Introduction
Sugarcane (Saccharum officinarum L.) is a vital crop for sugar and energy production in China, with Guangxi being the primary cultivation region. According to the National Bureau of Statistics, Guangxi’s sugarcane planting area has remained at more than 75 million ha, with sugar production around 6 million Mg (1000 kg) in recent years (Liu et al. Reference Liu, Meng and Li2023). High precipitation and warm temperatures provide ideal conditions for sugarcane cultivation. However, these conditions also support the growth of diverse weed species, significantly constraining sugarcane yields in Guangxi. Weed infestations can cause 20% to 30% yield reductions, and in severe cases, losses might exceed 50%, rendering fields unproductive (Li Reference Li2023; Lu et al. Reference Lu, Li, Xu, Yin, Wang and Huang2011).
Weed species composition in Guangxi sugarcane fields indicates low variation across the region, with most species belonging to subtropical families and smaller proportions from tropical and temperate zones (Sun et al. Reference Sun, Ren, Hu, Jiang, Ma, Wang, Song, Ma and Ma2019). The predominant species include hairy crabgrass [Digitaria sanguinalis (L.) Scop.], Indian goosegrass [Eleusine indica (L.) Gaertn.], bermudagrass [Cynodon dactylon (L.)], knotgrass (Paspalum distichum L.), Canada thistle [Cirsium arvense (L). Scop.], stickywilly (Galium aparine L.), yellow foxtail [Setaria viridis (L.) P. Beauv.], hyssop (Hyssopus officinalis L.), and purple nutsedge (Cyperus rotundus L.) (Li et al. Reference Li, Zhang, Huang, Yin, Luo, Wang and Shan2016). The Gramineae and Compositae families are the most diverse, with Gramineae weeds being the most harmful, followed by broadleaf weeds and less detrimental Cyperaceae weeds (Li Reference Li2023). Effective and timely weed management is essential for maintaining and improving sugarcane production.
Chemical herbicides play a pivotal role in weed control in sugarcane fields, particularly as the rising costs of rural labor have increased reliance on herbicides. More than 80% of sugarcane fields in Guangxi use herbicides for weed management (Guan et al. Reference Guan, XL, Tang and Zeng2015). MCPA-ametryn-diuron (MAD), a formulation commonly used to control weeds, combines systemic and contact herbicidal effects (Lima et al. Reference Lima, Müller, Costa, Batista, Dalvi and Domingos2017; Rangani et al. Reference Rangani, Rouse, Saski, Noorai, Shankar, Lawton-Rauh, Werle and Roma-Burgos2022; Sun et al. Reference Sun, Guo, Wang, Chang, Xia and Du2021). Ametryn (a triazine herbicide) and diuron (a phenylurea herbicide) inhibit photosynthesis by disrupting electron transfer in photosystem II. MCPA-sodium (a phenoxy carboxylic acid hormone herbicide) interferes with plant hormone functions, leading to uncontrolled growth and death of broadleaf weeds (Negri et al. Reference Negri, Flores, Mercurio, Mueller and Collier2015; Zhang et al. Reference Zhang, He, Zhang and Wang2022). A weed control efficacy of 87.9% against C. rotundus was reportedly achieved within 15 d by applying a 65% MAD formulation at 2,047.5 g ha−1 in 675 kg of water (Li et al. Reference Li, Zhang, Huang, Yin, Luo, Wang and Shan2016). The 81% MAD wettable powder, composed of low-toxicity raw materials, meets national standards for efficiency, low toxicity, and environmental sustainability (Huo et al. Reference Huo, Zhao, Zhang, Xing, Zhang, Dong and Fan2018). Its rapid absorption through roots, stems, and leaves effectively controls a broad spectrum of annual and perennial weeds, making it a typical solution for sugarcane weed management. This formulation has yielded an annual economic benefit of 1.87 million yuan in recent years (Huang et al. Reference Huang, Liang, Wang, Wu, Huang and Xu2015).
Using herbicides is currently the most efficient and time-saving approach for weed management in sugarcane cultivation. However, herbicides can lead to abnormal growth and development in sugarcane, resulting in varying degrees of damage that can negatively impact yield and quality (Hassan et al. Reference Hassan, Naz, Ali, Ali, Akram, Iqbal, Ajmal, Ali, Ercisli, Golokhvast and Hassan2023; Martins-Gomes et al. Reference Martins-Gomes, Silva, Andreani and Silva2022; Wang et al. Reference Wang, Riaz, Song, Song, Huang, Bai and Zhao2022). Developing and cultivating herbicide-tolerant varieties is an economical, environmentally sustainable, and effective strategy to mitigate herbicide-induced damage (Abou-Khater et al. Reference Abou-Khater, Maalouf, Jighly, Rubiales and Kumar2022). Therefore, tolerant genotypes are essential to ensure sustainable and cost-effective sugarcane production. Globally, researchers are working to identify tolerant varieties to enhance the efficiency of herbicide management (Koutouan et al. Reference Koutouan, Le Clerc, Suel, Hamama, Claudel, Halter, Baltenweck, Hugueney, Chich, Moussa, Champlain, Huet, Voisine, Pelletier and Balzergue2023). Moreover, the variability in herbicide effects on sugarcane poses challenges for accurate identification and monitoring, emphasizing the need for robust classification standards and evaluation frameworks (Singh et al. Reference Singh, Shukla, Kaur, Girdhar, Malik and Mohan2024). However, research on herbicide-tolerant breeding and evaluation in sugarcane in China remains limited (Cheng et al. Reference Cheng, Harikrishna, Redwood, Lit, Nath and Chua2022; Su et al. Reference Su, Peng, Ling, You, Wu, Xu and Que2022). This study aimed to establish a robust classification standard for herbicide tolerance of sugarcane genotypes, develop a comprehensive evaluation framework to assess sugarcane tolerance to these chemicals, and identify tolerant germplasm resources. The findings will enable the diagnosis and prediction of herbicide-induced sugarcane phytotoxicity, promoting sustainable production and advancing the sugarcane industry in China.
Materials and Methods
Experimental Sites
Field experiments were conducted at the Longzhou, Nongqin, and Qufeng Agricultural Experimental Stations in Guangxi, China, from 2021 to 2023. The soil type in all three experiments was red loam with sugarcane as the preceding crop. The Longzhou site is located at 22.3333°N, 106.7833°E, at an elevation of 115.4 m above sea level. The region has a subtropical monsoon climate characterized by an average annual temperature of 22.2 °C, a frost-free period of approximately 350 d, and an average annual precipitation of 1,300 mm. The area gets a mean annual sunshine duration of 1,695.2 h and a total solar radiation of 107.5 kcal cm−2. The second and third experimental sites, Nongqin and Qufeng, are located in Fusui County (22.6418°N, 107.9191°E) at an elevation of 69.5 m above sea level. This area receives a mean annual sunshine duration of 1,693 h and a total solar radiation of 108.4 kcal cm−2. The climate in Fusui features an average frost-free period of 346 d, annual rainfall ranging from 1,050 to 1,300 mm, and an average annual temperature of 22.4 °C (Supplementary Figure S1).
Weed Survey in the Sugarcane Field
The weed populations were surveyed using the W9 inverted point sampling method (Supplementary Figure S2) in the sugarcane fields at Longzhou, Nongqin, and Qufeng. Nine sampling sites were examined within each test plot, covering an area of 0.25 m2. Weed assessments were conducted three times at each sampling point before herbicide application and on day 7 and day 15 after application. The collected data were then used to calculate the plant control efficiency using the following formula:
$${\rm{Plant}}\;{\rm{control}}\;{\rm{efficiency}}\;\left( \% \right) = {{{\rm{CK}} - {\rm{PT}}} \over {{\rm{CK}}}} \times 100$$
where CK represents the number of weed plants in the control area, and PT represents the number of weed plants in the treatment area.
Herbicide Application
An ADJB-20 knapsack sprayer (Yongxing Machinery, Hebei Province, China) equipped with a fan-shaped nozzle and a constant-pressure valve was used for herbicide application of the 81% MAD wettable powder supplied by Shandong Shengbang Luye Chemical Co., Ltd. (Weifang City, Shandong Province, China) at an average flow rate of 830 ml min−1. The application rates of herbicide and water consumption per m2 were 0.84 g and 90 g, respectively, with an additional 2.5 g of auxiliary agent per m2. The treatments were applied when the sugarcane plants were at either the 5-leaf stage (seedling stage) or the 9-leaf stage (tillering stage). The herbicide was sprayed uniformly and vertically using the backpack sprayer before the closure of the sugarcane canopy. To ensure maximum effectiveness, the applications were scheduled to avoid rainfall within 6 h after spraying. A non-treated control was included for comparison.
Experimental Design and Field Management
The experiment was carried out in a randomized complete block design with three replications per genotype. Sugarcane genotypes were first planted at Nongqin and Longzhou in March 2021. The plants were maintained for 3 yr, which included 1 yr of new planting (2021), followed by 2 yr of ratoon cropping (2022 to 2023). Each genotype was planted in a single row per replication, with 2-m-long rows spaced 1.3 m apart at a planting density of 60 shoots per row. In March 2023, the identical 222 genotypes were planted at Qufeng and maintained through the first ratoon cropping cycle in 2024. At this site, the rows were 3-m-long, spaced 2 m apart, and planted at a density of 80 shoots per row, with three replications per genotype. To ensure consistency, fertilizer management practices were standardized across all sites, following local commercial sugarcane production methods. Additionally, the genotypes YT71-210 and ZZ6 were included at all locations as controls, representing susceptible and tolerant standards, respectively.
Field Evaluation
Field data were systematically collected over 3 yr (2021 to 2023) at the Longzhou site, 2 yr (2022 to 2023) at the Nongqin site, and 1 yr (2023) at the Qufeng site. For each genotype, we recorded the total number of plants (N), the number of plants exhibiting herbicide phytotoxicity (m), and the severity of phytotoxicity during the seedling stage (May to July). Phytotoxic severity was evaluated using a visual scale from 0 to 4 based on the symptoms observed on sugarcane leaves (Figure 1).
Figure 1. Symptom of 81% MCPA-ametryn-diuron (MAD) phytotoxicity on sugarcane. (A) level 0; (B) level 1; (C) level 2; (D) level 3; (E) level 4.
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Level 0: No visible herbicide injury; plants exhibited normal growth.
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Level 1: Mild symptoms, such as temporary yellowing at leaf tips and small damaged spots, which recovered quickly without affecting plant growth.
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Level 2: Moderate yellowing on fewer than half of the leaves, with continuous damage, chlorosis, and slight growth inhibition. Recovery was achievable, and yield remained unaffected.
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Level 3: Severe yellowing and drying on more than half of the leaves, accompanied by stunted plant growth, resulted in significant yield reductions and partial plant mortality, making recovery challenging.
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Level 4: Extensive yellowing and leaf death, severe growth suppression, widespread plant mortality, and substantial yield losses, potentially leading to complete crop failure.
Data from all three sites were combined to calculate the herbicide phytotoxic index, comprehensively assessing genotype responses under varying field conditions.
The herbicide phytotoxic percentage was calculated using the following formula:
$$Q = {{m}\over{N}} \times 100$$
where Q represents herbicide phytotoxic percentage (%), m denotes the number of plants exhibiting herbicide phytotoxicity, and N represents the total number of plants observed.
Depending on phytotoxic severity, the herbicide phytotoxic index was calculated using the following formula:
$${\rm{PI}} = {{\sum {\left( {n \times s} \right)} } \over {S \times N}} \times 100$$
where PI denotes herbicide phytotoxic index, n represents the number of plants at each phytotoxic severity level, s denotes the assigned value for the severity grade, S indicates the highest possible severity grade, and N represents the total number of plants observed.
These calculations provided a comprehensive measure of herbicide impact on sugarcane across the three experimental sites.
Statistical Analysis
The weed control efficiency data from Longzhou, Nongqin, and Qufeng were processed and analyzed using Data Processing System software (v. 7.05, Zhejiang University, Hangzhou, China). An arcsine square-root transformation was applied to standardize the percentage of herbicide phytotoxicity across 222 sugarcane genotypes. The data were subsequently analyzed using ANOVA in R software (v. 3.5.0; Verma et al. Reference Verma, Song, Yadav, Degu, Parvaiz, Singh, Huang, Mustafa, Xu and Li2022) to assess variation among genotypes, crop cycles, and locations. Specifically, a one-way ANOVA was performed on data from Longzhou and Nongqin to evaluate variations across different crop cycles. The model clearly defines nesting relationships and interactions to address data imbalance across various locations and times. Data were analyzed using a mixed linear model as follows:
$${Y_{ijklm}} = u + {\rm{ }}{C_i} + {\rm{ }}{G_j} + {L_k} + {Y_l}\left( {{L_k}} \right){\rm{ }} + {R_m}\left( {{L_k}} \right){\rm{ }} + {\rm{ }}{\left( {G{\rm{ }} \times L} \right)_{jk}} \\\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!+ {\rm{ }}{\left( {G{\rm{ }} \times {\rm{ }}C} \right)_{ji}} + {\rm{ }}{\left( {G \times L \times C} \right)_{jki}} + {E_{ijklm}}$$
where Y ijklm is the herbicide phytotoxicity percentage of the jth genotype from the mth replication in the kth location of the lth year of the ith crop cycle; u is the overall mean; C i is the fixed effect of the ith crop cycle; G j is the random effect of the jth genotype; L k is the fixed effect of the kth location; Y l (L k ) is the lth year nested within the kth location; R m (L k ) is the mth replication nested within the kth location; (G × L) jk is the interaction effect of the jth genotype and the kth location; (G × C) ji is the interaction effect of the jth genotype and the ith crop cycle; (G × L× C) jki is the interaction effect of the jth genotype and the kth location and the ith crop cycle; E ijklm is the experimental residual error.
Broad-sense heritability (H2 B), defined as the proportion of phenotypic variance attributable to genetic variance, was estimated for individual years and through a combined analysis across multiple years and locations. The formula used for the combined analysis is as follows:
$${H^2}_B = {\rm{ }}{\sigma ^2}_g/({\sigma ^2}_g + {\sigma ^2}_{gc}/c + {\rm{ }}{\sigma ^2}_{gl}/l + {\rm{ }}{\sigma ^2}_{glc}/lc + {\rm{ }}{\sigma ^2}_e/rlc)$$
where σ2 g, σ2 e, σ2 gc, σ2 gl, and σ2 glc refer to genotypic variance, error variance, genotype × crop cycle interaction, genotype × location interaction, and genotype × location × crop cycle interaction, respectively; c, l, and r represent the crop cycle, locations, and replications.
A hierarchical cluster analysis was conducted on the herbicide phytotoxic percentage and index mean and maximum values for each site using Ward’s method and Euclidean distance, as described by Lešková et al. (Reference Lešková, Giehl, Hartmann, Fargašová and von Wirén2017) with R implementation adapted from Hintikka et al. (Reference Hintikka, Munukka, Valtonen, Luoto, Ihalainen, Kallonen, Waris, Heinonen, Ruuskanen and Pekkala2022). After clustering, discriminant analysis was performed to evaluate clustering accuracy using DPS software (v. 7.05, Zhejiang University, Hangzhou, China). The discriminant analysis revealed that the maximum values were the most significant metrics for differentiating between clusters based on their high discrimination accuracy rates. The 222 genotypes were classified into five distinct tolerance categories, and an LSD post hoc analysis was conducted for each category using GraphPad Prism software (v. 8, GraphPad Software, Inc., San Diego, CA, USA).
Results and Discussion
Sugarcane-Field Weeds and Control Efficiencies of 81% MAD
Weeds were present at all stages of sugarcane growth and had a significant impact on the crop. During the spring, sugarcane experiences slow growth before canopy closure, leaving fields exposed for prolonged periods (Hajeb et al. Reference Hajeb, Hamzeh, Kazem Alavipanah, Neissi and Verrelst2023). This exposure and high temperatures and humidity promote rapid weed proliferation, impairing sugarcane tillering and seedling development (Mzabri et al. Reference Mzabri, Rimani, Charif, Kouddane and Berrichi2022). This leads to substantial reductions in yield and quality, ranging from 30% to 70% under moderate weed infestation, with severe cases resulting in total crop failure (Wen et al. Reference Wen, Xiufen, Ping, Zhenling, Xiaojuan, Hengrui, Hongliang and Suyun2021). A comprehensive survey of weed species in sugarcane fields identified 14 species from 8 families in Longzhou, 19 from 12 families in Nongqin, and 19 from 11 families in Qufeng (Table 1). Five weed species were found to be shared across all three locations, including Gramineae: C. dactylon and D. sanguinalis; Compositae: Oriental false hawksbeard [Youngia japonica (L.) DC.]; Convolvulaceae: ivy woodrose [Merremia hederacea (Burm. f.) Hallier f.] and white-edge morningglory [Ipomoea nil (L.) Roth]; Cyperaceae: C. rotundus; and Solanaceae: black nightshade (Solanum nigrum L.). A preliminary investigation, supported by a review of relevant literature, identified 116 weed species from 27 families as the dominant weed flora in the sugarcane-growing areas of Guangxi (Fu Reference Fu2008; Lu and Ma Reference Lu and Ma2003; Mayor and Dessaint Reference Mayor and Dessaint1998; Qin and Huang Reference Qin and Huang2014; Xue et al. Reference Xue, Yang, Zhang and Ma2010; Yang Reference Yang2012). Our study investigated 35 species from 16 families after removing duplicates in three locations. This identification provides a foundation for further weed research in the region and is crucial for selecting the right herbicides and enhancing weed control efficiency in sugarcane fields.
Table 1. Investigation on weed control efficacy in sugarcane fields from Longzhou, Nongqin, and Qufeng, China.
Phenylurea (e.g., diuron), phenoxy (e.g., MCPA-sodium), and triazine (e.g., ametryn) are selective systemic herbicides that absorb through roots, stems, and leaves, disrupting photosynthesis or hormone regulation in meristems to control weeds in crops (Liu et al. Reference Liu, Ma, Lu, Jiang, Wu and Yang2017). After application of 81% MAD, the control efficiency against C. rotundus varied after 7 d, with 26.14% in Longzhou, 36.26% in Nongqin, and 94.93% in Qufeng. However, by day 15, the control efficiency increased significantly, reaching 94.95%, 90.53%, and 96.77% at the respective locations. For C. dactylon, the control efficiency was 88.07% after 7 d and reached 100% by day 15, indicating a rapid and effective response to treatment. For S. nigrum, control efficiency varied based on plant size; smaller plants responded rapidly. However, the control efficiency across all sites reached 100% within 15 d. Similar responses were observed for M. hederacea and I. nil, with 90% control in 7 d and 100% by day 15. Most other weed species exhibited control efficiencies exceeding 90% by 15 d posttreatment, except E. indica, which demonstrated herbicide tolerance due to its advanced maturity stage. The application of 81% MAD effectively controlled more than 90% of weed species in sugarcane fields within 15 d. However, certain sugarcane varieties might experience growth issues due to sensitivity to these herbicides (Vyver et al. Reference Vyver, Conradie, Kossmann and Lloyd2013). A 3-yr study conducted in Longzhou, Nongqin, and Qufeng found that herbicide phytotoxicity percentage in sugarcane from May to July varied from 0% to 100%. Symptoms appear 5 to 10 d after application. Mild damage includes dry leaf tips with yellow strips under 5 cm. Severe damage causes leaf chlorosis, desiccation, shrinkage, deformation, stunted growth, and withering of growing points, potentially leading to the plant’s death (Ji Reference Ji2024; Shan et al. Reference Shan, Qin, Yan, Zhou, Pang, Tang, Pan and Tang2020). Each symptom was assessed at varying levels of severity and categorized into a standardized grading system ranging from 0 to 4 across five levels. The phytotoxicity significantly reduces sugarcane yield and sugar content, leading to substantial economic losses (Huang et al. Reference Huang, Lu, Chen, Sun and An2021). To minimize such losses, it is crucial to identify herbicide-sensitive cultivars and provide farmers with clear guidance on selecting suitable herbicides for specific sugarcane varieties (Taak et al. Reference Taak, Tiwari and Koul2020). Additionally, the variability in herbicide effects on sugarcane poses a significant challenge in accurately identifying and monitoring herbicide-induced damage, making it a crucial factor in herbicide safety evaluation (Landau et al. Reference Landau, Hager, Tranel, Davis, Martin and Williams2021). To address these challenges, it is imperative to develop comprehensive technical guidelines for assessing sugarcane tolerance to herbicides and to establish enhanced classification standards for herbicide damage (Wang et al. Reference Wang, Tan, Zhen, Liang, Gao, Zhao, Liu and Zha2023). This systematic approach provides a reliable method for recording and analyzing herbicide-induced damage in sugarcane. These efforts will contribute to establishing a unified framework for diagnosing and predicting the extent of herbicide damage, ensuring sustainable production and advancement in the sugarcane industry of China (Salgado et al. Reference Salgado, Wilson, Penn, Richard and Way2022; Wang et al. Reference Wang, Riaz, Song, Song, Huang, Bai and Zhao2022).
Variance Analysis of Herbicide Phytotoxicity in Sugarcane
The percentage and index of herbicide phytotoxicity in sugarcane demonstrated a broad distribution across various fields, rendering them highly suitable for tolerance assessment (Figure 2). Combined variance analysis of all collected phenotypic data demonstrated highly significant differences in herbicide phytotoxic percentage and index among genotypes (G, P < 0.001), locations (L, P < 0.001), and crop cycle (C, P < 0.001), as well as their interactions. Such significant interactions were observed for G × L (P < 0.001), G × C (P < 0.001), and G × L × C (P < 0.001). Genotype contributed 44.62% (45.12%) to the variance in herbicide phytotoxicity percentage (index), while location accounted for 0.29% (0.42%) and crop cycle for 1.61% (1.28%). The G × L interaction accounted for 15.46% (15.93%), the G × C interaction for 21.76% (25.03%), and the G × L × C interaction for 8.23% (7.79%) (Table 2). Sugarcane herbicide tolerance was primarily influenced by genotype and crop cycle, although environmental factors also played a significant role.
Figure 2. Frequency distribution, heritability of 81% MCPA-ametryn-diuron (MAD) herbicide phytotoxic percentage (Q) and herbicide phytotoxic index (PI) of different genotypes across three sites: Longzhou, Nongqin, and Qufeng in Guangxi, China.
Table 2. Variance analysis for phytotoxic percentage and index.
***P ≤ 0.001. MS: Mean Square; SS%: Percentage contribution to total sum of squares.
A variance analysis of phenotypic data collected in 2023 across three locations revealed that genotype (G) (P < 0.001), location (L) (P < 0.001), crop cycle (C) (P < 0.001), the G × L (P < 0.001) interaction, and the G × C (P < 0.001) interaction significantly influenced herbicide phytotoxic percentage and index. Specifically, genotype accounted for 59.80% of the variance in herbicide phytotoxic percentage, location for 1.90%, crop cycle for 0.59%, G × L for 23.25%, and G× C for 13.60%. Similarly, for the phytotoxic index, genotype accounted for 59.76%, location for 2.22%, crop cycle for 0.32%, G × L for 24.60%, and G× C for 12.82% (Table 3). This suggests that sugarcane tolerance to herbicide phytotoxicity is primarily determined by genotype and crop cycle, while environmental conditions also play a significant role.
Table 3. Variance analysis on the 2023 phenotypic data for phytotoxic percentage and index.
***P ≤ 0.001.
MS, Mean Square; SS%, Percentage contribution to total sum of squares.
ANOVA over the first and the second ratoon cane at the same location indicated significant differences in the ratoon sugarcane in Nongqin. In contrast, no significant differences between the plant cane and the ratoon cane were found in Longzhou (Figure 3). This suggests that sugarcane exhibits varying tolerance levels to herbicide phytotoxicity, with ratoon cane demonstrating greater sensitivity than plant cane. Furthermore, the first ratoon cane shows heightened sensitivity relative to those in the second ratoon cane. Plant cane has a robust root system with high metabolic activity, enabling efficient herbicide phytotoxicity and reduced sensitivity. In contrast, ratoon cane relies on aging roots with diminished nutrient absorption, leading to decreased herbicide tolerance. Its shallow bud points are more exposed to herbicide residues, increasing the risk of damage. The first ratoon cane is weakened by mechanical damage and environmental stress, while the second ratoon cane develops a stronger tolerance over time through root regeneration and soil microorganisms. Plant cane improves stress tolerance through tail fertilizer and moisture-retaining film. In contrast, ratoon cane (especially the first) often suffers from poor management and untimely fertilization, increasing herbicide sensitivity due to malnutrition. Long-term cultivation may trigger adaptive mechanisms, with the second stage potentially reducing herbicide absorption by upregulating tolerance-related genes like ABC transporters (Haj Yasein et al. Reference Haj Yasein, Jensen, Vindedal, Gundersen, Klungland, Ottersen, Hvalby and Nagelhus2011; Qamar et al. Reference Qamar, Nasir, Abouhaidar, Hefferon, Rao, Latif, Ali, Anwar, Rashid and Shahid2021; Thibane et al. Reference Thibane, Soni, Phali and Mdoda2023).
Figure 3. One-way ANOVA for 81% MCPA-ametryn-diuron (MAD) phytotoxic percentage and index for different crop cycles.
Root conditions, soil residues, and management practices influence the ratoon cane’s sensitivity to herbicides. Future research should integrate molecular biology (e.g., tolerance gene screening) with optimized field management (e.g., precise pesticide application) to develop phased herbicide plans. Promoting tolerant varieties like ‘Guitang42’ and microbial remediation can mitigate pesticide damage and support sustainable production (Cheeke et al. Reference Cheeke, Rosenstiel and Cruzan2012).
Tolerance Evaluation by Cluster and Discriminant Analysis
Hierarchical cluster analysis constructs a hierarchy of clusters by evaluating the similarity or distance between data samples (Gupta et al. Reference Gupta, Dhar, Kumar, Choudhary, Dass, Sharma, Shukla, Upadhyay, Das, Jinger, Rajpoot, Sannagoudar, Kumar, Bhupenchandra and Tyagi2022; Kalogiouri et al. Reference Kalogiouri, Manousi, Klaoudatos, Spanos, Topi and Zachariadis2021). The maximum and average phytotoxic percentages and indices were calculated independently and in combination for each experimental site. Hierarchical cluster analysis classified 222 sugarcane genotypes into five categories, each undergoing individual self-discriminant analysis (Figure 4). The accuracy of cluster analysis based on the maximum values was significantly higher than that based on the average values, demonstrating that using maximum values resulted in more accurate and stable classifications. Therefore, the maximum values were utilized in subsequent analyses.
Figure 4. Composite maximum stratified cluster analysis circle plot. HT, highly tolerant; HS, highly susceptible; MT, moderately tolerant; T, tolerant; S, susceptible.
The discriminant analysis classifies groups based on eigenvalues under categorical conditions (Ramsey et al. Reference Ramsey, Maginnis, Wong, Brock and Cummings2012). The interaction discrimination among the three experimental sites and the combined clustering analysis results revealed that the combined maximum values achieved an accuracy of greater than 95%. The discriminant analysis accuracy was 95.05% for Longzhou, 98.65% for Nongqin, and 98.65% for Qufeng (Table 4). This indicates that cluster analysis using combined maximum values provides a more accurate and widely applicable classification. Combining these methods offers a robust approach for accurately categorizing experimental genotypes (Xu et al. Reference Xu, Wei, Jiang, Pan, Wang, Deng and Zhang2023).
Table 4. Discriminant accuracy of different clustering metrics.
Initially, mean and maximum values were used in clusters and discriminant analyses. However, the maximum values yielded higher discriminant accuracy, indicating that maximum incidence is a more reliable measure of varietal tolerance (Jiang et al. Reference Jiang, Xu, Wei, Khan, Wu, Li, Chen, Zhang, Zeng and Zhang2024). To accurately assess herbicide phytotoxic tolerance in sugarcane, widely cultivated genotypes representing different tolerance levels were selected as reference controls (Table 5). Over 3 yr of trials conducted in Longzhou, Nongqin, and Qufeng, ROC22 consistently demonstrated tolerance, with a herbicide phytotoxic percentage below 20%, establishing it as a reliable tolerant control. Conversely, highly susceptible genotypes such as YT94-128, ROC25, YT71-210, and ROC27 exhibited maximum phytotoxic percentages ranging from 50% (susceptible) to greater than 85% (highly susceptible). These results confirm the utility of these genotypes as susceptible controls due to their consistently high damage levels across all tested regions. This study applied self-discriminatory and interactive discriminatory analyses to evaluate individual locations and perform comprehensive cross-site assessments. A total of 222 sugarcane genotypes were classified into five distinct categories using clustering and discriminant analysis based on the combined maximum value approach. The classifications are as follows: 21 highly tolerant (HT) genotypes (9.5%), 68 tolerant (T) genotypes (30.6%), 75 moderately tolerant (MT) genotypes (33.8%), 18 susceptible (S) genotypes (8.1%), and 40 highly susceptible (HS) genotypes (18%) (Table 6; Supplementary Table S1). The classifications derived from individual locations were validated across other experimental sites, establishing widely applicable classification criteria (Li et al. Reference Li, Du, Li, Yao and Zhang2024). The significant contribution of the G × L interaction underscores the complexity of tolerance mechanisms. These findings emphasize the vital role of parental genotype selection in breeding programs aimed at developing sugarcane cultivars with improved herbicide tolerance. This might also explain the challenges associated with breeding for herbicide tolerance.
Table 5. Evaluation criteria and reference genotype for assessing 81% MCPA-ametryn-diuron (MAD) field tolerance in sugarcane.
a HT, highly tolerant; HS, highly susceptible; MT, moderately tolerant; T, tolerant; S, susceptible.
b MAD, MCPA-ametryn-diuron; Q, herbicide phytotoxic percentage (%); PI, herbicide phytotoxic index.
Table 6. Variation in the identification indicators of sugarcane tolerance to 81% MCPA-ametryn-diuron (MAD) phytotoxicity among experimental genotypes.
a HT, highly tolerant; HS, highly susceptible; MT, moderately tolerant; T, tolerant; S, susceptible.
Evaluation of Sugarcane Parents for Tolerance to 81% MAD
Parental traits significantly influence progeny tolerance (Xu et al. Reference Xu, Wu, Gillani, Chen, Li, Wei, Jiang, Zhang, Zeng and Zhang2025). In our study, most parents exhibited pedigrees of tolerant or highly tolerant sugarcane varieties, including CT89-103, ROC22, and YZ89-7. The progenies of CT89-103 × ROC22 comprised 21 genotypes, with 18 exhibiting moderate to high tolerance (Figure 5A; Supplementary Table S2). Moreover, other parents crossed with ROC22, such as Co1001 (HT), YT93-124 (MT), and GT92-66 (MT), exhibited high or moderate tolerance to the herbicide. Among their 18 offspring genotypes, 16 exhibited moderate to high tolerance, including GT04-1001 (MT), GT05-378 (MT), 14-2802 (T), and 16-0812 (HT), while only two exhibited susceptibilities (Supplementary Table S2). This suggests that using tolerant parents increases the likelihood of obtaining tolerant progeny. The progeny of ROC25 × YZ89-7 were also examined, revealing that eight of the genotypes evaluated in this study exhibited moderate or high tolerance. Conversely, zero genotypes exhibited susceptibility (Figure 5B). The inclusion of tolerant parents appeared more favorable for obtaining tolerant progeny. Lineage analyses of ROC22, CT89-103, ROC25, and YZ89-7 indicated these varieties are closely related, with their lineages, YC71-374, F146, and F152, being utilized multiple times (Figure 5). When both parents are tolerant, the majority of progeny inherit tolerance. A substantial proportion of progeny may still exhibit tolerance if one parent is susceptible. However, when both parents are susceptible, the likelihood of producing tolerant progeny decreases significantly (Brahimi et al. Reference Brahimi, Mesli, Rahmouni, Zeggai, Khaldoun, Chebout and Belbachir2020). Lineage analysis revealed that ROC22, CT89-103, ROC25, and YZ89-7 are closely related, with lineages YC71-374, F146, and F152 frequently utilized. Herbicide-tolerant sugarcane progenitors are pivotal in breeding herbicide-tolerant varieties.
Figure 5. Parent traceability analysis of four important sugarcane varieties. (A) Progeny tolerance distribution of cross CT89-103 × ROC22; (B) Progeny tolerance distribution of cross ROC25 × YZ89-7. A question mark (?) indicates no parental information available. HT, highly tolerant; HS, highly susceptible; MT, moderately tolerant; T, tolerant; S, susceptible.
Conventional breeding is the most commonly used method in crop genetic breeding, and the effectiveness of a parent depends on its ability to select and breed suitable progeny varieties (Li et al. Reference Li, Xu, Duan, Bian, Hu, Shen, Li and Jin2018). The current study identified 89 tolerant genotypes, including ROC22 and its hybrid progenies 16-1715 and 16-041, displaying a maximum herbicide phytotoxic percentage and index of less than 20% and 10%, respectively. Conversely, 58 susceptible varieties, such as ROC25 and YT71-210, exhibited maximum phytotoxic percentage and index exceeding 50% and 85%, respectively. Notably, only 9.5% of the genotypes were classified as HT, indicating the urgent need to develop more herbicide-tolerant cultivars. Future breeding efforts should enhance these progenitors’ genetic diversity and prioritize using herbicide-tolerant parents to facilitate genetic improvement.
This study identified valuable genetic material among sugarcane parental lines and hybrid progenies, providing critical resources for breeding programs to develop herbicide-tolerant varieties and expand the pool of HT genotypes. A total of 21 HT genotypes were identified as strong candidates for use as parents in breeding programs aimed at enhancing tolerance to herbicide damage. By strategically integrating cluster and discriminant analyses, this study provides a robust framework for evaluating herbicide tolerance across diverse experimental sites, which has the potential for broader applications in assessing various forms of herbicide damage. Future research should prioritize evaluating novel germplasm, identifying tolerance-related genes, and optimizing breeding strategies to accelerate the development of sugarcane varieties with improved herbicide tolerance. This systematic approach enhances our understanding of tolerance mechanisms and paves the way for improving sugarcane tolerance, productivity, and sustainability in the face of herbicide challenges.
Supplementary material
To view supplementary material for this article, please visit https://doi.org/10.1017/wsc.2025.10033
Acknowledgments
We thank Charles A. Powell from the University of Florida for critically revising and editing the manuscript. The authors thank the reviewers for their constructive feedback on this manuscript.
Funding statement
This study was supported by the China Agriculture Research System of MOF and MARA (CARS170109) and the Science and Technology Major Project of Guangxi (Gui Ke AA22117001).
Competing interests
The authors declare no conflicts of interest.
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