Sugarcane (Saccharum officinarum L.) is a perennial grass of the Poaceae family, distinguished by its thick, segmented and fibrous culms that serve as the primary reservoir for sucrose buildup (Ali et al., 2021). The crop produces a well-branched and deep root structure, which enables strong anchorage and effective water and nutrient absorption, promoting rapid growth and increasing resistance to environmental challenges (Armiato et al., 2025). Sugarcane is thought to have originated in New Guinea and Southeast Asia, where wild Saccharum species were domesticated millennia ago, with archaeological evidence indicating early domestication in New Guinea as early as 8000 BC (Dinesh et al., 2022).
       
Sugarcane covers about 100,000 hectares in Haryana and is a major contributor to the state’s agriculture sector (Fig 1). The crop has an average productivity of 70-80 t ha^-1, which depends on irrigation availability, varietal selection and crop management approaches. Sugarcane is grown on 5 million hectares in India, producing around 400 million tons annually with average yields of 70-75 tonnes per hectare. Sugarcane is grown on about 26 million hectares worldwide, with an annual production of more than 1.9 billion tonnes. Sugarcane productivity averages around 70 t ha-¹ globally, however yields vary by region due to meteorological conditions, soil characteristics and crop management approaches.


       
Weed management is an important part of sugarcane agriculture because of the crop’s long growth cycle and wide row spacing, which produce ideal circumstances for weed infestation (Vasantha et al., 2023). Weeds, if not controlled, can result in yield losses ranging from 20 to 40% or greater, especially during the early stages of crop establishment (Rajbir et al., 2024). Weed control in sugarcane requires the use of both pre-and post-emergence herbicides, such as atrazine, metribuzin, sulfentrazone, clomazone, pendimethalin, halosulfuron and topramezone, either alone or in appropriate combinations. Furthermore, mechanical procedures such as manual weeding and inter-row cultivation are important, particularly when pesticide selectivity is limited or weed resistance is a concern (Msomba et al., 2024). Integrating chemical and mechanical technologies results in a more sustainable and efficient approach to weed management throughout the sugarcane development cycle (Begum and Bordoloi, 2016).
       
Recent advances in weed management highlight the use of precision agriculture techniques such as drone-assisted weed surveillance and site-specific herbicide administration, which allow for real-time assessment and more efficient use of agrochemical inputs (Thamoonlest et al., 2025). Such technologies are becoming increasingly crucial as climate change affects sugarcane productivity, necessitating flexible and adaptable weed management tactics. The combination of traditional weed management approaches and innovative technological interventions enables growers to maximize resource use, reduce environmental impacts and improve the long-term sustainability of sugarcane production systems (Msomba et al., 2024).
       
Despite the availability of several herbicides for weed management in sugarcane, their effectiveness largely depends on the composition of weed flora and agro-climatic conditions of a particular region. In North Haryana, sugarcane fields are commonly infested with a complex weed flora consisting of grasses, sedges and broad-leaved weeds, which often reduces crop growth and yield due to intense crop-weed competition. However, limited information is available regarding the comparative performance of different herbicide combinations for effective control of the prevailing weed flora under the specific soil and climatic conditions of this region. Therefore, the present study was undertaken to evaluate suitable weed management strategies for efficient weed control and improved productivity of sugarcane under North Haryana conditions.

Study area and climatic conditions
 
The field experiment was conducted during the spring season of 2024-25 at the Crop Research Farm of Maharishi Markandeshwar University, Mullana, located in Ambala district of Haryana, India. The experimental site is situated at approximately 30°37′N latitude and 76°77′E longitude with an altitude of about 264 m above mean sea level. The region falls under the subtropical climatic zone of northwestern India characterized by hot summers and cool winters.
       
The average annual temperature of the region is around 23-24°C, with summer temperatures often rising up to 40-41°C during May-June and winter temperatures occasionally dropping to around 7-9°C. The area receives an average annual rainfall of approximately 900-1000 mm, most of which occurs during the southwest monsoon period from July to September. Such climatic conditions are favorable for sugarcane cultivation as well as the development of diverse weed flora.
       
The experimental soil was sandy loam in texture, well drained and moderately fertile. The soil contained available nitrogen (225 kg ha^-1), phosphorus (41.8 kg ha^-1), potassium (261.2 kg ha^-1) and organic carbon content of 0.87%.
 
Variety (Sugarcane)
 
Co 118 is an early-maturing, high-yielding sugarcane cultivar that has gained popularity among farmers due to its exceptional agronomic performance and versatility. It has a high sucrose content and excellent juice quality, resulting in improved sugar recovery and profitability for sugar mills. It has greatly increased productivity and farm profitability because to its advantageous mix of early maturity, yield potential and juice quality.
 
Weed analysis of sugarcane
 
The experimental field was infested with a mixture of grassy, sedge and broad-leaved weeds. The dominant grassy weeds included Echinochloa colona, Cynodon dactylon and Dactyloctenium aegyptium. The sedge species mainly observed was Cyperus rotundus, while major broad-leaved weeds included Amaranthus viridis, Chenopodium album, Commelina benghalensis and Parthenium hysterophorus.
       
Weed data were recorded 150 days after sowing (DAS) to assess the efficacy of various treatments in controlling weed development. Weed samples were gathered from each plot using a 0.25 m² quadrat set randomly at two sites. The measured counts were then translated to weed density per square meter. Densities of grasses, sedges and broad-leaved weeds were measured separately and then combined to calculate overall weed density. To estimate dry matter, weed samples were oven-dried at 65±2°C until a consistent weight was reached. Weed control efficiency (WCE) was calculated to quantify the percentage reduction in weed dry matter relative to the weedy control and the weed index (WI).
       
Weed control efficiency (WCE) and weed index (WI) were calculated to evaluate the effectiveness of different weed management treatments. Weed control efficiency was computed using the formula:

 
Where,
DWC= The dry weight of weeds in the weedy check (control) plot.
DWT= The dry weight of weeds in the treated plot.
       
Weed index (WI) was calculated to determine the reduction in crop yield due to weed competition using the following formula:

 
Where,
Y_wf= The yield obtained from the weed-free treatment.
Y_t= The yield recorded under a particular treatment.
 
Statistical analysis
 
The recorded data were first entered into Microsoft Excel for preliminary calculations and computation of treatment means. The data were then subjected to statistical analysis using the CVSTAT software at the 5% level of significance (p<0.05). The significance of differences among treatments was tested using the Least Significant Difference (LSD) test and the standard error of mean (SEM) values were calculated to compare treatment effects.

Density of grasses (No. m^-2)
 
At 150 days after sowing (DAS), grass weed density (Table 1) varied significantly between treatments, ranging from 1.93 No. m^-2 (2.74 No. m^-2) to 5.82 No. m^-2 (32.84 No. m^-2). Treatment T₂, which included post-emergence applications of atrazine and metribuzin (1000+1000 g ha^-1), resulted in a minimal grass weed density of 1.93 No. m^-² (2.74 No. m^-2). The weedy check (T₁₀) had the highest grass weed density (5.82 No. m^-2; (32.84 No. m^-2). Previous research has shown that metribuzin is effective at low treatment rates in suppressing grass weeds like Poa annua and other related species in a variety of agricultural settings (Barua et al., 2021). Abdallah et al. (2021) reported similar findings, observing considerably higher weed populations in untreated plots compared to those treated with efficient herbicides.


 
Density of sedges (No. m^-2)
 
At 150 days after sowing (DAS), sedge weed density (Table 1) varied significantly between treatments, ranging from 2.05 No. m^-2 (3.21 No. m^-2) to 5.97 No. m^-2 (34.59 No. m^-2). Treatment T₂, which included post-emergence applications of atrazine and metribuzin (1000+1000 g ha^-1), resulted in the lowest sedge density of 2.05 No. m^-2 (3.21 No. m^-2). The weedy check (T₁₀) had the highest sedge density, with 5.97 No. m^-2 (34.59 No. m^-2). Treatment T₂ (atrazine + metribuzin) effectively suppresses sedge populations due to their complimentary mechanisms of action. Atrazine primarily inhibits photosynthesis in susceptible broad-leaved weeds and certain grasses, whereas metribuzin, a triazinone herbicide, provides residual control of grasses and some sedge species, especially at higher doses as used in the current study (Odero and Shaner, 2021). Herbicide treatments, including those containing metribuzin, were significantly more effective than non-chemical or untreated controls in lowering sedge and total weed populations in sugarcane fields (Dhankar et al., 2020).
 
Density of broad-leaved weeds (No. m^-2)
 
At 150 days after sowing (DAS), the density of broad-leaved weeds (Table 1) varied significantly between treatments, ranging from 2.21 No. m^-2 (3.89 No. m^-2) to 7.99 No. m^-2 (62.77 No. m^-2). Treatment T₂, which included post-emergence application of atrazine + metribuzin (1000+1000 g ha^-¹), resulted in the lowest broad-leaved weed density of 2.21 No. m^-² (3.89 No. m^-²). The weedy check (T₁₀) had the highest density of broad-leaved weeds (7.99 No. m^-2; 62.77 No. m^-2). Treatment T₂ effectively controls broad-leaved weeds by a synergistic combination of atrazine and metribuzin, resulting in a significant drop in density. Previous research has shown that herbicide combinations like atrazine and metribuzin are more effective at reducing broad-leaved weed density than single herbicide applications or untreated controls, especially when applied at the right rates and timings, reducing weed competition and increasing crop productivity. In contrast, the weedy check (T₁₀) allowed unfettered development of broad-leaved weeds, leading to significantly increased weed density. Similar trends have been consistently reported in field studies, where untreated plots had significantly higher broad-leaved weed infestations than plots treated with effective herbicide combinations, emphasizing the importance of integrated herbicide strategies for sustainable weed management (Odero and Shaner, 2021).
 
Total weed density (No. m^-2)
 
At 150 days after sowing (DAS), total weed density (Table 1) differed significantly between treatments, ranging from 3.29 No. m^-2 (9.84 No. m^-2) to 11.45 No. m^-2 (130.20 No. m^-2). Treatment T₂, which included post-emergence application of atrazine and metribuzin (1000+1000 g ha^-1), resulted in the lowest total weed density of 3.29 No. m^-2 (9.84 No. m^-2). The weedy check (T₁₀) had the highest total weed density, measuring 11.45 No. m^-2 (130.20 No. m^-2). The significant decrease in overall weed density observed under treatment T₂  is consistent with recent research showing that atrazine and metribuzin herbicide combinations offer broad-spectrum control of both grassy and broad-leaved weeds, lowering weed competition and increasing crop productivity (Acharya et al., 2023). Even at relatively lower doses, metribuzin-based treatments have been shown in previous studies to significantly reduce total weed density in comparison to untreated controls in wheat. For example, metribuzin applied at 250 g ha^-¹ at 25 DAS reduced total weed density by about 33% when compared to the weedy check. Furthermore, it has been observed that improvements in metribuzin formulations, notably nano-based solutions, cause significant photosynthetic impairment in weeds, leading to notable decreases in weed density and biomass even at lower application rates. The significance of integrated herbicide techniques for attaining efficient and sustainable weed management is shown by the fact that the untreated control (T₁₀) consistently reported the greatest total weed density, a tendency frequently seen in field tests (Takeshita et al., 2025).
 
Dry weight of grasses (g m^-2)
 
The dry weight of grassy weeds (Table 2) differed greatly between treatments at 150 days after sowing (DAS), ranging from 1.30 g m^-2 (0.69 g m^-2) to 3.03 g m^-2 (8.21 g m^-2). Treatment T₂, which included the post-emergence application of atrazine + metribuzin (1000+1000 g ha^-1), resulted in the lowest dry weight of grasses, 1.30 g m^-2 (0.69 g m^-2). On the other hand, the weedy check (T₁₀) had the highest grass weed dry weight of 3.03 g m^-2 (8.21 g m^-2). Metribuzin, a triazinone herbicide that offers residual control and is frequently used in conjunction with atrazine to accomplish broad-spectrum and persistent weed management, enhances the weed-suppressive action even further (Barbaś et al., 2020). On the other hand, because of unchecked weed development and competition, the weedy check (T₁₀) continuously displayed the largest dry weight of grass weeds. For efficient and long-term management of grass weeds in intensive agricultural systems, these results emphasize the significance of integrated herbicide treatments (Ofosu et al., 2023).


 
Dry weight of sedges (g m^-2)
 
The dry weight of sedge weeds (Table 2) varied substantially between treatments at 150 days after sowing (DAS), ranging from 1.34 g m^-2 (0.80 g m^-2) to 3.11 g m^-2  (8.65 g m^-2). With treatment T₂, which involved applying atrazine + metribuzin (1000+1000 g ha^-1) post-emergence, the lowest sedge dry weight of 1.34 g m-² (0.80 g m^-2) was observed. On the other hand, the weedy check (T₁₀) had the highest sedge dry weight of 3.11 g m^-2 (8.65 g m^-2). The herbicide combination effectively suppresses sedge growth and biomass accumulation. When compared to untreated controls, Bhatti et al. (2022) found that herbicide mixes that combine atrazine with triazinone herbicides, including metribuzin, are very effective in reducing sedge and total weed biomass. The results of the current study are supported by additional field research showing that these combinations considerably reduce sedge density and dry weight. Due to the lack of weed control measures, which allowed weeds to grow and compete unchecked, the weedy check (T₁₀) regularly reported the highest sedge dry weight (Bhatti et al., 2022).
 
Dry weight of broad-leaved weeds (g m^-2)
 
The dry weight of broad-leaved weeds (Table 2) at 150 days after sowing (DAS) varied greatly between treatments, ranging from 1.40 g m^-2 (0.97 g m^-2) to 4.09 g m^-2 (15.69 g m^-2). Treatment T₂, which included the post-emergence application of atrazine + metribuzin (1000+1000 g ha^-1), produced the lowest broad-leaved weed dry weight of 1.40 g m^-2 (0.97 g m-²). Conversely, the weedy check (T₁₀) showed the highest dry weight of broad-leaved weeds, 4.09 g m^-2 (15.69 g m^-2). The herbicide combination effectively suppresses the growth and biomass accumulation of broad-leaved weeds. According to Mueller and Henry (2024), broad-leaved weed biomass is considerably reduced by herbicide mixes, especially those that combine atrazine with triazinone herbicides like metribuzin, as opposed to single-herbicide treatments or untreated controls. These combinations significantly reduce the density and dry weight of broad-leaved weeds, according to supporting data from field studies, which supports the current study’s conclusions (Carvalho et al., 2023).
 
Total weed dry weight (g m^-2)
 
Total weed dry weight (Table 2) differed considerably between treatments at 150 days after sowing (DAS), ranging from 1.86 g m^-2 (2.46 g m^-2) to 5.79 g m^-2 (32.55 g m^-2). Atrazine + metribuzin (1000+ 1000 g ha^-1) was applied post-emergence under treatment T₂, which resulted in the lowest overall weed dry weight of 1.86 g m^-2 (2.46 g m^-2). Conversely, the weedy check (T₁₀) had the highest total weed dry weight, 5.79 g m^-2 (32.55 g m^-2). When compared to untreated controls, herbicide mixtures of atrazine and metribuzin are highly effective in suppressing overall weed biomass. This is consistent with the significantly lower total weed dry weight observed under treatment T₂, which involves the application of atrazine + metribuzin (Silburn et al., 2023). The lack of weed management measures, on the other hand, led to unregulated weed growth and fierce competition, which is why the weedy check (T₁₀) regularly recorded the highest total weed dry weight (Takeshita et al., 2025).
 
Weed control efficiency (%)
 
The treatments’ weed control efficiencies (WCE) (Table 3) ranged from 0.00% to 92.44%. Treatment T₂, which included the post-emergence application of atrazine + metribuzin (1000+1000 g ha^-1), had the highest WCE (92.44%). High WCE values of 91.40%, 89.83% and 88.93% were also recorded by treatments T4 [sulfentrazone + clomazone (750+750 g ha^-1) PRE, T7  [metribuzin + topramezone (1000 +25 g ha^-1) PoE] and T6 [pendimethalin + metribuzin (1000+1000 g ha^-1) PRE. These treatments were found to be statistically comparable to treatment T₂. On the other hand, the weedy check (T₁₀) had the lowest weed control efficacy (0.00%). According to Barbieri et al. (2022), single-herbicide treatments or untreated controls are less successful at reducing weed density and improving weed control effectiveness than herbicide combos such atrazine + metribuzin, sulfentrazone + clomazone and metribuzin + topramezone. Montgomery et al. (2024) reported similar results, highlighting the benefit of herbicide mixtures in attaining broad-spectrum and long-lasting weed control.


 
Weed index (%)
 
The weed index (WI) (Table 3), which ranged from 6.83% to 60.58%, differed considerably amongst the treatments. Treatment T₂, which applied atrazine + metribuzin (1000+1000 g ha^-1) post-emergence, had the lowest weed index (6.83%), showing that weed control was effective and that yield reduction was minor. On the other hand, the weedy check (T10) had the highest weed index, 60.58%, which indicated significant yield losses brought on by unmanaged weed competition. The great efficacy of weed control attained by the combined action of metribuzin and atrazine is demonstrated by the low weed index observed in treatment T₂. Javaid et al. (2022) found similar results, indicating that atrazine and metribuzin herbicide combinations considerably reduced weed density and weed index in comparison to other treatments.

The present study on weed management strategies in sugarcane under North Haryana’s agro-climatic conditions clearly demonstrates that good weed control is critical to boosting crop development and output. The most effective treatment was T₂, which involved applying atrazine and metribuzin (1000+1000 g ha^-1) after emergence. This treatment considerably reduced weed density and dry matter accumulation, hence increasing crop vigor and yield-contributing characteristics. This combination’s exceptional performance implies that it is a feasible and cost-effective weed management strategy in sugarcane farming. As a result, sugarcane growers in North Haryana can rely on post-emergence applications of atrazine and metribuzin for weed control. Further research is needed to evaluate the long-term environmental safety and residual effects of this herbicide combination in order to promote sustainable weed management.

The authors would like to express their gratitude to Department of Agronomy, Maharashi Markandeshwar (Deemed to be University) Mullana, Ambala, Haryana, India for providing the required facilities to conduct the current study and co-authors for completion of the manuscript.
 
Disclaimers
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.

The authors declare that there are no conflicts of interest regarding the publication of this article.