Elucidating the interactive effects of drought, weeds, and herbicides on the physiological, biochemical, and yield characteristics of rice

doi:10.21203/rs.3.rs-4309426/v1

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Research Article

Elucidating the interactive effects of drought, weeds, and herbicides on the physiological, biochemical, and yield characteristics of rice

https://doi.org/10.21203/rs.3.rs-4309426/v1

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published 01 Oct, 2024

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Aims

Rice yields are significantly influenced by both biotic and abiotic factors, like drought stress and weed infestation being prominent contributors to substantial crop losses. Environmental conditions, including drought stress, can impact the effectiveness of herbicides. The aim of this study is to investigate the impact of drought stress on the efficacy of the herbicide (Cyhalofop + Penoxsulam) against the weed species, Echinochloa colona (L.) Link, and Alternanthera paronychioides A. St.-Hil. Additionally, the study aims to assess the potential consequences of improper weed control, including the survival of weeds and their subsequent adverse effects on rice.

Methods

The herbicide was applied to rice plants under two distinct conditions: well-watered (WW) and drought-stressed (DS) at 8 days following the suspension of irrigation. The herbicide's effectiveness against two weed species, E. colona and A. paronychioides, assessed by quantifying weed growth and biomass.

Results

The findings elucidate a reduction in the herbicide efficacy against both the weeds under DS conditions. However, under DS the decline in herbicide effectiveness was more significant against E. colona than A. paronychioides, leading to inadequate weed control. As a result, the survival of these weeds further exacerbating oxidative stress in rice plants. The magnitude of oxidative stress was excess in rice with E. colona than A. paronychioides, and it significantly reduced the yield under both WW and DS.

Conclusions

The study highlighted that under drought conditions rice is more susceptible to E. colona infestation than to A. paronychioides with increased oxidative stress and reduced yield.

Drought stress

rice

A. paronychioides

E. colona

herbicide efficacy and Cyhalofop + Penoxsulam

Rice (Oryza sativa L.) stands as a crucial global food crop, responsible for nourishing nearly half of the world's population. In India alone, rice cultivation encompasses an extensive area of approximately 43.8 million hectares, yielding around 117.5 million tons (Directorate of Economics and Statistics 2019). On a global scale, the year 2019 witnessed rice production reaching an impressive 750 million tons (FAO 2019). However, as we look ahead to a projected global population of 9.7 billion by 2050 (World population prospects, 2019), the imperative of doubling current agricultural output becomes apparent to meet the burgeoning demand. Despite this, the average annual increase in rice yields hovers at a modest 1%, insufficient to attain the targeted levels by 2050 (Ray et al. 2013).

The path toward higher rice productivity is obstructed by an array of abiotic and biotic stresses, acting as prominent barriers (Zafar et al. 2017; Oliva et al. 2019; Ahmed et al. 2021). Rice, a water-dependent crop, is extremely susceptible to both drought stress and weed infestations. These stresses induce the synthesis of Reactive Oxygen Species (ROS) (Abdelaal et al. 2022; Sreekanth et al. 2023b) encompassing species such as H₂O₂, hydroxyl radicals (OH·), O₂·-, and singlet oxygen (O₂) (Li et al. 2022). When ROS production surpasses thresholds, oxidative stress within cells ensues, leading to damage in lipids, carbohydrates, proteins, and nucleic acids, ultimately culminating in cell death (Saad-Allah et al. 2021). This phenomenon detrimentally affects various physiological and biochemical traits of crops, encompassing water intake, carbon assimilation, relative water content, electrolyte leakage, antioxidant compounds, and yield (Abdelaal et al. 2022).

Drought stress can hinder the growth, maturation, and reproductive processes of both crops and weeds (Ziska et al. 2004). Nonetheless, specific weeds like Hordeum murinum L., Bromus tectorum L., and Lactuca serriola L. demonstrate heightened adaptability to such climatic factors because of phenotypic versatility (Jabran and Dogan 2018). The challenge of crop production is anticipated to intensify due to well-adapted weeds in a climate-change situation with high CO₂ and water scarcity (Ziska 2016).

In most cases, it is commonly acknowledged that during periods of drought, the competition between weeds and crops for water intensifies, often benefiting certain weed species (Patterson 1995). Drought-induced modifications in crop physiology, biochemistry, and molecular responses often impede the movement and uptake of herbicides, ultimately limiting their efficacy and promoting weed survival (Miller and Norsworthy 2018; Skelton et al. 2016; Sreekanth et al. 2023a; Wu et al. 2019). The advent of climate change adds another layer of complexity to the interaction between environmental factors and herbicide efficiency, influenced by variables like soil moisture, relative humidity, light, and temperature (Varanasi et al. 2016). Earlier investigations revealed the decreased effectiveness of certain herbicides, like fenoxaprop, under drought (Xie et al.1993). Xie et al. (1996) stated a neutral effect of drought on some herbicides, like imazamethabenz. Assessing the efficacy of herbicides on weed species under drought stress is necessary. Additionally, if there is a reduction in herbicide performance, it is required to estimate the degree of the reduction and adjust the herbicide rates to achieve effective weed control. Thus, understanding the impact of climate change variables, such as drought stress, on weed growth and efficacy of herbicide is crucial for efficient weed management (Varanasi et al. 2016).

For efficient weed management and maximal crop productivity, understanding the interplay of factors such as drought stress and herbicide efficacy is indispensable (Heap and Duke 2018; Heap 2014; Matzrafi et al. 2019). Notably, drought stress can impede herbicide efficacy, particularly affecting the weeds response to herbicides and limiting the active ingredient's ability to reach its intended site (Weller et al. 2019).

E.colona and A. paronychioides infestation in rice fields can result in significant reductions in grain yield, between 30 to 100% (Rao et al. 2007; Chauhan and Johnson 2010) and 62–83% (Pawar et al. 2022), respectively. Several investigations have explored the reactions of numerous weed species to water stress (Matloob et al. 2015), as well as the physiological responses of rice to drought (Abdelaal et al. 2022).

Knowledge about the influence of drought on weeds and the efficacy of herbicides is crucial for optimizing the effectiveness of herbicide applications (Alizade et al. 2021). It was hypothesized that the drought stress lowers the efficacy of the herbicide (Cyhalofop + Penoxsulam) against the targeted weeds, A. paronychioides and E. colona. This decrease in herbicide efficacy results in improper control of weeds, and the weeds that do survive due to reduced herbicide efficacy are likely to adversely affect rice physiological, biochemical traits yield attributes. Therefore, considering these facts and the growing menace of A. paronychioides and E. colona in rice production, a study was carried out with rice (Shahabagi) solely and rice with its predominant weeds Echinochloa colona (L.) Link. and Alternanthera paronychioides A. St.-Hil. along with ready-mix post-emergence herbicide (Cyhalofop + Penoxsulam) under drought with the following objective: The aim of current study is to explore the impact of drought stress on the efficacy of the herbicide (Cyhalofop + Penoxsulam) against the targeted weed species, A. paronychioides and E. colona. Moreover, the investigation aims to assess the probable consequences of improper weed control, including the survival of weeds and their subsequent adverse effects on rice physiological, biochemical traits, and yield attributes. This research reinforces the importance of understanding the intertwined influences of biotic and abiotic stressors in shaping agricultural outcomes. The identified correlations between drought stress, herbicide efficacy, weed growth, and rice physiology provide a stepping stone for future endeavors aimed at fortifying agricultural systems against the challenges posed by these stresses.

The study investigates the following research question: How does drought stress impact the efficacy of the herbicide (Cyhalofop + Penoxsulam) against the targeted weeds, A. paronychioides and E. colona, and what are the potential effects of surviving weeds on rice physiological, biochemical traits, and yield attributes under drought conditions.

A pot experiment was conducted during Kharif-2022 and 2023 in a green-house at the Indian Council of Agricultural Research - Directorate of Weed Research (ICAR-DWR), Jabalpur, Madhya Pradesh, India, utilizing rice variety Shahabagi and its primary weeds, Alternanthera paronychioides and Echinochloa colona. The green-house was carefully regulated to ensure controlled conditions (12 h light, 28 ± 2°C temperature, 60–75% RH and light intensity of 500–1000 µmol photons m^− 2 s^− 1). The soil exhibits a clay loam texture, possesses a low organic carbon matter, and has a pH level of 7.6. The study was carried out using rice alone and rice grown with its primary weeds, E. colona and A. paronychioides. The plants were grown in earthen pots (42 cm height X 46 cm diameter) and filled with 58 kg soil. Seedlings of A. paronychioides were kept at a density of 25/m² (5 weeds/pot), while E. colona seedlings were maintained at a density of 50/m² (10 weeds/pot). Additional weed seedlings were uprooted through hand weeding after 7 days. Weed-free rice pots were kept as ‘weed-free’ by uprooting the emerged weeds after every 5–7 days interval (Table 1). Fertilizers were administered at the recommended dosage (120:60:40 N, P₂O₅, K₂O Kg/ha) to all experimental pots. Phosphorus (P) and potassium (K) fertilizers were used as a basal dose, while nitrogen (N) fertilizer was distributed in three distinct stages: 50% at sowing, 25% during active tillering, and the remaining 25% at the booting stage (Bahuguna et al. 2015). Experimental pots were organized randomly as per completely randomized design (CRD) with each treatment having five replications (i.e., five pots per treatment).

Table 1

Experimental treatments details
Treatments		Description
Control (Well-watered, WW)	WW-RWF	Well-watered Weed free rice
	WW-RA	Well-watered Rice + A. paronychioides
	WW-RAH	Well-watered Rice + A. paronychioides + Herbicide
	WW-RE	Well-watered Rice + E. colona
	WW-REH	Well-watered Rice + E. colona + Herbicide
Drought (Drought stress, DS)	DS-RWF	Drought-stressed Weed free rice
	DS-RA	Drought-stressed Rice + A. paronychioides
	DS-RAH	Drought-stressed Rice + A. paronychioides + Herbicide
	DS-RE	Drought-stressed Rice + E. colona
	DS-REH	Drought-stressed Rice + E. colona + Herbicide

Drought imposition and application of herbicide

Well-watered (WW) pots were frequently watered, while drought stress (DS) was initiated by refraining from irrigation starting at 25 days after sowing (DAS) for a duration of 15 days. A post-emergence herbicide ready mix [Cyhalofop-buty + Penoxsulam (135 g ai/ha^− 1)] was employed to WW and DS pots at 8 days after irrigation was withheld. The plants were rewatered after noticing drought indications like leaf yellowness, drying, and leaf rolling.

Soil moisture content (SMC)

Soil samples from both the top 5 cm and bottom 5 cm of each pot were collected in triplicate. These samples were obtained using sampling auger equipment and placed into pre-weighed boxes.

To assess their fresh weight, soil samples all treatments were weighed initially. Subsequently, these samples were dried up to one day in an oven set to 105°–110°C. Soil moisture content (SMC) was then determined according to Schmugge et al. (1980):

SMC = [Weight of wet soil– Weight of dry soil / Weight of wet soil] X 100

Assessment of physiological and biochemical indices

Prior to rewatering, relative water content (RWC), membrane stability index (MSI), and gas exchange traits were determined. Leaf samples were aseptically collected from both WW and DS pots for biochemical analysis, RWC and MSI, etc.

Relative water content (RWC)

Fresh leaf samples (FW) were submerged in petri plates filled with double distilled water for 4 hours to record the turgid weight (TW). After overnight oven drying at 70°C, the dry weight (DW) was measured, and RWC was estimated from Barrs and Weatherley (1962) equation.

RWC (%) = (FW − DW) / (TW − DW) × 100

Membrane stability index (MSI)

The MSI of the fresh leaves was assessed using the protocol proposed by Sairam et al. (1994). The MSI was assessed as:

MSI = [1−(C1/C2)] × 100

Total chlorophyll content

A 0.1 g leaf sample was incubated in a 10 mL of acetone (80%). Absorbance was noted at 663 nm and 645 nm (Lichtenthaler 1987).

Measurement of gas exchange parameters

The LI-COR portable photosynthesis system was utilized to record the net photosynthesis rate, stomatal conductance, and transpiration. These indices were assessed from the flag leaf between 10:00–12:00 hours (Leakey et al. 2004).

Oxidative Stress Markers

Superoxide Anion Content (O₂.⁻)

The O₂.⁻ levels was estimated by measuring absorbance at 530 nm with sodium nitrite standard and specified as µmol g^− 1 FW (Wu et al. 2010).

Hydrogen peroxide content (H₂O₂)

H₂O₂ levels was determined using Mukherjee and Choudhari's (1983) protocol, recording absorbance at 415 nm and quantifying it with an H₂O₂ standard curve, mentioned as µmol g^− 1 FW. Malondialdehyde (MDA) analysis

The MDA was assessed using extinction coefficient of 155 mM^− 1 cm^− 1 by taking absorbance at 532 and 600 nm and specified as µg g^− 1 FW (Heath and Packers 1968).

Antioxidative enzymes assay

A 1g leaf sample was ground in an extraction solution containing 0.1 M phosphate buffer, 0.5 mM EDTA, 1% polyvinyl pyrrolidone, and 1 mM PMSF. After centrifugation at 12,000 g for 30 minutes at 4°C, the resulting supernatant was utilized to evaluate SOD, CAT, and POD enzymatic activities. The quantification of protein levels was done by Lowry et al. (1951) method.

Assessment of SOD (EC 1.15.1.1) activity was done by measuring absorbance at 560 nm and measured in units of mg protein^− 1 min^− 1 (Beauchamp and Fridovich 1971).

Activity of CAT (E.C. 1.11.1.6) was done using extinction coefficient of 45.2 mM^− 1 cm^− 1 by measuring the consumption of H₂O₂ at 240 nm for 1 minute (Aebi 1984).

Peroxidase (POD, EC 1.11.1.7) activity was examined following the protocol proposed by Kar and Mishra (1976), tracking purpurogallin generation at 420 nm using a blank.

Non-enzymatic antioxidants

Proline content

Quantification of proline was done by measuring absorbance at 520 nm (Bates et al.1973).

Total phenolic content (TPC)

The TPC was evaluated by methodology of Singleton and Rossi (1965) by measuring the OD at 765nm.

Ascorbic acid content (AsA)

AsA was assessed following Hodges et al. (1996) procedure, with absorbance recorded at 525 nm. AsA content was determined using the L-ascorbic acid standard curve.

Starch content

The leaf's starch content was investigated by the methodology given by McCready et al. (1950). Liquid nitrogen was used to grind the leaf samples. 0.1g of the sample was mixed with 80% ethanol (V/V) and then centrifuged. The pellet obtained after centrifugation was dried, and perchloric acid was supplemented to extract starch from the sample. This mixture was centrifuged again and then analyzed using an anthrone reagent.

Grain yield

Grain yield at maturity was estimated by randomly sampling five plants from each treatment.

Weed growth and biomass traits

Plant fresh weight, dry weight, leaf area and root length of weeds were recorded at 7 days after herbicide application.

Statistical analysis

An analysis of variance (ANOVA) was assessed by SPSS Inc., Chicago, IL, USA (Version 16.0) to compare the variances among treatments and their interactions. A post-hoc test (Tukey's) was carried out to determine the differences between treatments with a least significant difference (LSD) of 5% and 1%. The correlation and PCA analysis were performed using Minitab.

Soil moisture content (SMC)

SMC significantly varied across treatments. In weed-free rice under well-watered (WW) conditions, SMC measured 21.60% (at top 5 cm) and 22.56% (below 5 cm soil). However, under drought stress (DS), SMC decreased to 10.51% (top 5 cm soil) and 11.55% (below 5 cm soil). With the interference of A. paronychioides, SMC dropped to 17.15% (top 5 cm soil) and 18.24% (below 5 cm soil) in WW conditions, and even further to 8.86% (top 5 cm soil) and 9.87% (below 5 cm soil) in DS conditions. In contrast, E. colona interference reduced SMC to 16.90% (top 5 cm) and 17.85% (below 5 cm soil) in WW conditions, and drastically to 6.70% (top 5 cm) and 7.61% (below 5 cm soil) in DS conditions. Similar SMC trends were observed in herbicide-treated pots. E. colona presence during DS led to a significant SMC reduction, suggesting extreme drought stress on rice by E. colona (see Supplementary Fig. 1 a & b).

Physiological and biochemical traits

RWC

A significant variability across WCH (Rice alone; rice + weeds; rice + weeds + herbicide) (P < 0.001), T (Control and drought) (P < 0.001) and WCH X T (P < 0.001) for RWC was noticed. The RWC values under WW and DS conditions in weed-free rice were 86.74% and 71.60%, respectively (Table 2). In both WW and DS circumstances, weed interference (A. paronychioides and E. colona) substantially lowered the RWC. The presence of A. paronychioides led to a notable decrease in RWC by 15.16% and 18.95% in WW and DS, respectively, compared to the weed-free control. Likewise, E. colona interference resulted in RWC reductions by 22.17% and 24.45% under WW and DS, respectively. This highlights severe drought stress experienced by rice due to both weed species in both WW and DS conditions (Fig. 1; Supplementary Fig. 2 a and b). The application of herbicide resulted in decreased weed stress on rice plants, leading to a remarkable increase in the RWC of rice grown with A. paronychioides (12.97% and 13.71% during WW and DS conditions, respectively) and E. colona (6.38% and 3.32% during WW and DS, respectively), compared to their respective herbicide unsprayed controls (Table 2).

Figure 1. Effect of drought and weed control treatments on rice morphological and physiological responses. Rice with A. paronychioides (a and b), rice with E. colona (c and d) and weed free rice (e and f) under well-watered (WW) and drought stress (DS).

MSI

There was a noteworthy variation seen for MSI among WCH (P < 0.001), T (P < 0.001), and WCH X T (P < 0.05). The MSI values for weed-free rice were 82.64% and 77.63% in WW and DS, respectively (Table 2). A. paronychioides presence led to MSI reductions of 16.05% and 23.45% in WW and DS compared to the weed-free control. Conversely, E. colona caused MSI decreases to 22.86% and 25.18% in WW and DS relative to the control. Rice grown with A. paronychioides (11.06% and 21.35% at WW and DS, respectively) and E. colona (20.14%% and 10.40% at WW and DS, respectively) had significantly higher MSI than their respective herbicide-unsprayed controls due to the reduction of weed stress caused by herbicide application (Table 2).

Total chlorophyll content

A notable impact was observed for both WCH (P < 0.001) and T (P < 0.001) on total chlorophyll levels. Whereas, the interaction effect (WCH × T) was found to be nonsignificant. Total chlorophyll content levels in weed-free rice were 6.27 mg g^− 1 FW and 4.67 mg g^− 1 FW in WW and DS, respectively (Table 2). A. paronychioides interference led to a decrease of 43.62% and 62.77% in total chlorophyll content in WW and DS, respectively. E. colona interference resulted in reductions to 54.38% and 70.21% in WW and DS, respectively compared to the weed-free control. Nevertheless, in comparison to their respective controls where herbicide was not applied, rice grown with A. paronychioides (5.66% and 31.43% in WW and DS conditions, respectively) and E. colona (32.92% and 5.89% in WW and DS, respectively) exhibited a significant rise in total chlorophyll content attributed to the alleviation of weed stress resulting from herbicide application (Table 2).

Table 2

Effects of drought and weed management on the RWC, MSI, and total chlorophyll levels of rice plants
Weed, Crop and Herbicide (WCH)	Treatment (T)	RWC (%)	MSI (%)	Total chlorophyll content (mg g^− 1 FW)
RWF	WW	84.21 ± 0.82a	82.64 ± 0.33a	6.27 ± 0.24a
	DS	74.25 ± 0.61d	77.63 ± 0.83b	4.67 ± 0.13b
RA	WW	71.44 ± 0.49e	69.37 ± 0.78c	3.53 ± 0.24cd
	DS	68.26 ± 0.69f	63.26 ± 1.41e	2.33 ± 0.18fg
RAH	WW	80.70 ± 0.67b	77.05 ± 0.82b	3.73 ± 0.18c
	DS	77.62 ± 0.86c	76.76 ± 4.02b	3.07 ± 0.18de
RE	WW	65.54 ± 0.10g	63.74 ± 0.52de	2.86 ± 0.27ef
	DS	63.62 ± 0.36h	61.83 ± 1.40e	1.87 ± 0.13g
REH	WW	69.72 ± 0.69ef	76.58 ± 0.86b	3.80 ± 0.23c
	DS	65.74 ± 0.33g	68.26 ± 1.04cd	1.98 ± 0.10g
	WCH	0.806***	2.07***	0.259***
LSD < 0.05	T	1.274***	3.273***	0.41***
	WCH X T	1.802***	NS	NS
The data provided above represent the Mean ± SE (n = 5). WW: Well-watered; DS: Drought stress; RWF: Weed free rice; RA: Rice + A. paronychioides; RAH: Rice + A. paronychioides + Herbicide; RE: Rice + E. colona; REH: Rice + E. colona + Herbicide; RWC: relative water content; MSI: membrane stability index. Distinct lower letters in a column designates a significant difference at P = 0.05. , , and ** designates significant difference at P < 0.05, 0.01, and 0.001 levels, respectively, while "ns" indicates non-significance. WCH: refers to combinations of weeds, crop, and herbicide; T: denotes treatment; and WCH X T: represents the interaction between combinations of weeds, crop, herbicide, and treatment.

Gas exchange parameters

The rate of photosynthetic (PN) showed significant differences for WCH, T, and WCH X T (P < 0.001). Under WW and DS conditions, the PN values of weed-free rice were recorded as 22.70 and 14.38 (µmol CO₂ m^–2s^–1), respectively. A. paronychioides interference led to significant reductions of 48.12% and 58.46% in PN in WW and DS, respectively. E. colona interference diminished PN to 59.49% and 63.41% in WW and DS, respectively, compared to the weed-free control. Compared to their respective controls without herbicide application, rice plants grown with A. paronychioides (38.81% and 35.03% in WW and DS conditions, respectively) and E. colona (13.59% and 13.24% in WW and DS, respectively) exhibited notable enhancements in their PN due to reduced weed stress induced by herbicide application.

Significant variations (P < 0.001) in stomatal conductance (gs) were observed among WCH, T, and WCH × T. The gs values in WW and DS conditions in weed-free rice were 0.64 and 0.33 [mol (H₂O) m^–2s^–1], respectively (Fig. 2 b). Furthermore, the presence of E. colona resulted in the highest reduction (56.77%) in gs under DS compared to the weed-free control. Herbicide application led reduction of weed stress on rice which led significantly enhancement in the gs of rice grown with A. paronychioides (5.88% and 27.71% at WW and DS, respectively) and E. colona (11.76% and 30.77% at WW and DS, respectively) relative to their respective herbicide unsprayed controls.

A noteworthy difference was observed for E for WCH (P < 0.001), T (P < 0.001), and WCH X T (P < 0.05). Under WW and DS, the E values for weed-free rice were 5.43 and 9.49 [mol (H₂O) m^–2s^–1], respectively (Fig. 2 c). A. paronychioides led to reductions of 32.21% and 45.31% in E under WW and DS, while E. colona caused decreases to 48.96% and 59.64% in WW and DS, respectively, compared to the weed-free control. The use of herbicides led to decrease in weed stress on rice, which in turn led to a notable improvement in the E of rice grown with A. paronychioides (12.54% and 14.84% at WW and DS, respectively) and E. colona (19.68% and 9.05% at WW and DS, respectively) when compared to herbicide-unsprayed controls.

Figure 2. Effect of drought stress and weed control treatments on rate of photosynthesis (a), stomatal conductance (b) and transpiration (c) of rice in well-watered (WW) and drought stress (DS) conditions. RWF: weed free rice; RA: rice + A. paronychioides; RAH: rice + A. paronychioides + herbicide; RE: rice + E. colona; REH: rice + E. colona + herbicide. The data provided above represent the Mean ± SE (n = 5). Distinct lower letters in a column designates a significant difference at P = 0.05. *, **, and *** specifies significant difference at P < 0.05, 0.01, and 0.001 levels, respectively, while "ns" specifies non-significance. WCH: refers to combinations of weeds, crop, and herbicide; T: denotes treatment; and WCH X T: represents the interaction between combinations of weeds, crop, herbicide, and treatment.

Oxidative Stress Markers

Superoxide ion

Superoxide ion exhibited a significant variance across WCH (P < 0.001), T (P < 0.001) and WCH X T (P < 0.01) was noted. In WW and DS, the superoxide ion levels in weed-free rice was 3.50 and 6.06 µmol g^− 1 FW, respectively (Fig. 3 a). Superoxide ion levels under WW and DS, respectively, increased significantly by 1.15 and 1.63 times in the existence of A. paronychioides than weed-free control. Similarly, compared to weed-free control, E. colona interference elevated the superoxide ion level to 2.19 and 2.95 times in WW and DS, respectively. In comparison to their respective herbicide unsprayed controls, the superoxide ion content in rice planted with A. paronychioides (44.12% and 43.98% at WW and DS, respectively) and E. colona (43.28% and 36.95% at WW and DS, respectively) was significantly reduced upon herbicide application.

H₂O₂

A significant variation WCH, T and WCH X T (P < 0.001) was noted for H₂O₂ content. The H₂O₂ content values under WW and DS conditions in weed-free rice were 1.59 and 2.98 (µmol g^− 1 FW), respectively. A significant rise of 1.30fold and 2.02fold in H₂O₂ content was observed in the presence of A. paronychioides in WW and DS, respectively, compared to the weed-free control (Fig. 3 b). Likewise, E. colona interference led to an increase in H₂O₂ level to 1.56fold and 3.47fold in WW and DS, respectively, than weed-free control. The application of herbicide reduced weed stress on rice, resulting in a significant decrease in H₂O₂ levels in rice grown with A. paronychioides (by 45.05% and 45.94% in WW and DS conditions, respectively) and E. colona (by 35.85% and 49.72% in WW and DS, respectively), compared to their respective controls without herbicide application.

Lipid peroxidation (MDA content)

A significant effect for MDA content across WCH, T and WCH X T (P < 0.001) was noticed. In rice without weeds, the MDA content values were 11.17 µg g^− 1 FW in WW and 21.45 µg g^− 1 FW under DS conditions. In both WW and DS conditions, the presence of A. paronychioides resulted in a remarkable upsurge of 1.95fold and 3.21fold in MDA content, respectively, compared to the weed-free control (Fig. 3 c). Similarly, E. colona interference elevated the MDA content to 3.73fold and 4.59fold in WW and DS, respectively, than weed-free control. The application of herbicide reduced weed stress on rice, resulting in a significant decrease in MDA content in rice grown with A. paronychioides (61.27% and 20.28% in WW and DS conditions, respectively) and E. colona (72.06% and 6.94% in WW and DS, respectively), compared to their respective controls without herbicide application.

Figure 3. Effect of drought stress and weed control treatments on superoxide ion content (a), H₂O₂ content (b) and MDA content (c) of rice in well-watered (WW) and drought stress (DS) conditions. RWF: weed free rice; RA: rice + A. paronychioides; RAH: rice + A. paronychioides + herbicide; RE: rice + E. colona; REH: rice + E. colona + herbicide. The data provided above represent the Mean ± SE (n = 5). Distinct lower letters in a column indicates a significant difference at P = 0.05. *, **, and *** indicates significant difference at P < 0.05, 0.01, and 0.001 levels, respectively, while "ns" specifies non-significance. WCH: refers to combinations of weeds, crop, and herbicide; T: denotes treatment; and WCH X T: represents the interaction between combinations of weeds, crop, herbicide, and treatment.

Antioxidant enzymes

Significant differences in SOD, CAT, and POD were observed among WCH (P < 0.001) and T (P < 0.001). However, while the interaction effects (WCH × T) were significant for SOD and CAT (P < 0.001), and non-significant for POD.

In rice without weeds, there was a significant 65.94% increase in SOD in DS than WW weed-free control. In the existence of A. paronychioides, SOD levels increased by 2.45fold and 3.11fold in WW and DS conditions, respectively, compared to the weed-free control (Fig. 4a). However, E. colona interference remarkably raised SOD levels to 1.26fold and 2.60fold in WW and DS, respectively, than control. Herbicide application alleviated weed stress on rice, leading to a considerable reduction in SOD levels in rice cultivated with A. paronychioides (63.21% and 15.86% during WW and DS conditions, respectively) and E. colona (21.64% and 21.16% during WW and DS, respectively), compared to their respective controls untreated with herbicide.

In rice without weeds, CAT showed a significant increase of 40.51% under DS than WW weed-free control. In the occurrence of A. paronychioides, CAT levels increased by 1.02-fold and 1.20-fold in WW and DS, respectively, compared to the weed-free control (Fig. 4 b). Likewise, in WW and DS, respectively, E. colona occurrence greatly raised CAT to 68.12% and 78.71% in comparison to the control. Herbicide application reduced weed stress on rice, resulting in a significant decrease in CAT levels in rice grown with A. paronychioides (46.49% and 5.62% during WW and DS conditions, respectively) and E. colona (2.60% and 1.48% during WW and DS, respectively), compared to their respective controls without herbicide application.

POD in weed-free rice increased (41.54%) significantly under DS compared to WW. A. paronychioides interference remarkably accelerated POD by 1.80fold and 2.48fold in WW and DS, respectively, compared to weed-free control (Fig. 4 c). In contrast, compared to weed-free control, E. colona existence upsurged the POD to 66.56% and 1.03fold in WW and DS, respectively. Herbicide application reduced weed stress on rice, resulting in a significant decrease in POD levels in rice grown with A. paronychioides (46.38% and 48.38% during WW and DS, respectively) and E. colona (10.77% and 26.52% during WW and DS, respectively), compared to their respective controls without herbicide application.

Figure 4. Effect of drought stress and weed control treatments on superoxide dismutase (a), catalase (b) and peroxidase (c) of rice in well-watered (WW) and drought stress (DS) conditions. RWF: weed free rice; RA: rice + A. paronychioides; RAH: rice + A. paronychioides + herbicide; RE: rice + E. colona; REH: rice + E. colona + herbicide. The data provided above represent the Mean ± SE (n = 5). Distinct lower letters in a column specifies a significant difference at P = 0.05. *, **, and *** indicates significant difference at P < 0.05, 0.01, and 0.001 levels, respectively, while "ns" specifies non-significance. WCH: refers to combinations of weeds, crop, and herbicide; T: denotes treatment; and WCH X T: represents the interaction between combinations of weeds, crop, herbicide, and treatment.

Non enzymatic antioxidants

Proline

Significant variability among WCH, T and WCH X T (P < 0.001) was noticed for proline content. The proline quantity values under WW and DS conditions in weed-free rice were 14.67 mg g^− 1 FW and 20.33 mg g^− 1 FW, respectively. Proline levels in WW and DS, respectively, significantly increased 1.18 and 3.00 times in the presence of A. paronychioides compared to weed-free control (Supplementary Fig. 3 a). In contrast, compared to weed-free control, E. colona existence elevated the proline to 68.18% and 2.52fold in WW and DS, respectively. Herbicide application decreased weed stress on rice, resulting in a significant reduction in proline accumulation in rice grown with A. paronychioides (51.04% and 29.55% during WW and DS, respectively) and E. colona (35.14% and 41.94% during WW and DS, respectively), compared to their respective controls without herbicide application.

Total phenolic content (TPC)

A significant variation WCH, T and WCH X T (P < 0.001) was noted for TPC. In WW and DS, the TPC values for weed-free rice were 11.22 mg g^− 1 FW and 19.07 mg g^− 1 FW, respectively. In comparison to weed-free control, the existence of A. paronychioides resulted in a notable increase in TPC content in WW and DS of 1.78 and 2.51fold, respectively. (Supplementary Fig. 3 b). On the other hand, compared to weed-free control, E. colona existence upsurged the TPC content to 1.03 and 1.61fold in WW and DS, respectively. The application of herbicide reduced weed stress on rice, resulting in a significant decrease in TPC in rice grown with A. paronychioides (58.72% and 62.63% during WW and DS conditions, respectively) and E. colona (42.72% and 42.68% during WW and DS, respectively).

Ascorbic acid content (AsA)

Ascorbic acid varied significantly for WCH (P < 0.001), T (P < 0.001) and WCH X T (P < 0.05). The AsA values in weed-free rice were 9.25 µg g^− 1 FW and 14.58 µg g^− 1 FW under WW and DS, respectively. The content of AsA significantly increased in both WW and DS in the existence of A. paronychioides, by 1.58 and 2.04fold, respectively, compared to the weed-free control (Supplementary Fig. 3 c). In contrast to weed-free control, E. colona existence amplified the AsA level to 90% and 1.27fold in WW and DS, respectively. Herbicide application decreased weed stress on rice, resulting in a significant reduction in AsA levels in rice grown with A. paronychioides (54.70% and 57.10% during WW and DS conditions, respectively) and E. colona (25.59% and 28.17% during WW and DS, respectively), compared to their respective controls without herbicide application.

Starch content

The starch content exhibited significant differences among WCH and T (P < 0.001), however the interaction effect (WCH × T) was not significant. The starch levels in weed-free rice were 22.21 mg/g DW under DS and 26.23 mg/g DW in WW, respectively. A. paronychioides interference significantly reduced the starch content in WW and DS, respectively, by 36.98% and 44.87%, than weed-free control (Fig. 5 a). However, in comparison to weed-free control, E. colona existence significantly decreased the starch to 53.89% and 69.35% in WW and DS, respectively. The application of herbicide reduced weed stress on rice, leading to a significant increase in rice starch levels when grown with A. paronychioides (39.96% and 31.84% during WW and DS conditions, respectively) and E. colona (75.78% and 1.1-fold during WW and DS, respectively), compared to their respective controls without herbicide application.

Yield

There was a significant difference across WCH, T and WCH X T (P < 0.001) noticed for yield. The yield values for weed-free rice in WW and DS conditions were 9.32 and 4.74 (g/plant), respectively. Whereas, weed interference caused a notable reduction in yield when exposed to DS. Compared to the weed-free control, the interference of A. paronychioides led to a drop in yield by 32.40% under WW and by 66.81% in DS (Fig. 5 b). In contrast, compared to weed-free control, E. colona existence decreased the yield to 78.22% and 80.86% in WW and DS, respectively. The application of herbicide reduced weed stress on rice, leading to a significant increase in rice yield when grown with A. paronychioides (5.12% and 37.38% during WW and DS, respectively) and E. colona (52.05% and 45.18% during WW and DS, respectively), compared to their respective controls without herbicide application.

Figure 5. Effect of drought stress and weed control treatments on starch (a) and yield (b) of rice in well-watered (WW) and drought stress (DS) conditions. RWF: weed free rice; RA: rice + A. paronychioides; RAH: rice + A. paronychioides + herbicide; RE: rice + E. colona; REH: rice + E. colona + herbicide. The data provided above represent the Mean ± SE (n = 5). Distinct lower letters in a column indicates a significant difference at P = 0.05. *, **, and *** indicates significant difference at P < 0.05, 0.01, and 0.001 levels, respectively, while "ns" specifies non-significance. WCH: refers to combinations of weeds, crop, and herbicide; T: denotes treatment; and WCH X T: represents the interaction between combinations of weeds, crop, herbicide, and treatment.

Weeds growth, biomass traits

Significant variations were observed in the growth and biomass traits of weed species (A. paronychioides and E. colona) in reaction to herbicide application under DS. A significant (P < 0.001) WH (weeds: A. paronychioides; E. colona and herbicide) and WH X T effect was noticed for weed fresh weight. However, the T (control and drought) was non-significant. The A. paronychioides fresh weight values were 8.53 and 6.47 g in WW and DS, respectively. Similarly, E. colona fresh weight values were 5.50 and 4.93 g in WW and DS respectively. Herbicide application led to a decrease in the weed's fresh weight i.e., A. paronychioides (70.70% and 61.86% at WW and DS, respectively) and E. colona (63.03%% and 41.89% at WW and DS, respectively) in comparison to their respective herbicide unsprayed controls (Table 3).

A notable variation was observed among WH (P < 0.001), T (P < 0.05), and the interaction between WH and T (P < 0.01) for weed dry weight. Herbicide application led to a decline in the weeds dry weight i.e., A. paronychioides (58.13% and 51.55% at WW and DS, respectively) and E. colona (36.84%% and 31.76% at WW and DS, respectively) in comparison to their respective herbicide unsprayed controls (Table 3).

A significant variation WH, T and WH X T (P < 0.001) was noticed for leaf area. Herbicide application led to a reduction in the weeds leaf area i.e., A. paronychioides (55.00% and 49.48% at WW and DS, respectively) and E. colona (31.11%% and 19.62% at WW and DS, respectively) compared to their respective herbicide unsprayed controls (Table 3).

Root length varied significantly for WH (P < 0.001) and T (P < 0.01) was noted for root length. Whereas, the interaction effect (WH X T) was non-significant. Herbicide application led to a decrease in the weeds root length i.e., A. paronychioides (24.14% and 22.86% at WW and DS, respectively) and E. colona (10.26%% and 6.98% at WW and DS, respectively) compared to their respective herbicide unsprayed controls (Table 3).

Remarkably, the growth and biomass parameters of A. paronychioides and E. colona were shown to decrease upon the application of herbicide under DS. Whereas, the intensity of drop was significantly lesser in E. colona than in A. paronychioides, signifying a higher tolerance against DS. The level of minimal growth decline of E. colona in DS is thought to have a direct correlation with the lowered herbicide efficacy. This observation led us to infer that E. colona exhibits more effective and pronounced adaptive mechanisms to DS than A. paronychioides. Additionally, the application of herbicide was less effective in drought conditions.

Table 3

Effect of weed control treatments on weed growth and biomass traits under drought stress
Weeds and herbicide combination (WCH)	Treatment (T)	Fresh weight (g)	Dry weight (g)	Leaf area (cm²)	Root length (cm)
A. paronychioides	WW	8.53 ± 0.98a	0.96 ± 0.13a	96.95 ± 1.44a	9.67 ± 0.33cd
A. paronychioides	DS	6.47 ± 0.28b	0.65 ± 0.05b	63.67 ± 1.68b	11.67 ± 0.88bc
A. paronychioides + Herbicide	WW	2.50 ± 0.17d	0.40 ± 0.03cd	43.63 ± 1.13d	7.33 ± 0.67e
A. paronychioides + Herbicide	DS	2.47 ± 0.23c	0.31 ± 0.02bc	32.17 ± 1.80de	9.00 ± 0.58de
E. colona	WW	5.50 ± 0.23bc	0.63 ± 0.06b	54.43 ± 1.44c	13.00 ± 0.58ab
E. colona	DS	4.93 ± 0.48d	0.57 ± 0.12d	40.77 ± 0.94f	14.33 ± 0.88a
E. colona + Herbicide	WW	2.03 ± 0.18d	0.40 ± 0.02cd	37.50 ± 0.92e	11.67 ± 0.88bc
E. colona + Herbicide	DS	2.87 ± 0.35d	0.39 ± 0.03cd	32.77 ± 1.34f	13.33 ± 0.33ab
LSD = P < 0.05	WH	***	***	***	***
	T	NS	*	***	**
	WH X T	***	**	***	NS
The data provided above represent the Mean ± SE (n = 5). Distinct lower letters in a column designates a significant difference at P = 0.05. , , and ** designates significant difference at P < 0.05, 0.01, and 0.001 levels, respectively, while "ns" designates non-significance. WW: Well-watered; DS: drought stress; WCH: refers to combinations of weeds, crop, and herbicide; T: denotes treatment; and WCH X T: represents the interaction between combinations of weeds, crop, herbicide, and treatment.

Principal component analysis (PCA)

The first two principal components (PCs) obtained from the PCA of the rice's responses to several treatments had eigenvalues more than 1 and were statistically significant (Supplementary Table 1; Fig. 6). The PC-1 and 2 together explained 88.2% (PC-1:72.1% and PC-2: 16.1%) of variation (Fig. 6) among 12 parameters. In PC-1, the highest variance was attributed to SOD (0.337), CAT (0.323), MDA (0.322), and Proline (0.316), while total PN (-0.336), RWC (-0.322), and yield (-0.300) contributed the least variance. Conversely, in PC-2, superoxide (0.296) and MDA (0.208) contributed the most variance, whereas POD (-0.488) and yield (-0.404) attributed the low variance.

Figure 6. The principal component analysis (PCA) of physiological, biochemical traits and yield of rice in various weed control treatments under WW and DS conditions. RWF: weed free rice; RA: rice + A. paronychioides; RAH: rice + A. paronychioides + herbicide; RE: rice + E. colona; REH rice + E. colona + herbicide; WW: well-watered condition; DS: drought stress; RWC: relative content; MDA: malondialdehyde content, Superoxide: superoxide ion content; Proline: proline content; AsA: ascorbic acid content; SOD: superoxide dismutase; CAT: catalase; POD: peroxidase; P_N: rate of photosynthesis; Yield: grain yield.

Pearson’s correlation analysis

The results of this study were additionally confirmed through Pearson's correlation analysis. This analysis were examined for 15 physiological and biochemical traits of rice under different treatment conditions, encompassing both WW and DS conditions. The correlation matrices are presented in Fig. 7. The analysis indicated significant positive correlations (P < 0.01) between yield and POD (0.946**), RWC (0.903**), total chlorophyll (0.857**), and starch content (0.786**), while significant negative correlations (P < 0.01 and P < 0.05) were observed with superoxide (-0.794**), MDA (-0.760*), and H₂O₂ (-0.699*). Conversely, PN showed significant positive correlations (P < 0.01) with RWC (0.932**), total chlorophyll (0.904**), starch content (0.831**), and MSI (0.828**), while significant negative correlations (P < 0.01 and P < 0.05) were found with superoxide (-0.798**), MDA (-0.768**), CAT (-0.760*), SOD (-0.701*), and H₂O₂ (-0.727*).

Figure 7. Correlogram showing the relationship between various physiological, biochemical traits and yield of rice affected by weed control treatments in WW and DS conditions. Asterisks (*, **, ***) indicate significance at < 0.05; <0.01; <0.001, respectively. RWC: relative water content; MSI: membrane stability index; Total chl: total chlorophyll content; Proline: proline content; AsA: ascorbic acid content; TPC: total phenolic content; MDA: malondialdehyde content; H₂O₂: H₂O₂ content, Superoxide: superoxide ion content; SOD: superoxide dismutase; CAT: catalase; POD: peroxidase; Starch: starch content; P_N:rate of photosynthesis; Yield: grain yield.

Drought stress stands as a formidable abiotic challenge globally, impacts crop productivity (Lesk et al. 2016; Matiu et al. 2017). In both irrigated and non-irrigated agricultural contexts, weeds persist as a pivotal biological limitation despite advancements in crop varieties (Mahajan et al. 2015). The drought stress, significantly impairs the efficacy of herbicides in managing weeds, often resulting in reduced effectiveness (Weller et al. 2019). In current examination, we investigate how drought stress affects the performance of the herbicide (Cyhalofop + Penoxsulam) against A. paronychioides and E. colona. Additionally, we assess the potential consequences of inadequate weed control, including the survival of weeds and their adverse effects on rice's physiological, biochemical traits, and yield.

In the context of weed management, drought-affected weeds create difficulties for their management using post-emergent herbicide. Herbicides require soil moisture to function effectively, but their efficacy can be diminished by drought (Singh et al. 2011). Herbicides struggle to penetrate plant tissues due to increased cuticle thickness and leaf hairs caused by drought-induced responses (Patterson 1995). These traits influence the growth of crops and weeds and the recovery process after herbicide application. In drought-prone areas, herbicide volatilization intensifies, while regular rainfall limits safe herbicide application windows in agriculture (Froud-Williams 1996). These interconnected factors create complex challenges in weed management. Likewise, in the current investigation, the effecacy of the herbicide (Cyhalofop + Penoxsulam) was reduced against, A. paronychioides and E. colona, during drought conditions. Consequently, the weeds that managed to survive due to this diminished herbicide efficacy exerted a noteworthy adverse impact on the physiological, biochemical, and yield-related characteristics of rice.

To retain normal functionality, plants that are growing in challenging environments modify their metabolic processes (Hussain et al. 2019; Ansari et al. 2021). Drought adversely affects plant morphology, physiology, and yield by reducing leaf area, chlorophyll content, and RWC while increasing ROS (Lata and Prasad 2011; Salehi-Lisar and Bakhshayeshan-Agdam 2016). Plants go through several morpho-physiological and biochemical changes to lessen these negative effects of drought. In this study, it was observed that the reduction in various rice traits was more pronounced in the occurrence of E. colona compared to A. paronychioides. This effect was consistent across both herbicide-treated and non-treated rice, with DS conditions exacerbating the decrease in traits more significantly than under WW conditions. This specifies that rice is extremely susceptible to E. colona under DS. The degree of growth lessening under DS is believed to be directly linked to the diminished efficacy of herbicides against E. colona.

RWC emerges as a pivotal physiological marker influencing water potential, stomatal resistance, and transpiration (Hartmann et al. 2013). Weed presence in soybean led to reduced leaf RWC, which improved upon herbicide application (Khaffagy et al. 2022). Notably, decreased RWC and MSI (membrane stability index) are associated with drought stress (Bangar et al. 2019).

Our study emphasizes that rice, when subjected to DS, exhibits a greater decline in RWC and MSI in the occurrence of E. colona than A. paronychioides, regardless of herbicide application.

Water scarcity has a detrimental negative effect on various physiological characteristics of rice, i.e., the gas exchange parameters (Zhu et al. 2020). Additionally, weed-infested soybean plants had a lower rate of assimilation of carbon dioxide, which led to a notable drop in carbon allocation (McKenzie et al. 2019). Correspondingly, our study demonstrates distinctive decreases in these (P_N, gs, and E) rice traits due to E. colona in contrast to A. paronychioides, regardless of herbicide treatment, under DS conditions than WW conditions.

The low chlorophyll content is commonly considered a typical indication of oxidative stress triggered by drought conditions (Faisal et al. 2019). The deterioration in chlorophyll content caused by drought stress can be linked to the photooxidation of pigments and the deterioration of chlorophyll (Nezhadahmadi et al. 2013). Similarly, it was noticed that the level of reduction in total chlorophyll content of rice was more in the existence of E. colona than A. paronychioides, in both herbicide-treated and non-treated rice under DS than WW condition. Due to various stresses, the drop in photosynthetic pigment concentrations, such as chlorophyll, could directly limit the photosynthetic activities (Farooq et al. 2009).

MDA, is a byproduct of lipid peroxidation, is a vital indicator of weed stress. Increased electrolyte leakage via the plasma membrane during weed infection can trigger the production of ROS (Tanaka et al. 1993). Plants may experience a number of negative effects from drought stress, including disruption of redox equilibrium and potential oxidative damage. Over production of ROS such as O₂.⁻, OH and H₂O₂ results in oxidative stress. This can then result in membrane degradation, electron leakage, and lipid peroxidation. Furthermore, apart from damaging proteins and nucleic acids, ROS also prevents stomata closing and interferes with enzyme and photosynthesis (Polle 2001; Asada 2006; Jaspers and Kangasjärvi 2010; Maksup et al. 2014). Likewise, in our present study, the extent of enhancement of ROS (O₂⁻, H₂O₂) and MDA generation were greater in rice due to E. colona than A. paronychioides, in both herbicide-treated and non-treated rice under DS than WW conditions.

To lower the detrimental effects of drought, plants trigger their defense system by enhancing the level of antioxidants (e.g., SOD, CAT, and POD) and non-enzymatic antioxidants (TPC and AsA). Antioxidant, AsA is essential in mitigating the harm induced by oxidative stress (Foyer and Noctor 2005). In numerous plant species, TPC levels (Wrobel et al. 2005), proline (Mohammadi et al. 2018) may rise under abiotic stress. Several physiological alterations were noted under weed infestation. Among these, the excess generation of ROS and modifications in antioxidant substances are paramount. Weed infestation resulted in reduced activity of SOD, while levels of H₂O₂ and oxidized AsA were increased. It was observed that the lipid peroxidation, superoxide, and CAT activity remained unchanged in soybean, indicating no significant alteration (McKenzie-Gopsill et al. 2019), as were changes in leaf antioxidants such as AsA and glutathione and other antioxidant enzymes (Yamazaki 2010; Afifi and Swanton 2012; Gal et al. 2015; Agostinetto et al. 2017). The degree of APX, CAT and SOD were dropped with Cyperus rotundus L. competition in soybean (Darmanti et al. 2016). It creates a notable shift in proline accumulation (Jabran et al. 2018). Similar trends are evident in our study, wherein the occurrence of E. colona induced greater reductions in antioxidant enzyme accumulation, TPC, AsA, and proline content in rice compared to A. paronychioides, particularly under DS conditions with and without herbicide treatment.

The starch is broken down under DS to supply energy and carbon when photosynthetic activity is limited, results in lower starch content (Siaut et al. 2011). In our study, we found a greater decrease in rice starch content, particularly notable in the presence of E. colona. Both herbicide-treated and non-treated rice experienced a substantial reduction in starch content under DS compared to WW conditions, with E. colona causing more significant reductions than A. paronychioides.

Rice production is significantly constrained by drought (Sasi et al. 2021). Likewise, it was noticed that under DS, rice exhibited a more significant yield decrease in E. colona than A. paronychioides. This phenomenon was observed in both herbicide-treated and untreated rice under DS, indicating rice's higher vulnerability to the negative impacts of E. colona. The degree of growth decline under stress is believed to directly correlate with the diminished effectiveness of herbicides against E. colona.

Unforeseen drought events disrupt water availability and the equilibrium of crop-weed competition, intensifying competition magnitude (Ramesh et al. 2017). Under DS, Abutilon theophrasti Medic. and Anoda cristata (L.) Schltdl. became more competitive with cotton (Patterson and Highsmith 1989). The adoption of direct-seeded rice, driven by water scarcity, is fostering the spread of troublesome grassy weeds such as Dactyloctenium aegyptium, Eleusine indica, Leptochloa chinensis, and weedy rice (Oryza sativa) in rice fields (Chauhan et al. 2014; Matloob et al. 2015). Similarly, our study found that E. colona has a greater impact on rice under drought stress (DS) conditions compared to A. paronychioides. This could be attributed to the C₄ photosynthetic pathway of E. colona. DS is a main abiotic stress that has a considerable impact to herbicides, primarily decreasing their efficacy (Weller et al. 2019). It has been documented that the effectiveness of herbicides decreases under DS (Miller and Norsworthy 2018; Skelton et al. 2016; Wu et al. 2019). Plants can survive the application of herbicides by naturally possessing the ability to metabolize (break down or detoxify) sufficient amounts of the herbicide or by augmenting this ability under conditions of environmental stress (Nandula et al. 2019). Conversely, in the present investigation the efficacy of herbicide was reduced against A. paronychioides and E. colona. On the other hand, compared to A. paronychioides, the amount of herbicide efficacy reduction against E. colona was substantially higher, suggesting a greater drought tolerance. Hence, it is imperative to comprehend how global climate change factors, such as drought, affect weeds and herbicide efficacy to optimize herbicide usage and achieve efficient weed management.

The current investigation aims to explore the influence of drought stress on the effectiveness of the herbicide (Cyhalofop + Penoxsulam) against the weed species, A. paronychioides and E. colona. Additionally, the study aims to assess the potential consequences of improper weed control, including the survival of weeds and their subsequent adverse effects on rice physiological, biochemical traits, and yield attributes. The herbicide Cyhalofop + Penoxsulam showed reduced efficacy under drought stress in controlling the growth and biomass of the weeds, E. colona and A. paronychioides, which resulted in inadequate weed control of both E. colona and A. paronychioides. It is noteworthy to mention that, under DS the decline in herbicide effectiveness was more significant against E. colona than A. paronychioides. The oxidative stress was induced by the survived weeds was significantly more pronounced in rice plants exposed to E. colona compared to A. paronychioides, under DS. In the presence of E. colona, the mechanisms appear to be less effective, with lowered activities of antioxidant enzymes and reduced production of total phenolic content, ascorbic acid, and proline observed in rice under drought stress. E. colona influence led to more significant reductions in rice starch content compared to A. paronychioides under both WW and DS. This study further revealed that the presence of E. colona exacerbates yield losses more significantly than A. paronychioides under drought stress, emphasizing E. colona competitive advantage during challenging conditions. To counter the intricate challenges posed by drought and weed infestations, a holistic understanding of their synergistic effects on crops is imperative. This study's insights contribute to this endeavor, calling for ongoing research to unravel the intricate mechanisms underlying weed responses to environmental stressors, and ultimately enabling sustainable and effective weed management practices in the face of evolving climatic conditions.

Author contribution

Dasari Sreekanth, Pawar Deepak Vishwanath, Shobha Sondhia, JS Mishra: Conceptualization Supervision, Project administration; Dasari Sreekanth, Pawar Deepak Vishwanath: Methodology; Dasari Sreekanth, Pawar Deepak Vishwanath, Survi Mahesh, Shobha Sondhia, C.R. Chethan, P.S. Basavaraju: Validation; Dasari Sreekanth, Nagaraju Mukkamula, B Kiran Kumar: Formal analysis; Dasari Sreekanth: Investigation; Dasari Sreekanth, Pawar Deepak Vishwanath: Data Curation; Dasari Sreekanth, Survi Mahesh: Writing - Original Draft; Dasari Sreekanth, Pawar Deepak Vishwanath, Survi Mahesh, JS Mishra, PK Singh, Nagaraju Mukkamula, B Kiran Kumar: Writing - Review & Editing; Dasari Sreekanth, Survi Mahesh, C.R. Chethan, P.S. Basavaraju, Nagaraju Mukkamula: Visualization.

Acknowledgment

The authors express gratitude to the ICAR-Directorate of Weed Research for the essential facilities and financial assistance.

Funding source: Not available.

Declaration of competing interest

The authors state no identifiable financial conflicts or personal associations that could impact the presented findings.

Data availability

Data from this study can be obtained by contacting the corresponding author, Dasari Sreekanth, upon a reasonable request.

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Elucidating the interactive effects of drought, weeds, and herbicides on the physiological, biochemical, and yield characteristics of rice

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Journal Publication

Version 1

Abstract

Aims

Methods

Results

Conclusions

Figures

Introduction

Materials and Methods

Drought imposition and application of herbicide

Soil moisture content (SMC)

Assessment of physiological and biochemical indices

Relative water content (RWC)

Membrane stability index (MSI)

MSI = [1−(C1/C2)] × 100

Total chlorophyll content

Measurement of gas exchange parameters

Oxidative Stress Markers

Superoxide Anion Content (O2.−)

Hydrogen peroxide content (H2O2)

Antioxidative enzymes assay

Non-enzymatic antioxidants

Proline content

Total phenolic content (TPC)

Ascorbic acid content (AsA)

Starch content

Grain yield

Weed growth and biomass traits

Statistical analysis

Results

Soil moisture content (SMC)

Physiological and biochemical traits

RWC

MSI

Total chlorophyll content

Gas exchange parameters

Oxidative Stress Markers

Superoxide ion

H2O2

Lipid peroxidation (MDA content)

Antioxidant enzymes

Non enzymatic antioxidants

Proline

Total phenolic content (TPC)

Ascorbic acid content (AsA)

Starch content

Yield

Weeds growth, biomass traits

Principal component analysis (PCA)

Pearson’s correlation analysis

Discussion

Conclusion

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1

Superoxide Anion Content (O₂.⁻)

Hydrogen peroxide content (H₂O₂)

H₂O₂