zhang WF, Christie P, Goulding KWT, Vitousek PM, Zhang F (2010) Significant acidification in major Chinese croplands. Science 327: 1008 - 1010. https://doi.org/10.1126/science.1182570.\u003c/li\u003e\n\u003cli\u003eGupta N, Gaurav SS, Kumar A (2013) Molecular basis of aluminium toxicity in plants: A review. Am J Plant Sci 4: 21-37. https://doi.org/10.4236/AJPS.2013.412A3004.\u003c/li\u003e\n\u003cli\u003eHeath RL, Packer L (1968) Photoperoxidation in isolated chloroplasts. I. Kinetics and stoichiometry of fatty acid peroxidation. Arch Biochem Biophys 125 1: 189-198.\u003c/li\u003e\n\u003cli\u003eHu D, Li R, Dong S, Zhang J, Zhao B, Ren B, Ren H, Yao H, Wang Z, Liu P (2022) Maize (\u003cem\u003eZea mays\u003c/em\u003e L.) responses to salt stress in terms of root anatomy, respiration and antioxidative enzyme activity. BMC Plant Biol 22: 602. https://doi.org/10.1186/s12870-022-03972-4.\u003c/li\u003e\n\u003cli\u003eJafari A, Paknejad F, Jami AL-Ahmadi M (2012) Evaluation of selection indices for drought tolerance of corn (\u003cem\u003eZea mays\u003c/em\u003e L.) hybrids. Int J Plant Prod 3: 33-38. https://doi.org/10.22069/ijpp.2012.661.\u003c/li\u003e\n\u003cli\u003eKhan R, Gao F, Khan K, Shah MA, Ahmad H, Fan ZP, Zhou XB (2024) Evaluation of maize varieties via multivariate analysis: Roles of ionome, antioxidants, and autophagy in salt tolerance. Plant Physiol 196: 195-209. https://doi.org/10.1093/plphys/kiae335.\u003c/li\u003e\n\u003cli\u003eKochian LV (1995) Cellular mechanisms of aluminum toxicity and resistance in plants. Annu Rev Plant Biol 46: 237-260. doi: 10.1146/annurev.pp.46.060195.001321.\u003c/li\u003e\n\u003cli\u003eMa JF, Nagao S, Sato K, Ito H, Furukawa J, Takeda K (2004) Molecular mapping of a gene responsible for Al-activated secretion of citrate in barley. J Exp Bot 55: 1335-1341. https://doi.org/10.1093/jxb/erh152.\u003c/li\u003e\n\u003cli\u003eMaron LG, Guimar\u0026atilde;es CT, Kirst M, Albert PS, Birchler JA, Bradbury PJ, Buckler ES, Coluccio AE, Danilova TV, Kudrna D, Magalhaes JV, Pi\u0026ntilde;eros MA, Schatz MC, Wing RA, Kochian LV (2013) Aluminum tolerance in maize is associated with higher \u003cem\u003eMATE1\u003c/em\u003e gene copy number. Proc Natl Acad Sci 110: 5241-5246. https://doi.org/10.1073/pnas.1220766110.\u003c/li\u003e\n\u003cli\u003eNakano Y, Asada K (1987) Purification of ascorbate peroxidase in spinach chloroplasts; its inactivation in ascorbate-depleted medium and reactivation by monodehydroascorbate radical. Plant Cell Physiol 28: 131-140. https://doi.org/10.1094/Phyto-77-112.\u003c/li\u003e\n\u003cli\u003eNavakode S, Weidner A, Lohwasser U, R\u0026ouml;der MS, B\u0026ouml;rner A (2008) Molecular mapping of quantitative trait loci (QTLs) controlling aluminium tolerance in bread wheat. Euphytica 166: 283-290. https://doi.org/10.1007/s10681-008-9845-8.\u003c/li\u003e\n\u003cli\u003eNieves‐Cordones M, L\u0026oacute;pez-Delacalle M, R\u0026oacute;denas R, Mart\u0026iacute;nez V, Rubio F, Rivero RM (2019) Critical responses to nutrient deprivation: A comprehensive review on the role of ROS and RNS. Environ Exp Bot 161: 74-85. https://doi.org/10.1016/j.envexpbot.2018.10.039.\u003c/li\u003e\n\u003cli\u003ePoschenrieder C, Llugany M, Barcel\u0026oacute; J (1995) Short‐term effects of pH and aluminium on mineral nutrition in maize varieties differing in proton and aluminium tolerance. J Plant Nutr 18: 1495-1507. https://doi.org/10.1080/01904169509364998.\u003c/li\u003e\n\u003cli\u003eQian P, Sun R, Ali B, Gill RA, Xu L, Zhou W (2013) Effects of hydrogen sulfide on growth, Antioxidative capacity, and ultrastructural changes in oilseed rape seedlings under aluminum toxicity. J Plant Growth Regul 33: 526-538. https://doi.org/10.1007/s00344-013-9402-0.\u003c/li\u003e\n\u003cli\u003eSun F, Chen Q, Chen Q, Jiang M, Gao W, Qu Y (2021) Screening of key drought tolerance indices for cotton at the flowering and boll setting stage using the dimension deduction method. Front Plant Sci 12: 619926. https://doi.org/10.3389/fpls.2021.619926.\u003c/li\u003e\n\u003cli\u003eTalebi R (2009) Effective selection criteria for assessing drought stress tolerance in durum wheat (\u003cem\u003eTriticum durum\u003c/em\u003e Desf.). Gen Appl Plant Physiol35: 64-74.\u003c/li\u003e\n\u003cli\u003eTandzi LN, Mutengwa CS, Ngonkeu ELM, Gracen VE (2018) Breeding maize for tolerance to acidic soils: A review. Agronomy 8: 84. https://doi.org/10.3390/agronomy8060084.\u003c/li\u003e\n\u003cli\u003eTang C, Weligama C, Sale P (2013) Subsurface Soil Acidification in Farming Systems: Its Possible Causes and Management Options. In: J Xu, DL Sparks (eds) Molecular Environmental Soil Science. Dordrecht, Springer Netherlands , pp 389-412.\u003c/li\u003e\n\u003cli\u003eWang GY, Ahmad S, Wang Y, Wang BW, Huang JH, Jahan MS, Zhou XB, Shi CQ (2023) Multivariate analysis compares and evaluates drought and flooding tolerances of maize germplasm. Plant Physiol 193: 339-355. https://doi.org/10.1093/plphys/kiad317.\u003c/li\u003e\n\u003cli\u003eWang Y, Xing G, Qin G, Fu B, Bo W (2017) Evaluation of drought resistance in Iris germanica L. based on subordination function and principal component analysis. Emir J Food Agric 29: 770-778. https://doi.org/10.9755/ejfa.2017.v29.i10.1260.\u003c/li\u003e\n\u003cli\u003eYamamoto Y, Kobayashi Y, Devi SR, Rikiishi S, Matsumoto H (2003) Oxidative stress triggered by aluminum in plant roots. Plant Soil 255: 239-243. https://doi.org/10.1023/A:1026127803156.\u003c/li\u003e\n\u003cli\u003eZhang M, Lu X, Li C, Zhang B, Zhang C, Zhang X-s, Ding Z (2018) Auxin efflux carrier ZmPGP1 mediates root growth inhibition under aluminum stress. Plant Physiol 177: 819-832. https://doi.org/10.1104/pp.17.01379.\u003c/li\u003e\n\u003cli\u003eZhang Y, Xu S, Mo L (2020) Screening of grape genotypes for aluminum tolerance and evaluation on selecting indices. Soil Sci Plant Nutr 66: 560-568. https://doi.org/10.1080/00380768.2020.1779572.\u003c/li\u003e\n\u003cli\u003eZhao XQ, Chen RF, Shen RF (2014) Coadaptation of plants to multiple stresses in acidic soils. Soil Science 179: 503-513. https://doi.org/10.1097/ss.0000000000000086.\u003c/li\u003e\n\u003cli\u003eZhu Q, de Vries W, Liu X, Hao T, Zeng M, Shen J, Zhang F (2018) Enhanced acidification in Chinese croplands as derived from element budgets in the period 1980-2010. Sci Total Environ 618: 1497-1505. https://doi.org/10.1016/j.scitotenv.2017.09.289.\u003c/li\u003e\n\u003cli\u003eZhu Q, Liu X, Hao T, Zeng M, Shen J, Zhang F, de Vries W (2020) Cropland acidification increases risk of yield losses and food insecurity in China. Environ Pollut 256: 113145. https://doi.org/10.1016/j.envpol.2019.113145.\u003c/li\u003e\n\u003cli\u003eZhu X, Ros GH, Xu M, Xu D, Cai Z, Sun N, Duan Y, Vries WD (2024) The contribution of natural and anthropogenic causes to soil acidification rates under different fertilization practices and site conditions in southern China. Sci Total Environ 934: 172986. https://doi.org/10.1016/j.scitotenv.2024.172986.\u003c/li\u003e\n\u003cli\u003eZhu XF, Shen RF (2023) Towards sustainable use of acidic soils: Deciphering aluminum-resistant mechanisms in plants. Fundam Res4: 1533-1541. https://doi.org/10.1016/j.fmre.2023.03.004.\u003c/li\u003e\n\u003cli\u003eZou J, Hu W, Li Y, He J, Zhu H, Zhou Z (2020) Screening of drought resistance indices and evaluation of drought resistance in cotton (\u003cem\u003eGossypium hirsutum\u003c/em\u003e L.). J Integr Agr 19: 495-508. https://doi.org/10.1016/s2095-3119(19)62696-1.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Acid soil, Maize, Acid tolerance mechanism, Antioxidant enzymes, Comprehensive evaluation","lastPublishedDoi":"10.21203/rs.3.rs-6021236/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6021236/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims\u003c/h2\u003e \u003cp\u003eAcid soils, characterized by nutrient deficiencies and metal ion toxicity, severely limit maize yields. Cultivating acid soil tolerant maize represents a promising strategy to address these edaphic constraints.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThrough controlled pot experiments, 50 maize hybrids were subjected to acidic soil stress (AS) and optimal soil conditions (CK), evaluating 15 morpho-physiological traits at the V5 stage. Multivariate statistical approaches were employed to identify critical tolerance indicators, with subsequent field validation conducted on four selected genotypes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAcidic soil stress induced significant alterations across all measured parameters compared to control conditions and revealed substantial genotypic variation in stress responses. Cluster analysis classified the 50 hybrids into five distinct tolerance categories, with two predominant adaptation strategies. Antioxidant-dependent resistance characterized by elevated peroxidase (POD), ascorbate peroxidase (APX), and catalase (CAT) activities. This strategy prioritized oxidative defense at the expense of biomass production (acid-sensitive varieties). Nutrient optimization strategy demonstrated by superior nitrogen and phosphorus acquisition efficiencies, enabling sustained growth under stress conditions (acid-tolerant varieties). Stepwise regression identified six critical evaluation parameters: plant height, fresh weight, stem diameter, leaf area, total nitrogen and phosphorus accumulation, complemented by antioxidant enzyme profiles and reactive oxygen species levels.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study establishes a comprehensive evaluation framework incorporating 11 validated indicators for screening adaptive maize varieties in acid soil conditions. Field validation confirmed the accuracy of multivariate analysis in selecting acid soil tolerant varieties.\u003c/p\u003e","manuscriptTitle":"Multivariate analysis unveils antioxidant-nutrient trade-offs in Maize Hybrids: A hierarchical framework for acid soil tolerance evaluation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-07 05:57:51","doi":"10.21203/rs.3.rs-6021236/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-04-04T00:40:37+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-03T22:12:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-03T03:27:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2025-04-02T02:13:45+00:00","index":"","fulltext":""},{"type":"decision","content":"Minor revisions","date":"2025-03-15T18:05:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"59297d19-9203-412f-8e8c-0020d6465f1a","owner":[],"postedDate":"April 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T16:01:47+00:00","versionOfRecord":{"articleIdentity":"rs-6021236","link":"https://doi.org/10.1007/s11104-025-07525-0","journal":{"identity":"plant-and-soil","isVorOnly":false,"title":"Plant and Soil"},"publishedOn":"2025-05-13 15:57:48","publishedOnDateReadable":"May 13th, 2025"},"versionCreatedAt":"2025-04-07 05:57:51","video":"","vorDoi":"10.1007/s11104-025-07525-0","vorDoiUrl":"https://doi.org/10.1007/s11104-025-07525-0","workflowStages":[]},"version":"v1","identity":"rs-6021236","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6021236","identity":"rs-6021236","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}