Impact of Irrigation Techniques on Water-Use Efficiency, Economic Returns, and Productivity of Rice

Abstract

The growing global population and water scarcities are putting pressure on researchers to develop new techniques for food production that require less water. In agriculture, sustainable irrigation methods considerably improve water-use efficiency (WUE), economic returns, and crop productivity. A field trial was carried out during 2022 and 2023 to examine the impact of different irrigation techniques—furrow irrigation (FI), alternate wetting and drying (AWD), and continuous flooding (CF)—on water-use efficiency, economic benefits, cost of production, and productivity of different high-yielding rice varieties (i.e., V₁: Super gold 2019, V₂: Super basmati 2019, V₃: Kissan basmati 2016, V₄: Punjab basmati 2016, V₅: Chenab basmati 2016). Findings showed that yield and yield parameters were statistically higher in AWD than FI and CF. Chenab basmati 2016 was superior in productivity as compared to the remaining varieties in both years. Water-use efficiency, net income, and cost/benefit ratio were highest in AWD as compared to CF and FI. AWD irrigation methods coupled with Chenab basmati 2016 were the most effective combination of treatments for obtaining more grain yield with maximum water savings, net income, and cost/benefit ratio.

1. Introduction

Rice is a staple food crop and a major contributor to Pakistan’s economy. It occupies about 3.90 million hectares (around 10% of the total cropped area) and accounts for nearly 17.8% of the value added in agriculture and 4.2% of GDP [1]. As the second most important food grain after wheat, rice plays a vital role in national food security and is a key source of income for millions of rural households. Furthermore, Pakistan is among the top rice-exporting countries, with basmati rice being a major export commodity [1]. In agriculture, one of the prominent issues is water scarcity, and its severity is getting worse with time. Water saving has become the most important factor in crop production [2]. The developing water problems pose a serious warning to food security in rice-growing countries [3]. Global food demand is increasing because of expanding population, and food security appears to be a huge task due to depletion of natural resources, especially water. Sustainable water resources are necessary to ensure global food security [4]. Continuous flooded rice is incredibly effective for weed control, but it is water-intensive and has adverse environmental effects, such as harming soil quality, raising methane emissions, and developing dangerous substances such as mercury and arsenic [5]. Rice cultivation in Pakistan accounts for over 60% of total irrigation water use, placing severe pressure on groundwater resources. Punjab province, the principal rice-growing region, faces rising water scarcity and soil degradation [6]. Under these constraints, optimizing irrigation practices is essential for sustaining productivity, profitability, and environmental sustainability. It is essential to adopt water-management practices that minimize water consumption while maintaining rice yields to support increasing populations [7]. In order to significantly increase water-use efficiency (WUE), different water-saving methods have been studied over several decades [7]. Currently, furrow irrigation and alternate wetting and drying (AWD) are commonly employed water-saving technologies in rice cultivation [8].

Furrow irrigation/ridge planting is a successful approach for increasing rice productivity [9]. Crops sown on ridges have distinct advantages over crops sown on flats, including reduced water and other input consumption, improved crop management, and increased crop productivity [10]. Ridge planting of crops results in improved aeration, reduced crop lodging, increased water-use efficiency, non-crusting, and minimal soil surface cracking, all of which reduce moisture loss through evaporation and boost groundwater recharge [10]. Furthermore, the adoption of technology has positive environmental effects due to lower methane emissions, more efficient nitrogen application, and lower nitrous-oxide emissions [11]. In paddy fields, alternate wetting and drying (AWD) is a widely recognized method for conserving water during irrigation in rice. This method involves alternating between periods when the soil is soaked and times when it is not. The field is re-flooded once moisture falls below a set level [12]. The drying stress caused by AWD can lead to an increase in bulk density due to soil consolidation [13]. This change can interact with the drier soil moisture, resulting in higher penetration resistance in the field [14]. Inefficient irrigation water losses from seepage and percolation can be minimized by AWD [15]. AWD increases aerobic activities in paddy soil during the drying period, which reduces CH₄ emissions [16]. The varietal selection in rice is critical under water-scarce conditions, as each rice variety has a unique mechanism for managing drought, whether through tolerance, avoidance, or escape. Choosing the right variety largely depends on the demography and water availability for irrigation. Selecting the right variety based on its adaptation to the local rice-farming system is crucial, as various varieties require distinct techniques to tolerate drought [3]. A combined approach of efficient water management and cultivation of high-yielding varieties adapted to water-limited conditions is expected to reduce yield loss [3].

Farmers adopt agricultural technology based on its economic viability, ease of use, and environmental sustainability. Economic returns from rice production are shaped by a combination of yield potential, input costs, and external market dynamics [17]. In many rice-growing regions, the cost of resources, including labor, seed, fertilizers, water, and machinery, plays a significant role in determining profitability. Thus, a comprehensive understanding of economic returns in rice farming must account for both the internal factors related to farm management and the broader external factors that shape market conditions and environmental sustainability [18]. Previous research has demonstrated the potential of alternate wetting and drying (AWD) to improve water productivity and reduce methane emissions compared to continuous flooding. In Pakistan, it was reported that AWD reduced irrigation water by up to 30% without significant yield penalties [8]. However, limited studies have assessed AWD performance across multiple basmati cultivars under farmers’ field conditions. This research fills this gap by evaluating different irrigation techniques in diverse high-yielding varieties. Previous studies generally compared the yield attributes, WUE, and economic outcomes of different irrigation management approaches separately. However, a comparative study on the economics of furrow irrigation (FI), alternate wetting and drying (AWD), and continuous flooding (CF), along with water-use efficiency (WUE), has not yet been conducted. The research objective/scope is to assess the impact of different irrigation techniques on the WUE, economics, and productivity of rice varieties. The economic outcomes of cultivating different varieties under different irrigation practices have been rarely assessed in previous studies. In the current study, it was hypothesized that different irrigation techniques would significantly improve water-use efficiency and rice productivity, leading to higher economic returns.

2. Materials and Methods

2.1. Study Site and Experimental Descriptions

For two consecutive years, field experiments were conducted on sandy clay loam soil (Lyallpur series) at the Agronomic Research Area of the University of Agriculture, Faisalabad (31°25′0″ N 73°5′28″ E), during the summer seasons in 2022 and 2023. Soil samples from experimental sites were analyzed before sowing crops to assess soil nutritional status. Samples were collected from several sites in a zigzag pattern, blended to form composite samples, air dried, and crushed to a particle size that could pass through a 2 mm sieve. A soil auger at 30 cm depth was used to collect the soil samples, and then soil physiochemical analysis was performed by employing the facilities of District Soil and Water Testing Laboratory, Ayub Agriculture Research Institute (AARI), Faisalabad. Results of the soil analysis are detailed in

Table 1. Soil physiochemical traits before planting.

Years	Texture	pH	EC (dS m−1)	Exchangeable Sodium (mmol 100 g−1)	Total Nitrogen (%)	Available Phosphorus (mg kg−1)	Exchangeable Potassium (mg kg−1)	OM (%)	BD (g m–3)
2022	Sandy clay loam	7.75	1.30	0.41	0.05	15.40	187.50	0.92	1.48
2023	Sandy clay loam	7.65	1.68	0.39	0.04	11.55	149	0.68	1.53

EC = electrical conductivity, OM = organic matter, BD = bulk density.

. The meteorological cell at the University of Agriculture, Faisalabad, Pakistan, provided information on weather data. Various weather parameters, such as minimum temperature, maximum temperature, relative humidity, and rainfall, were recorded from sowing time to crop harvest during the two consecutive years of research, represented in

Table 2. Weather data for the study area over two years.

Month	Minimum Temperature (°C)		Maximum Temperature (°C)		Relative Humidity (%)		Rainfall (mm)
	2022	2023	2022	2023	2022	2023	2022	2023
June	29.53	29.84	43.29	41.99	28.29	40.22	1.75	0.43
July	28.31	29.82	35.99	39.02	69.08	55.91	9.93	3.24
August	26.07	30.38	34.24	41.97	77.21	43.71	4.67	0.19
September	25.25	28.85	36.97	41.04	59.27	36.75	0.86	0.27
October	19.94	21.21	35.48	36.26	41.58	31.40	0.06	0.006
November	13.75	13.75	28.81	28.81	44.34	44.34	0.30	0.30

.

Proper land preparation ensures plant stability and enhances nutrients and water uptake. Proper tillage for crop planting improves water retention. The puddled field was prepared by two ploughings with a cultivator and two plankings. After the traditional land preparation, ridges were constructed with the help of a ridger. For transplanted rice, the nursery was sown on the 7th and 10th of June, and thirty days subsequently; old seedlings were transplanted into puddled field conditions on the 7th and 10th of July each year. The nursery transplantation was carried out manually. The spacing between plants and rows was maintained at 22.5 cm. Rice was manually harvested at maturity from each experimental unit. After harvesting, crops remained in their respective plots for 4–5 days for sun drying to reduce moisture content. After achieving 14% grain moisture content, grains were threshed manually by beating against a metal drum. A portable microprocessor grain moisture meter (MC-7825G) was used to measure the paddy moisture content.

Water inputs were measured with a cutthroat flume, field depth with PVC tubes, and soil moisture tension (SMT) on a daily basis using tensiometers. All experimental units were initially saturated and then irrigated based on the defined threshold level.

2.2. Treatments and Data Measurements

Three irrigation techniques (I₁: furrow irrigation (FI), I₂: alternate wetting and drying (AWD), I₃: continuous flooding (CF)) and five high-yielding rice varieties (V₁: Super gold 2019, V₂: Super basmati 2019, V₃: Kissan basmati 2016, V₄: Punjab basmati 2016, V₅: Chenab basmati 2016) were used in the experiment. The rice varieties included in this study are all conventional (non-hybrid) cultivars belonging to Oryza sativa L., subspecies indica. These varieties are widely grown in the Punjab region for their basmati grain quality and adaptability to irrigated conditions. None of the cultivars used were hybrid rice; rather, all are improved traditional basmati selections developed through conventional breeding methods. A set of 15 treatments with three replications was arranged in a randomized complete block design (RCBD) with factorial arrangement for both years, with a net plot size of 5 × 1.8 m. Complete potash + phosphorus and 50% nitrogen were broadcast before sowing, and the rest of the nitrogen was applied in two splits at tillering and the panicle initiation stage. Weeds were tackled with the stale seedbed method and herbicides. Oxadiargyl^® as a pre-emergence herbicide and ethoxysulfuron^® as a post-emergence herbicide were implemented in all experimental units using the shake-bottle method and flood methods, respectively. Zinc sulfate (33%) was applied 10 days after transplanting at the rate of 15 kg ha⁻¹.

2.3. Irrigation Management and AWD Tube Characteristics and Installation

Rice requires higher water input, which was met through canal and tube well water. Using a cutthroat flume, the total volume of applied water was measured to calculate water-use efficiency. In AWD treatments, AWD tubes were used to maintain the field water depth at a safe level. The field was flooded whenever the water level reached the perched water table, i.e., 15 cm below the soil surface, which is also known as safe AWD. A week after transplanting seedlings, AWD was performed and then maintained until 1–2 weeks before maturity. A “field water tube” was used to observe the water level in a field while implementing AWD safely. When the water depth gradually decreased after irrigation, the rice fields were re-flooded to a fixed depth of around 5 cm after the water level fell to about 15 cm below the soil surface, to lessen the risk of infertile branches brought on by water-deficit stress during this delicate period. Polyvinyl chloride (PVC) pipes 30 cm in length and 15 cm in diameter were obtained from a local market. To obtain perforated parts (15 cm), holes having a 5 mm diameter were made with a drill, maintaining a hole-to-hole distance of 2 cm. The perforated portion (15 cm) of the AWD tubes was inserted in the soil, while the remaining 15 cm (intact) portion was kept above the soil surface. The soil was removed from the tubes after installation for easy movement of water from soil to tube and vice versa, and to check water movement within the AWD tubes [8]. A self-driven puddler made furrows during flooded conditions, while a ridger made ridges. Ridges were divided by a developed furrow that was 30 cm broad and 22.8 cm deep.

2.4. Economic Analysis

The economic analysis was carried out based on the guidance of CIMMYT [19]. Fixed and variable costs were added to calculate the total expenditure for each treatment. Land preparation, nursery sowing, nursery uprooting and transplanting, weedicide and plant protection (spray + granules + spray), fertilizer, land rent, agricultural income tax, and management charges were taken into consideration as fixed costs, as they were applied uniformly to every treatment, while irrigation and harvesting with threshing were considered as variable costs, as per the nature of the treatments [20]. The gross income was calculated by multiplying the paddy yield (kg ha⁻¹) and straw yield (kg ha⁻¹) by the local market price. Net income was calculated by subtracting all expenses from the gross income. The determination of the benefit/cost ratio (BCR) involves dividing the net income by total expenditure. For benefit/cost ratio calculation, the following method was used [21]. Total income in PKR was estimated using the local market’s per-unit prices for grain and straw. Subtracting the total expenditure from the gross income of the relevant treatment gave the net income. The benefit/cost ratio (BCR) is calculated by the following formula:

$$\mathrm{B}\mathrm{C}\mathrm{R}={\displaystyle \frac{{\mathrm{N}\mathrm{e}\mathrm{t} \mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e} (\mathrm{P}\mathrm{K}\mathrm{R} \mathrm{h}\mathrm{a}}^{-1})}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{e}\mathrm{x}\mathrm{p}\mathrm{e}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{e} \left({\mathrm{P}\mathrm{K}\mathrm{R} \mathrm{h}\mathrm{a}}^{-1}\right)}}$$

The economic study was in Pakistani rupees (PKR). One USD equals PKR 279.03. Costs and prices were calculated according to the Agriculture Marketing Information Service (AIMS), Crop Reporting Service (CRS), and Kala Shah Kaku (KSK).

2.5. Data Recording Procedure for Growth and Yield Attributes

The leaf-area index refers to the ratio of the leaf area to the land area of the plants. A SunScan Canopy Analysis System type SSI model was employed to measure the leaf-area index. The SunScan system was placed at three distinct positions inside each plot, and the average value derived from these measurements and the final subject trait were measured using the following formula:

$$\mathrm{Leaf}\text{-}\mathrm{area}\mathrm{index}={\displaystyle \frac{\mathrm{L}\mathrm{e}\mathrm{a}\mathrm{f}\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{a}}{\mathrm{G}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d}\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{a}}}$$

The height of the rice plants at harvest maturity was recorded by determining the length of three plants per treatment in each block with a meter rod from the base of the soil surface to the tip of the panicle. The data were averaged using a Microsoft Excel spreadsheet. Productive and non-productive tillers were calculated after panicle initiation from each experimental unit of 1 m². Tiller-bearing panicles are counted as productive tillers, and those without panicles are considered non-productive tillers. A wooden scale was used to measure panicle length from the last node of the stem at the start of the panicle to the tip of the panicle. The length of the panicles was measured from each experimental unit and then averaged. The randomly selected panicles from each experimental unit were separated from the plant with scissors, and the number of branches on each panicle was carefully counted.

The working board was used to separate the grains from three randomly selected panicles, and after that, the number of grains per panicle was counted and averaged. An electric seed counter was employed to count 1000 grains. Grains were dried in an oven at 80 °C until a constant weight was achieved before weighing. An area of 1 m² was manually harvested from three replications and then averaged, and the grains were manually threshed from the plant. The harvest index was calculated using the formula of Beadle [22], as follows:

$$\mathrm{H}\mathrm{I}={\displaystyle \frac{\mathrm{G}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n}\mathrm{y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d}}{\mathrm{B}\mathrm{i}\mathrm{o}\mathrm{l}\mathrm{o}\mathrm{g}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d}}}\times 100$$

2.6. Water-Use Efficiency

Water-use efficiency was estimated according to the formula of Jenses [23], as described below:

$$\mathrm{W}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{u}\mathrm{s}\mathrm{e}\mathrm{e}\mathrm{f}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y}={\displaystyle \frac{\mathrm{S}\mathrm{e}\mathrm{e}\mathrm{d}\mathrm{y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d}\left(\mathrm{k}\mathrm{g}{\mathrm{h}\mathrm{a}}^{-1}\right)}{\mathrm{S}\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{l}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{u}\mathrm{m}\mathrm{p}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{u}\mathrm{s}\mathrm{e}\mathrm{o}\mathrm{f}\mathrm{w}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r}\left(\mathrm{m}\mathrm{m}\right)}}$$

2.7. Statistical Analysis

Data were statistically analyzed through the computer program Statistix 8.1. Fisher’s analysis of variance (ANOVA) techniques and Tukey’s (HSD) test were used to compare the treatment means [24]. Graphical data were presented using Origin-Pro software v2021 (Originlab, Northampton, MA, USA).

3. Results

Statistical analysis of the studied traits for the two years comprising leaf-area index, plant height, productive tillers, non-productive tillers, panicle length, number of branches per panicle, number of grains per panicle, 1000-grain weight, grain yield, straw yield, biological yield, harvest index, and water-use efficiency is presented in

Table 3. F-values for the following traits of rice varieties.

Traits	2022		2023
	I	V	I	V
Leaf-area index 1	7.59 **	8.38 **	18.78 **	4.48 **
Leaf-area index 2	2.66 ns	4.39 **	12.67 **	4.36 **
Leaf-area index 3	21.23 **	3.42 *	29.42 **	5.76 **
Leaf-area index 4	8.61 **	3.78 *	16.14 **	5.04 **
Leaf-area index 5	6.06 **	2.53 ns	8.73 **	3.65 *
Leaf-area index 6	5.92 **	2.65 *	8.01 **	5.00 **
Leaf-area index 7	15.40 **	5.11 **	7.81 **	9.78 **
Plant height	0.39 ns	45.48 **	0.38 ns	39.27 **
Productive tillers	6.20 **	0.43 ns	9.14 **	0.49 ns
Non-productive tillers	26.95 **	5.79 **	42.65 **	10.23 **
Panicle length	1.40 ns	1.10 ns	0.50 ns	0.62 ns
No. of branches per panicle	3.59 *	1.50 ns	2.82 ns	0.94 ns
No. of grains per panicle	5.94 **	0.25 ns	4.66 *	0.19 ns
1000-grain weight	1.12 ns	1.45 ns	2.00 ns	1.98 ns
Grain yield	1.14 ns	22.44 **	0.75 ns	11.75 **
Straw yield	0.05 ns	8.55 **	0.79 ns	7.32 **
Biological yield	0.33 ns	16.92 **	0.83 ns	9.50 **
Harvest index	0.67 ns	3.22 *	0.26 ns	10.66 **
Water-use efficiency	119.43 **	20.06 **	70.77 **	11.47 **

* and **, significant at p ≤ 0.05 and p ≤ 0.01, respectively. ns mean not significant; I: irrigation and V: variety. The seven leaf-area indices correspond to measurements taken at different growth stages.

. During 2022 and 2023, the interaction effect seemed non-significant.

3.1. Impact of Irrigation Techniques on Leaf-Area Index of Rice Varieties

In addition, the interactive effects shown by using stacked columns are presented in Figure 1. The LAI of the rice varieties was substantially affected by the irrigation techniques throughout the duration of the experiments. After 30 days of transplanting, the first LAI was taken, which showed minute values that were then computed after every 15-day interval. It was observed that AWD with Chenab basmati (I2V5) gave higher values for LAI1 (21.30%, 25.22%), LAI2 (31.51%, 41.16%), LAI3 (25.93%, 24.40%), LAI4 (14.47%, 14.67%), LAI5 (24.91%, 14.36%), LAI6 (31.80%, 25.47%), and LAI7 (65.09%, 53.45%) relative to CF with Super gold (I3V1) in both years. On the other hand, AWD with Chenab basmati (I2V5) gave higher results than FI with Super gold (I1V1) for LAI1 (27.75%, 37.84%), LAI2 (40.48%, 50.73%), LAI3 (35.93%, 27.14%), LAI4 (20.56%, 20.65%), LAI5 (34.18%, 26.57%), LAI6 (45.60%, 39.80%), and LAI7 (122%, 69.76%) in both years. The LAI followed a distinctive pattern characterized by a gradual increase up to LAI4, where it reached its peak. This initial rise suggests an accumulation of leaf biomass or an enhancement in canopy development, likely driven by favorable growth conditions or the maturation of plant tissues. After reaching the maximum value at LAI4, the leaf-area index began to decrease, indicating a decline in leaf area beyond this point.

3.2. Impact of Irrigation Techniques on Yield Attributes of Rice Varieties

The findings on yield attributes, as demonstrated by box-and-whisker plots, are shown in Figure 2, Figure 3 and Figure 4. During 2022 and 2023, plant-height data showed distinct trends for various rice varieties under different irrigation techniques. Results showed that in the year 2022, the highest plant height (12.70%, 10.89%) was observed in AWD with Chenab Basmati (I2V5), statistically at par (12.05%, 10.25%) with CF on Chenab basmati (I3V5) in relation to CF used with Super gold (I3V1) and FI with Super gold (I1V1). Regarding plant height, the maximum value (7.85%, 11.35%) was recorded using AWD in conjunction with Chenab basmati (I2V5) when compared with CF coupled with Super gold (I1V3) and FI with Super gold (I1V1) during 2023. During 2022 and 2023, AWD with Chenab basmati (I2V5) increased (12.89%, 12.85%) over CF with Super gold I3V1, while AWD with Chenab basmati (I2V5) decreased (2.79%, 3.20) compared to FI combined with Chenab basmati (I1V5). Moreover, concerning results regarding non-productive tillers, data showed that AWD in association with Chenab basmati (I2V5) decreased (25.36%, 12.37%) in comparison with CF with Super gold (I3V1) and FI with Super gold (I1V1) in 2022. During 2023, AWD with Chenab basmati (I2V5) decreased by (30.05%, 17.89%) compared to CF with Super gold (I3V1) and FI with Super gold (I1V1). As the number of productive tillers increases in a given treatment, the number of non-productive tillers decreases, indicating a trade-off between the two. This suggests that higher allocation of resources to productive tillers may lead to a reduction in non-productive tillers. The results regarding panicle length exhibited the maximum panicle length (12.45%, 13.56%) in AWD coupled with Chenab basmati (I2V5) when measured against CF in combination with Super gold (I3V1) and FI with Super gold (I1V1) during 2022. For 2023, AWD combined with Chenab basmati (I2V5) performed better (11.31%, 14.21%) in relation to CF with Super gold (I3V1) and FI with Super gold (I1V1). Data determined the leading number of branches per panicle was harvested with interaction (I2V4) as mentioned (14.16%, 14.37%) against CF in the presence of Super gold (I3V1) for the years 2022 and 2023, respectively, and (16.66%, 16.15%) relative to FI with Super gold (I1V1) during 2022 and 2023, respectively. However, the number of grains per panicle was affected by the employment of subject factors, and the highest value (7.07%, 13.96%) was collected from the interaction of AWD with Chenab basmati (I2V5), opposed to CF with Super gold (I3V1), and FI with Super gold (I1V1) during 2022. At the same time, during 2023, the interaction of AWD with Chenab basmati (I2V5) also performed better percentage-wise (6.41%, 13.95%) in comparison with CF with Super gold (I3V1) and FI with Super gold (I1V1), respectively. During the 2022 period, under the interaction treatments, AWD with Chenab basmati (I2V5) resulted in a significant increase in 1000-grain weight (6.78%, 10.27%) in comparison to CF with Super gold (I3V1) and FI with Super gold (I1V1). The interaction of AWD with Chenab basmati (I2V5) in 2023 also showed an increased percentage (14.99%, 15.39%) compared to CF with Super gold I3V1 and FI with Super gold I1V1. Statistically, the leading grain yield (33.33%, 36.36%) was harvested from AWD with Chenab basmati (I2V5) when measured against CF with Super gold (I3V1) and FI with Super gold (I1V1) during 2022. The year 2023 showed that AWD with Chenab basmati (I2V5) also achieved a good percentage (34.05%, 37.03%) compared to CF with Super gold (I3V1) and FI with Super gold (I1V1). Data regarding straw yield demonstrated that in 2022, AWD with Chenab basmati (I2V5) increased (19.93%, 17.46%) compared to CF with Super gold (I3V1) and FI with Super gold (I1V1). In the next year, AWD with Chenab basmati (I2V5) also showed noticeable results (18.41%, 18.83%) compared to CF with Super gold (I3V1) and FI with Super gold (I1V1). Data determined the leading biological yield occurred with the interaction of AWD with Chenab basmati (I2V5), as mentioned (24.22%, 23.61%), compared with CF with Super gold (I3V1) for the years 2022 and 2023, respectively, and (23.34%, 24.81%) measured against FI with Super gold (I1V1) during 2022 and 2023, respectively. The mentioned data regarding the harvest index illustrate that during 2022, the interactive treatment of AWD with Chenab basmati (I2V5) performed higher (7.46%, 10.43%) than CF in combination with Super gold (I3V1) and FI with Super gold (I1V1). On the other hand, during the year 2023, the same interaction of AWD with Chenab basmati (I2V5) showed a higher percentage (8.40%, 9.69) compared to CF with Super gold (I3V1) and FI with Super gold (I1V1).

3.3. Impact of Irrigation Techniques on Water-Use Efficiency for Rice Varieties

AWD showed higher water-use efficiency (17.11%, 53.13%) in 2022, assessed against FI and CF, respectively. Data determined the highest water-use efficiency (100.32%, 53.65%) observed in AWD through employing the variety Chenab basmati (I2V5) compared to CF in concert with Super gold (I3V1) and FI using Super gold (I1V1) in the year 2022. During 2023, AWD achieved higher results (17.60%, 53.64%) relative to FI and CF, respectively. In the case of the year 2023, the highest water-use efficiency was calculated (101.40%, 54.40%) in AWD through employing the variety Chenab basmati (I2V5) against CF under Super gold (I3V1) and FI in collaboration with Super gold (I1V1) in the year 2023, respectively. Data regarding water-use efficiency are presented in the chord diagram in Figure 5.

3.4. Pearson Correlation Analysis of Growth Traits, Yield Attributes, and Water-Use Efficiency

The correlation analysis for 2022 and 2023 assesses the relationship of the studied traits in Figure 6. During 2022, LAI negatively correlated with plant height, productive tillers, and non-productive tillers, but a strongly positive and highly significant correlation was seen with panicle length, number of branches per panicle, number of grains per panicle, 1000-grain weight, grain yield, straw yield, harvest index, and water-use efficiency. Productive tillers showed a highly positive correlation with water-use efficiency and an unexpected positive correlation with non-productive tillers, as well as a negative correlation with panicle length, number of branches per panicle, number of grains per panicle, 1000-grain weight, grain yield, straw yield, and harvest index. Grain yield had a highly positive correlation with straw yield, biological yield, harvest index, and WUE. Except for non-productive tillers, water-use efficiency seemed to have strongly positive correlations with all traits. However, the same trend was observed in the year 2023, with unexpected differences observed.

3.5. Economic Analysis of Irrigation Techniques

Data regarding economic analysis are reported in

Table 4. Economics of different irrigation techniques (ITs) with rice varieties and their impact on gross income, total expenditure, net income, and benefit/cost ratio for 2022 and 2023. Values are expressed in Pakistani rupees (PKR), with equivalents in USD shown in parentheses (1 USD = PKR 279.03).

Irrigation Techniques		2022				2023
	Varieties	Gross Income	Total Expenditure	Net Income	BCR	Gross Income	Total Expenditure	Net Income	BCR
FI	Super gold 2019	235,000	230,938.75	4061.25	0.017586	240,000	232,188.75	7811.25	0.033642
FI	Super basmati 2019	250,000	242,688.75	7311.25	0.030126	255,000	243,938.75	11,061.25	0.045344
FI	Kissan basmati 2019	265,000	256,438.75	8561.25	0.033385	270,000	257,688.75	12,311.25	0.047776
FI	Punjab basmati 2016	290,000	277,688.75	12,311.25	0.044335	296,666.5	279,355.375	17,311.125	0.061968
FI	Chenab basmati 2016	305,000	281,438.75	23,561.25	0.083717	310,000	282,688.75	27,311.25	0.096612
AWD	Super gold 2019	245,000	236,313.75	8686.25	0.036757	250,000	237,563.75	12,436.25	0.052349
AWD	Super basmati 2019	260,000	248,063.75	11,936.25	0.048118	268,333.5	250,147.125	18,186.375	0.072703
AWD	Kissan basmati 2019	275,000	256,813.75	18,186.25	0.070815	283,333.5	258,897.125	24,436.375	0.094386
AWD	Punjab basmati 2016	300,000	278,063.75	21,936.25	0.078889	303,333.5	278,897.125	24,436.375	0.087618
AWD	Chenab basmati 2016	315,000	281,813.75	33,186.25	0.11776	323,333.5	283,897.125	39,436.375	0.138911
CF	Super gold 2019	240,000	235,438.75	4561.25	0.019373	245,000	236,688.75	8311.25	0.035115
CF	Super basmati 2019	255,000	246,188.75	8811.25	0.035791	260,000	247,438.75	12,561.25	0.050765
CF	Kissan basmati 2019	270,000	258,938.75	11,061.25	0.042718	275,000	260,188.75	14,811.25	0.056925
CF	Punjab basmati 2016	295,000	280,188.75	14,811.25	0.052862	301,666.5	281,855.375	19,811.125	0.070288
CF	Chenab basmati 2016	310,000	285,938.75	24,061.25	0.084148	316,666.5	287,605.375	29,061.125	0.101045

. Analysis showed that in irrigation techniques, AWD with the Chenab basmati showed more net income (627.56%, 717.143%) and benefit/cost ratio (507.85%, 569.62%) than CF and FI under Super gold in the year 2022. In the next year, 2023, AWD with Chenab basmati showed more net income (374.49%, 404.86%) and benefit/cost ratio (295.58%, 312.90%) than CF and FI under Super gold.

4. Discussion

4.1. Impact of Irrigation Techniques on Leaf-Area Index of Rice Varieties

LAI is most sensitive to water deficits because water stress results in the inhibition of cell division and expansion, which consequently disturbs the leaf-cell expansion and turgor [25]. At mid-tillering, AWD ensured optimum water supply, which facilitated the photosynthates’ translocation to the panicle and improved tillering and growth as compared to CF, where a large portion of irrigation is wasted through evaporation and percolation [26]. Typically, furrow irrigation results in lower LAI compared to continuous flooding and AWD, as the water is not evenly distributed across the field, leading to uneven growth and LAI in rice [27].

4.2. Impact of Irrigation Techniques on Yield Attributes of Rice Varieties

Plant height is a genetically controlled trait that could be influenced by crop management strategies and the environment to some extent. Water stress may hamper plant height due to limiting cell division and expansion [28]. Rice under furrow irrigation often has shorter plant height, likely due to uneven water distribution that can limit root development and reduce overall plant growth. Varieties vary in their ability to tolerate and recover from water stress associated with AWD. AWD-adapted rice varieties can maximize growth potential during re-flooding periods and show resilience to water stress, therefore optimizing plant height results [29]. Improvement of tillering under AWD could be attributed to the fact that a large portion of water in CF is wasted through evaporation and percolation, while satisfactory rice growth can be achieved with water-saving irrigation. It is essential to effectively control AWD cycles, including the timing and duration of the drying and re-flooding periods. Timely re-flooding ensures that plants recover adequately, minimizing the harmful impacts of water stress on tiller formation. During the drying periods of AWD, varieties with improved stress tolerance mechanisms, such as effective root systems and improved physiological responses to water stress, can maintain tiller growth and development [26].

Rice is highly sensitive to water stress at critical stages such as stand establishment, panicle initiation, anthesis, and the grain-filling stage compared to other crops [25]. The temporary inhibition of panicle elongation during drying periods by water stress may lead to shorter panicles. Re-flooding allows plants to recover and resume growth, promoting panicle elongation and supporting longer panicles compared to prolonged water-stress conditions. Higher panicle length may result from enhanced root development on ridges, which promotes tiller production and growth. In rice farming, the combination of AWD and rice varieties with nutrition availability, water management, and genetic factors affects panicle length [29]. This may be attributable to the fact that AWD considerably enhances the number of branches per panicle and confirms from earlier research that the main aspect behind the better rice yield is the number of branches per panicle [30]. Certain varieties are recognized for their capacity to produce a greater number of branches, which might potentially lead to an increase in spikelets and total grain production.

Because of the increased activities of enzymes in the rice kernel and stem (sucrose synthase or SuS), the elevated ABA concentration throughout the AWD dry cycle promotes the translocation of photosynthates towards developing the number of grains per panicle [31]. It is believed that sucrose phosphate synthase (SPS) promotes the resynthesis of sucrose. SPS activity increases in response to an increase in ABA concentration during mild-AWD soil drying. In response to soil drying, higher SPS increases the formation of disaccharides while simultaneously maintaining the photosynthates’ transfer from leaves to grain [32]. Under moderate AWD, soil drying increases the ABA level, which mobilizes carbon translocation to the sink as a result of increased activity of the key enzyme participating in grain filling. Increased levels of cytokinin in leaves under mild AWD during the re-wetting cycle enhance the photosynthetic rate, which results in an increase in the number of grains per panicle and inferior spikelet grain filling [33].

Davatgar et al. [25] highlighted that water stress at the flowering stage results in grain abscission and flower abortion, which increases the percentage of sterile spikelets and unfilled grains, affecting the 1000-grain weight and paddy yield. Similarly, significant water stress in water-efficient techniques results in reducing the photosynthetic rate, growth, and translocation of photosynthates to the sink. Moreover, electrolyte leakage and hormonal imbalance due to water stress result in inferior produce [34]. Davatgar et al. [25] demonstrated that AWD increased rice yield through improved root oxidation, higher photosynthetic rates, and more active sucrose-to-starch conversion enzymes during grain filling. Meanwhile, stress during the reproductive stage resulted in a larger percentage of empty grains [35]. Better yield performance in our AWD treatment could be attributed to a potential soil water range that remained ≥ −20 kPa, ensuring sufficient soil moisture for optimal crop growth.

Under AWD, along with many other biochemical and physiological changes, the enzyme involved in grain filling and ammonia assimilation [36] or those which convert sucrose to starch in grains will increase significantly. Results of this experiment expressed that biological yield was significantly higher, which is contrary to the findings of Liu et al. [37], who described that dry biomass did not differ significantly. Different rice varieties exhibit varying genetic traits and physiological characteristics that can directly influence the harvest index. Varieties with traits such as higher grain-to-straw ratio, better disease resistance, efficient nutrient use, and drought tolerance tend to have higher harvest indices. By periodically allowing the soil to dry out, AWD reduces water consumption with respect to continuous flooding. This efficiency can improve overall plant health and reduce stress during critical growth stages, potentially enhancing grain filling, harvest index, and yield [38].

4.3. Impact of Irrigation Techniques on Water-Use Efficiency Under Rice Varieties

During both rice seasons, AWD used less water on average than transplanted rice and furrow irrigation [39]. Chakrabarti et al. [40] showed that the cultivation of AWD decreases the overall water impact and blue water footprint but raises the green water footprint. The overall amount of water used in the continuously flooded rice was higher, because flooding to a deeper depth in rice fields results in significant water loss through seepage, surface runoff, percolation, and evaporation [41]. On the other hand, the outcomes of the water-saving AWD irrigation system are supported by several previous studies. Studies demonstrated that across diverse climatic and soil situations, adoption of AWD reduces water input by 57% [42], 21–56% [43], and 25.7% [44] as compared with continuous flooding. Recent studies have demonstrated that alternate wetting and drying (AWD) can achieve significant water savings without compromising rice yields [45]. In Pakistan, Akbar et al. [46] reported up to a 25–30% water-use reduction with AWD compared to continuous flooding, aligning with our findings on improved water-use efficiency and profitability. Undoubtedly, the AWD production system does not include standing water, but the soil must retain moisture at the optimal level, although partial soil-drying under AWD led to extensive root system proliferation and physiological alterations in leaves that conserve water [47]. The modeling studies of Massey et al. [48] showed that using AWD irrigation greatly reduces water losses from deep drainage. Rainfall during the drying phase of AWD can delay re-irrigation, which minimizes surface runoff losses [49]. Cultivation of early-maturing varieties may help to significantly minimize the quantity of water input. However, practical adoption depends on farmer awareness, access to proper field leveling, and reliable irrigation scheduling. The feasibility of AWD can be enhanced through targeted extension services and demonstration trials. Moreover, policy incentives such as subsidized water-saving technologies and training programs could improve acceptability. These results highlight that integrating AWD into existing farming systems offers a promising strategy to address water scarcity while maintaining economic returns. Globally, water scarcity will be a key barrier to effective rice production in the future. It will be necessary to boost water productivity for rice cultivation [50].

4.4. Economic Feasibilty of Irrigation Techniques

Research has extensively explored water-saving irrigation strategies for rice to maximize economic profitability while minimizing adverse environmental effects. Economic analysis indicated that AWD was the most cost-efficient method for growing rice among the different irrigation treatments [51]. Profitability and net income are the most critical indicators of the effectiveness of any crop or agricultural practice. A higher gross income from increased production costs may not be ideal, as much of the income could be offset by the high costs [52]. Higher yields and water productivity are only cost-effective if input costs do not rise with crop yield [53]. These results are in line with Ishfaq et al. [8], who found that AWD resulted in the highest BCR, primarily due to increased grain yield. AWD recorded the highest BCR among all treatments, indicating strong economic viability. Its adoption could lead to significant water savings, reduction in production costs, and enhanced profitability for rice growers. However, widespread adoption may be limited by certain feasibility issues such as limited farmers’ awareness, lack of technical training, and inadequate access to water monitoring tools like field water tubes. Socio-economic factors, including risk aversion and traditional irrigation practices, may also influence willingness to adopt AWD. Policy support, including extension services, subsidies for water-saving equipment, and awareness campaigns, could enhance the adoption of AWD, aligning economic gains with sustainable water-management goals.

5. Conclusions

Considering the increasing threat of water scarcity due to global warming, it is essential to adopt irrigation practices in rice cultivation that consider both water needs and economic efficiency. The findings suggest that AWD combined with a high-yield rice variety (Chenab basmati 2016) offers a sustainable solution to the challenges of water scarcity in rice production. AWD enhances water efficiency without compromising yields, making it a promising strategy for both sustainable rice farming and improved economic returns. Adopting AWD lowers total water input compared to furrow irrigation and continuous flooding while maintaining or improving the yield economically. Overall, AWD represents a pragmatic approach for addressing global food security while minimizing water use. With the rising population and limited resources, this study could help save water and increase agricultural productivity and profitability.

6. Outlook

Future studies on irrigation methods in rice may also explore long-term impacts of irrigation methods on soil compaction, microbial activity, and greenhouse gas emissions. This will strengthen the adaptation of water-saving irrigation methods through policy management and the promotion of best-suited techniques in farming communities through targeted subsidies and farmer training programs. Decision-makers must integrate irrigation methods into national water conservation strategies. Stakeholders should collaborate on scaling up water-saving technologies for broader implementation.