Performance of Post-Emergence Herbicides for Weed Control and Soybean Yield in Thailand

Abstract

Soybean (Glycine max (L.) Merr.) is an essential legume crop in Thailand, valued for its high protein content and economic significance. However, weed competition can reduce yields by up to 82% if not managed effectively. This study evaluates the efficacy of post-emergence herbicides for weed control and their impact on soybean yield. A field experiment was conducted during the 2023 rainy and 2024/2025 dry seasons at Khon Kaen University using a split-plot design with four replications. Weed management treatments included hand weeding, an untreated control, and three herbicides, fluazifop-P-butyl + fomesafen, clethodim + fomesafen, and quizalofop-P-tefuryl + fomesafen, applied to two soybean varieties (Morkhor60 and CM60). Quizalofop-P-tefuryl + fomesafen was found to be the most effective herbicide, achieving 87.66% weed control efficiency (WCE) in the dry season and 72.43% in the rainy season. Hand weeding produced the highest yield (1324.00 kg ha⁻¹), followed by quizalofop-P-tefuryl + fomesafen (1148.90 kg ha⁻¹). Morkhor60 outperformed CM60 in yield and growth performance. These findings highlight the importance of selecting suitable herbicide treatments to optimize weed control and enhance soybean productivity under different seasonal conditions.

1. Introduction

Soybean (Glycine max (L.) Merr.) is a legume species with a long history of cultivation in Asia. Since the 1990s, soybeans have been widely recognized for their superior nutritional value and exceptionally high protein content compared to other legumes. With protein constituting 35–40% of mature raw seeds’ dry weight, soybeans stand out as one of the richest plant-based protein sources available [1]. This high protein quantity is complemented by excellent protein quality, with all essential amino acids present in balanced proportions, making soybean protein nutritionally comparable to many animal proteins [2]. The combination of high protein content and quality distinguishes soybeans from most other legumes, solidifying their position as a premium plant-based protein option.

In Thailand, increasing domestic consumption reflects growing consumer awareness of these nutritional benefits, particularly the value of soy protein as a healthy plant-based alternative [3]. Additional nutritional components include 20% lipids, 9% dietary fiber, and 8.5% moisture in mature raw seeds [1]. Despite high soybean consumption, Thailand’s domestic soybean production remains marginal, forecasted at only 52,000 metric tons in 2025/2026, with no significant expansion expected due to economic constraints and regulatory restrictions on transgenic varieties [4]. Consequently, Thailand depends heavily on imports, projected to reach 3.8 million metric tons in 2025/2026, primarily from Brazil and the United States. This import reliance underscores the need to enhance domestic soybean yield through improved crop management.

In an effort to enhance domestic soybean production in Thailand, several soybean varieties have been developed to achieve high productivity. Thailand’s Department of Agriculture launched its soybean breeding program in 1960, releasing 15 cultivars nationwide, with CM60 becoming the most widely adopted for its high yield, adaptability, and preferred consumption traits [5,6]. Meanwhile, Morkhor60, developed by Khon Kaen University, represents a promising alternative with potential for improved yield and stress tolerance [7]. Recent multi-environment trials have demonstrated that Morkhor60 consistently outperforms CM60 in yield, with a 12.8% and 7.5% yield advantage in 2022 and 2023, respectively [8]. Furthermore, Morkhor60 exhibits robust environmental adaptation and high phenotypic stability across diverse growing conditions, indicating superior genetic mechanisms for stress tolerance [9,10,11]. However, its response to herbicide treatments and weed competition remains under-investigated. The selection of CM60 and Morkhor60 for this study is based on their contrasting agronomic and physiological profiles, which are hypothesized to lead to differential responses to herbicide applications. For instance, while both are intermediate-maturity types, Morkhor60 exhibits more vigorous early growth and a longer period to flowering, indicating fundamental physiological differences that could influence herbicide interception, retention, and metabolism [9].

Weed competition is a major biotic constraint limiting soybean yield, with unmanaged weeds capable of causing yield losses of up to 82% through competition for water, nutrients, and light during early crop growth stages [12]. Effective weed control during the critical period, typically the first four weeks after emergence, is essential to safeguard yield potential [13]. Although manual weeding remains common, it is labor-intensive and increasingly impractical for large-scale production. Chemical weed control, particularly through post-emergence herbicides, offers a practical solution for managing a diverse range of weed species in soybean fields. However, the complexity of weed flora with varying susceptibility to herbicidal modes of action necessitates strategic combinations of herbicides to broaden the control spectrum and mitigate resistance risks. As reported by Aekrathok et al. [14], dominant weed species in Thailand’s sugarcane production, such as Brachiaria distachya, Dactyloctenium aegyptium, Praxelis clematidea, and Pennisetum polystachion, require species-specific control approaches, as adequate weed management is case-specific. This indicates that combining herbicides with complementary modes of action (e.g., enzyme inhibition and physiological disruption) simultaneously targets multiple biological processes in weeds, reducing the likelihood of resistance development [15,16]. This approach is exemplified by successful combinations, such as fluazifop-P-butyl plus fomesafen and flumiclorac-pentyl plus clethodim, which achieve high weed suppression and yield improvement [17,18,19,20]. Supporting this approach, a study by Jaipala et al. [21] demonstrated that in CM60, the application of fluazifop-P-butyl + fomesafen at a rate of 150 + 250 g ai ha⁻¹ is effective in controlling both narrow- and broad-leaved weeds, confirming the suitability and relevance of this herbicide treatment for research application, despite inducing slight to moderate phytotoxicity to soybeans after paddy rice irrigation. In Thailand, several post-emergence herbicides, including ACCase and PPO inhibitors, are authorized for use in soybeans [22].

However, the performance of these mixtures is not solely dependent on their biochemical properties; it is also significantly influenced by environmental conditions. For instance, Stewart et al. [23] demonstrated that excessive precipitation (>60% above the monthly average) shortly after application can decrease the effectiveness of certain herbicides, highlighting how seasonal rainfall patterns can lead to synergistic or antagonistic interactions and alter the required doses [24]. Therefore, understanding herbicide modes of action, their interactions, and their environmental dependencies is critical for selecting appropriate herbicides, diagnosing injury symptoms, and implementing sustainable management practices that support long-term crop productivity [15,16,25].

Despite these advances, a lack of comprehensive data remains on the effectiveness and crop safety of post-emergence herbicide mixtures on locally adapted Thai soybean varieties, including CM60 and Morkhor60, under the specific agro-climatic conditions of the region. This knowledge gap is critical, as varietal differences in canopy architecture, growth rates, and physiological maturity can significantly influence herbicide efficacy and crop tolerance. Therefore, this study aims to evaluate the efficacy of selected post-emergence herbicide mixtures on weed control and soybean yield across the CM60 and Morkhor60 varieties in Thailand. The results are expected to provide evidence-based recommendations for integrated weed management strategies that enhance sustainable soybean production and reduce reliance on imports by optimizing herbicide use tailored to local conditions.

2. Materials and Methods

2.1. Plant Material, Growing Conditions, and Experimental Design

A field experiment was conducted at the Agronomy Field, Faculty of Agriculture, Khon Kaen University, Thailand, during the rainy season (planting date: 29 July 2023) and the dry season (planting date: 26 October 2024) to evaluate the effectiveness of post-emergence herbicides for weed control in soybean cultivation. The experimental site was characterized by sandy loam soil, with sand, silt, and clay fractions of 73.36%, 20.87%, and 5.77% in the rainy season, and 73.90%, 17.40%, and 8.70% in the dry season, respectively. Soil pH values measured in a 1:1 soil-to-water suspension were 6.19 and 5.40 for the rainy and dry seasons, respectively. Weather data were collected from the closest meteorological station (Agricultural Weather Station, Land Crop Section, Faculty of Agriculture, Khon Kaen University), located approximately 100 m from the experimental site. The average rainfall and temperature during the rainy season of 2023 were recorded at 5.96 mm and 28.6 °C, respectively, while during the dry season of 2024/2025, the averages were 0.01 mm and 26.4 °C, as illustrated in Figure 1.

The experimental design was a split-plot in a randomized complete block design (RCBD) with four replications. The main plots were randomly assigned to weed management treatments, which included HW every two weeks, a weedy control (no weed control), and three post-emergence herbicide combinations: fluazifop-P-butyl plus fomesafen, clethodim plus fomesafen, and quizalofop-P-tefuryl plus fomesafen. Consequently, these herbicides were combined to provide a broader spectrum of weed control. Herbicides were applied at 22 days after planting (DAP) during the rainy season on 20 August 2023, and at 28 DAP in the dry season on 23 November 2024, which corresponded to the 3–5 leaf stage of weed growth. Detailed information on herbicide treatments and application rates is provided in

Table 1. Weed control treatments were used during the experiment.

Treatment	Class	Active Ingredient + Formulation	Trade Name/	Doses
			Company Name	(g a.i. ha−1) */
Hand weeding	-	-	-	-
Weedy	-	-	-	-
Fluazifop-P-butyl	A	15% w/v EC	Fluzilate/Syngenta	150
+	+	+	+	+
fomesafen	E	25% w/v SL	Ople/Sotus	250
Clethodim	A	24% w/v EC	Pantera/Sotus	150
+	+	+	+	+
fomesafen	E	25% w/v SL	Ople/Sotus	250
Quizalofop-P-tefuryl	A	4% w/v EC	Select/Arysta	75
+	+	+	+	+
fomesafen	E	25% w/v SL	Ople/Sotus	250

*^/ grams active ingredient per hectare; hand weeding = plots were maintained weed-free throughout the study period by manual hoeing, weedy = plots were left unweeded for the duration of the experiment. Class = the categorization of herbicide mechanisms of action as defined by the Weed Science Society of America (WSSA) and the Herbicide Resistance Action Committee (HRAC) (A: Inhibition of acetyl-CoA carboxylase (ACCase) enzyme and primarily effective against annual and perennial grass weeds; E: Inhibitors of protoporphyrinogen oxidase and mainly effective against annual broadleaf weeds). EC: emulsifiable concentrate; SL: soluble concentrate; and w/v: weight per volume.

. Herbicide applications were performed using a calibrated 15 L knapsack sprayer with a flooding fan nozzle (500 L ha⁻¹ spray volume) at a controlled speed for uniform plot coverage. Environmental conditions were continuously monitored during spraying operations using a UNI-T UT333 digital thermo-hygrometer and a glass soil thermometer (5 cm depth). During the 2023 rainy season applications, conditions averaged 32 ± 5 °C (air), 78 ± 17% RH, and 27 ± 0.3 °C (soil), while the 2024/2025 dry season recorded 35 ± 0.3 °C (air), 43 ± 0.4% RH, and 26 ± 2 °C (soil). All herbicide applications were uniformly conducted in the early morning under low wind conditions (<5 km h⁻¹) with thorough solution agitation to maintain suspension homogeneity and minimize drift.

Subplots consisted of two soybean cultivars, Morkhor60 (previously identified as breeding line 35*Sj-32) and CM60 (Chiang Mai 60), all of which were developed and obtained from the Plant Breeding Research Center for Sustainable Agriculture at Khon Kaen University. The cultivars were selected for their contrasting agronomic profiles and local adaptation. CM60 is characterized by white flowers, a seed protein content of 44%, and resistance to bacterial pustule and mosaic virus [5]. In contrast, Morkhor60 exhibits purple flowers, a protein content of 41%, and resistance to leaf pustule and powdery mildew [9]. Each subplot measured 25 m² (5 m × 5 m), with soybean seeds sown using a jab planter at a spacing of 0.25 m between plants within rows and 0.5 m between rows. Fertilizer was initially applied before planting at a rate of (9.38 kg N ha⁻¹, 4.09 kg P ha⁻¹, and 7.79 kg K ha⁻¹). A top dressing was applied 45 DAP, supplying (23.48 kg N ha⁻¹, 10.22 kg P ha⁻¹, and 19.45 kg K ha⁻¹). All other agronomic practices were implemented according to regional recommendations for soybean production.

2.2. Data Collection and Measurement

2.2.1. Weed Data

The impact of herbicide application on weed populations was evaluated by identifying weed species and measuring weed density and dry biomass at periodic intervals (0, 3, 7, 14, 28, and 42 DAA). Weed sampling at each assessment involved two 0.5 m² quadrats (1.0 × 0.5 m) per plot, systematically placed in inter-row and intra-row positions (total 1.0 m²). Sampling locations were shifted between assessments to prevent resampling and avoid confounding effects from prior harvests. Weeds were identified and counted by species, with dry biomass determined following a 72 h oven-drying period at 80 °C. WCE was evaluated in the dry weight of each of the broad-leaved, grassy, and total weeds. The percentage of WCE (%) was calculated following Hasan et al. [26] and Mani et al. [27]:

$$\mathrm{WCE} (\%) = {\displaystyle \frac{\mathrm{Weed} \mathrm{biomass} \mathrm{in} \mathrm{weedy} \mathrm{plots}-\mathrm{Weed} \mathrm{biomass} \mathrm{in} \mathrm{treated} \mathrm{plots}}{\mathrm{Weed} \mathrm{biomass} \mathrm{in} \mathrm{weedy} \mathrm{plots}}} \times 100$$

(1)

The Summed Dominance Ratio (SDR) for each weed species in untreated plots was calculated as a percentage according to Janiya and Moody [28], cited in Hasan et al. [26], using the following formula:

$$\mathrm{SDR} = {\displaystyle \frac{\mathrm{Relative} \mathrm{density} \left(\mathrm{RD}\right) + \mathrm{relative} \mathrm{dry} \mathrm{weight} \left(\mathrm{RDW}\right)}{2}}$$

(2)

where

$$\mathrm{RD}={\displaystyle \frac{\mathrm{Density} \mathrm{of} \mathrm{a} \mathrm{given} \mathrm{species}}{\mathrm{Total} \mathrm{density}}} \times 100$$

(3)

$$\mathrm{RDW}={\displaystyle \frac{\mathrm{Dry} \mathrm{weight} \mathrm{of} \mathrm{a} \mathrm{given} \mathrm{species}}{\mathrm{Total} \mathrm{dry} \mathrm{weight}}} \times 100$$

(4)

2.2.2. Herbicide Phytotoxicity and Soybean Growth

Soybean phytotoxicity was visually assessed on a scale from 0 to 100%, where 0 indicated no visible injury and 100 represented complete plant mortality and yield loss, following the EWRC rating scale [29]. Soybean phytotoxicity was evaluated at 1, 14, 28, and 42 DAA. The average height and node number of five plants were measured from ground level to the tip [30].

2.2.3. Yield Components and Soybean Yield

The number of pods on branches, pods on the main stem, and seeds per pod were collected from five plants, randomly selected in an area of 2 × 2 m, differing from the weeds’ random area. The weight of 100 seeds (g) was calculated, and the grain yields were converted into kg ha⁻¹ by calculation [31]. Yield loss percentage was computed as the relative decrease in grain production when measured against the hand-weeding control, calculated with the formula:

$$\mathrm{Grain} \mathrm{yield} \mathrm{loss} (\%) = {\displaystyle \frac{\mathrm{Yield} \mathrm{in} \mathrm{hand} \mathrm{weeding}-\mathrm{Yield} \mathrm{in} \mathrm{weed} \mathrm{control}}{\mathrm{Yield} \mathrm{in} \mathrm{hand} \mathrm{weeding}}} \times 100$$

(5)

2.3. Statistical Analysis

The data for all measured features (e.g., WCE, weed biomass, weed density, soybean injury, height, node number, yield, and yield components) were statistically analyzed with the Statistix^® version 10.0 (1985–2013) tool (Analytical Software, Tallahassee, FL, USA). Primarily, analysis of variance (ANOVA) was performed, and the Least Significant Difference (LSD) test (p ≤ 0.05) was used to test for differences in treatment means.

3. Results

3.1. Effect of Weed Control Treatment and Soybean Variety on Weeds Data

3.1.1. Weeds Species Associated with Soybean in the Experimental Site

Analysis of summed dominance ratios (SDR) in untreated plots revealed that the weed community composition was primarily shaped by five dominant species across both the rainy season of 2023 and the dry season of 2024/2025 (

Table 2. Summed dominance ratio (%) in untreated plots at 0, 14, 28, and 42 DAA during the rainy season of 2023 and the dry season of 2024/2025.

Weed Species	Family	Summed Dominance Ratio of Weed Species (%)
		Rainy Season of 2023				Dry Season of 2024/2025
		0 DAA 1/	14 DAA	28 DAA	42 DAA	0 DAA	14 DAA	28 DAA	42 DAA
Broad-Leaved
Oldenlandia corymbosa L.	Rubiaceae	0.71	3.63	19.94	14.34	0.90	4.98	8.81	9.28
Trianthema portulacastrum L.	Aizoaceae	54.95	29.53	5.11	1.20	5.53	3.59	2.90	0.66
Cleome rutidosperma	Cleomaceae	0.05	-	0.06	0.06	0.99	0.88	1.21	0.99
Praxelis clematidea R.M.King & H.Rob.	Asteraceae	0.08	0.24	-	0.35	0.79	1.34	1.16	2.38
Alternanthera sessilis	Amaranthaceae	0.43	0.18	0.00	0.93	0.59	0.80	1.15	0.70
Indigofera hirsuta L.	Fabaceae	- 2/	-	-	-	0.98	0.55	0.94	1.53
Lindernia ciliata (Colsm.) Pennell	Linderniaceae	-	-	3.08	5.59	0.00	-	0.76	0.40
Ludwigia hyssopifolia (G.Don) Exell	Onagraceae	-	-	-	-	-	-	0.08	-
Portulaca oleracea Linn.	Portulacaceae	-	-	-	-	-	-	0.08	-
Wrighia arborea (Dennst.) Mabb.	Apocynaceae	-	-	-	-	0.00	0.31	0.05	0.00
Amaranthus viridis L.	Amaranthaceae	-	0.00	0.00	0.00	0.00	0.06	0.00	0.00
Ipomoea pes-tigridis L.	Convolvulaceae	-	-	-	-	-	-	0.00	0.23
Sida cordifolia L.	Malvaceae	-	-	-	-	0.00	0.00	0.00	0.00
Borreria alata (Aubl.) DC.	Rubiaceae	-	-	-	-	-	0.16	0.00	0.00
Xanthium strumarium L.	Asteraceae	-	-	-	-	0.04	0.16	-	0.00
Ipomoea gracilis R.Br.	Convolvulaceae	-	-	-	-	0.00	-	-	0.06
Phyllanthus amarus Schum. & Thonn.	Phyllanthaceae	-	-	-	0.00	-	-	-	-
Grasses
Digitaria ciliaris (Retz.) Koeler	Poaceae	20.58	42.16	55.74	52.96	51.55	63.03	65.15	56.03
Dactyloctenium aegyptium (L.) P. B.	Poaceae	3.16	4.76	5.41	10.64	3.93	2.56	4.24	8.95
Cynodon dactylon (L.) Pers.	Poaceae	-	-	-	-	4.76	3.43	1.29	1.48
Eragrostis pectinacea (Michx.) Nees	Poaceae	-	-	-	-	-	0.16	0.34	-
Eleusine indica (L.) Gaertn.	Poaceae	-	-	0.49	-	0.73	0.00	0.18	0.93
Sedges
Cyperus rotundus L.	Cyperaceae	15.61	18.56	7.78	11.40	29.28	17.89	11.24	15.85
Cyperus esculentus	Cyperaceae	0.00	-	-	0.26	-	0.03	0.24	0.38
Cyperus difformis L.	Cyperaceae	-	-	-	-	-	0.09	0.15	0.19
Cyperus compressus L.	Cyperaceae	4.44	0.95	2.38	2.26	0.00	-	0.09	0.00
Cyperus iria L.	Cyperaceae	-	-	-	-	-	0.03	-	-
Total		100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00

^1/ Days after application, ^2/ No weed emergence was recorded during the experimental period.

). During the rainy season of 2023, Trianthema portulacastrum L., a broad-leaved weed, exhibited initial dominance, accounting for 54.95% of the SDR at 0 days after application (DAA). However, its proportion declined sharply over time, reaching only 1.20% at 42 DAA. In contrast, the grass species Digitaria ciliaris (Retz.) Koeler increased steadily in dominance, from 20.58% at 0 DAA to 55.74% at 28 DAA, and remained the most prevalent species at 42 DAA (52.96%). Cyperus rotundus L., a perennial sedge, maintained a considerable presence throughout the season, with SDR values ranging from 15.61% to 18.56% in the early stages and 7.78% to 11.40% in the later stages. Oldenlandia corymbosa L. and Dactyloctenium aegyptium (L.) P. B. also increased in relative abundance as the season progressed, particularly after 14 DAA.

In the dry season of 2024/2025, D. ciliaris was the overwhelmingly dominant species, comprising 51.55% of the SDR at 0 DAA and increasing to 65.15% at 28 DAA before slightly declining to 56.03% at 42 DAA. C. rotundus was the second most prevalent species at the start of the season (29.28% at 0 DAA), though its dominance gradually decreased over time. The broad-leaved weed O. corymbosa demonstrated a gradual increase, from 0.90% at 0 DAA to 9.28% at 42 DAA. D. aegyptium remained consistently present, with SDR values rising from 3.93% at 0 DAA to 8.95% at 42 DAA. T. portulacastrum while initially less dominant than in the rainy season, was still among the top five species, particularly at early time points.

Overall, these results indicate that D. ciliaris, T. portulacastrum, C. rotundus, O corymbosa, and D. aegyptium were the principal weed species affecting soybean fields in both seasons. The dynamic shifts in their dominance ratios highlight the necessity for integrated weed management strategies that target both grass and broad-leaved weeds, as well as persistent sedges. Such an approach is essential to reduce weed competition and effectively safeguard soybean yield in Thailand effectively.

3.1.2. Effect of Herbicide Treatments on Weed Control Efficiency (WCE)

Analysis of WCE across treatments revealed that hand weeding consistently provided complete weed suppression, maintaining 100% WCE at all evaluation intervals in both the rainy season of 2023 and dry season of 2024/2025 (

Table 3. Effects of post-emergence herbicide and soybean variety on weed control efficiency (%) in the rainy and dry seasons.

Variable	Weed Control Efficiency (%)
	Rainy Season of 2023				Dry Season of 2024/2025
	3 DAA 1/	7 DAA	14 DAA	28 DAA	3 DAA	7 DAA	14 DAA	28 DAA
Weed Control (WC)
Hand weeding	100.00 a 2/	100.00 a	100.00 a	100.00 a	100.00 a	100.00 a	100.00 a	100.00 a
Weedy	0.00 c	0.00 c	0.00 c	0.00 d	0.00 c	0.00 c	0.00 d	0.00 e
Fluazifop-P-butyl + Fomesafen	37.04 b	56.64 b	69.31 b	63.84 c	44.94 b	57.23 b	71.79 b	78.62 c
Clethodim + Fomesafen	37.74 b	52.14 b	67.64 b	73.21 b	42.03 b	47.71 b	54.68 c	70.21 d
Quizalofop-P-tefuryl + Fomesafen	35.40 b	56.99 b	64.92 b	72.43 bc	44.75 b	54.85 b	77.39 b	87.31 b
LSD 0.05	7.63	5.54	9.18	8.97	8.06	10.39	7.59	7.80
Variety (Var)
Morkhor60	43.59	58.42 a	64.54 a	65.26 a	42.31 b	56.17 a	59.48 b	67.51
CM60	40.49	47.88 b	56.20 b	58.54 b	50.37 a	47.74 b	62.07 a	66.96
LSD 0.05	3.76	5.55	6.14	3.19	5.63	4.16	2.04	3.30
F-tests
Weed Control (WC)	** 3/	**	**	**	**	**	**	**
Variety (Var)	ns	**	**	**	**	**	*	ns
WC × Var	**	* 4/	ns 5/	**	**	*	**	ns
CV WC	16.65	9.57	13.95	13.31	15.97	18.35	11.46	10.65
CV WC × Var	13.25	15.5	15.08	7.65	18.04	11.89	4.98	7.28

^1/ Days after application, ^2/ means sharing a common letter are not significantly different based on the LSD test (p < 0.05), ^3/ significant at p < 0.01, ^4/ significant at p < 0.05, and ^5/ not significant.

and Table S1, and Figure 2). All weed control treatments showed highly significant differences in efficiency (p < 0.01). In the dry season of 2024/2025, quizalofop-P-tefuryl + fomesafen demonstrated the highest numerical WCE, particularly at 28 DAA (87.31%), which was significantly greater than that of clethodim + fomesafen (70.21%) and fluazifop-P-butyl + fomesafen (78.62%) as indicated by the same letter notation (b) in the data (Figure 2b; Table 3). In the rainy season of 2023, however, quizalofop-P-tefuryl + fomesafen achieved a lower WCE of 72.43% at 28 DAA, which was not significantly different from clethodim + fomesafen (73.21%) but higher than fluazifop-P-butyl + fomesafen (63.84%) (Figure 2a).

The superior efficacy of quizalofop-P-tefuryl + fomesafen, particularly in the dry season, may be attributed to its enhanced activity against dominant grass weeds such as D. ciliaris, which accounted for over 65% of the SDR in untreated plots. The increasing WCE over time suggests that the combination provides both rapid initial suppression and sustained residual control, likely due to the complementary modes of action of the two herbicides. Seasonal differences in efficacy, especially the reduced performance during the rainy season, could be explained by increased rainfall leading to herbicide leaching or dilution, as well as more favorable conditions for weed emergence and growth.

Varietal differences were also significant, particularly in the early and mid-growth stages. Morkhor60 generally exhibited higher WCE than CM60 in the rainy season of 2023 (65.26% versus 58.54% at 28 DAA; p < 0.01), suggesting better weed suppression, possibly due to a denser canopy or more vigorous early growth (Figure 2c). In the dry season of 2024/2025, variety effects were less pronounced at 28 DAA, but CM60 showed slightly higher initial WCE at 3 DAA (Figure 2d). These results indicate that both the choice of herbicide and soybean variety, as well as seasonal conditions, are critical for optimizing weed management in soybean production systems in Northeast Thailand.

3.1.3. Effect of Herbicide Treatments on Dominant Weeds

The analysis of weed density and biomass data revealed significant variations in the efficacy of herbicide treatments against dominant weeds in soybean fields in Northeast Thailand (

Table 4. The effect of weed control treatment and variety of soybean against dominant weeds of soybean on weed density at 0, 14, 28, and 42 days after application (DAA).

Variable	Weed Density (Plants m−2)
	Broad-Leaved Weeds								Grassy Weeds								Sedge Weeds
	Trianthema portulacastrum Linn.				Oldenlandia corymbosa L.				Digitaria ciliaris (Retz.) Koel.				Dactyloctenium aegyptium (L.) P.B.				Cyperus rotundus Linn.
	0 DAA 1/	14 DAA	28 DAA	42 DAA	0 DAA	14 DAA	28 DAA	42 DAA	0 DAA	14 DAA	28 DAA	42 DAA	0 DAA	14 DAA	28 DAA	42 DAA	0 DAA	14 DAA	28 DAA	42 DAA
Weed Control	Rainy Season of 2023
Hand weeding	0.00 b 2/	0.00 c	0.00 c	0.00 c	0.00	0.00 b	0.00	0.00	0.00	0.00 b	0.00 b	0.00 b	0.00	0.00 b	0.00	0.00 c	0.00	0.00 c	0.00	0.00
Weedy	226.63 a	102.75 ab	9.13 bc	2.25 bc	7.38	18.88 a	47.38	32.00	86.13	104.88 a	54.50 a	44.75 a	7.25	8.88 a	5.88	5.50 abc	35.88	61.38 a	14.75	18.13
Fluazifop-P-butyl + Fomesafen	277.12 a	97.125 ab	37.63 a	11.13 ab	2.88	2.00 b	48.38	34.38	100.63	19.25 b	3.13 b	0.88 b	9.00	8.25 a	4.13	6.63 ab	22.25	13.00 bc	11.63	14.63
Clethodim + Fomesafen	238.75 a	88.88 b	28.00 ab	10.63 ab	3.50	5.25 b	27.88	19.13	58.63	19.38 b	6.75 b	7.38 b	4.63	3.88 ab	1.88	9.50 a	56.88	57.13 ab	50.00	40.88
Quizalofop-P-tefuryl + Fomesafen	200.25 a	135.38 a	43.88 a	18.38 a	6.13	6.38 b	39.25	77.00	104.13	23.25 b	5.25 b	2.88 b	9.25	2.50 ab	0.00	1.38 bc	59.75	22.38 abc	16.75	13.63
LSD 0.05	126.96	43.21	19.35	8.97	7.82	11.00	48.12	39.48	82.52	30.37	25.84	14.21	7.57	7.58	5.37	6.26	81.37	47.45	40.15	32.72
Variety
Morkhor60	204.00	78.85	23.50	9.25	4.10	7.65	34.90	27.05	79.45	29.20 b	13.60	10.55	7.45	4.35	3.10	6.20	30.40	30.85	17.30	21.35
CM60	173.10	90.80	23.95	7.70	3.85	5.35	30.25	37.95	60.35	37.50 a	14.25	11.80	4.60	5.05	1.65	3.00	39.50	30.70	19.95	13.55
LSD 0.05	46.11	29.14	13.08	3.62	5.50	7.27	21.73	26.45	41.30	6.86	8.19	4.28	6.12	3.81	3.38	3.59	39.69	9.21	18.51	9.38
Weed Control	Dry Season of 2024/2025
Hand weeding	0.00 b	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00 b	0.00 c	0.00 b	0.00 c	0.00	0.00	0.00 c	0.00 b	0.00 b	0.00 b	0.00 b	0.00 b
Weedy	20.25 a	12.00	7.38	1.13	5.88	22.00	30.13	26.88	194.88 a	182.38 a	100.75 a	53.38 a	8.13	4.38	3.13 bc	5.75 ab	64.13 a	49.50 a	25.00 a	31.75 a
Fluazifop-P-butyl + Fomesafen	17.50 a	6.13	2.75	2.38	9.63	7.63	16.50	13.63	85.63 b	13.50 bc	2.63 b	0.38 c	2.00	8.38	8.38 a	8.88 a	62.88 a	57.25 a	36.25 a	32.63 a
Clethodim + Fomesafen	21.75 a	9.38	6.63	2.00	3.38	13.00	13.63	8.63	65.13 b	56.50 b	16.75 b	12.63 b	6.25	7.38	5.25 ab	3.38 ab	85.13 a	61.88 a	43.00 a	23.13 a
Quizalofop-P-tefuryl + Fomesafen	17.38 a	12.00	6.63	3.50	6.88	9.50	14.88	27.25	86.38 b	9.63 bc	0.50 b	0.00 c	6.13	0.88	0.25 c	0.63 b	63.63 a	42.63 a	32.88 a	25.00 a
LSD 0.05	12.60	9.38	7.06	3.15	9.00	19.58	19.86	25.29	90.56	50.61	24.57	8.17	9.60	7.52	4.52	6.00	41.47	27.62	20.02	18.78
Variety
Morkhor60	14.65	10.20 a	3.60	2.55 a	6.05	14.25 a	21.10	18.90	97.90	74.65 a	31.75	13.15	5.75	0.15 b	2.10	2.40 b	60.70	39.95	26.50	19.50
CM60	16.10	5.60 b	5.75	1.05 b	4.25	6.60 b	8.95	11.65	74.90	30.15 b	16.50	13.40	3.25	8.25 a	4.70	5.05 a	49.60	44.55	28.35	25.50
LSD 0.05	8.38	3.76	3.95	1.32	6.49	6.53	14.92	16.97	65.76	29.76	24.00	7.95	7.95	4.06	4.36	6.00	21.91	16.69	9.35	9.68

The data were measured per square meter (1 m²). ^1/ Days after application, and ^2/ means sharing a common letter are not significantly different based on the LSD test (p < 0.05).

,

Table 5. The effect of weed control treatment and variety of soybean against dominant weeds of soybean on weed biomass at 0, 14, 28, and 42 days after application (DAA).

Variable	Weed Biomass (g m−2)
	Broad-Leaved Weeds								Grassy Weeds								Sedge Weeds
	Trianthema portulacastrum Linn.				Oldenlandia corymbosa L.				Digitaria ciliaris (Retz.) Koel.				Dactyloctenium aegyptium (L.) P.B.				Cyperus rotundus Linn.
	0 DAA 1/	14 DAA	28 DAA	42 DAA	0 DAA	14 DAA	28 DAA	42 DAA	0 DAA	14 DAA	28 DAA	42 DAA	0 DAA	14 DAA	28 DAA	42 DAA	0 DAA	14 DAA	28 DAA	42 DAA
Weed Control	Rainy Season of 2023
Hand weeding	0.00	0.00 c 2/	0.00 c	0.00 c	0.00	0.00 b	0.00 b	0.00 c	0.00	0.00 b	0.00 b	0.00 b	0.00	0.00 b	0.00 b	0.00	0.00	0.00	0.00	0.00
Weedy	27.89	35.60 a	8.18 bc	1.46 c	0.04	0.80 a	14.20 a	6.58 bc	8.39	70.11 a	176.85 a	110.23 a	3.02	10.48 a	19.56 a	27.00	6.32	27.25	8.98	7.35
Fluazifop-P-butyl + Fomesafen	35.39	25.39 ab	36.39 a	5.59 bc	0.06	0.15 b	13.33 a	14.16 ab	11.06	9.29 b	7.40 b	0.73 b	0.99	4.10 b	13.94 a	26.13	2.22	3.63	10.29	9.38
Clethodim + Fomesafen	31.76	16.68 b	20.60 ab	10.44 ab	0.03	0.18 b	5.00 ab	7.54 bc	4.19	7.35 b	23.49 b	15.83 b	0.55	2.41 b	5.44 b	37.69	7.10	23.41	29.24	41.75
Quizalofop-P-tefuryl + Fomesafen	21.14	33.40 a	28.00 a	13.06 a	0.05	0.20 b	17.60 a	24.13 a	8.58	8.05 b	9.80 b	1.35 b	0.87	0.83 b	0.00 b	0.36	1.97	6.28	15.19	7.76
LSD 0.05	30.83	14.05	17.14	6.72	0.09	0.45	12.80	12.65	7.80	23.50	42.52	25.63	3.58	5.84	7.17	36.56	13.68	30.65	23.27	30.21
Variety
Morkhor60	29.12	22.01	18.04	6.36	0.04	0.34	9.78	9.26	5.57	17.29 b	47.11	22.38	0.76	4.12	8.93	23.45	4.47	12.90	11.55	12.10
CM60	17.35	22.42	19.23	5.86	0.03	0.19	10.27	11.70	7.32	20.63 a	39.91	28.88	1.41	3.01	6.65	13.02	2.58	11.33	13.93	14.40
LSD 0.05	18.35	7.71	7.28	3.98	0.06	0.31	9.64	8.57	5.31	2.97	25.39	9.51	2.75	3.66	11.79	24.68	8.40	7.90	8.89	11.45
Weed Control	Dry Season of 2024/2025
Hand weeding	0.00 b	0.00	0.00	0.00	0.00	0.00	0.00	0.00 b	0.00 b	0.00 c	0.00 b	0.00 c	0.00	0.00	0.00 b	0.00	0.00	0.00 c	0.00	0.00 c
Weedy	4.51 a	5.12	2.87	0.25	0.49	1.50	3.67	5.20 a	77.30 a	124.28 a	207.93 a	110.44 a	10.43	6.78	17.44 a	19.55	70.88	27.14 b	20.68	15.84 a
Fluazifop-P-butyl + Fomesafen	4.64 a	2.15	1.62	0.51	1.68	0.26	1.78	1.41 b	59.34 a	10.14 bc	7.57 b	0.06 c	4.86	3.78	17.93 a	20.27	78.58	32.92 ab	23.99	10.23 ab
Clethodim + Fomesafen	3.34 a	3.13	2.84	0.51	0.15	0.46	0.98	1.11 b	41.49 ab	38.25 b	26.85 b	20.19 b	8.11	1.59	8.00 ab	13.54	101.33	50.90 a	31.69	8.46 b
Quizalofop-P-tefuryl + Fomesafen	3.21 a	2.00	2.71	1.18	0.42	0.47	2.47	3.21 ab	38.88 ab	6.36 bc	0.27 b	0.00 c	3.73	0.89	0.12 b	0.78	74.74	27.14 b	22.69	8.13 b
LSD 0.05	3.21	3.92	3.54	0.91	1.18	1.39	2.45	3.38	46.21	34.42	48.00	17.02	10.10	7.82	10.59	18.57	70.86	21.44	21.07	6.51
Variety
Morkhor60	2.38	2.57	1.08	0.54	0.82	0.71 a	2.22	1.91	50.02	40.12	52.70	24.55	5.18	0.26 b	8.22	10.20	69.47	24.05	14.88 b	5.74 b
CM60	3.90	2.39	2.93	0.44	0.28	0.36 b	1.34	2.46	36.78	31.49	44.35	27.73	5.68	4.95 a	9.17	11.45	60.74	31.19	24.74 a	11.32 a
LSD 0.05	2.92	1.84	2.43	0.45	0.80	0.33	1.67	2.86	30.04	15.82	29.35	13.69	9.31	3.52	12.49	11.25	26.18	15.88	7.60	5.27

The data were measured per square meter (1 m²). ^1/ Days after application, and ^2/ means sharing a common letter are not significantly different based on the LSD test (p < 0.05).

, Tables S3 and S4). During the rainy season of 2023, untreated (weedy) plots exhibited high initial densities of T. portulacastrum (226.63 plants m⁻²) and D. ciliaris (86.13 plants m⁻²), which declined naturally over time but remained substantial at 42 DAA. In contrast, herbicide treatments demonstrated marked reductions in weed pressure. For instance, fluazifop-P-butyl + fomesafen reduced T. portulacastrum density to 11.13 plants m⁻² and biomass to 5.59 g m⁻² by 42 DAA, correlating with a WCE of 63.84–69.31% during the same period. Similarly, quizalofop-P-tefuryl + fomesafen showed superior efficacy in suppressing D. ciliaris, reducing its density to 2.88 plants m⁻² and biomass to 1.35 g m⁻² by 42 DAA, aligning with its high WCE of 87.31% in the dry season.

The summed dominance ratio (SDR) highlighted D. ciliaris as the most persistent species in untreated plots, dominating 55.7–65.2% of the weed community by 28 DAA across seasons. Herbicides targeting this species, such as quizalofop-P-tefuryl + fomesafen, effectively countered its dominance, underscoring their strategic value. Hand weeding achieved 100% WCE but was labor-intensive, whereas herbicide combinations like clethodim + fomesafen and fluazifop-P-butyl + fomesafen provided statistically significant (p < 0.05) and practical alternatives, reducing both the density and biomass of key weeds. Varietal differences were minimal, though Morkhor60 exhibited marginally better weed suppression in some treatments. These findings emphasize the critical role of tailored herbicide applications in managing dominant weeds, particularly D. ciliaris and T. portulacastrum, to enhance soybean productivity in the region.

3.2. Effect of Weed Control Treatment and Soybean Variety on Soybean Data

3.2.1. Phytotoxicity Effect of Post-Emergence Herbicides on Soybean

The analysis of phytotoxicity data revealed significant trade-offs between herbicide efficacy and crop safety in soybean fields in Northeast Thailand. During the dry season of 2024/2025, the combination of fluazifop-P-butyl and fomesafen induced considerable phytotoxicity, peaking at 60.40% at 1 DAA (European Weed Research Council/EWRC score 7 “increasing severity of damage”), and subsequently diminishing to 3.62% by 42 DAA (EWRC score 4 “substantial chlorosis and or stunting; most effects probably reversible”; Figure 3b; Table S2). This decline in phytotoxicity corresponded with its high WCE, which ranged from 71.79% to 78.62% (Figure 2b) against predominant grasses like D. ciliaris. This species was particularly prevalent in untreated plots, showing a species dominance ratio of 51.55% to 65.15% across seasons. It was effectively controlled by the combination of quizalofop-P-tefuryl and fomesafen, which exhibited lower phytotoxicity of 35.19% at 1 DAA (EWRC score 6 “increasing severity”) and achieved the highest WCE of 87.31% at 28 DAA, ultimately reducing D. ciliaris density to zero plants per square meter by 42 DAA.

Meanwhile, the combination of clethodim and fomesafen displayed minimal phytotoxicity during the dry season of 2024/2025 (9.94% at 1 DAA, EWRC score 4; Figure 3b) and demonstrated a moderate WCE of 54.68% to 70.21% (Table 3). However, it was less effective against sedges such as C. rotundus, which maintained a dominant SDR of 15.85% in untreated areas. Although hand weeding resulted in zero phytotoxicity (0%, EWRC score 1 “indicating no effect”), it was impractical for large-scale implementation. Significant varietal differences in phytotoxicity response were observed. In the dry season, Morkhor60 and CM60 showed high initial phytotoxicity (22.96% versus 19.25% at 1 DAA, approaching EWRC score 5 “strong chlorosis/stunting; thinning of stand”; Figure 3d). However, Morkhor60 demonstrated better recovery by 42 DAA (0.62%, EWRC score 2 “very slight effects; some stunting and yellowing just visible”) versus CM60 (1.84%, EWRC score 3 “slight effects; stunting and yellowing; effects reversible”). In the rainy season, both varieties showed milder responses, with Morkhor60 (6.35% at 1 DAA, EWRC score 4; Figure 3c) and CM60 (4.55%, EWRC score 4) showing reversible damage. Both varieties achieved complete recovery (EWRC score 2) by 42 DAA (0.28%). These results indicate that while Morkhor60 exhibits greater initial herbicide sensitivity (particularly in dry conditions), its superior recovery capacity suggests better tolerance mechanisms compared to CM60.

These findings highlight the necessity for balancing herbicide efficacy with the risks of phytotoxicity. This balance is particularly crucial in post-emergence applications aimed at controlling dominant weeds like D. ciliaris. While transient phytotoxicity at EWRC score 5–6 (10–25% damage, indicating strong but reversible effects) may momentarily impact crop esthetics. Conversely, treatments exhibiting EWRC score 1–4 (below 10% phytotoxicity, defined as slight to substantial but non-damaging and reversible) present a viable compromise for sustainable weed management. Seasonal considerations are essential. High-efficacy herbicides, such as quizalofop-P-tefuryl + fomesafen, are crucial during the dry season to combat intense weed pressure. In contrast, moderate alternatives with minimal phytotoxicity, such as clethodim + fomesafen, are adequate in the rainy season. This strategic approach enhances soybean productivity while protecting crop health in the variable agroecological conditions of Northeast Thailand.

3.2.2. Effect of Post-Emergence Herbicides on Soybean Growth

The analysis of soybean plant height and node development demonstrated significant variations influenced by WCE and phytotoxicity levels across different treatments and varieties. During the rainy season of 2023, hand weeding (HW) consistently resulted in the tallest plants, reaching 53.75 cm at 28 DAA, significantly exceeding the heights of other treatments (Figure 4a). This outcome corresponds with its 100% WCE (Figure 2a), which effectively eliminates weed competition, thus enhancing resource availability for soybean growth. In contrast, herbicide-based treatments, such as fluazifop-P-butyl + fomesafen (FF), showed lower plant heights of 48.83 cm at 28 DAA (Figure 4a) despite achieving moderate WCE (63.84–69.31%). This disparity is likely attributable to phytotoxicity stress (14.63% at 1 DAA; Figure 3a), which can hinder physiological processes. Additionally, the weedy control, which recorded 0% WCE, resulted in stunted growth (48.05 cm at 28 DAA; Figure 4a) due to severe weed competition (Table 3).

Node development exhibited similar trends across the board. HW achieved the highest node count, recording 4.15 nodes at 0 DAA (Figure 5a), a result of continuous early growth in weed-free conditions. In contrast, FF and the combination of quizalofop-P-tefuryl and fomesafen (QF) displayed lower node counts of 3.85 and 3.65 nodes at 0 DAA, respectively, correlating with their levels of phytotoxicity (14.63% and 7.75% at 1 DAA; Figure 3a). The 2024/2025 dry season data revealed different response patterns. Statistical analysis showed no significant differences (p > 0.05) in plant height between treatments at any growth stage, with final heights ranging from 30.38 cm (FF) to 33.775 cm (weedy control) at 42 DAA (Figure 4b). However, the node number at 28 DAA showed significant treatment effects (p < 0.05). HW and QF treatments produced the highest node counts (8.97 and 8.80 nodes, respectively), while the weedy control showed significantly lower node production (7.66 nodes; Figure 5b). This response differential suggests that while herbicide treatments did not significantly affect final plant height during the dry season, their weed control efficiency (particularly QF’s 87.31% WCE at 28 DAA; Figure 2b) supported better node development compared to weedy conditions. The high phytotoxicity observed with FF treatment (60.40% at 1 DAA; Figure 3b) did not result in significant growth reductions, possibly indicating soybean recovery capacity or seasonal adaptation mechanisms.

Varietal differences significantly influenced growth outcomes. Morkhor60 outperformed CM60 in plant height (64.47 cm compared to 53.20 cm at 42 DAA; Figure 4c), consistent with its inherent genetic vigor and resilience to suboptimal conditions. However, the number of nodes at 42 DAA during the rainy season 2023 did not differ significantly between the two varieties (10.42 for Morkhor60 versus 10.34 for CM60; Figure 5c), as indicated by the F-test (p > 0.05). This suggests that while Morkhor60 exhibits superior vegetative growth, nodal development in the later stages may be less sensitive to varietal differences under rainy season conditions. In contrast, CM60’s consistently lower height and occasional reductions in node number (e.g., 7.83 vs. 9.20 at 28 DAA in 2024/2025; Figure 5d) may reflect its diminished adaptability to residual weed pressure or herbicide-induced stress.

In summary, optimal plant height and node development were driven by high WCE (eliminating resource competition) and minimal phytotoxicity, as exemplified by HW. Herbicide efficacy was counterbalanced by phytotoxic effects, underscoring the need for balanced weed management strategies. These findings align with the weed density and biomass data (Table 4 and Table 5), where HW’s zero weed presence directly supported robust soybean growth.

3.3. Effect of Weed Control Treatment and Soybean Variety on Yield and Yield Components

The results demonstrated significant variations in soybean yield and yield components across different weed control treatments, which can be attributed to variations in WCE, phytotoxicity, plant height, and nodal development. HW and quizalofop-P-tefuryl + fomesafen (QF) consistently achieved the highest grain yields in both seasons (1324 kg ha⁻¹ and 745.34 kg ha⁻¹ in the rainy and dry seasons, respectively;

Table 6. Effect of weed control treatments and variety of soybean on yield and yield components.

Variable		Yield and Yield Component of Soybean
	Rainy Season of 2023						Dry Season of 2024/2025
	Pods on Branches	Pods on Main Stem	Seed Number/Pod	Weight of 100 Seeds (g)	Grain Yield (kg ha−1)	Grain Yield Loss (%)	Pods on Branches	Pods on Main Stem	Seed Number/Pod	Weight of 100 Seeds (g)	Grain Yield (kg ha−1)	Grain Yield Loss (%)
Weed Control (WC)
Hand weeding	20.65 a 1/	16.76 bc	2.35	16.91	1324.00 a	0.00	6.85 b	12.23 bc	2.23	16.57	732.72 ab	0.00
Weedy	8.14 c	14.44 d	2.13	16.06	354.90 d	73.20	3.96 d	11.18 c	2.21	15.91	579.72 c	20.88
Fluazifop-P-butyl + Fomesafen	19.68 ab	19.43 a	2.33	15.36	969.70 bc	26.76	5.14 c	16.87 a	2.17	16.38	706.27 ab	3.61
Clethodim + Fomesafen	18.21 b	17.84 ab	2.30	16.40	895.20 c	32.39	6.86 b	12.92 bc	2.24	16.01	654.47 bc	10.68
Quizalofop-P-tefuryl + Fomesafen	21.55 a	14.90 cd	2.33	16.89	1148.90 ab	13.22	9.41 a	13.90 b	2.18	16.41	745.34 a	−1.72
LSD 0.05	2.00	2.14	0.20	1.29	194.71	ND	1.03	2.09	0.13	1.06	87.94	ND
Variety (Var)
Morkhor60	22.45 a	18.78 a	2.38 a	15.34 b	1128.10 a	ND 5/	7.63 a	12.20 b	2.31 a	15.82 b	720.53 a	ND
CM60	12.84 b	14.57 b	2.20 b	17.32 a	749.00 b	ND	5.25 b	14.64 a	2.10 b	16.70 a	646.87 b	ND
LSD 0.05	2.24	1.73	0.08	0.74	74.34	ND	0.60	1.22	0.06	0.76	60.55	ND
F-tests
Weed Control (WC)	** 2/	**	ns 4/	ns	**	ND	**	**	ns	ns	**	ND
Variety (Var)	**	**	**	**	**	ND	**	**	**	* 3/	*	ND
WC × Var	**	ns	**	ns	ns	ND	**	**	ns	ns	ns	ND
CV WC	10.45	11.81	8.21	7.28	19.04	ND	14.63	14.28	5.44	6.01	11.81	ND
CV WC × Var	18.85	15.35	5.05	6.69	11.75	ND	13.79	13.49	4.39	6.90	13.14	ND

^1/ means sharing a common letter are not significantly different based on the LSD test (p < 0.05), ^2/ significant at p < 0.01, ^3/ significant at p < 0.05, ^4/ not significant, and ^5/ ND = not determined.

and Table S5). These treatments also exhibited superior WCE values (72.43–87.31% for QF; Figure 2), indicating effective weed suppression that minimized resource competition, thereby enhancing pod formation during the rainy season 2023 (21.55 pods on branches for QF) and seed weight (16.89 g/100 seeds). The high WCE of QF correlated with its moderate phytotoxicity (7.75% at 1 DAA in the rainy season; Figure 3a), which was significantly lower than fluazifop-P-butyl + fomesafen (FF; 14.63% phytotoxicity). Reduced phytotoxicity likely preserved photosynthetic capacity, supporting sustained growth and yield.

Plant height and nodal development trends partially aligned with yield outcomes. While HW resulted in numerically taller plants (61.125 cm at 42 DAA; Figure 4a) and higher nodal counts (10.45 nodes at 42 DAA; Figure 5a) during the rainy season 2023, statistical analysis revealed no significant differences among treatments for these traits (F-tests: p > 0.05 for WC at 42 DAA). This suggests that environmental factors or minimal weed interference during this stage may have homogenized vegetative growth. In contrast, during the dry season (2024/2025), Quizalofop-P-tefuryl + Fomesafen (QF)-treated plants exhibited robust growth (31.24 cm height; Figure 4b) and maintained high nodal numbers (9.15 nodes; Figure 5b), which were statistically significant at 28 DAA. These structural advantages likely contributed to their superior “pods on branches” and “seed number/pod” (Table 6), underscoring seasonal variability in treatment efficacy.

In contrast, weedy plots showed the lowest yields, with a 73.20% yield loss in the rainy season (354.90 kg ha⁻¹ versus 1324.00 kg ha⁻¹) and 20.88% in the dry season (579.72 kg ha⁻¹ versus 732.72 kg ha⁻¹; Table 6). This drastic reduction was attributed to minimal WCE (0%) and severe weed competition, which stunted plant height (48.05 cm at 28 DAA; Figure 4a) and reduced nodal development (8.16 nodes; Figure 5a). Dominant weeds like T. portulacastrum and D. ciliaris (Table 4) outcompeted soybean plants for resources, severely limiting photosynthetic efficiency and pod formation. Similarly, FF, despite moderate WCE (63.84–78.62%), caused high phytotoxicity (60.40% at 1 DAA in the dry season), likely impairing metabolic processes and limiting yield potential (706.27 kg ha⁻¹).

Varietal differences also influenced growth and yield parameters. Morkhor60 outperformed CM60 in yield (720.53 versus 646.87 kg ha⁻¹ in the dry season) due to its taller stature (35.50 cm versus 29.14 cm; Figure 4d) and greater nodal counts (9.67 versus 7.91 nodes; Figure 5d), which enhanced pod-bearing capacity. In summary, effective weed control (high WCE), coupled with minimal phytotoxicity and optimal plant architecture (height and nodes), synergistically improved yield components. These interlinked parameters underscore the importance of selecting treatments that balance weed suppression with crop safety to maximize soybean productivity.

4. Discussion

The interplay of weed dominance, herbicide efficacy, environmental factors, and varietal adaptability critically shapes soybean productivity, as evidenced by this study. Effective weed management begins with identifying dominant species, which compete aggressively for resources, reducing crop quality, and escalating production costs [32,33]. Among the 27 weed species observed, D. ciliaris (grassy weed), T. portulacastrum (broadleaf), and C. rotundus (sedge) dominated across seasons (Table 2). Their suppression required tailored herbicide strategies, as efficacy varied by weed type and season. Quizalofop-P-tefuryl + fomesafen (QF) emerged as the most effective treatment against grassy weeds (D. ciliaris), reducing their density to near-zero levels (Table 4 and Table 5). This aligns with its mode of action: inhibition of acetyl-CoA carboxylase, which disrupts fatty acid synthesis in grasses while sparing broadleaf crops like soybean [34]. In contrast, fluazifop-P-butyl + fomesafen (FF) and clethodim + fomesafen showed moderate efficacy against broadleaf weeds (T. portulacastrum), reflecting their limited spectrum of control. In addition, a previous report by Jaipala et al. [21] indicated that fluazifop-P-butyl + fomesafen at a rate of 150 + 250 g ai ha⁻¹ applied to CM60 after paddy rice irrigated displayed effective in controlling both narrow- and broad-leaved weeds for up to 45 days after spraying.

Seasonal dynamics profoundly influenced weed management efficacy. QF achieved a higher WCE in the dry season (87.66%) compared to the rainy season (72.43%), attributable to reduced weed vigor under water stress (Figure 2). This observation aligns with the established principle that moisture scarcity compromises weed growth, thereby enhancing their susceptibility to herbicides. Supporting this finding, research on ACCase-inhibiting herbicides in Brazilian soybean production systems has demonstrated their potential for high efficacy under water-limited conditions, although performance remains highly dependent on specific herbicide and weed species interactions [35]. Conversely, the reduced efficacy in the rainy season can be linked to excessive rainfall, which can leach herbicides and reduce their bioavailability [36,37], This phenomenon is corroborated by Stewart et al. [23], who documented that precipitation exceeding 60% above the monthly average within 12 days post-application significantly diminished control effectiveness for various post-emergence herbicides in soybean.

The population density of C. rotundus, for instance, declined by approximately 50% in the dry season (Table 4), likely reflecting its particular sensitivity to soil moisture deficits. Conversely, FF’s phytotoxicity spiked to 66.27% in the dry season (Figure 3b), exacerbated by drought stress, which intensifies herbicide injury by impairing soybean detoxification pathways [38,39]. This observed phytotoxicity aligns with the findings of Jaipala et al. [21], who reported that fluazifop-P-butyl + fomesafen at a rate of 150 + 250 g ai ha⁻¹ showed slight to moderate phytotoxicity to CM60 after paddy rice irrigation. This is consistent with findings that environmental stress can amplify phytotoxicity, as reviewed by Cieslik et al. [40], who noted that factors like humidity and temperature significantly alter herbicide uptake and plant response. Dry conditions also reduce stomatal conductance and nutrient translocation, amplifying the phytotoxic effects [41,42]. These findings underscore the necessity of aligning herbicide applications with seasonal environmental conditions to strike a balance between efficacy and crop safety.

Soybean growth and yield components were directly linked to WCE and varietal resilience. HW, with 100% WCE, maximized resource allocation, producing the tallest plants (61.125 cm at 42 DAA; Figure 4a) and highest nodal counts (10.45 nodes; Figure 5a), which correlated with superior pod formation (20.65 pods/branch) and yield (1324 kg ha⁻¹; Table 6). QF mirrored this trend, achieving 745.34 kg ha⁻¹ in the dry season despite lower absolute plant height (31.238 cm), as its high nodal plasticity (9.15 nodes; Figure 5b) facilitated pod retention. In contrast, weedy plots suffered a 73.20% yield loss in the rainy season (354.90 kg ha⁻¹ versus HW) due to unchecked competition from T. portulacastrum and D. ciliaris, which reduced plant height (48.05 cm; Figure 4a) and nodal development (8.16 nodes; Figure 5a).

Varietal differences significantly influenced agronomic performance and herbicide response. Morkhor60 outperformed CM60 in yield (720.53 versus 646.87 kg ha⁻¹; Table 6), which was attributed to its taller stature (35.50 cm versus 29.14 cm; Figure 4d) and higher nodal abundance (9.67 versus 7.91 nodes; Figure 5d). These contrasting morphological and developmental traits are characteristic of the two genotypes; Morkhor60 exhibits a longer period to 50% flowering (41.5 days) and a more vigorous early growth structure compared to the relatively compact CM60 (35.8 days to 50% flowering) [9]. In Morkhor60, these traits facilitated accelerated canopy closure, enhancing light interception and vascular efficiency, which is critical for optimizing the partitioning of photoassimilates to pods [43,44]. The rapid canopy closure in Morkhor60 likely suppressed weed germination and establishment by limiting light penetration to the soil surface, as evidenced by its superior WCE compared to CM60. This aligns with Rezvani et al. [45], who demonstrated significant varietal differences in weed suppression ability among soybean genotypes, linking competitiveness to traits like biomass accumulation and canopy architecture. Furthermore, these inherent physiological distinctions are critical as they can directly influence herbicide interception, retention, and metabolism [9], potentially explaining the observed varietal differences in responses to herbicide treatments. The significant genotype-by-environment interactions documented for these varieties [9] underscore that genetic makeup profoundly shapes performance under various stresses, including the chemical stress imposed by post-emergence herbicides.

The genotypic superiority of Morkhor60 reflects inherent stress tolerance, aligning with the framework proposed by Rose et al. [46], which identifies the canopy closure rate as a key determinant of soybean competitiveness against weeds. Furthermore, Norsworthy and Shipe [47] argue that crop competitiveness arises from complex trait combinations, such as height, nodal development, and light-use efficiency, traits prominently expressed in Morkhor60. These findings underscore the role of genotype-environment interactions in soybean adaptability [7], where Morkhor60’s genetic makeup likely integrates traits that synergistically enhance productivity and weed suppression [8]. As Vollmann et al. [48] emphasize, breeding for such trait combinations is critical to advancing sustainable weed management in soybean cultivation.

Phytotoxicity served as a key determinant of yield stability. QF’s transient phytotoxicity (7.75% at 1 DAA) minimally disrupted photosynthesis, whereas FF’s persistent damage (60.40% at 1 DAA in the dry season) impaired chloroplast function, reducing carbon assimilation and yield (706.27 kg ha⁻¹). Drought exacerbated this by limiting metabolic recovery, as reported in studies where herbicides like lactofen caused severe injury under iron-deficient, dry conditions [39].

5. Conclusions

This study demonstrates that quizalofop-P-tefuryl + fomesafen is the most efficient herbicide for controlling dominant weeds, particularly grassy weeds (Digitaria ciliaris (Retz.) Koel.), with minimal adverse effects on soybean growth. While HW remains the most effective weed control method, its labor-intensive nature makes chemical control a viable alternative. Morkhor60 outperformed CM60 in yield and growth attributes, indicating its potential as a preferred cultivar. However, the observed phytotoxicity of herbicides and lower yields compared to the rainy season suggest that farmers should carefully balance weed control benefits with potential crop stress, particularly when selecting dry-season herbicide applications.

It is crucial to note that these conclusions are derived from a study conducted under the specific agro-climatic conditions of Northeast Thailand. To enhance the applicability and robustness of these findings, future research should prioritize: (1) multi-location trials to validate herbicide performance across diverse soil types and microclimates; (2) long-term monitoring to assess the evolution of resistance in target weed species, especially against quizalofop-P-tefuryl; and (3) investigating the integrated effects of this herbicide within holistic weed management systems, including cover crops and optimized planting densities.