Effect of integrated weed management practices on growth, yield, quality and economics of green gram (Vigna radiata L.)

Introduction

Green gram (Vigna radiata L.), commonly known as mung bean, is one of the most important short-duration pulse crops of India, valued for its high protein content, nitrogen-fixing ability, and suitability for intensive and relay cropping systems. Despite these agronomic advantages, average national productivity of green gram remains well below its genetic potential, and uncontrolled weed growth is consistently identified as one of the principal biotic constraints limiting yield, particularly because the crop's short stature, slow initial growth and wide row spacing during the early vegetative phase leave it poorly competitive against fast-establishing weed flora (Hirani et al., 2024) [1] . Weeds compete with the crop for light, moisture, nutrients and space during the critical crop-weed competition period and, if left unmanaged, can depress green gram yield severely (Abbas et al., 2018) [2] . Traditional single-tactic weed management, whether a single pre-emergence herbicide application, a single post-emergence spray, or one round of manual weeding, often fails to provide season-long control because different weed species emerge and flush at different times through the crop's growth cycle, and a single intervention timed for one flush inevitably leaves later-emerging cohorts uncontrolled (Riemens et al., 2022) [3] . This has driven a broad shift in weed science towards Integrated Weed Management (IWM), which deliberately combines chemically and mechanistically diverse control tactics, pre-emergence and post-emergence herbicides with different modes of action, together with mechanical or manual intervention, to intercept se quential weed flushes and reduce reliance on any single tactic (Gonzalez-Andujar, 2023) [4] . Empirical support for the yield and profitability advantage of such integrated, multi-tactic regimens over single-tactic approaches has been documented across several crops, including maize (Aslam et al., 2024) [5] , (Bin Lukangila et al., 2024) [6] and (Simić et al., 2025) [7] , and cereal-legume rotations (Mishra et al., 2022) [8] , but comparable systematic, multi-parameter evidence specific to green gram, evaluating a range of pre-emergence, postemergence and mechanical/manual treatment combinations together for their effect on growth, yield, quality and economics under Indian agro-climatic conditions, remains comparatively limited (Das et al., 2024) [9] . Beyond the direct yield outcome, weed management decisions in green gram cultivation are also an economic decision for resourceconstrained smallholder growers, who must weigh the cost of herbicide inputs and labour against the value of the yield protected, making the profitability of any recommended weed management package, and not only its biological efficacy, of direct practical relevance (Kumar et al., 2024) [10] . Against this background, a two-year field investigation was undertaken with the specific objective of evaluating the effect of nine integrated pre-emergence, post-emergence and mechanical/manual weed management combinations, together with an unweeded control, on the growth attributes, yield attributes, seed yield, seed quality (protein content) and economics of cultivation of green gram cv. PDM 139, so as to identify the most effective and economically viable weed management package for the region.

Materials and methods

A field experiment was conducted over two consecutive kharif seasons (2025 and 2026) at the Agriculture Research Farm, Faculty of Agriculture, Rabindranath Tagore University (RNTU), Raisen, Madhya Pradesh, India. The experimental site lies in the central Narmada valley agro-climatic zone, with medium-to-deep black soil (Vertisol), clay-dominant texture, near-neutral pH (7.23) and low initial organic carbon (0.38%). The experiment was laid out in a Randomized Block Design (RBD) with ten treatments and three replications, giving thirty plots of 5.00 m x 4.00 m (gross = net plot size). Green gram variety PDM 139 was sown at a spacing of 30 cm (row to row) x 10 cm (plant to plant) with a seed rate of 13 kg/ha, on 20 June in both years, and fertilized uniformly at 20:40:20 kg N:P 2 O 5 :K 2 O per ha. The ten treatments evaluated (T 1 -T 10 ) are summarised in Table 1. Nine were integrated combinations of a pre-emergence herbicide (pendimethalin 1.0 kg/ha, flumioxazin 0.6 kg/ha or pyroxasulfone 0.75 kg/ha) with a post-emergence herbicide (quizalofop-P-ethyl 75 g/ha, propaquizafop 100 g/ha, imazamox 75 g/ha or halosulfuronmethyl 75 g/ha) and/or a mechanical hoeing or hand-weeding operation at 20-50 days after sowing (DAS); the tenth treatment (T 10 ) was an unweeded control. Herbicides were applied using a knapsack sprayer fitted with a flat-fan nozzle and 500 L/ha spray volume; preemergence herbicides were applied within 24-48 hours of sowing. Five randomly selected net plants (for growth and yield attributes) or 1 m 2 quadrats (for weed observations), excluding border rows, were sampled from each plot. Plant height, number of branches per plant, and plant dry weight were recorded at 20, 40, 60 DAS and at harvest. Yield attributes (pods/plant, seeds/pod, seed index), seed yield, stover yield, biological yield and harvest index were recorded at harvest.

Seed protein content was estimated by the micro-Kjeldahl method, using the standard nitrogen-to-protein conversion factor of 6.25 (AOAC, 2000) [11] . Cost of cultivation, gross returns (based on prevailing minimum support price of seed and local market rate of stover), net returns and benefit-cost (B:C) ratio were computed for each treatment. Data recorded in each year were subjected to analysis of variance (ANOVA) appropriate to a Randomized Block Design; where the error variances of the two years were found homogeneous, a pooled analysis of variance across years was carried out, following the procedure of (Panse and Sukhatme) [12] . Standard error of mean (SEm±), critical difference at 5 per cent level of significance (CD at P = 0.05) and coefficient of variation (CV%) were computed for each parameter, and pooled means (of two years) are reported in the results. 11.57 q/ha seed yield; 23.93% protein), followed by T 5 (10.60 q/ha) and T 3 (10.22 q/ha). The unweeded control (T 10 ) recorded the lowest values throughout (12.78 pods/plant; 7.37 q/ha seed yield; 20.35% protein), with T 8 representing a 66.5 per cent increase in seed yield over the control and T 9 a 57.0 per cent increase. Treatments relying on a single herbicide application without a complementary post-emergence or mechanical/manual follow-up (T 4 , T 6 and T 7 ) recorded seed yields of 7.50, 8.10 and 8.52 q/ha respectively, only marginally higher than the control.

Results

Discussion

The consistently superior growth and yield performance of T 8 (pendimethalin fb imazamox fb mechanical hoeing) and T 9 (pyroxasulfone fb halosulfuron-methyl fb hand weeding) over the remaining treatments is attributable to their three-tier structure: an effective preemergence herbicide controlling the first flush of germinating weeds, a post-emergence herbicide with a complementary mode of action controlling weeds escaping the pre-emergence layer, and a physical (mechanical or manual) intervention giving a final, non-chemical pass ahead of the crop's critical reproductive stages. This is consistent with reports that integrated, multi-tactic weed management outperforms single-tactic approaches by intercepting sequential weed flushes that a single intervention cannot (Riemens et al., 2022) [3] , (Aslam et al., 2024) [5] , (Bin Lukangila et al., 2024) [6] and (Simić et al., 2025) [7] , and mirrors similar findings in a rice-wheat-mungbean rotation, where integrated tillage and weed management improved crop stand and yield relative to conventional single-pass practice (Mishra et al., 2022) [8] . The marked improvement in growth parameters (plant height, branches/plant and dry weight) under T 8 and T 9 reflects reduced weed competition for light, moisture, space and nutrients during the crop's early vegetative phase, when green gram, with its short stature and slow initial growth, is otherwise poorly competitive against faster-establishing weed flora (Hirani et al., 2024) [1] . This growth advantage translated directly into yield attributes: the higher number of pods per plant, seeds per pod and seed index recorded under T 8 and T 9 indicate that effective early-season weed suppression allowed greater assimilate partitioning to reproductive sink development, consistent with the general principle that weed-free conditions during the critical cropweed competition window disproportionately benefit yield-determining, rather than purely vegetative, growth (Abbas et al., 2018) [2] . The higher seed protein content recorded under T 8 and T 9 is consistent with improved nitrogen availability to the crop under effective weed control, since weeds otherwise compete directly with the crop for available soil nitrogen; this nutritional dimension of weed management, beyond the yield outcome itself, adds to the case for integrated weed management as a quality-protecting, and not merely yield-protecting, practice. From an economic standpoint, T 8 's combination of the highest net returns with a moderate cost of cultivation, lower than T 1 , T 3 or T 5 , confirms that the most profitable treatment need not be the most input-intensive one; its advantage rests on effectively timed, complementary interventions rather than on additional spray or labour cost. This finding reinforces the importance of evaluating weed management packages on profitability, and not on biological efficacy alone, particularly for resource-constrained smallholder growers (Kumar et al., 2024) [10] and (Das et al., 2024) [9] . T 5 's favourable B:C ratio, achieved through a simpler two-herbicide regimen without a mechanical or hand-weeding pass, further suggests that where labour availability or machinery access is a constraint, a well-timed two-tactic herbicide sequence remains a practically attractive alternative to the top-ranked three-tactic treatments, consistent with reports that herbicide-only integrated regimens can approach the profitability of more input-intensive combinations under favourable conditions (Gonzalez-Andujar, 2023) [4] . Treatments relying on a single herbicide application without a complementary post-emergence or mechanical/manual follow-up (T 4 , T 6 and T 7 ) consistently ranked in the lower half of the treatment set, only marginally outperforming the unweeded control. This confirms that a single herbicide application, however well chosen, is generally insufficient for season-long weed management in green gram, and that combining it with a second, differently timed or differently acting intervention is necessary to realise the crop's full growth and yield potential.

Conclusion

Integrated weed management combining a pre-emergence herbicide with a post-emergence herbicide and a mechanical or manual followup operation significantly improved the growth, yield attributes, seed yield, seed quality and profitability of green gram over single-tactic weed management approaches. Among the ten treatments evaluated, pendimethalin 1.0 kg/ha (PE) followed by imazamox 75 g/ha (30 DAS) followed by mechanical hoeing (45 DAS) (T 8 ) recorded the highest seed yield (12.27 q/ha, 66.5% over control), seed protein content (24.09%), net return (Rs 95,467/ha) and benefit-cost ratio (3.10), and is recommended as the most effective and economical weed management package for green gram cultivation in the central Narmada valley agro-climatic zone of Madhya Pradesh and comparable regions. Pyroxasulfone 0.75 kg/ha (PE) followed by halosulfuronmethyl 75 g/ha (25 DAS) followed by hand weeding (45 DAS) (T 9 ) is recommended as an equally effective alternative where labour is preferred over mechanical operations.

Acknowledgement

AcknowledgementThe authors gratefully acknowledge the Faculty of Agriculture, Rabindranath Tagore University, Raisen, Madhya Pradesh, for providing the experimental farm, laboratory facilities and technical support necessary to carry out this investigation, and the Department of Agronomy for its continued academic guidance throughout the study.

References

1. Hirani AK, Korav S, Rajanna GA, Elansary HO, Mahmoud EA. Determination of critical crop-weed competition period: Impact on growth, nutrient dynamics and productivity of green gram (Vigna radiata). Heliyon. 2024;10(17):e36855.
2. Abbas T, Zahir ZA, Naveed M, Kremer RJ. Limitations of Existing Weed Control Practices Necessitate Development of Alternative Techniques Based on Biological Approaches. Adv Agron. 2018;147:239-280.
3. Riemens M, Sønderskov M, Moonen AC, Storkey J, Kudsk P. An Integrated Weed Management framework: A pan-European perspective. Eur J Agron. 2022;133:126443.
4. Gonzalez-Andujar JL. Integrated Weed Management: A Shift towards More Sustainable and Holistic Practices. Agronomy. 2023;13(10):2646.
5. Aslam MT, Maqbool R, Khan I, Chattha MU, Nawaz M, Shah AN et al. Efficacy of Different Pre and Post Emergence Herbicide Application on Late Sown Maize Crop Under Variable Planting Density. Int J Plant Prod. 2024;18(2):229-238.
6. Bin Lukangila MA, Tabu HI, Mugisho DB, Lubobo AK, Mbuyi Wa Mbombo AK. Evaluating the effects of manual hoeing and selective herbicides on maize (Zea mays L.) productivity and profitability. Heliyon. 2024;10(13):e33294.
7. Simić M, Dragičević V, Tataridas A, Krachunova T, Srdić J, Gazoulis I et al. Integrated effects of crop rotation and different herbicide rates in maize (Zea mays L.) production in central Serbia. Crop Prot. 2025;187:106913.
8. Mishra JS, Kumar R, Mondal S, Poonia SP, Rao KK, Dubey R et al. Tillage and crop establishment effects on weeds and productivity of a rice-wheat-mungbean rotation. Field Crops Res. 2022;284:108577.
9. Das TK, Behera B, Nath CP, Ghosh S, Sen S, Raj R et al. Herbicides use in crop production: An analysis of cost-benefit, non-target toxicities and environmental risks. Crop Prot. 2024;181:106691.
10. Kumar S, Rana SS, Abdelrahman K, Uddin MG, Fnais MS, Abioui M. Impact of conservation agriculture and weed management on carbon footprint, energy efficiency, and sustainability in maize-wheat cropping systems. Energy. 2024;309:133131.
11. AOAC. Official Methods of Analysis. Edn 17, Association of Official Analytical Chemists, Washington DC; 2000.
12. Panse VG, Sukhatme PV. Statistical Methods for Agricultural Workers. Edn 2, Indian Council of Agricultural Research, New Delhi; 1967.
13. How to cite this article: Kokate HG, Verma H, Namdeo B, Rajput B, Verma AK. Effect of integrated weed management practices on growth, yield, quality and economics of green gram (Vigna radiata L.). International Journal of Research in Agronomy. 2026;9(9):688-692. doi: https://www.doi.org/10.3354