Herbicide and irrigation management options in conventionally-tilled wheat: deciphering water and energy budgeting, and grain and monetary output in north-Indian plains

Abstract

In South Asia, declining water tables due to increased irrigation and labor shortages for manual weeding pose significant challenges for wheat production. Additionally, herbicide resistance, often resulting from poor management practices, further complicates weed problems. The objective of this study was to assess the impacts of traditional irrigation regimens (IRs) and herbicide application on wheat crops. The findings showed that when irrigation was applied at 100 mm CPE (IR₄), and at 40 mm CPE (IR₁), different combinations of herbicide to weed managment were tested. In comparison to the other treatments, application of irrigation at 40 mm cumulative pan evaporation (IR₁) along with the Pendimethalin₁₀₀₀ g ha⁻¹ (pre-em) in combination with clodinafop-propargyl 10% + metribuzin 22% + sulfosulfuron 4.2% at a rate of 1125 g ha⁻¹ at 30 DAS (WM₁) produced the best results in terms of crop yields, economic returns, relative water content, consumptive use, rate of water use, water use efficiency, water productivity, energy input–output, energy returns, energy productivity, energy intensity, specific energy, energy efficiency, maximum field capacity, available soil water, and soil profile moisture extraction pattern. The only exceptions were Pendimethalin₁₀₀₀g ha⁻¹ (pre-em) combined with carfentrazone ethyl 20% + sulfosulfuron 25%WG), at the rate of 100 g ha⁻¹ at 30 DAS (WM₂) and the weed-free treatment (WM₅), where the differences were not statistically significant. The yield of wheat grain (14.26 kg ha⁻¹) and straw (14.41 kg ha⁻¹) decreased as the unit dry matter production of weeds increased. The study recommends exploring additional weed control strategies and irrigation management options in future improve wheat yields in conventionally-tilled systems.

Introduction

India has the highest population density globally, leading to an increasing demand for food grain production. To address these challenges, it is essential to implement measures that will boost food grain production in the country. Increasing food grain production is our primary focus, and this cannot be accomplished by simply expanding arable land horizontally due to its limited potential. Twenty percent of the world’s calories come from the vital cereal crop wheat (Triticum aestivum)¹. With an average productivity of 3.47 tons per hectare, it was grown on 220 million hectares (32% of the global area) in 2019–20, yielding 764.4 million tons (USDA 2020).Wheat covers an area of 29.32 m ha (14% of the global area), producing 103.6 mt annually (12.92% of global production) with a productivity of 3.53 t ha^-1 (USDA 2019–20). This makes a substantial contribution to the nation’s overall food grain production, guaranteeing the majority of the population’s access to food and nutrition^2,3.

Global drought and diminishing water supplies have intensified the danger to sustainable crop production across the globe posed by a changing climate^4,5. Due to continuously growing population, urbanization, and industrialization, there is now rivalry for freshwater with the agricultural industry⁶. According to estimates from Tillman et al.⁷, the demand for food grain over the world will double by 2050 and for sustainable crop production and food grain security irrigation is also inevitable⁸. In order to meet the current agricultural demand and the population’s daily food grain requirement, the current situation highlights the need for increased food grain production with limited water supply⁹. Due to water scarcity, irrigation water resources should be effectively utilized, keeping in mind high crop yields¹⁰. In this respect, irrigating crop plants with deficit water regime is a practicable tactic to save water and obtaining reasonable productivity^11,12,13. However, reduction in soil water content results in decline in nutrients availability and uptake^14,15. Deficit irrigation imposed the plants to the adverse effects of drought, leading to improper availability and utilization of nutrients, weakening yield and quality^{16,17,18,19,20}. Physiologically, drought caused disturbance in photosynthetic systems^21,22,23 and degradation in plant pigments with stomata closure, reducing dry mater accumulation^24,25.Effective planning and management of the available water resources in agriculture can meet this need. Carefulirrigation of the crop will undoubtedly cut down on losses from drainage and soil evaporation.

The best strategy for raising wheat yield and water productivity is to apply water during crucial crop growth phases. Because less water is used throughout the full crop growing season without affecting crop output, irrigation systems are widely accepted and utilized throughout the nation^26,27. Numerous studies^28,29,30 have shown that irrigation is a promising strategy when it comes to wheat crops, especially during the critical stages of crop growth. Nonetheless, these outcomes differ significantly between various wheat cultivars and cultivation conditions. Crop water requirements are directly influenced by crop management practices and local climate, so irrigation during critical stages of crop growth leads to differing water productivity across different regions.An increase in irrigation frequencies significantly increased wheat grain yield, according to Verma et al.³¹, and Verma et al. (2017). The most critical growth stages of the crop, including the emergence of flag leaves, the soft dough stage, the hard dough stage, and the beginning of crown roots, observed the greatest increase in irrigation frequency³². Additionally,³³ found that if irrigation was not provided at the critical stages of tillering, elongation, flowering, and grain maturity, the grain yield of the crop reduced by 8%, 20.8%, 14.2%, and 12.2%, respectively.

Weed infestation is a significant concern, especially in wheat cultivated in central parts of Uttar Pradesh^{5,31,34,35,36}. Some of the common weeds found in these areas include Phalaris minor, Avena ludoviciana, Avena fetua, Cynodondactylon, Chenopodium album, Convolvulus arvensis, Melilotus indica, Rumex dentatus, Anagallis arvensis, Lathyrus aphaca, Vicia sativa, Vicia hirsuta, Cyperus rotundusandSpergula arvensisetc. Crop productivity is detrimentally influenced by weeds as biotic stressor^37,38. Weeds compete with crop plants for water and nutrients suppressing plant growth and development, and reducing yields³⁹. Accordingly, weed can cause indirect drought impact via removing soil moisture, hence yield and quality declined^18,19,20,40. To overcome the hazardous impacts of weeds, efficient weed control tactics should be implemented in crop fields^{16,18,19,20,41}. The use of herbicides is one of the effective tools implemented in cereals to control the associated weeds^18,19,20. As for weed control in wheat as the main cereal crop, application of herbicides has achieved more than 90% efficiency in managing various weed types while obtaining high yields⁴². The relationship between water and weed management is well understood in modern agriculture, where weed dynamics in wheat are greatly influenced by irrigation frequency⁴³.There are excessive pressures on the production of food grains from agricultural lands due to factors such as limited irrigation infrastructure, population growth, and a lack of water for crop development. Water management demands prompt attention, which can only be achieved by more effectively applying water to the crop.Numerous methods for planning irrigation in wheat have been studied. The irrigation strategy based on the crucial stages has, however, received the most acceptance. Since there is a lack of information on how Varanasi’s irrigation regimes (IRs) and weed control methods (WMs) affect water conservation. Therefore, necessity of appropriate WMs to avoid the critical crop weed competition in the wheat and timely supplementation of irrigations on the basis of cumulative pan evaporation (CPE) of wheat crop to save water and to increases water use efficiency(WUE) and water productivity (WP)was felt. A two-year field experiment was therefore conducted to examine the impact of irrigation methods and herbicide-based weed killers (WMs) on water economization in wheat grown in the irrigated Varanasi district of Uttar Pradesh, India.

Materials and methods

Site description and soil

The field experiment was conducted in the eastern wheat-growing region of Uttar Pradesh, India with the permission of Head of department, in the fourth week of November in the years 2018–19 and 2019–20, respectively. The Northern Indo-Gangetic plans place the experimental field, Banaras Hindu University, Varanasi, at 128.9 m above mean sea level and 25.18’ N, 83.03’ E. The experimental site’s surface soil (0–30 cm layer) had a sandy clay loam texture (55.1% sand, 20.4% silt, and 24.5% clay), with a saline reaction (pH 7.6). Table 1 shows that the top soil has a low organic carbon content (0.42%), medium accessible phosphorus and potassium, and low levels of available nitrogen, sulfur, and zinc.

Table 1 Initial physico-chemical properties of top soil.

Climate and weather

The study area has a semi-arid sub-humid climate, with severely hot and dry summers and frigid winters with minimal precipitation. The experimental site receives 868 mm of rainfall on average per year, with the monsoon season between June to September accounting for about 89% of that total. The potential annual evapotranspiration (PET) of the testing site is 1560 mm. The monsoon typically begins and ends in the fifth week of June and extends into the end of September (sometimes even into the first week of October) (Table 2).

Table 2 Average monthly meteorological data collectedduring experimentation.

Treatments and design of experiments

The study included a split plot design, three replications, and twenty combination treatments. The main plots received four different irrigation schedules, and the sub plots received five different herbicidal treatments (Table 3).

Table 3 Treatment details of the experiment.

Crop management

On November 24 and 25, of 2018–19 and 2019–20, respectively, 100 kg ha^–1 seed was used to sow the wheat variety “HD 2967” at a line separation of 22.5 cm apart by opening cuttings with a seed-drill machine.Seeds were obtained from the Department of Genetics at the Institute of Agricultural Sciences,BHU. Every plot received the same treatment in terms of inputs and other agronomic techniques. The recommended dose of fertilizers N, P₂O₅, and K₂O (120:60:60 kg ha⁻¹) was evenly applied throughout the field using urea (46% N), DAP (46% P₂O₅), and murate of potash (60% K2O), respectively. As a basal dosage, P, K, and half of the N were given in full. However, the remaining half dose of N was top-dressed in two equal splits during the stages of active tillering and panicle commencement, respectively. Herbicides were sprayed on weeds using a backpack sprayer fitted with a flat fan nozzle in accordance with the procedures. The wheat cultivar "HD 2967," well-known for its encouraging outcomes in India’s North Western Plain Zone, was employed in the current investigation . This wheat variety can be sown in timely irrigation conditions. It has a 5.5–6.0 t ha⁻¹ yield potential. Normally, the cultivar matures in 130–140 days. When fully grown, a plant is 80–100 cm tall. White glumes cover the medium-density, tapering ears. Chapati-making benefits from the variety. Its grain is medium in size, oval in form, and semi-hard in texture.

Data recorded

To record data, standard procedures were implemented. Using quadrats positioned at random in each plot, the total weed density was measured at 30 and 60 DAS (0.5 × 0.5 m²). During these phases, the biomass of the weeds was measured after they were chopped at the soil’s surface and oven-dried for 72 h at ± 65 °C. The following formula was used to determine weed control efficiency (WCE) (Verma et al. 2016):

$${\text{WCE}}\left( \% \right) = \frac{{{\text{D}}{\text{.M}}{\text{.C}} - {\text{D}}{\text{.M}}{\text{.T}}{.}}}{{{\text{D}}{\text{.M}}{\text{.C}}{.}}} \times {100}$$

(1)

where, WCE = weed control efficiency, DMC- dry weight of weeds (g m⁻²) from weedy check, and DMT- dry weight of weeds(g m⁻²) from treated plot.

The following formula was used to determine the reduction in grain yield caused by weeds:

$${\text{WI}} = \frac{{{\text{X}} - {\text{Y}}}}{{\text{Y}}} \times 100$$

(2)

where, WI = weed index, X = grain yield of weed free plot, Y = grain yield of treated plot.

$${\text{Nutrient depletion by weeds }}\left( {{\text{kg ha}}^{{ - {1}}} } \right) = \frac{{{\text{Nutrient content in weed (\% )}}}}{{{100}}} \times {\text{Dry mattter of weeds (kg/ha)}}$$

(3)

Soil moisture was monitored during planting, two days before and after each irrigation, and at harvest. The results were expressed as a percentageof total profile use. Dastane⁵² states that the soil moisture content and available meteorological data were used to calculate the water consumption, rate of water use, water usage efficiency, and moisture extraction pattern. On the other hand, water productivity was calculated in rupees per millimeter and reported using gross returns and the total amount of water used by the crop⁵³.

$${\text{WP }} = {\text{ gross returns }}\left( {{\text{Rs}}./{\text{ha}}} \right)/{\text{total water used }}\left( {{\text{mm}}} \right)$$

(4)

Barrs and Weatherley⁵⁴ developed the following formula to calculate relative water content (RWC):

$${\text{RWC }} = \, \left( {{\text{FW }} - {\text{ DW}}} \right)/\left( {{\text{FW }} - {\text{ TW}}} \right) \, \times {1}00$$

(5)

whereas, RWC: is the relative water content, FW: is the leaf fresh weight, DW: is the leaf dry weight, and TW: is the turgid leaf weight.

The economic was computed using the input and output prices found in the local market. The input and product prices were essentially constant during the course of the two research years. The total cost of production was calculated by adding the prices paid for all of the goods and services utilized in the production of crops. The marketable values of the grain and straw were added to determine the gross returns (GR). To obtain the net returns (NR), the manufacturing cost was deducted from the gross returns (GR). To compare the returns from each treatment, the following equation was used to do partial gross margin (PGM) analysis⁵⁵:

$${\text{PGM }} = \, \left( {{\text{Y}}*{\text{P}}} \right) \, {-}{\text{W}}$$

(6)

whereas, PGM: Partial gross margin, Y: Yield of wheat per hectare, P: Average price of wheat and W: Cost of irrigation water.

Energy input–output relationship

Both operational (direct) and non-operational (indirect) energy are inputs. Non-operational energy included things like seed, manures, chemical fertilizers, and pesticides, whereas operational energy included things like manual labor, fuel, machinery, etc. To calculate energy consumption based on energy equivalents, primary data on various inputs and management techniques were employed (Table 4). The energy output from the products (grain and straw yield of component crops) was calculated by multiplying the appropriate production levels by their corresponding energy equivalents (Table 4). Energy use indices were computed using standard procedures^56,57,58.

Table 4 Equivalent energy used in crop production for both input and output.

Energy input (MJha⁻¹) = A

Energy output (MJha⁻¹) = B

$$\text{Energy return or energy balance }(\text{MJ}/\text{ha}) =\text{ B}-\text{A}$$

(7)

$$\text{Energy productivity }(\text{kgMJ}-1) =\frac{\text{Economic yield }\left(\text{kg}/\text{ha}\right)}{\text{ Energy input }\left(\text{MJ}/\text{ha}\right)}$$

(8)

$$\text{Energy use efficiency }=\frac{\text{Energy output }(\text{MJ}/\text{ha})}{\text{Energy input }(\text{MJ}/\text{ha})}$$

(9)

$$\text{Energy intensity}=\frac{\text{Gross energy output }(\text{MJ}) }{\text{Cost of cultivation }()}$$

(10)

$${\text{Specific energy}} = \frac{{{\text{Energy}}\;{\text{input}}}}{{{\text{Yield}}({\text{kg}}/{\text{ha}})}}$$

(11)

On April 4, 2019, and April 9, 2020, the net plot area of each treatment plot was physically harvested. The harvested produce was initially bundled, marked, and left to sun dry in the field for a few days before being transported to the threshing floor. Following threshing and cleaning, the yield of grain and straw was measured at 14% moisture.

Statistical analysis

Data obtained annually were averaged out replication-wise in order to do statistical analysis using analysis of variance procedures in accordance with the split-plot design (4 × 5) described by Gomez and Gomez ⁶³. The statistics on the density and dry weight of the weed were first converted using the square-root transformation ($\sqrt {x + 0.5}$)and then assessed in order to normalize the count data. The "F" test was used to assess the treatment effects’ significance. The least significant difference (LSD) was calculated at the 5% level of probability (p = 0.05) for comparing treatment means.

Results

Climate

During both the crop seasons scantly rainfall was received during February to April. A rainfall of 19.3 mm and 21.2 mm was recorded during crop period of 2018–19 and 2019–20, respectively. In 2018–19 and 2019–20, the experimental site’s average maximum and minimum temperatures,ranged from 20.7–37.8 °C and 6.5–19.9 °C to 17.1–33.2 °C and 6.5–18.2 °C, respectively. The maximum (3.6–4.5 °C) and minimum temperature (0.1–1.7 °C) were higher in 2018–19 than 2019–20. In the beginning, from December to March, as the crop became older, the relative humidity (RH) increased; after that, it steadily declined as the temperature rose. Bright sunshine hours over the investigation period were generally consistent with small fluctuations.

Relative composition of weeds

In the experimental crop, the most prominent weed flora was found in all the treatments included Cynodandactylon, Phalaris minor, Avena ludoviciana, Chenopodium album, Vicia sativa, Anagallis arvensis, Melilotus indica, Rumex denticulate, Cyperus rotundus and Cyperus esculantus. Further among grassy weeds, Cynodandactylon (16.08%) was the most dominant followed by Phalaris minor (115.58%, Fig. 1A). Amongst three dominant broad leaved weeds, (BLW), Chenopodium album (17.48%) showed dominance over Vicia sativa (113.28%) and Anagallis arvensis (11.39%, Fig. 1B). For sedges, Cyperus rotundus (7.16%) recorded dominance over Cyperus esculantus (3.84%, Fig. 1C). Overall, BLW accounted for higher percentage (56%) of the total weeds followed by grassy weeds (33%) and Sedges (11%, Fig. 2 & 2a).

Fig. 1

Species-wise density of (A) grassy weeds (B) broad leaf weeds and (C) sedges in weedy check plots at 60 days after sowing (pooled data of two years).

Fig. 2

Relative composition of grasses, BLWs and sedges in weedy check plot at 60 DAS (pooled data of two years). (a) Relative composition of all weeds at 60 DAS in weedy-check plot (pooled data of two years).

Weed density

The density of all weed flora was lower at 30 DAS, but as the crop progressed toward its maximum development stage, the density of weeds rose, reaching its maximum at 60 DAS. All of the irrigation rates (IRs) markedly changed the density of weeds; IR4’s watering at 100 mm CPE produced the lowest amounts of weed flora (grasses, BLWs, and sedges), followed by IR3. The plots under the IR₁ followed by IR₂ were infested mostly all groups of weed flora. Among the WMs, WM₁-WM₃ and WM₅ showed a lethal effect on weeds and recorded the lower weed number over weedy- check (Table 2). Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2% at 1125 g ha⁻¹ 30 DAS (WM₁) recorded significantly lowest density of all groups of weeds though it was statistically at par with Pendimethalin₁₀₀₀ (pre-em) fbcarfentrazone ethyl 20% + sulfosulfuron 25%WG) at 100 g ha⁻¹ at 30 DAS (WM₂) and Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 15% + metsulfuron 1% at 400 g ha⁻¹ 30 DAS (WM₃), respectively.

Total density and dry weight of weed

Weedy-check had the highest dry weight and weed density, indicating a significant association between the two. However, irrigation at 100 mm CPE (IR4) resulted in the lowest total weed density and dry weight when compared to IR3, IR2, and IR1. The WMs, WM1–WM3, showed a noticeable effect on weeds; they assessed lower dry weight and overall weed density in comparison to the weedy control.Within the WMs, Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2% at 1125 g ha⁻¹ 30 DAS (WM₁) was the excellent one in reducing the total weed density and their dry weight followed by Pendimethalin₁₀₀₀ (pre-em) fbcarfentrazone ethyl 20% + sulfosulfuron 25%WG) at 100 g ha⁻¹ at 30 DAS (WM₂) and Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 15% + metsulfuron 1% at 400 g ha⁻¹ 30 DAS (WM₃), respectively (Table 5).

Table 5 Effect of IRs and WMs on density and dry weight of weeds.

Fig. 3

Interaction effect of IRs and WMs on weed density at 30 DAS (Pooled data of two years). (a) Interaction effect of IRs and WMs on weed density at 60 DAS (Pooled data of two years). (b) Interaction effect of IRs and WMs on total weed density at 30 and 60 DAS (Pooled data of two years).

Fig. 4

Interaction effect of IRs and WMs on total weed dry weight at 30 and 60 DAS (Pooled data of two years).

Weed Index (WI) and WeedControlEfficiency (WCE)

The degree of crop-weed interference’s severity determines the WI value. In comparison to IR3, IR2, and IR1, respectively, IR4 recorded the highest WI and WCE (Figs. 5 and 6). When compared to the other treatments, WM1 showed the highest WCE and the lowest WI value among the WMs. The WCE also followed the sequence WM1 > WM2 > WM3 and WI in reverse.

Fig. 5

Effect of IRs and WMs on WCE at 30 and 60 DAS (Pooled data of two years). Figure 5a. Interaction effect of IRs and WMs on WCE at 30 and 60 DAS (pooled data of two years).

Fig. 6

Interaction effect of IRs and WMs on weed index (pooled data of two years).

Nutrient content and their depletion by weeds

The irrigation at 40 mm CPE (IR1) showed the highest weed depletion and the lowest concentration of N, P, and K when compared to other IRs. As anticipated, the maximum concentrations of N, P, and K and the least amount of weed depletion were found while hand weeding at 20 and 40 DAS (weed free) conditions in comparison to the other WMs. However, within herbicidal treatments, Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha⁻¹ at 30 DAS was an excellent one in reducing the mining of N, P and K by weeds (Table 6 and Fig. 7).

Table 6 Effect of IRs and WMs on nutrient content and their depletion by weeds.

Fig. 7

Interaction effects of IRs and WMs on nutrient depletion by weeds(pooled data of two years)

Consumptive use (CU) and Rate of WaterUse (RWU)

Since wheat’s water requirements would not have been influenced by the ground water table, the CU values in the current experiment may, most likely, be underestimated.The irrigation at 40 mm CPE (IR1), then, produced higher CU, RWU, and RWC than the other IRs (Table 7), although IR4 had the highest WUE and WP. Among WMs, the CU, WUE, RWC, WP and RWU was obtained maximum with the application of Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha⁻¹ at 30 DAS (WM₁) and it was statistically at par with WM₂. None of the WMs as effective as WM₅ (weed free) with respect to increase in CU and RWU.

Table 7 Direct effect of IRs and WMs on CU, WUE, RWU, water productivity and relative water content (pooled data of two years).

Water UseEfficiency (WUE) and wWaterProductivity (WP)

WUE and WP estimated for the IRs and WMs, the highest WUE and WP values were observed in IR₄ followed by IR_3, IR₂ and least in IR₁, respectively. Wheat displayed the highest WUE and WP in the weed-free condition compared to the other WMs, as was to be expected. Nevertheless, Pendimethalin₁₀₀₀(pre-em) fb clodinafop-propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha-1 at 30 DAS (WM1) was the most effective herbicidal treatment for increasing the WUE and WP, with WM2 and WM3 following suit (Table 8).

Table 8 Interaction effect of IRs and WMs on CU, RWU, WUE and WP.

Relative WaterContent (RWC) and Field Capacity (FC)

RWC and FC were often more accustomed to higher irrigation regimens. The irrigations at 40 mm CPE (IR1), followed by those at 60, 80, and 100 mm CPE, respectively, reported the highest RWC and FC. WMs made progressive increase in the RWC and FC over weedy-check plot. It would also be explained from the Table 6 & Fig. 8 within the herbicidal treatments, application of Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha⁻¹ at 30 DAS recorded the maximum RWC and FC followed by same was with WM₂ and WM₃, respectively.

Fig. 8

Effect of IRs and WMs on field capacity (pooled data of two years).

Available SoilMoisture (ASM) and MoistureExtractionPattern (MEP)

The ASM and MEP value depends on the extent of available soil moisture. At 0–30 and 30–60 cm soil depths, maximum ASW and MEP was recorded for IR₁ compared to IR₂, IR₃ and IR₄, respectively.Conversely, the maximum ASW and soil moisture were retrieved at soil depths of 60–90 cm with irrigations at 100 mm CPE (IR4), followed by IR3. Among WMs, WM₁ exhibited the highest value of ASM and MEP than the other treatments. Further, the ASM and MEP followed the order WM₁ > WM₂ > WM₃. However, WM₅ (weed free) was the best one for ASM and MEP over all the WMs (Figs. 9, 10). MWs showed nearly similar trends for ASW and MEP in 60–90 cm soil depth. Overall, ASW and SME were greater from the top soil layer (0–30 cm).

Fig. 9

Effect of IRs and WMs on ASM at 90 DAS (pooled data of two years).

Fig. 10

Effect of IRs and WMs onmoisture extraction pattern (pooled data of two years).

Yield attributes

IRs showed the substantial difference on yield attributes. The IR₁ irrigated crop, which was statistically at par with IR₂, that had the maximum effective tillers, length of spike, grains spike⁻¹, weight of grains spike⁻¹ and 1000 grain weight compared to IR₃ and IR₄ irrigated crop showed the lower value of the same attributes (Fig. 11). Within WMs, WM₁ the wheat tillers, spike length, grains spike⁻¹, weight of grains spike⁻¹ and 1000 grain weight were all significant with Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha⁻¹ at 30 DAS (WM₁) over Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 15% + metsulfuron 1%, 400 g ha⁻¹ at 30 DAS (WM₃) and was statistically similar with Pendimethalin ₁₀₀₀ (pre-em) fbcarfentrazone ethyl 20% + sulfosulfuron 25%WG, 100 g ha⁻¹ at 30 DAS (WM₂).

Fig. 11

Effect of IRs and WMs on yield attributes (pooled data of two years).

Wheat yield

The different IRs had a substantial gain in wheat outcome. Among IRs, IR₁ increased the grain and straw yield by 4.97 and 1.46% over IR₂, 13.83 and 2.04% over IR3 and 18.17 and 4.28% over IR4, respectively. The impact of WMs was also good on wheat yield; Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha⁻¹ at 30 DAS enhanced grain, straw and biological yield significantly over Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 15% + metsulfuron 1%, 400 g ha⁻¹ at 30 DAS and weedy check and statistically similar to WM₂. Further, the grain and straw yield followed the order WM₁ > WM₂ > WM₃ > WM_4, respectively. In comparison to WM2, WM3, and WM4, WM1 had greater grain and straw yields of 4.86 and 3.56%, 13.83 and 9.34%, and 23.04 and 15.31%, respectively. The maximum yield of grain, straw, and biological yield was undoubtedly produced by the weed-free (WM5) condition and was noticeably higher than that of the other WMs (Table 9). Given the limited water supply and the more effective weed control technique (WM1), the grain production was reduced in IR1.The Interaction effect of IRs and WMs on wheat yield are shown in Table 10.

Table 9 Effect of IRs and WMs on wheat yield and weed index.

Table 10 Interaction effect of IRs and WMs on wheat yield (pooled data of two years).

Gross Returns (GR), Net Rturns (NR) and Partial GrossMargin (PGM)

Significantly the highest GR, NR (₹66,154 ha⁻¹) and PGM (₹79,135 ha⁻¹) were recorded with irrigations at 40 mm CPE (IR₁) followed by IR₂ and IR_3, respectively (Table 11). Whereas, least GR, NR (₹ 53,622 ha⁻¹) and PGM (₹ 65,574 ha⁻¹) were recorded with irrigations at 100 mm CPE (IR₄). Among IRs, IR₁ increased the NR by 4.99, 14.41 and 18.94% and PGM by 4.34, 13.04 and 17.14%, respectively over IR_2, IR₃ and IR₄ treatments. WMs showed significant advantage over weedy-check in maximizing GR, NR and PGM. Within the WMs, WM₁ recorded the highest GR, NR and PGM followed by the rest of WMs. Further, the GR and PGM followed the order WM₅ > WM₁ > WM₂ > WM₃ > WM_4, respectively. However, NR followed the order WM₁ > WM₅ > WM₂ > WM₃ > WM_4, respectively.

Table 11 Direct effect of IRs and WMs on gross returns, net returns and partial gross margin (pooled data of two years).

Energy budgeting

Energy input and energy production were considerably (p = 0.05) impacted by the IRs and MWs (Table 12). With IR1 and IR4, respectively, the highest energy input (18,595 MJ ha⁻¹) and lowest energy input (15,959 MJ ha⁻¹) were measured. Despite having the largest energy input, IR1 had significantly higher energy production (165,890 MJ ha⁻¹), return (147,294 MJ ha⁻¹), productivity (0.229 MJ ha⁻¹) and intensity (4.15 MJ ha⁻¹) than IR3 and IR4,but remained statistically similar to IR₂. IR₁ increase the energy output by 9.4%, energy return 8.8%, energy productivity 6.6% and energy intensity 1.4% over IR₄. However, IR₄ recorded higher energy efficiency (9.42 MJ ha⁻¹) and specific energy (4.68 MJ ha⁻¹) over all the IRs treatments. In WMs, the highest (17,351 MJ ha⁻¹) and lowest (16,692 MJ ha⁻¹) energy input were obtained with WM₁ and WM₄ treatment, respectively. Among WMs treatments, WM₅ (weed free) was found to be best over WM₄, WM₃ and WM₂ and it were statistically similar to WM₁, recorded higher energy output (173,072 MJ ha⁻¹), energy return (155,752 MJ ha⁻¹), energy productivity (0.245 MJ ha⁻¹) and energy efficiency (9.99 MJ ha⁻¹) and these were higher by 20.3%, 22.1%, 22.9% and 17.3%, respectively over WM4. Concerning the highest energy intensity (4.43 MJ ha⁻¹) and specific energy (5.30 MJ ha⁻¹) was observed with WM₁ and WM₄ treatment, respectively.

Table 12 Effect of IRs and WMs on energy input–output relationship (pooled data of two years).

Irrigation Regimes (IRs) x WeedMPractices (WMs)

The density of all weed groups (Fig. 3, 3a), total weed density (Fig. 3b), total weed dry weight (Fig. 4), at 30 and 60 DAS, WCE (Fig. 5a), WI (Fig. 6), nutrient depletion by weeds (Fig. 7), CU, WUE, RWU and WP (Table 7), grain, straw, and biological yield (Table 9), GR, NR, and PGM (Table 11).The density of grass, BLWs, and sedges was considerably lower in the IR4 plots treated with Pendimethalin₁₀₀₀(pre-em) fb clodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha-1 at 30 DAS compared to all other combinations of IRs and WMs at 30 and 60 DAS.

For total density of weed at 30 and 60 DAS, IR₁ plot treated with Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha⁻¹ at 30 DAS (WM₁) was did not differ significantly from Pendimethalin ₁₀₀₀ (pre-em) fbcarfentrazone ethyl 20% + sulfosulfuron 25%WG, 100 g ha⁻¹ at 30 DAS (WM₂). However, IR4 plots treated with Pendimethalin₁₀₀₀(pre-em) fb clodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha-1 at 30 DAS recorded significantly lower (7–28% and 85–97%) total density of weeds than all IRs × (WM1, WM2,WM3) combinations; the reduction was still more when this treatment combination was compared with weedy-check at 30 and 60 DAS, respectively. The total density of weed was significantly higher in all the IRs(WM1, WM2, and WM3) combinations than in all the WMs with IR1 combinations.

Dry weight of weed in IR₄ was 9.84–11.78% and 31.0–33.5% lower at 30 and 60 DAS, respectively than IR₃, IR₂ and IR₁ under WM₁, WM₂ and WM₃, respectively. For weedy check plots, IR₄ recorded 1.60, 2.80, 5.0%; and 4.4, 10.0, 12.2% lower dry weight of weed at 30 and 60 DAS, respectively than IR₃, IR₂, IR₁, respectively. Weed free and weedy check plots leaving aside, dry weight of weed from IR₁ plots applied with Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 15% + metsulfuron 1%, 400 g ha⁻¹ at 30 DAS (WM₃) was significantly higher than all the IRs × WMs combinations; the increase ranged from 7.41 to 38.4%.

The IR₁ plots applied with Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha⁻¹ at 30 DAS resulted significantly higher grain (12.69%), straw (9.18%) and biological yield (10.70%) than WM₃ and was statistically at par to WM₂. IR₄ plots applied with Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha⁻¹ at 30 DAS (4.66–16.19%) or Pendimethalin ₁₀₀₀ (pre-em) fbcarfentrazone ethyl 20% + sulfosulfuron 25%WG, 100 g ha⁻¹ at 30 DAS (4.89–17.70%) or Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 15% + metsulfuron 1%, 400 g ha⁻¹ at 30 DAS (5.46–19.37%) or weed free (4.49–16.34%) and weedy-check plots (6.12–21.42%) lower grain yield was recorded. Again, Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha⁻¹ at 30 DAS (WM₁) did not differ from weed free under IR₁ and IR₂, but did express its superiority under IR3 and IR₄ (Table 13). Also weed free plot exhibited (16.34 and 12.43%), (3.98 and 2.69%) and 9.39 and 6.86%), respectively higher grain, straw and biological yield was obtained under IR₁ and IR₂ than under IR₄.

Table 13 Interaction effect of IRs and WMs on economics of wheat (pooled data of two years).

For the WCE, irrigation at 100 mm CPE (IR₄) plots treated with Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha⁻¹ at 30 DAS recorded maximum WCE than all IRs × (WM₂, WM₃) combinations. The IR₁ × WM₃ combination recorded significantly lower WCE than all the combinations of these WMs (WM₁-WM₂) with IR₂, IR₃ and IR₄. However for WI, the IRs applied with Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha⁻¹ at 30 DAS resulted in significantly lowest WI than all the combinations of IRs and WMs; and IR₄ applied with Pendimethalin₁₀₀₀ (pre-em) fbclodinafop- propargyl 15% + metsulfuron 1%, 400 g ha⁻¹ at 30 DAS resulted significantly higher WI than all other combinations of IRs and WMs.

The depletion of nutrients (N, P, and K) by weeds was found to be significantly reduced in IR4 plots treated with Pendimethalin₁₀₀₀ (pre-em) fb clodinafop-propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha-1 at 30 DAS, compared to all combinations of IR1-IR2 × (WM1-WM3) and on par with all combinations of IR3 × (WM1-WM3).

Regression analysis

The results of regression analysis revealed a substantial moderate to strong negative connection between weed density (R2 = 0.373 & 0.612, Fig. 12) and dry weight (R2 = 0.366 & 0.614, Fig. 13) and grain and straw yield. Figure 14 shows the relationship between wheat yield and WCE, with grain and straw yield significantly positively correlated with WCE (R2 = 0.366 and 0.614). Figure 15 shows a significant positive relationship between unit increases in water use efficiency and wheat grain and straw yield (R2 = 0.007 & 0.084).

Fig. 12

Relationship between weed density and wheat yield.

Fig. 13

Relationship between weed dry weight and wheat yield.

Fig. 14

Relationship between WCE and wheat yield.

Fig. 15

Relationship between water use efficiency and wheat yield.

Discussion

The weather during the experiment’s two years was generally favorable for wheat growth. From February to April, there was little rainfall, and cooler temperatures from December to January supported crop development. The temperature range was beneficial, and while relative humidity was optimal early on, it increased until March before declining as temperatures rose from March to April. Sunshine hours were mostly consistent, with slight variations due to clouds and rain.

Low temperatures have several beneficial effects on crop growth and development. Low temperatures can enhance enzyme activities in crop, such as sucrose synthase and glutamine synthase. Cooler temperatures can inhibit the development of certain pests and diseases, reducing the need for chemical controls.

Addressing water shortages through proper hydration and resource management is essential for maintaining health and preventing these physiological impacts. Moisture stress significantly impacts plant growth, development, and yield, presenting a major challenge for sustainable agriculture. Its effects are not uniform; while some processes, like photosynthesis and nutrient uptake, are highly susceptible, others are less affected. Ultimately, moisture stress leads to reduced growth, quantity, and quality of crops, influencing the final yield by disrupting various physiological functions, including water relations and hormonal activities.

It has been reported that deficit water caused dangerous impacts on osmotic potential and disorders in nutrients uptake and utilization⁶⁴. Since drought stimulates the over generation of reactive oxygen species, critical damages occurred for chlorophylls, photosynthetic apparatus and several metabolic processes^21,22,65. Thus, crop growth and yield reduced under water deficiency^13,16.

Weed infestation is a significant challenge in conventional till irrigated wheat compared to zero or reduced tillage. To enhance productivity, effective irrigation regimes (IRs) and weed management strategies (WMs) are essential. Proper irrigation combined with suitable WMs can reduce weed density and biomass, lessening competition with crops and ultimately increasing wheat yields.

In recent assessments, broadleaf weeds (BLWs) were found to be more common than grasses and sedges. Weed density and dry weight increased until 60 days after sowing (DAS) and then declined, likely due to the ephemeral nature of most weeds. Higher irrigation levels (IR1 and IR2) resulted in greater weed density and biomass due to improved moisture availability, while reduced irrigation (IR4) led to lower weed growth, as the drier conditions inhibited weed development. Overall, increased irrigation promotes weed growth, while limited moisture reduces it, highlighting the importance of balanced irrigation and effective weed management in wheat cultivation. When compared to weedy-check, all of the WMs were able to significantly reduce dry weight and weed density. When administered at 30 DAS, Pendimethalin₁₀₀₀(pre-em) fb clodinafop-propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, 1125 g ha⁻¹, showed superior efficacy in killing weeds and hence lowering their growth parameters in comparison to WM2 and WM3, respectively.Three herbicides sprayed as a ready mix had a combined impact that effectively controlled all varieties of weed flora, which contributed to the treatment’s better efficacy. The WM1 and WM2 statistically outperformed the WM3. These findings are supported by Singh et al.'s³⁵, and Singh et al.'s⁵ research. Future weed flushes could not fully develop in the shade of wheat plants because the first flush of weeds was identified at 20 DAS later^5,34,66. Herbicidal treatments were shown to be inferior to HW at 20 and 40 DAS.

The efficacy of a particular treatment compared to untreated plots was assessed using WCE. In comparison to the other IRs in both years, IR₄ yielded superior WCE results. The decrease in soil moisture led to a reduction in the amount of dry weeds, which in turn increased the WCE⁸. In addition to the impact on WCE, WMs also demonstrated significant effects, alongside IRs. WM1 exhibited a higher WCE compared to WM2 and WM3. The combined impact of multiple herbicides might be responsible for the influence on weed flora. When herbicides are used in combination without HW, studies have shown that they can result in greater WCE^34,35,36,67. HW at 20 and 40 DAS, however, proved to be more effective than all herbicide treatments. HW demonstrated the highest WCE, effectively eliminating all weed biotypes, as reported by Verma et al. (2007a), Sangwan et al.², and Singh et al.⁵. WI provided evidence of the decrease in crop yield due to the presence of weeds compared to weed-free conditions. In the IR₁, a lower WI was observed, while in the IR₄, a comparatively higher WI was recorded. The lower WI was found to be linked to the IR₁ treatment due to the more effective weed control, resulting in reduced yield loss. Due to its exceptional ability to manage various types of weed flora, WM₁ demonstrated lower WI compared to other WMs in studies conducted by Singh et al.³⁴ and Lakra et al.⁸.

The depletion of NPK by weeds was influenced by the nutrient content found in the dry biomass of the weeds. Consequently, the depletion of N, K, and P were reduced when there is a decrease in weed dry biomass, and vice versa. Weeds under IR4 treatment accumulated higher amounts of N, P, and K compared to other treatments. This can be attributed to an increase in the yield of dry biomass from the weeds. While the nutrient depletion caused by weeds was lower in IR4 and IR3 compared to IR₂, it was likely because of the reduced weed dry biomass production. In a recent study by Lakra and Husain⁶⁸, it was found that weeds experienced lower levels of plant nutrient loss when subjected to reduced irrigation and a consistent water supply. On average, WM1, WM₂, and WM₃ resulted in significantly less depletion of total N, P, and K by weeds compared to the untreated control. The depletion rates for WM1, WM2, and WM3 were 88.6%, 86.0%, and 84.7% lower, respectively. The weeds of WM1 resulted in the lowest level of nitrogen depletion. Because of the herbicides’ wide-ranging effects on weed growth, the use of WM1 (Pendimethalin₁₀₀₀ (pre-em) combined with clodinafop-propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, at the rate of 1125 g ha⁻¹ at 30 DAS) could potentially result in improved weed control, as suggested by studies conducted by Verma et al. (2008), Ansari et al., and Singh et al.^34,35,36.

The amount of soil water that is accessible strongly influences crop development, enhancement of yield qualities, and yield increase⁶⁹. According to Li et al.⁷⁰, it is suggested that the optimal soil moisture level during the anthesis and grain filling stage should be maintained between 55–70% up to a soil depth of 80 cm. For optimal growth and development in wheat crops, it is recommended to maintain a soil moisture level of 50–60% at FC⁷¹. The outcome of a crop is influenced by various factors related to its growth and yield. In this particular study, it was found that this relationship held true. Both IRs and WMs played a role in influencing wheat yield trait and yield. There have been significant variations observed in wheat yield trait and yield as indicated by IRs. The irrigated wheat crop with 40 mm CPE (IR₁) showed similar results to the crop with 60 mm CPE (IR₂) in terms of effective tillers, spike length, grains spike⁻¹, grain weight spike⁻¹, 1000 grain weight, grain, straw, and biological yield. However, the crops with 80 mm CPE (IR₃) and 100 mm CPE (IR₄) had lower values for these parameters. The crop’s yield saw a significant increase due to its robust growth and the presence of traits that enhance yield, all due to an ample supply of moisture⁷². Research conducted by Meena et al.²⁸, Dhaliwal et al.³³, and Lakra et al.⁸ has shown that increasing the number of irrigations significantly improves both yield traits and overall crop yield. Based on previous research, it was found that wheat that underwent irrigation at specific growth stages showed increased yield-enhancing traits and overall yield. These stages include CRI, booting, stem elongation, tillering, and after-flower initiation. Improved crop growth and development were observed under different irrigation regimes due to the increased soil moisture. This enhancement in water availability contributes to the maintenance of the plant’s internal water balance^26,30. Regular irrigation and an optimal growing environment are crucial factors that enhance crop yield²⁷. Several studies have also reported the quadratic relationship between wheat yield and water availability, including the research conducted by Islam et al. (2018), Thamer et al.²⁹, and Dhaliwal et al.³³.

In addition to IRs, the impact of water management on wheat yield characteristics and overall yield was found to be significant. Among the different treatments, WM₁ showed a decrease in crop-weed competition, potentially resulting in higher yield traits and overall yield compared to WM3 and the weedy check. These results were statistically similar to WM₂. Due to the wide-ranging effectiveness of the herbicides used, the density and biomass of weeds were reduced in WM₁. As a result, crop nutrient uptake, growth, yield characteristics, and overall yield all experienced significant improvements. The findings were supported by research conducted by Singh et al.³, Shivran et al.⁶⁷, Sharma et al.⁷³, and Singh et al.⁵. Combining herbicides in joint applications proved to be more effective in preventing weed growth and enhancing wheat yield traits and overall yield, as demonstrated in recent studies^5,8. As a result of the underwhelming performance of wheat yield traits and increased competition from weeds in the weedy-check plots, the lowest contributing traits and yield were observed. Verma et al.³¹ also found comparable findings. Based on the research conducted by Singh et al.⁵⁸, Verma et al.³¹, Zagar et al.⁷⁴, and Singh et al.⁵, it was found that maintaining weed-free conditions (HW at 20 and 40 DAS) had a positive impact on enhancing wheat yield and yield-related characteristics.

Profitability is the key factor that determines the adoption of new strategies in modern farming. An economic return is more significant than a higher yield in today’s context. The increased NR observed in the IR1 condition can be primarily attributed to a higher yield of biological components, such as grain and straw. The findings were supported by the research findings of Verma et al.³¹ and Lakra et al.⁸. Other studies conducted by Sangwan et al.², Kumar et al.²⁷, and Sharma et al.⁷³ also highlighted similar findings. They observed that chemical weeding proved to be a more cost-effective approach compared to weedy check and hand-weeded treatments. The Weedy Check had the lowest net returns because of increased interference from weeds, leading to a decline in wheat yield⁵.

Considering that the groundwater table did not play a role in supplying water to the wheat, it is possible that the values of FC, RWC, RWU, and CU in the current experiment might be underestimated. In terms of the IRs, irrigation at 40 mm CPE (I1) led to the highest values of FC, RWC, RWU, and CU. The rise in these factors was primarily caused by a higher availability of soil moisture. Verma and Idnani (2012), Verma et al.³¹, Kumar et al.²⁷, and Lakra and Verma⁵³ also reached a similar conclusion. Out of the different IRs, the FC, RWC, RWU, and CU values showed a comparatively smaller range of variation. It was worth noting that the soil moisture studies were conducted within a specific depth range of 0 to 90 cm, which aligned with the depth at which wheat roots were found. Additionally, the IR1 treatment received a sufficient amount of irrigation water due to appropriate irrigation practices, while the IR2, IR3, and IR4 treatments received comparatively less irrigation water. As a result, this disparity in irrigation water led to lower values of FC, RWC, RWU, and CU in these treatments. When it comes to WMs, the highest values of FC, RWC, RWU, and CU were observed when Pendimethalin₁₀₀₀ (pre-em) was applied in combination with clodinafop-propargyl 10% + metribuzin 22% + sulfosulfuron 4.2% at a rate of 1125 g ha⁻¹ at 30 DAS. This treatment was found to be statistically similar to WM2. This was caused by improved crop growth due to increased nutrient availability, resulting in higher water consumption. In addition, an analysis of the pan evaporation data revealed a noticeable rise in evaporation rates between November and April. In the 2018–19 period, the evaporation increased from 1.4 to 4.2 mm, while in the 2019–20 period, it ranged from 1.0 to 4.2 mm. The later stages of these treatments’ increased CU and RWU were also impacted by the rise in pan evaporation. It was evident that a soil surface with higher water content will experience a faster rate of water loss compared to a surface with lower moisture levels. The findings were supported by the research conducted by Verma and Idnani (2012), Verma et al.³¹, and Verma et al. (2017). The HW outperforms all the WMs in terms of FC, RWC, RWU, and CU at 20 and 40 DAS under weed-free conditions(Verma 2014;⁵).

Tables 6, 7, 8 demonstrate the estimated WUE and WP values for theIRs and WMs. Based on the results, it was observed that reducing the frequency of irrigations led to an increase in WUE and WP. Conversely, increasing the frequency of irrigations resulted in a decrease in WUE and WP values. The IR4 treatment showed the highest water use efficiency (WUE) and water productivity (WP) values, followed by the IR3 treatment. On the other hand, the IR1 and IR2 treatments had the lowest values for WUE and WP, respectively. Due to the crop’s ability to achieve higher yields with less applied water, water usage efficiency and WP reached their peak levels^28,33,75. As per the study conducted by Zaman et al.⁷⁶, an increase in water supply leads to a decrease in water use efficiency. Reducing the frequency of irrigation and, as a result, the amount of irrigation water used, has the potential to increase WUE and WP, as suggested by several studies^30,72,77,78. Among the different treatments, WM1 exhibited the highest values of WUE and WP, with WM₂ and WM₃ following closely behind. The reason behind this phenomenon was that the weeds were sparser and had a greater weight under these conditions, resulting in decreased water usage by the weeds and increased water uptake by the crop. As a result, the WUE and WP were enhanced. The increase in crop productivity and efficient water usage resulted in improved water use efficiency and water productivity, which can be attributed to the higher crop yield (Verma et al. 2017). Applying herbicidal treatments was found to effectively decrease the density and dry weight of weeds. This, in turn, helps to minimize water loss in weeds and promotes higher water use and water use efficiency in crops^5,31. The levels of ASW and SME tended to be higher in the upper 0–30 cm layer of soil. The results aligned with the findings of Maurya et al.⁷⁹. Studies have shown that an increase in weed density leads to a decrease in both ASW and crop yields^3,5.

The energy input was determined based on the input materials used and their energy equivalents. IR₄ and IR₃ recorded a lower energy input among the IRs, while IR₁ recorded a higher energy input.The maximum energy input, energy output, energy return, energy productivity, and energy efficiency achieved with IR1 may be attributed to a higher yield.Among the WMs, WM₄ (weedy check) and WM₁ were found to have the lowest and highest energy input, respectively. WM5 and WM₁ had the highest energy input, energy output, energy return, energy productivity, and energy efficiency, while also having the lowest energy intensity and specific energy. This could be attributed to an increase in biological yield.

When it comes to maximizing economic gain, it was observed that the IRs and WMs have a statistically significant advantage over weedy checks. Possible reasons for the higher values of GR, NR, and PGM observed with irrigation at 40 mm CPE (IR₁) compared to the other IRs could be attributed to the improved crop yield. Studies conducted by Verma (2014), Singh et al.⁵, and Laklra et al. (2022a) have shown consistent results in terms of improved farm profitability in relation to weed management and irrigation. WM₁ exhibited significantly higher levels of GR, NR, and PGM compared to WM₃, and was found to be statistically similar to WM₂. This can be attributed to the higher wheat yield. Singh et al. (2013) and Sharma et al.⁷³ also presented similar findings. In a series of studies conducted by different researchers, it was found that there is a consistent variation in returns among treatments. This variation can be attributed to differences in yield and the expenses associated with each treatment^{5,31,34,35,36,66}. Poor yield was observed in the weedy check plots, which can be attributed to increased crop-weed interference. This finding was supported by several studies (Verma et al. 2007a; Ansari et al. 2009;^27,68).The interaction effect of different IRs and WMs revealed that a specific combination of irrigation and herbicide application resulted in a significant reduction in the density and biomass of weeds. This combination showed better weed control efficacy at different time points compared to other treatment combinations. This may be attributed to the combined impact of IRs and the application of herbicides to enhance weed control across various types of plant species. On the subject of water application and yield gain, it was worth noting that the WM₁ treatment resulted in a lower water input compared to IR₁, despite achieving a higher yield.When the IRs and WMs were combined, the results showed a significant improvement in various factors such as CU, RWU, WUE, WP, yields, GR, NR, and PGM. This particular combination of IR₁ and WM1 was found to be statistically similar to WM2 and the weed-free plots (WM₅), and it outperformed all other combinations of IRs and WM choices. It was possible that the combined impact of IR1 and WM1-WM2 on weeds and yield enhancement was more powerful, leading to a WCE that was much greater and a WI that was significantly lower than that of IR₂-IR₄ × WM₃. The occurrence of this was due to the optimal soil moisture levels and enhanced efficiency of herbicide application for effective weed control. The minimal dry biomass of the weed resulted in significantly reduced nutrient depletion when IR₄ and WM₁ treatments were combined. Verma et al.³¹, Lakra et al.⁸, and Lakra et al. (2022a) all made similar observations regarding the impact of certain factors on yield attributes, yield, and economic returns.

Conclusions

This research highlights the significant impact of effective weed management and irrigation on wheat production in eastern Uttar Pradesh, India. The application of irrigation at 40 mm cumulative pan evaporation (IR1) along with the Pendimethalin ₁₀₀₀ (pre-emergence) combined with clodinafop- propargyl 10% + metribuzin 22% + sulfosulfuron 4.2%, applied at 1125 g ha⁻¹ at 30 days after sowing proved to be the most effective treatment for controlling weeds while enhancing wheat yield and profitability. The study recommends exploring additional weed control strategies and irrigation management options to further improve wheat yields in conventionally-tilled systems.

Disclosure statement

No potential conflict of interest was reported by the author(s) in this research paper.