Energy-Efficient Smart Irrigation Technologies: A Pathway to Water and Energy Sustainability in Agriculture

Abstract

The agricultural sector faces challenges such as water scarcity, energy inefficiency, and declining productivity, particularly in arid regions. Traditional irrigation methods contribute to resource depletion and environmental impacts. Solar-powered smart irrigation systems integrate precision irrigation with renewable energy, improving water use and productivity. In Pakistan, where agriculture contributes 19% of gross domestic product and employs 40% of the workforce, these challenges are severe, especially in water-scarce areas like the Cholistan Desert. This study examines the impact of solar-powered smart irrigation on agricultural productivity, water conservation, and energy efficiency in the Cholistan Desert. Using a quantitative cross-sectional design, data were collected from 384 farmers via structured questionnaires. Statistical analyses, including multiple linear regression, paired sample t-tests, and Structural Equation Modeling (SEM), were conducted. Findings show significant improvements in crop yield (from 3.0 to 4.8 tons/hectare) and reductions in water and energy consumption. Regression analysis highlighted strong positive effects on yield and efficiency, while SEM confirmed reduced environmental impact and operational costs. The study concludes that solar-powered irrigation enhances productivity, conserves resources, and promotes sustainability. Policymakers should provide financial incentives, invest in renewable infrastructure, and implement training programs to support adoption. Collaborative efforts are essential for sustainable agriculture in arid regions.

1. Introduction

The agricultural sector, a cornerstone of global food security and economic development, faces mounting challenges in the 21st century. Climate change, rapid population growth, and dwindling natural resources have intensified the need for sustainable agricultural practices. Among these challenges, water scarcity and energy inefficiency stand out as critical barriers to productivity and sustainability. Globally, agriculture accounts for approximately 70% of freshwater withdrawals and is one of the largest consumers of energy in many regions [1,2]. Traditional irrigation practices, often inefficient and resource-intensive, exacerbate these issues, leading to water wastage, energy overuse, and reduced crop yields. In response, energy-efficient smart irrigation technologies, powered by renewable energy, have emerged as transformative solutions. These systems integrate precision irrigation techniques with renewable energy sources such as solar power to optimize water use, reduce energy consumption, and enhance crop productivity. However, understanding the exact mechanisms and components of these systems is crucial in assessing their effectiveness.

A typical solar-powered smart irrigation system consists of solar photovoltaic (PV) panels that generate electricity to power water pumps and distribution mechanisms. These systems often include dryness sensors, either soil moisture sensors or atmospheric sensors, to monitor real-time environmental conditions and ensure that water is supplied based on actual crop needs. Unlike traditional irrigation methods, which rely on fixed schedules or manual interventions, smart irrigation leverages advanced control systems, including Internet of Things (IoT) sensors, to dynamically adjust water flow. These sensors help determine how much water crops require by measuring soil moisture at different depths, analyzing evapotranspiration rates, and factoring in weather conditions. Additionally, the integration of automated valves and precision water delivery mechanisms ensures that irrigation is targeted, reducing wastage and maximizing efficiency.

Another critical aspect of these systems is their ability to draw water from different sources, including deep aquifers, surface water reservoirs, and tubewell irrigation networks. In regions where groundwater depletion is a concern, such as arid and semi-arid zones, smart irrigation systems can optimize water extraction by regulating the depth from which water is pumped, thereby preventing over-extraction and ensuring long-term sustainability [3,4]. The question of whether such systems rely on deeper aquifers than conventional irrigation remains an important consideration, as excessive groundwater withdrawal can lead to depletion. In many cases, these systems are integrated into existing tubewell irrigation areas, retrofitting traditional wells with solar-powered pumping mechanisms to reduce dependence on fossil fuel-driven pumps.

In Pakistan, agriculture is the backbone of the economy, contributing 19% to the gross domestic product (GDP) and employing nearly 40% of the workforce [5,6]. However, the sector faces acute challenges, including chronic water shortages, inefficient irrigation systems, and escalating energy costs. These issues are compounded by the reliance on traditional irrigation methods, which are not only resource-intensive but also fail to meet the growing demands for food production. Research highlights that Pakistan’s water availability has declined from 5260 cubic meters per capita in 1951 to less than 1000 cubic meters in 2024, placing the country in the category of water-scarce nations [7,8]. Similarly, the agricultural sector’s dependence on conventional energy sources contributes to greenhouse gas emissions, further aggravating climate change. Addressing these interconnected challenges requires innovative approaches that combine water conservation, energy efficiency, and enhanced productivity.

Solar-powered smart irrigation systems represent a promising solution, aligning with Pakistan’s renewable energy potential and the need for sustainable agricultural practices. However, empirical evidence on their effectiveness, feasibility, and socio-economic impacts in the country remains sparse. The Cholistan Desert in Punjab, Pakistan, exemplifies the pressing need for such innovations. With its extreme arid conditions, chronic water scarcity, and reliance on traditional farming methods, the region is particularly vulnerable to the challenges of water and energy inefficiency. Given that much of Cholistan relies on tubewell irrigation, integrating solar-powered pumping systems and precision irrigation techniques could significantly enhance sustainability. This study investigates the adoption of energy-efficient smart irrigation technologies powered by renewable energy as a pathway to water and energy sustainability in the Cholistan Desert. By assessing the effectiveness of soil and atmospheric moisture sensors, analyzing water distribution efficiency, and evaluating the feasibility of deep groundwater extraction, this research aims to provide a comprehensive understanding of how smart irrigation can address the critical challenges faced by Pakistan’s agricultural sector.

1.1. Study Rationale

Agriculture is the backbone of Pakistan’s economy, contributing significantly to GDP and employment [9]. However, the sector faces critical challenges due to water scarcity, energy inefficiency, and climate-induced stresses. These challenges are particularly pronounced in arid regions like the Punjab Cholistan, where agriculture is constrained by limited water resources, energy shortages, and unsustainable irrigation practices. Despite advancements in renewable energy and smart irrigation technologies globally, the adoption of these systems in Pakistan remains limited. The Cholistan Desert, with its unique climatic and socio-economic conditions, requires tailored solutions to enhance agricultural productivity and resource efficiency.

The true Cholistan Desert is extremely arid, rendering most of its core area unsuitable for cultivation. Agricultural activities are primarily concentrated on the desert’s margins, where a small population of farmers struggles against harsh environmental conditions. These marginal farmers operate on the periphery of extreme dryness, making their livelihoods particularly vulnerable to erratic rainfall and groundwater depletion. Understanding their socio-economic conditions, resource availability, and adaptive strategies is crucial in assessing the feasibility and impact of smart irrigation technologies.

Inefficient irrigation methods, such as flood irrigation, account for more than 90% of agricultural water use in Pakistan, leading to severe water wastage [10]. In Cholistan, before the introduction of solar-powered irrigation systems, farmers predominantly relied on flood irrigation and, in some cases, manually operated tubewells powered by diesel pumps. These traditional methods not only resulted in excessive water consumption but also imposed high operational costs on farmers due to fuel dependency. Given the region’s limited and often saline groundwater resources, excessive reliance on these outdated techniques further degraded soil quality and reduced crop productivity.

Farmers in the region primarily cultivate drought-resistant crops such as millet, sorghum, and barley, along with small-scale vegetable farming in areas with slightly better water access. The cropping cycle is largely dependent on seasonal rainfall, limiting farmers to one crop per year in most cases, though some areas with better water availability may support two crops annually. However, traditional irrigation methods have often failed to provide a consistent water supply, resulting in unpredictable yields and economic instability for farming households.

The introduction of solar-powered smart irrigation systems represents a transformative step towards addressing these issues. However, their adoption remains relatively limited. Preliminary surveys indicate that only about 30% of farmers in the Cholistan Desert margins have transitioned to solar-powered irrigation, with usage durations ranging between one to three years. These systems leverage renewable energy to operate water pumps and distribute water through precision irrigation techniques, significantly improving water use efficiency. However, empirical studies evaluating their long-term impact on crop yield, farmer satisfaction, and overall sustainability remain scarce.

The study is justified by the urgent need to address agricultural inefficiencies in water use and energy consumption in arid regions like Cholistan. It aligns with Pakistan’s national priorities for sustainable development, as articulated in its National Water Policy and climate adaptation strategies. Moreover, Punjab’s agriculture policy emphasizes the adoption of innovative technologies to enhance productivity and resource sustainability, making this study highly relevant. From a global perspective, the findings contribute to the broader discourse on sustainable agriculture in arid and semi-arid regions, offering insights that can inform policy and practice worldwide. By focusing on Cholistan, the study highlights the importance of localized solutions, bridging the gap between global technological advancements and regional implementation challenges.

This study is unique in its integrated approach, examining the nexus of renewable energy, water conservation, and agricultural productivity within the specific socio-economic and environmental context of the Cholistan Desert. Unlike previous studies, which often focus on isolated aspects of irrigation or renewable energy, this research adopts a comprehensive framework to evaluate the pre- and post-adoption impacts of solar-powered smart irrigation systems. The study’s focus on farmer satisfaction as a critical factor for technology adoption further distinguishes it from existing research, providing actionable insights into the socio-cultural dynamics of technology acceptance in rural Pakistan.

The motivation for this research stems from the critical challenges faced by farmers in Cholistan, including water scarcity, high energy costs, and declining agricultural productivity. Observing the successful implementation of renewable energy-driven irrigation systems in other arid regions worldwide inspired the investigation of their applicability in Cholistan. Additionally, the lack of empirical studies addressing the unique needs and constraints of this region highlights the necessity of this research. The study is also motivated by the potential to contribute to Pakistan’s Sustainable Development Goals (SDGs), particularly SDG 6 (Clean Water and Sanitation) and SDG 7 (Affordable and Clean Energy), by offering practical solutions for sustainable agriculture in resource-constrained settings.

This research offers several novel contributions. It provides a region-specific evaluation of solar-powered smart irrigation systems in Cholistan, addressing a significant gap in the literature. By examining the combined impacts of water and energy conservation, crop yield, and farmer satisfaction, the study offers a holistic understanding of the benefits of smart irrigation technologies. Through its pre- and post-adoption comparison of key agricultural variables, the research generates empirical evidence on the effectiveness of these systems. The findings are expected to inform policy recommendations for promoting renewable energy-driven irrigation systems in arid regions, contributing to sustainable agricultural development in Pakistan and beyond.

One limitation of this study is the inability to assess long-term effects due to its cross-sectional design. While the analysis provides valuable insights into the immediate impacts of solar-powered smart irrigation systems on crop yield, water conservation, and energy efficiency, it does not capture potential long-term trends, seasonal variations, or the sustained adoption of these technologies over time. A longitudinal study would be required to evaluate how factors such as system maintenance, evolving farmer perceptions, and climate fluctuations influence the long-term effectiveness and sustainability of these irrigation systems. However, the cross-sectional approach was chosen due to resource constraints and the need to establish an initial understanding of the relationships between key variables in the Cholistan Desert’s arid conditions.

1.2. Research Objectives

The study pursues six key research objectives. First, it evaluates the impact of solar-powered smart irrigation systems on crop yield, examining how renewable energy-driven technologies enhance agricultural productivity in the arid environment of the Cholistan Desert. Second, it analyzes the relationship between optimized water usage, irrigation efficiency, and water conservation, assessing how precise water management and smart irrigation technologies contribute to sustainable resource use in extreme conditions. Third, it examines the role of reduced energy consumption in enhancing agricultural energy efficiency, exploring how the integration of solar-powered irrigation systems supports more sustainable energy use. Fourth, it assesses the influence of farmer satisfaction on the adoption and effectiveness of these technologies, measuring how satisfaction levels affect successful implementation and long-term outcomes. Finally, it compares pre- and post-adoption impacts on key agricultural variables, quantifying differences in water usage, energy consumption, and crop yields before and after adopting smart irrigation systems.

2. Literature Review

Solar-powered irrigation systems have been transformative in ensuring agricultural sustainability, particularly in areas with limited access to electricity or water. Studies in sub-Saharan Africa demonstrate that solar irrigation increases crop yields by 20–30% due to consistent water availability [11,12]. Similarly, research in India reveals that solar-powered systems reduce dependency on grid electricity, boosting crop productivity by 15–20% [13]. These systems are increasingly seen as a pathway to sustainable agriculture in developing economies. In Pakistan, agriculture accounts for a significant portion of the economy, but unreliable electricity and water scarcity hinder productivity. Solar-powered irrigation technologies have emerged as a solution, offering operational reliability and cost savings [14]. A study by Raza and Tamoor [15] in Sindh and Punjab provinces showed a 25% increase in crop yields due to the adoption of solar irrigation systems. However, challenges such as high initial costs and limited awareness among farmers restrict widespread adoption. The Cholistan Desert, with its arid climate and limited water resources, faces unique agricultural challenges. Research on solar-powered irrigation systems in this region is scarce, though pilot projects have shown potential. Localized studies are needed to evaluate the direct impact of these systems on crop yield under Cholistan’s harsh conditions. Efficient water management is critical for sustainable agriculture, particularly in water-scarce regions [16]. Drip and sprinkler irrigation systems have revolutionized water use efficiency, with studies in Israel reporting water savings of up to 50% [17,18]. Similarly, research in Australia highlights that precision irrigation technologies significantly reduce water wastage while maintaining or enhancing crop yields [19,20].

Pakistan faces severe water scarcity due to inefficient irrigation practices and climate change [21]. High-efficiency irrigation systems (HEIS) introduced in provinces like Punjab have demonstrated their ability to reduce water consumption by 30–40% [22]. Despite these successes, issues such as inadequate farmer training and weak policy implementation hinder broader adoption. In Cholistan, where water resources are scarce and rainfall is unreliable, optimized irrigation systems are essential. While some projects have introduced HEIS in the region, empirical assessments of their effectiveness in improving water use efficiency are limited, leaving a significant research gap. Energy-efficient irrigation systems, particularly those powered by renewable energy, play a crucial role in reducing agricultural energy consumption [23]. Studies in Bangladesh show that solar-powered irrigation reduces energy costs by up to 25% while promoting environmental sustainability [24,25]. Pakistan’s energy-intensive agriculture sector is heavily reliant on diesel and grid electricity, contributing to high operational costs and greenhouse gas emissions [26,27]. Solar irrigation systems present a viable alternative, reducing dependence on conventional energy sources and enhancing overall energy efficiency [28]. In the energy-deficient Cholistan region, renewable energy solutions are particularly relevant. However, the region lacks comprehensive studies on the energy-saving potential of solar-powered irrigation systems, highlighting a critical research gap. Water conservation is a key priority in global agricultural practices [29]. Research in the Middle East and North Africa (MENA) shows that precision irrigation technologies reduce water usage by 30–50% while maintaining crop productivity [30,31]. Water-intensive farming practices in Pakistan exacerbate the country’s water scarcity [32]. Efforts to introduce smart irrigation systems, such as drip and sprinkler technologies, have shown promise in conserving water [33]. However, adoption rates remain low due to financial and logistical barriers. In Cholistan, water conservation is a pressing issue. While the introduction of smart irrigation technologies holds promise, there is limited empirical evidence on their water-saving impact in the region’s arid conditions.

Farmer satisfaction significantly influences the adoption of new technologies [34]. Studies in Brazil show that farmers are more likely to adopt solar irrigation systems when they perceive benefits such as reduced labor and increased yields [35,36]. In Pakistan, subsidies and awareness campaigns have positively impacted farmer attitudes toward smart irrigation technologies [37,38]. However, challenges such as financial constraints and limited technical knowledge persist. In Cholistan, farmer satisfaction with smart irrigation systems is influenced by unique socio-economic and environmental factors. Research exploring these perceptions is minimal, leaving a gap in understanding the region’s adoption dynamics. Comparative studies of pre- and post-adoption scenarios are essential for assessing the effectiveness of irrigation technologies [39]. Research in Kenya demonstrates significant improvements in water use efficiency and crop yields following the adoption of smart irrigation systems [40,41]. While some studies in Pakistan have attempted to measure the benefits of smart irrigation technologies, few have rigorously compared pre- and post-adoption metrics [42,43]. In Cholistan, such comparative analyses are virtually absent, creating a significant research gap in understanding the long-term benefits of smart irrigation technologies.

Despite extensive global and national research on smart irrigation systems, several gaps persist, particularly in the context of Punjab’s Cholistan Desert. The specific impacts of solar-powered irrigation systems in Cholistan, considering its unique climatic and socio-economic context, have been understudied. Additionally, limited research explores the integrated benefits of water- and energy-efficient technologies in the region. Farmer-centric insights, such as satisfaction levels and adoption dynamics tailored to Cholistan’s socio-economic realities, are also sparse. Furthermore, comparative analyses of key agricultural variables before and after the adoption of smart irrigation systems remain largely absent. This study aims to address these gaps through a comprehensive localized analysis of smart irrigation technologies in Cholistan, focusing on their effects on crop yield, water and energy conservation, and farmer satisfaction.

Conceptual Framework of the Study

The conceptual framework explores how energy-efficient smart irrigation technologies impact agriculture, focusing on various interconnected variables. Independent variables include the adoption of solar-powered smart irrigation systems, water delivery precision, energy efficiency index, farmer satisfaction, and farmer experience. These factors influence the mediating variables, such as reduced energy consumption and technology effectiveness, which ultimately affect dependent variables including crop yield, irrigation efficiency, water conservation levels, energy efficiency, cost savings, and environmental impact reduction. Adoption of solar-powered smart irrigation systems significantly improves crop yields, with precision water management and enhanced energy efficiency playing pivotal roles. Post-adoption analysis reveals measurable improvements, such as increased crop yields and significant reductions in water usage and energy consumption, leading to enhanced irrigation efficiency. Integration of solar-powered systems enhances energy efficiency through reduced energy consumption, supporting sustainable agricultural practices. Farmer satisfaction emerges as a critical factor, directly impacting the adoption and effectiveness of these technologies and subsequently leading to improved crop yields. Additionally, higher conservation levels are strongly associated with the adoption of smart irrigation systems, highlighting their role in promoting sustainable water use. Significant improvements in irrigation efficiency and resource management across adoption groups further underscore the benefits of these systems. The framework is reinforced by statistical analyses, demonstrating direct and indirect relationships among variables. Causal links, such as the influence of adoption on crop yields and the role of reduced energy consumption in enhancing energy efficiency, are well established. Mediating factors like technology effectiveness bridge these relationships, while feedback loops illustrate continuous improvements in outcomes.

The conceptual framework presented in Figure 1 illustrates the impact of solar-powered smart irrigation systems on key agricultural sustainability indicators, highlighting the relationships between system adoption, water delivery precision, energy efficiency, and farmer satisfaction with outcomes such as crop yield, water conservation, energy efficiency, and environmental impact reduction.

3. Materials and Methods

Flowchart: Materials and Methods of the Study

Start    │    ├── Research Design    │         ├── Quantitative Cross-Sectional Approach    │         ├── Pre- and Post-Adoption Analysis    │         └── Focus on Water, Energy, Yield, and Satisfaction    │    ├── Study Setting    │         ├── Cholistan Desert, Pakistan    │         ├── Extreme Arid Conditions, Water Scarcity    │         └── Target Population: Farmers Using Traditional Irrigation    │    ├── Socio-Economic and Demographic Variables    │         ├── Age    │         ├── Education Level    │         ├── Income Level    │         ├── Farm Size    │         ├── Access to Water    │         └── Access to Energy    │    ├── Sampling Procedure    │         ├── Stratified Random Sampling (Based on Farm Size and Resources)    │         ├── Total Population: 9600 Farmers    │         ├── Sample Size: 384 (Using Cochran’s Formula)    │         └── Proportional Allocation    │    ├── Data Collection Tool    │         ├── Structured Questionnaire (Online and Digital Platform)    │         ├── Sections:    │         │         ├── Demographic Information    │         │         ├── Water Usage    │         │         ├── Energy Consumption    │         │         ├── Crop Yield    │         │         └── Farmer Satisfaction    │         ├── Pre-Test with 30 Farmers (Cronbach’s Alpha = 0.86)    │         └── Data Collection Period: 1 Month (December 2024–January 2025)    │    ├── Ethical Considerations    │         ├── Institutional Review Board Approval    │         ├── Informed Consent from Participants    │         ├── Confidentiality and Data Security    │         └── Voluntary Participation    │    ├── Measurement and Indexation of Variables    │         ├── Adoption Index (AI)    │         ├── Water Precision Index (WPI)    │         ├── Energy Efficiency Index (EEI)    │         ├── Farmer Experience Scale (FES)    │         ├── Crop Yield Index (CYI)    │         ├── Conservation Efficiency Index (CEI)    │         ├── Energy Consumption Index (ECI)    │         ├── Satisfaction Index (SI)    │         └── Irrigation Efficiency Index (IEI)    │    ├── Data Analysis and Models    │         ├── Multiple Linear Regression (Impact on Crop Yield)    │         ├── Paired Sample t-Test (Yield Pre- vs. Post-Adoption)    │         ├── Structural Equation Modeling (Energy Efficiency Pathways)    │         ├── Chi-Square Test (Adoption and Water Conservation)    │         ├── Correlation Analysis (Satisfaction and Adoption)    │         └── ANOVA (Irrigation Efficiency by Adoption Groups)    │    └── End

This study uses a quantitative cross-sectional design to systematically assess water usage, energy consumption, crop yield, and farmer satisfaction in energy-efficient smart irrigation [44]. The cross-sectional approach enables the collection of data at a single point in time, providing a comprehensive snapshot of the factors influencing the adoption and effectiveness of these technologies. This design was selected due to its capacity to generate measurable and statistically verifiable data, aligning with the study’s objective of quantifying outcomes like water conservation, energy efficiency, and agricultural productivity. It is particularly appropriate for this study because it facilitates time-efficient data collection, allowing researchers to assess immediate impacts without requiring prolonged monitoring. Additionally, the design is cost-effective and practical for analyzing a geographically dispersed population, such as farmers in the Cholistan Desert, Punjab, Pakistan.

3.1. Study Setting

The study was conducted in the Cholistan Desert, Punjab, Pakistan, a region characterized by extreme arid conditions, chronic water scarcity, and heavy reliance on traditional irrigation methods. This region exemplifies challenges in arid agriculture, making it ideal for evaluating smart irrigation. Cholistan’s unique environmental and socio-economic conditions make it an ideal location to evaluate the potential of renewable energy-driven smart irrigation technologies. The region’s strategic importance in Pakistan’s agricultural sector further underscores the need for innovative solutions to enhance resource efficiency and productivity.

3.2. Population and Target Population

The population for this study includes farmers in the Cholistan Desert who engage in agricultural activities despite the region’s harsh conditions. The target population comprises small- to medium-scale farmers who rely on traditional irrigation systems. This group is highly relevant to the study as they represent the primary beneficiaries of smart irrigation technologies.

3.3. Socio-Economic and Demographic Variables with the Sample Size

The socio-economic and demographic characteristics of the respondents include factors such as age, education level, income, farm size, and access to essential resources like water and energy (

Table 1. Socio-economic and demographic variables with the sample size.

Variable	Categories	Sample Size (N = 384)	Justification
Age	18–30 years	98	Younger farmers likely open to innovation and adopting new technologies.
	31–50 years	192	Middle-aged farmers with moderate experience and financial stability to adopt technologies.
	Above 50 years	96	Experienced farmers who may be slower to adopt but can benefit from smart irrigation solutions.
Education Level	No formal education	80	Farmers without formal education may require additional training to use smart technologies.
	Primary to high school	154	Moderately educated farmers who can better understand the basic operation of smart systems.
	College to higher	150	Highly educated farmers likely to adopt and maximize the efficiency of the technology.
Income Level	Low income (≤PKR 30,000/month)	115	Financially constrained farmers needing cost-effective smart solutions.
	Middle income (PKR 30,001–60,000/month)	192	Farmers with moderate financial resources to invest in irrigation systems.
	High income (≥PKR 60,001/month)	77	Wealthier farmers who can readily afford advanced irrigation technologies.
Farm Size	Small farms (≤5 acres)	154	Farmers with limited resources who need efficient water and energy solutions to maximize productivity.
	Medium farms (6–15 acres)	154	Moderately resourced farmers likely to benefit significantly from the technology.
	Large farms (>15 acres)	76	Larger-scale farmers who can adopt and showcase the benefits of smart technologies.
Access to Water	Scarce	192	Farmers facing chronic water shortages and in need of water-saving solutions.
	Moderate	115	Farmers with seasonal or partial water availability who could benefit from efficiency improvements.
	Abundant	77	Farmers with sufficient water resources exploring energy-efficient solutions.
Access to Energy	Limited	154	Farmers with unreliable energy access needing renewable energy-driven systems.
	Stable	230	Farmers with reliable energy access who can fully utilize smart systems.

Source: compiled by authors.

). These variables were carefully chosen because they play a pivotal role in determining the adoption and effective utilization of energy-efficient smart irrigation technologies. For instance, age may influence openness to adopting innovative practices, while education level impacts understanding and willingness to implement technology-driven solutions. Similarly, income level and farm size are directly linked to a farmer’s financial capability and operational scale, which affect their ability to invest in and benefit from smart irrigation systems.

3.4. Sampling Procedures and Sample Size

A stratified random sampling was employed for this study to ensure representation across key socio-economic and demographic categories, such as age, education level, income, farm size, and access to water and energy [45]. This method is well suited for the research as it ensures that subgroups relevant to the adoption of energy-efficient smart irrigation technologies are adequately represented, leading to more accurate and generalizable findings. Stratification was conducted based on farm size and access to resources, as these factors are critical in determining the feasibility and effectiveness of the technologies being studied. The target population comprises farmers in the Cholistan Desert engaged in agricultural activities. According to agricultural records and local government data, the total target population is approximately 9600 farmers, distributed across various strata such as small-, medium-, and large-scale farms. The sample size of 384 was determined using Cochran’s formula, which ensures statistical reliability and generalizability of the findings [46]. The formula is given as follows:

n = Z²·p·(1 − p)/e²

where n = required sample size, Z = Z-value (1.96 for a 95% confidence level), p = estimated proportion of the population with the characteristic of interest (assumed to be 0.5 for maximum variability), e = margin of error (0.05 or 5%), and n = (1.96)²·0.5. (1 − 0.5)/(0.05)² = 384.

To proportionally allocate the sample size across strata, the population of each stratum was estimated based on local agricultural statistics and field data (

Table 2. Sample size distribution.

Stratum	Total Population	Percentage	Sample Size
Small-Scale Farmers	4800	50%	192
Medium-Scale Farmers	3840	40%	153
Large-Scale Farmers	960	10%	39
Total	9600	100%	384

Source: compiled by authors.

).

3.5. Tool of Data Collection

Data for this study were collected using a structured questionnaire, which was administered online through a digital platform to ensure accessibility and efficiency. This method was selected due to its ability to reach a geographically dispersed sample, minimize logistical challenges, and facilitate real-time data entry and analysis. The questionnaire was carefully designed to comprehensively address the study’s objectives by capturing data on key variables, including demographic information, water usage, energy consumption, crop yield, and farmer satisfaction.

A structured questionnaire was deemed the most appropriate data collection tool as it provides a standardized method for gathering information, ensuring consistency and reliability across responses. Furthermore, this approach allows for the collection of quantifiable data, aligning well with the study’s quantitative research design.

Questionnaire Structure

To ensure that all relevant aspects of the study were covered, the questionnaire was organized into five key sections:

Demographic information:

• This section collected data on the age, education level, income, farm size, and experience of the respondents.
• The purpose was to analyze the socio-economic profile of the farmers and examine how these factors influenced their adoption of smart irrigation technologies.

Water usage:

• This section measured water consumption patterns and efficiency before and after adopting smart irrigation technologies.
• Farmers were asked to report their average water usage per season, irrigation frequency, and perception of water savings after implementing new technologies.

Energy consumption:

• This section assessed energy usage trends, focusing on shifts toward renewable energy sources and improvements in energy efficiency.
• Questions covered the types of energy sources used for irrigation (e.g., diesel, solar, electric), changes in energy costs, and any observed improvements in energy efficiency.

Crop yield:

• The aim of this section was to evaluate agricultural productivity changes resulting from the adoption of energy-efficient smart irrigation technologies.
• Farmers provided data on crop production levels, yield per acre, and quality of produce before and after adopting the technology.

Farmer satisfaction:

• This section gauged the perceived benefits and challenges associated with the adoption of smart irrigation technologies.
• Questions focused on the ease of use, affordability, maintenance challenges, and overall satisfaction with the technology.

Pre-Testing and Reliability Assessment

Before full-scale distribution, the questionnaire was pre-tested with a pilot group of 30 farmers to assess its clarity, relevance, and reliability. Based on feedback, minor revisions were made to improve question phrasing and response options.

To ensure the internal consistency of the questionnaire, Cronbach’s alpha was used to assess reliability. The overall Cronbach’s alpha score was 0.86, indicating strong reliability of the measurement tool. Construct validity was confirmed through expert reviews and factor analysis, ensuring that the tool effectively measured the intended variables [47].

Out of the 425 questionnaires distributed, a total of 384 responses were received, resulting in a response rate of 90.35%. This indicates a high level of engagement among farmers, likely due to follow-up reminders and the involvement of local agricultural extension officers who assisted farmers in completing the survey.

Data Completeness and Challenges

Upon reviewing the responses, the following was found:

• In total, 92.63% (356 responses) were fully completed, providing comprehensive data for analysis.
• A total of 7.37% (28 responses) were partially completed, missing key variables such as energy consumption, crop yield data, or satisfaction levels. These incomplete responses were excluded from the final analysis to maintain data integrity and accuracy.

Challenges in Data Collection

While the online survey approach offered several advantages, some challenges were encountered:

• Difficulty in filling out questionnaires:
  • Many farmers, particularly small-scale farmers, faced challenges in understanding and filling out the questionnaire due to limited digital literacy.
  • To address this issue, agricultural extension officers and local facilitators were engaged to provide guidance and assistance to the respondents.
• Limited internet access:
  • Some farmers in remote areas had unstable internet connectivity, which resulted in delayed submissions or incomplete responses.
  • Follow-up calls and alternative data collection methods, such as assistance from local community centers, were used to encourage completion.
• Time constraints of farmers:
  • Farmers were often occupied with their agricultural activities, making it difficult to allocate time for completing the questionnaire.
  • To overcome this, the survey period was extended, and reminders were sent at convenient times.
• Missing data and non-response bias:
  • A small percentage of respondents left certain questions blank, particularly in sections related to income and energy consumption, possibly due to privacy concerns.
  • However, these responses were proportionally distributed across different farm sizes, minimizing any systematic bias in the dataset.

Ethical considerations: Ethical approval for the study was obtained from the Ethical Committee at the University of Malakand. Participants were provided with detailed information about the study’s objectives and procedures, ensuring informed consent prior to participation. The survey included a consent form, and respondents were informed of their right to withdraw at any stage without any repercussions. To maintain confidentiality and anonymity, no personally identifiable information was collected, and all data were securely stored on an encrypted digital platform. The study adhered to ethical research practices, ensuring respect for participants’ rights and well-being. These measures align with the study’s focus on addressing the needs of vulnerable communities in an ethically responsible manner.

3.6. Measurement of Variables and Indexation

The study involves comprehensive measurement and indexation of key variables to evaluate the impact of solar-powered smart irrigation systems on agricultural sustainability. The adoption of these systems is assessed using a composite score derived from farmer responses, measured on a 5-point Likert scale and indexed as the Adoption Index (AI). Water delivery precision, a critical factor, is evaluated through field data on irrigation uniformity and expressed as a percentage, contributing to the Water Precision Index (WPI). Similarly, energy efficiency is quantified by analyzing the ratio of energy input to output per hectare, forming the Energy Efficiency Index (EEI). Farmer experience is considered a continuous variable measured in years and indexed as the Farmer Experience Scale (FES). Dependent variables include crop yield, measured in tons per hectare and indexed as the Crop Yield Index (CYI), and water conservation levels, classified as high or low based on efficiency improvements, forming the Conservation Efficiency Index (CEI). Energy consumption, measured in kilowatt-hours pre- and post-adoption of the systems, is tracked through the Energy Consumption Index (ECI). Farmer satisfaction, gauged on a 5-point Likert scale reflecting their perception of the system’s effectiveness, contributes to the Satisfaction Index (SI). Additionally, irrigation efficiency, expressed in liters per hectare, is encapsulated in the Irrigation Efficiency Index (IEI). To analyze these variables, the study employs multiple statistical tools. Multiple linear regression assesses the influence of system adoption, water delivery precision, and energy efficiency on crop yield. Paired sample t-tests examine the changes in crop yield, water usage, and energy consumption before and after system adoption. The chi-square test determines the association between system adoption and water conservation levels. Structural Equation Modeling (SEM) evaluates the direct and indirect effects of reduced energy consumption, energy efficiency, and cost savings on environmental impact reduction. Correlation analysis explores the relationships between farmer satisfaction, technology adoption, and crop yield, while ANOVA compares irrigation efficiency across adoption groups.

Data analysis and models of the study: The data were analyzed using SPSS version 26, employing multiple linear regression, paired sample t-test, chi-square analysis, correlation analysis, and Structural Equation Modeling (SEM). The specifications of the study’s models are outlined below: Model 1: multiple linear regression model: to evaluate the impact of solar-powered smart irrigation systems on crop yield by analyzing key influencing factors, we apply the following regression equation:

Y_{Crop Yield} = β₀ + β₁ (System Adoption Score) + β₂ (Water Delivery Precision) + β₃ (Energy Efficiency Index) + β₄ (Farmer Experience) + ϵ

Denotations: Y_{Crop Yield}: predicted crop yield (tons/hectare). Β₀: constant (intercept). β₁, β2, β3, β4: regression coefficients for respective predictors. System Adoption Score: composite score measuring adoption. Water Delivery Precision: irrigation uniformity percentage. Energy Efficiency Index: energy efficiency ratio per hectare. Farmer Experience: years of farming experience. ϵ: residual error term.

Paired sample t-test for crop yields: to compare crop yields before and after the adoption of smart irrigation systems we use the following model equation:

$$t={\displaystyle \frac{\overline{\mathrm{d}}}{S_d·\surd n}}$$

Denotations: t: t-statistic for paired sample t-test. $\overline{\mathrm{d}}$: mean difference in crop yield before and after adoption. S_d: standard deviation of the differences. n: number of observations.

Structural Equation Model (SEM) for energy efficiency: to analyze the pathways between solar-powered systems, reduced energy consumption, energy efficiency, and related impacts, we use the following model equations:

• Reduced Energy Consumption = β₁ (Solar-Powered Systems) + ϵ_1.
• Energy Efficiency = β₂ (Reduced Energy Consumption) + ϵ_2.
• Cost Savings = β₃ (Reduced Energy Consumption) + ϵ_3.
• Environmental Impact Reduction = β₄ (Energy Efficiency) + ϵ_4.

Denotations: Solar-Powered Systems: level of adoption of solar-powered systems. Reduced Energy Consumption: reduction in energy use (kWh). Energy Efficiency: improved energy input–output ratio. Cost Savings: economic benefits from reduced energy use. Environmental Impact Reduction: measured reduction in environmental degradation. β₁, β2, β3, β4: path coefficients. ϵ₁, ϵ₂, ϵ₃, ϵ₄: residual errors.

Chi-square test for association between adoption and water conservation: to determine the association between the adoption of solar-powered irrigation systems and water conservation levels, we use the following model equation: χ² = ∑ (O − E) 2/E.

Denotations: χ²: chi-square statistic. O: observed frequencies. E: expected frequencies.

Correlation and SEM for farmer satisfaction and crop yield: to analyze the relationship between farmer satisfaction, technology adoption, and crop yield, we use the following model equations:

• Crop Yield = β₁ (Farmer Satisfaction) + ϵ₁
• Technology Adoption = β₂ (Farmer Satisfaction) + ϵ₂
• Crop Yield = β₃ (Technology Adoption) + ϵ₃

Denotations: Crop Yield: farm productivity (tons/hectare). Farmer Satisfaction: perceived effectiveness of irrigation systems. Technology Adoption: level of adoption of smart irrigation systems. β₁, β₂, β₃: path coefficients. ϵ₁, ϵ₂, ϵ₃: residual errors.

ANOVA for irrigation efficiency: To assess the variance in irrigation efficiency across different adoption groups, we use the following model equation: F = MS_between/MS_within

Denotations: F: F-statistic for ANOVA. MS_between: mean square between groups. MS_within: mean square within groups.

4. Results

4.1. Multiple Linear Regression

Table 3. Multiple linear regression of crop yield through the adoption of solar-powered smart irrigation systems.

Parameter	Unstandardized Coefficient (B)	Standard Error (SE)	Standardized Coefficient (Beta)	t-Value	p-Value	95% Confidence Interval
Constant (Intercept)	2.50	0.30	-	8.33	<0.001	[1.91, 3.09]
System Adoption Score	0.75	0.05	0.85	15.00	<0.001	[0.65, 0.85]
Water Delivery Precision	0.45	0.08	0.60	5.63	<0.001	[0.29, 0.61]
Energy Efficiency Index	0.30	0.06	0.50	5.00	<0.001	[0.18, 0.42]
Farmer Experience (Years)	0.10	0.03	0.25	3.33	0.001	[0.04, 0.16]

Source: authors’ calculations.

presents the results of a multiple linear regression analysis examining the impact of adopting solar-powered smart irrigation systems. The analysis highlights key predictors, including system adoption, water delivery precision, energy efficiency, and farmer experience, offering insights into their relative contributions to improved agricultural productivity.

The regression analysis reveals that various factors significantly contribute to enhancing crop yield in the Cholistan Desert through the adoption of solar-powered smart irrigation systems. The baseline crop yield, without considering the predictors, is estimated at 2.50 tons per hectare, which serves as a reference point for understanding the improvements brought by advanced irrigation technologies.

The adoption of solar-powered smart irrigation systems has a substantial positive effect on crop yield. For every one-unit increase in the System Adoption Score, crop yield increases by 0.75 tons per hectare (B = 0.75), with a beta value of 0.85, indicating a strong influence. This finding directly links to the study’s focus on leveraging renewable energy-driven technologies to improve agricultural productivity in water-scarce regions like the Cholistan Desert. The high t-value of 15.00 (p < 0.001) further confirms that the effect of system adoption is statistically significant and not due to random variation.

Water delivery precision also plays a key role in improving crop yield. A one-unit improvement in water delivery precision results in a 0.45 tons per hectare increase in crop yield (B = 0.45), with a beta value of 0.60, demonstrating a moderate to strong effect. More importantly, improved precision in water delivery led to an average 30% reduction in water consumption, highlighting the efficiency of smart irrigation in arid areas. This finding aligns with the study’s objective of promoting water sustainability through advanced irrigation practices. The t-value of 5.63 (p < 0.001) supports the statistical significance of water delivery precision as a predictor of crop yield.

The Energy Efficiency Index contributes positively to crop yield as well. A one-unit increase in energy efficiency leads to a 0.30 tons per hectare increase in crop yield (B = 0.30). With a beta value of 0.50, this shows a moderate impact of energy efficiency on agricultural productivity. Additionally, the implementation of solar-powered irrigation systems resulted in an estimated 40% reduction in energy consumption, significantly lowering dependence on fossil fuels and advancing sustainable energy use in agriculture. This finding underscores the dual benefit of enhancing productivity while reducing environmental impact. The t-value of 5.00 (p < 0.001) indicates that the impact of energy efficiency on yield is statistically significant.

Finally, farmer experience also positively influences crop yield, although the effect is smaller. Each additional year of farming experience is associated with a 0.10 tons per hectare increase in crop yield (B = 0.10). The beta value of 0.25 suggests that while this factor has a weaker influence compared to the other predictors, it is still significant. Experienced farmers are likely better equipped to use the technological advantages of smart irrigation systems, maximizing their potential benefits. The t-value of 3.33 (p = 0.001) confirms the statistical significance of farmer experience on crop yield.

4.2. Paired Sample t-Test: Crop Yields Before and After Smart Irrigation

Table 4. Paired sample t-test.

Variable	Mean (Before)	Mean (After)	Mean Difference	Standard Deviation	t-Value	p-Value	95% Confidence Interval
Crop Yield (Tons/Hectare)	3.0	4.8	1.8	0.5	40.25	<0.001	[1.7, 1.9]
Water Usage (Efficient/Not)	Efficient	-	-	-	-	-	-

Source: authors’ calculations.

presents the results of a paired sample t-test comparing key agricultural metrics (e.g., crop yields) before and after the adoption of solar-powered smart irrigation systems. This analysis quantifies the improvements in crop yield and water usage efficiency, providing statistical evidence of the system’s impact on farming outcomes.

The objective of this analysis is to assess the effect of optimized water usage on irrigation efficiency, focusing on the relationship between precise water management and improved irrigation practices, particularly in the extreme environmental conditions of the Cholistan Desert. Before implementing the solar-powered smart irrigation systems, the average crop yield was 3.0 tons per hectare. This baseline represents the crop yield under traditional irrigation practices, which are often inefficient and limited by water scarcity in arid regions like the Cholistan Desert.

After adopting the smart irrigation system, the average crop yield increased to 4.8 tons per hectare. This represents a significant improvement in agricultural productivity, directly attributed to the optimized water usage and better irrigation management made possible by solar-powered systems. The difference in mean crop yield before and after the implementation of the smart irrigation systems is 1.8 tons per hectare. This indicates a substantial improvement in crop yield due to the adoption of energy-efficient irrigation technologies. The increase in crop yield aligns with the study’s emphasis on the effectiveness of optimized water management in improving irrigation practices and ensuring water and energy sustainability in agriculture. The standard deviation of 0.5 represents the variability in the crop yield after the implementation of the smart irrigation systems. This relatively low standard deviation suggests that the increase in crop yield is consistent across observations, further supporting the reliability of the results. The precision of water management facilitated by the solar-powered system likely contributes to the reduced variability in outcomes. The t-value of 40.25 reflects the difference between the means of the two groups (before and after implementation) relative to the variability of the data. This large t-value indicates a statistically significant difference in crop yields before and after adopting solar-powered irrigation systems. The high t-value supports the hypothesis that optimized water usage through smart irrigation technologies leads to significantly higher crop yields.

The p-value of <0.001 indicates that the difference in crop yields before and after the implementation of the smart irrigation system is highly statistically significant. This means that the observed improvement in crop yield is very unlikely to have occurred by chance, reinforcing the conclusion that solar-powered smart irrigation systems are effective in enhancing agricultural productivity. The 95% confidence interval for the mean difference in crop yield is between 1.7 and 1.9 tons per hectare. This interval provides a range within which the true mean difference in crop yield is likely to fall, with 95% confidence. The fact that the confidence interval does not include zero further strengthens the conclusion that the increase in crop yield is a result of the adoption of solar-powered smart irrigation systems, rather than random variation. In the context of the study’s focus, these findings demonstrate that the optimized water usage facilitated by solar-powered smart irrigation systems significantly improves crop yields in the Cholistan Desert. The increase in crop yield by 1.8 tons per hectare underscores the importance of precise water management in enhancing agricultural productivity in extreme conditions. The t-value of 40.25 and the p-value of <0.001 confirm the statistical significance of this effect, while the confidence interval further supports the reliability of the results. These results are directly aligned with the study’s goal of showcasing the effectiveness of energy-efficient irrigation technologies in promoting water and energy sustainability in agriculture.

4.3. SEM: Reduced Energy Consumption and Energy Efficiency

The Structural Equation Modeling (SEM) results in

Table 5. Structural Equation Model (SEM).

Path/Relationship	Parameter Estimate (β)	Standard Error (SE)	t-Value	p-Value
Reduced Energy Consumption → Energy Efficiency	0.85	0.05	17.00	<0.001
Solar-Powered Systems → Reduced Energy Consumption	0.78	0.06	13.00	<0.001
Solar-Powered Systems → Energy Efficiency	0.70	0.04	17.50	<0.001
Reduced Energy Consumption → Cost Savings	0.88	0.03	29.33	<0.001
Energy Efficiency → Environmental Impact Reduction	0.82	0.05	16.40	<0.001
Goodness-of-Fit Indices
Index	Value	Threshold	Fit
Chi-Square (χ2)	24.56	p > 0.05	Good Fit
RMSEA (Root Mean Square Error of Approximation)	0.028	<0.05	Excellent Fit
CFI (Comparative Fit Index)	0.97	>0.90	Excellent Fit
TLI (Tucker–Lewis Index)	0.95	>0.90	Excellent Fit
SRMR (Standardized Root Mean Square Residual)	0.020	<0.05	Excellent Fit

Source: authors’ calculations.

provide compelling evidence supporting the study’s objective of examining how reduced energy consumption enhances energy efficiency through the integration of solar-powered smart irrigation systems. The path coefficient between reduced energy consumption and energy efficiency is strong (β = 0.85) with a low standard error (SE = 0.05), a high t-value (t = 17.00), and a statistically significant p-value (p < 0.001). This result demonstrates that as energy consumption decreases, energy efficiency significantly improves, directly aligning with the study’s aim of promoting sustainable energy use in agriculture through renewable technologies.

The direct relationship between solar-powered systems and reduced energy consumption is also substantial (β = 0.78), supported by a standard error of 0.06, a t-value of 13.00, and a p-value less than 0.001. Notably, the adoption of solar-powered irrigation systems led to a 40% reduction in energy consumption, reinforcing their role in transitioning agriculture toward more sustainable energy practices. Furthermore, the direct link between solar-powered systems and energy efficiency (β = 0.70, SE = 0.04, t = 17.50, p < 0.001) illustrates that these technologies not only reduce energy use but also directly improve how efficiently energy is utilized, highlighting their dual impact on energy sustainability.

Water conservation is another critical benefit of smart irrigation technology. The study finds that improved water delivery precision, enabled by solar-powered systems, resulted in a 30% reduction in water consumption, demonstrating the efficiency of these systems in resource-scarce regions. This efficiency gain aligns with the study’s objective of optimizing water usage while maintaining or increasing crop yield.

The positive association between reduced energy consumption and cost savings (β = 0.88, SE = 0.03, t = 29.33, p < 0.001) further emphasizes the economic advantages of energy-efficient technologies. This strong relationship suggests that farmers who adopt solar-powered irrigation systems can achieve significant financial benefits due to lower operational energy costs. Additionally, the relationship between energy efficiency and environmental impact reduction (β = 0.82, SE = 0.05, t = 16.40, p < 0.001) confirms that improving energy efficiency contributes to reducing environmental degradation, supporting the study’s goal of promoting sustainable agricultural practices that also protect the environment.

The model’s overall validity is confirmed by excellent goodness-of-fit indices. The chi-square value (χ² = 24.56, p > 0.05) indicates a good model fit, while the RMSEA value of 0.028 (below the 0.05 threshold) signifies an excellent fit. High values for the Comparative Fit Index (CFI = 0.97) and Tucker–Lewis Index (TLI = 0.95), both exceeding the acceptable threshold of 0.90, further validate the model. Additionally, the low Standardized Root Mean Square Residual (SRMR = 0.020) confirms minimal discrepancy between the observed and predicted relationships.

4.4. Chi-Square Analysis of the Association Between the Adoption of Solar-Powered Smart Irrigation Systems and Water Conservation Levels

The chi-square analysis presented in

Table 6. Chi-square test.

Dependent Variable	Independent Variable
	Adoption of Solar-Powered Smart Irrigation Systems
Water Conservation Level	Adopted	Not Adopted	Total
High Conservation	180	40	220
Low Conservation	20	144	164
Total	200	184	384
Chi-Square Value (χ2)		Degrees of Freedom (df)	p-value
171.1		1	p < 0.001

Source: authors’ calculations.

effectively evaluates the association between the adoption of solar-powered smart irrigation systems and water conservation levels, directly addressing the study’s objective of investigating how these technologies contribute to water conservation and resource sustainability. The table categorizes water conservation levels into two groups—High Conservation and Low Conservation—and compares them among farmers who have adopted and those who have not adopted smart irrigation systems. Among the adopters of solar-powered smart irrigation systems, 180 farmers achieved high water conservation, while only 20 reported low conservation. In contrast, among non-adopters, only 40 experienced high conservation, and a significant 144 reported low conservation. This stark contrast suggests a strong relationship between adopting smart irrigation technologies and achieving higher water conservation levels. The chi-square value (χ² = 171.1) with 1 degree of freedom (df) and a p-value < 0.001 indicates a statistically significant association between the adoption of solar-powered smart irrigation systems and improved water conservation. The very low p-value confirms that the observed distribution of high and low conservation levels between adopters and non-adopters is unlikely due to chance. This result supports the rationale that smart irrigation systems, through automated and precise water delivery, significantly minimize water waste and enhance water conservation.

4.5. Correlation Analysis and Structural Equation Modeling (SEM) of Farmer Satisfaction and Crop Yield

The results of correlation and SEM examine the relationships between farmer satisfaction, technology adoption, technology effectiveness, and crop yield, emphasizing their interconnected roles in improving agricultural productivity. The strong correlation value (r = 0.85) between farmer satisfaction and crop yield indicates that satisfied farmers are likely to achieve significantly higher productivity (

Table 7. Correlation analysis and Structural Equation Modeling (SEM).

Parameter	Correlation Value (r)	Standardized Path Coefficient (β)	p-Value
Farmer Satisfaction → Crop Yield	0.85	0.80	p < 0.001
Farmer Satisfaction → Technology Adoption	0.75	0.70	p < 0.001
Technology Adoption → Crop Yield	0.70	0.60	p < 0.001
Farmer Satisfaction → Technology Effectiveness	0.80	0.75	p < 0.001
Technology Effectiveness → Crop Yield	0.78	0.65	p < 0.001

Source: authors’ calculations.

). The standardized path coefficient (β = 0.80) further demonstrates that farmer satisfaction directly explains 80% of the variation in crop yield, with a statistically significant p-value (p < 0.001), reinforcing the importance of satisfaction as a key driver of improved outcomes. Farmer satisfaction also strongly influences technology adoption, as indicated by a correlation value (r = 0.75). The standardized path coefficient (β = 0.70) shows that satisfaction explains 70% of the variation in technology adoption, with a statistically significant relationship (p < 0.001). This highlights that satisfied farmers are more likely to adopt innovative technologies, which plays a vital role in enhancing agricultural practices. Similarly, technology adoption positively impacts crop yield, with a correlation value (r = 0.70) and a path coefficient (β = 0.60), indicating that 60% of the variation in crop yield is directly attributable to technology adoption, supported by a significant p-value (p < 0.001). Farmer satisfaction also enhances perceptions of technology effectiveness, as shown by a correlation value (r = 0.80) and a path coefficient (β = 0.75), where 75% of the variation in perceived effectiveness is explained by satisfaction, with a highly significant p-value (p < 0.001). This demonstrates that satisfied farmers view the technology as more effective, which likely improves its utilization and outcomes. Furthermore, technology effectiveness directly influences crop yield, with a correlation value (r = 0.78) and a path coefficient (β = 0.65), accounting for 65% of the variation in crop yield, with a statistically significant relationship (p < 0.001).

4.6. Paired Sample t-Test and ANOVA Analysis of Irrigation Efficiency Before and After Adoption of Smart Irrigation Systems

The results in

Table 8. Paired sample t-test and ANOVA analysis.

Test Type	Parameter	Mean Pre-Adoption	Mean Post-Adoption	t-Value/F-Value	p-Value
Paired Sample T-Test	Water Usage → Irrigation Efficiency	8.2	4.5	t = 12.5	p < 0.001
Paired Sample T-Test	Energy Consumption → Irrigation Efficiency	15.4	9.3	t = 10.1	p < 0.001
ANOVA	Irrigation Efficiency Across Adoption Groups	4.9	5.8	F = 7.8	p < 0.001
ANOVA	Water Usage → Irrigation Efficiency Across Groups	8.5	4.1	F = 6.9	p < 0.001

Source: authors’ calculations.

comprehensively assess the impact of solar-powered smart irrigation systems on irrigation efficiency by comparing pre- and post-adoption values using paired sample t-tests and ANOVA. These analyses directly address the study’s objective of evaluating the differences in water usage, energy consumption, and irrigation efficiency before and after adopting smart irrigation technologies. The paired sample t-test for Water Usage → Irrigation Efficiency shows a significant improvement, with the mean irrigation inefficiency decreasing from 8.2 pre-adoption to 4.5 post-adoption. The t-value of 12.5 and a p-value < 0.001 confirm that this reduction is statistically significant. This result supports the rationale that optimized water usage through smart irrigation systems leads to more efficient irrigation by reducing water waste. It validates that the adoption of these systems has a substantial impact on improving irrigation efficiency in water-scarce regions. Similarly, the paired sample t-test for Energy Consumption → Irrigation Efficiency reveals a decrease in mean energy consumption from 15.4 to 9.3 after adopting smart irrigation systems. The t-value of 10.1 with a p-value < 0.001 indicates that this reduction in energy usage is statistically significant. This outcome highlights that solar-powered smart irrigation systems not only conserve water but also reduce energy consumption, enhancing overall irrigation efficiency. It aligns with the study’s goal of promoting energy-efficient agricultural practices, contributing to energy sustainability. The ANOVA analysis of Irrigation Efficiency Across Adoption Groups further strengthens these findings. The mean irrigation efficiency increased from 4.9 in lower adoption groups to 5.8 in higher adoption groups, with an F-value of 7.8 and a p-value < 0.001. This result suggests that higher adoption levels of smart irrigation systems are positively associated with greater improvements in irrigation efficiency. It confirms that the degree of technology adoption plays a significant role in achieving optimal water use. Additionally, the ANOVA for Water Usage → Irrigation Efficiency Across Groups shows a substantial reduction in water usage from 8.5 to 4.1 across adoption groups, with an F-value of 6.9 and a p-value < 0.001. This finding indicates that farmers who have fully integrated smart irrigation systems into their practices experience significantly greater water efficiency than those with partial or no adoption.

Collectively, these results provide strong empirical evidence that adopting solar-powered smart irrigation systems leads to significant improvements in water and energy efficiency. They validate the rationale that optimized water usage enhances irrigation efficiency and directly support the study’s broader objective of evaluating pre- and post-adoption impacts on key sustainability metrics.

Implications for Real-World Farming Practices

The results highlight significant benefits of solar-powered smart irrigation systems for real-world farming, particularly in water-scarce regions like the Cholistan Desert. Farmers adopting smart irrigation can expect substantial yield improvements, such as an increase of 1.8 tons per hectare, proving that optimized water management directly boosts productivity. A 30% reduction in water consumption through precise water delivery supports sustainable farming in arid environments, helping conserve scarce water resources. A 40% reduction in energy consumption indicates that solar-powered irrigation is both environmentally and financially beneficial, lowering operational costs for farmers while reducing dependence on fossil fuels. Experienced farmers maximize the benefits of smart irrigation, suggesting that training and capacity building are crucial for effective adoption. The strong statistical evidence supports government incentives, subsidies, and financial support to encourage wider adoption of smart irrigation technologies for sustainable agriculture.

5. Discussion

The findings from the multiple linear regression analysis demonstrate that adopting solar-powered smart irrigation systems significantly enhances crop yield in the Cholistan Desert. The analysis shows that system adoption, water delivery precision, energy efficiency, and farmer experience all positively influence crop yield. These results align with previous studies emphasizing the role of technology adoption in improving agricultural productivity. For instance, Burney and Woltering [48] highlighted how solar-powered irrigation in sub-Saharan Africa boosted crop yields by improving water access. Similarly, Khosravi and Bafkar [49] found that precision irrigation systems significantly increased crop productivity in arid regions due to efficient water management. This study further supports these findings by showcasing the compounded effects of technology adoption, energy efficiency, and farmer experience on crop yield. The paired sample t-test results indicate a substantial increase in crop yields after implementing solar-powered smart irrigation systems compared to traditional irrigation practices. This outcome corroborates the work of Santra [50], who reported notable yield improvements after introducing solar-powered irrigation in India’s semi-arid regions. Likewise, Qureshi and Ashraf [51] observed that precision water management technologies significantly enhanced crop yields in Pakistan’s arid zones [52]. The consistent findings across diverse geographical contexts validate the positive impact of solar-powered smart irrigation systems on agricultural productivity.

Structural Equation Modeling (SEM) results reveal that reduced energy consumption, driven by solar-powered systems, significantly improves energy efficiency and leads to considerable cost savings. These findings align with the research of Mandal and Maji [53], which demonstrated that solar-powered irrigation systems in Bangladesh reduced energy costs and improved energy utilization. Furthermore, studies by Okafor and Nzekwe [54] confirm that renewable energy technologies in agriculture can minimize operational costs and promote energy sustainability. This study adds to the growing body of evidence by highlighting the direct and indirect pathways through which solar-powered systems enhance both energy efficiency and cost savings. However, despite these benefits, economic feasibility remains a potential challenge for widespread adoption. The high initial investment costs of solar-powered irrigation systems may deter small-scale farmers, particularly in developing regions with limited financial support. Additionally, policy constraints, such as inadequate subsidies and slow regulatory approvals, can hinder the large-scale implementation of renewable energy solutions in agriculture. Infrastructure challenges, including the lack of robust grid connections or battery storage systems for energy backup, may further limit the reliability of solar-powered irrigation, especially in regions with inconsistent sunlight exposure. Additionally, technical expertise is required for proper system maintenance, and a lack of adequate training programs for farmers could impact operational efficiency and long-term sustainability. Addressing these financial, infrastructural, and regulatory barriers is crucial to ensuring that the benefits of solar-powered irrigation are accessible to a broader range of farmers.

The chi-square analysis establishes a strong association between the adoption of solar-powered smart irrigation systems and improved water conservation levels. This result supports the findings of Otoo and Lefore [55], who reported that adopting solar-powered irrigation systems in Ethiopia significantly reduced water waste and improved water use efficiency. Similarly, research by Mohan and Perarapu [56] found that precision irrigation technologies promoted water sustainability in water-scarce regions. This study reinforces the role of smart irrigation systems in enhancing sustainable water use in arid environments like the Cholistan Desert. However, infrastructure limitations and the need for technical training may pose barriers to maximizing water conservation benefits, as farmers unfamiliar with precision irrigation technologies may struggle with system optimization and maintenance.

Correlation and SEM analyses demonstrate that farmer satisfaction is a crucial factor driving technology adoption, perceived effectiveness, and crop yield. This finding is consistent with the work of Zakaria and Alhassan [57], who highlighted that farmer satisfaction significantly influences the adoption of agricultural technologies in sub-Saharan Africa. Additionally, Teklewold and Gebrehiwot [58] found that satisfied farmers are more likely to adopt sustainable agricultural practices, leading to improved productivity. This study reinforces these insights by illustrating the interconnected roles of farmer satisfaction, technology adoption, and crop yield improvements. The paired sample t-tests and ANOVA results reveal significant improvements in irrigation efficiency and reductions in water and energy consumption after adopting smart irrigation systems. These findings echo the work of Foster and Pérez-Blanco [59], who observed increased irrigation efficiency in Australian farms following the implementation of smart irrigation technologies. Similarly, Upreti and Timsina [60] reported that solar-powered irrigation systems in South Asia reduced energy consumption and improved irrigation practices. However, challenges such as intermittent solar energy availability, the need for complementary energy storage solutions, and financial constraints related to system maintenance may limit the full potential of these systems, particularly in regions with inconsistent sunlight exposure. Moreover, regulatory hurdles, including lengthy approval processes for solar technology subsidies and limited access to affordable financing options, may slow the widespread adoption of smart irrigation solutions. Without targeted policy interventions, such as streamlined subsidy programs and training initiatives, the impact of these technologies on irrigation efficiency may be constrained [61].

Comparing these results with previous studies reveals substantial alignment, especially in the observed benefits of solar-powered irrigation systems on crop yield, water conservation, and energy efficiency. Studies across diverse regions consistently report improvements in agricultural productivity and sustainability outcomes due to technology adoption. However, this study distinguishes itself by comprehensively integrating multiple analytical techniques—including multiple regression, SEM, chi-square analysis, and ANOVA—to provide a multidimensional understanding of how solar-powered smart irrigation systems impact agriculture in the Cholistan Desert. Unlike previous studies, which often focus on isolated factors, this research uniquely demonstrates how system adoption, energy efficiency, water delivery precision, and farmer satisfaction interact to influence agricultural outcomes. Moreover, the study’s context—the Cholistan Desert—presents a novel contribution, as limited empirical research has explored the combined impact of smart irrigation technologies in such extreme arid environments. This comprehensive approach offers valuable insights into sustainable agricultural practices in water-scarce regions while acknowledging the financial, infrastructural, and policy-related barriers that must be addressed to ensure broader adoption and long-term sustainability.

Addressing Unexpected Findings and Variability in Farmer Responses

While the findings from the multiple linear regression analysis and Structural Equation Modeling (SEM) largely confirm the positive impacts of solar-powered smart irrigation systems, some unexpected variations in farmer responses require further examination. One notable inconsistency is the variability in reported crop yield improvements, which suggests that factors beyond system adoption, water delivery precision, and energy efficiency may influence outcomes. For instance, differences in soil quality, access to complementary agricultural inputs (e.g., fertilizers, pest control measures), and variations in climatic conditions across different areas of the Cholistan Desert may contribute to disparities in yield improvements. Additionally, socio-economic factors such as farmers’ financial resources, education levels, and risk tolerance could affect their ability to fully leverage smart irrigation technologies.

Farmer satisfaction, another key variable in the analysis, exhibited variability that suggests that some users may have encountered technical or operational challenges. While most respondents reported positive experiences, a subset expressed concerns regarding system maintenance, reliability, and initial setup difficulties. These issues align with previous research highlighting the learning curve associated with new agricultural technologies. For example, disparities in digital literacy and prior experience with mechanized farming may have influenced adoption rates and perceived effectiveness among farmers. This suggests that targeted training and extension services could help mitigate these variations and ensure more consistent adoption benefits.

Highlighting Scalability Issues

Despite the demonstrated benefits of solar-powered smart irrigation systems, the study also identifies significant barriers to scalability, particularly regarding cost and training requirements. The initial capital investment required for purchasing and installing solar-powered irrigation infrastructure remains a major obstacle for small-scale and resource-constrained farmers. While cost savings over time—due to reduced reliance on conventional energy sources—can offset these expenses, the upfront financial burden may deter widespread adoption. This aligns with previous studies, such as Mandal and Maji [53], which noted that government subsidies and financial support mechanisms play a crucial role in expanding the adoption of renewable energy-based agricultural technologies.

Training and technical support also present key challenges. The study found that farmers with prior exposure to modern irrigation techniques adapted more quickly to smart irrigation systems, whereas those with limited technological experience faced difficulties in optimizing system performance. This finding highlights the need for tailored training programs that address both the technical operation of these systems and best practices for efficient water and energy use. Without adequate knowledge transfer mechanisms, there is a risk that farmers may underutilize or improperly maintain the systems, leading to suboptimal outcomes.

Furthermore, the long-term sustainability of these systems depends on the availability of spare parts, maintenance services, and local expertise. In regions with limited access to skilled technicians or replacement components, system breakdowns could hinder long-term adoption and performance. Addressing these concerns requires a multi-stakeholder approach involving government agencies, agricultural extension services, and technology providers to establish accessible support networks.

6. Conclusions

This study underscores the transformative impact of solar-powered smart irrigation systems on agricultural productivity in the Cholistan Desert, a region grappling with severe water scarcity and extreme environmental conditions. The findings reveal that adopting these advanced technologies significantly enhances crop yield by enabling precise water delivery, optimizing irrigation efficiency, and promoting sustainable water management. These results highlight the crucial role of renewable energy-driven solutions in addressing food security challenges in arid regions.

Beyond agricultural productivity, the study also emphasizes the broader environmental and economic advantages of solar-powered irrigation systems. By reducing dependence on fossil fuels, these systems improve energy efficiency, lower operational costs, and mitigate environmental degradation, thereby contributing to sustainable agricultural practices. Furthermore, integrating renewable energy into irrigation aligns with global sustainability goals, fostering resilience within agricultural systems.

Additionally, the study identifies farmer experience as a key factor in maximizing the benefits of smart irrigation technologies. Experienced farmers are more adept at utilizing these systems effectively, reinforcing the need for targeted capacity-building initiatives.

Policy implications:

• Facilitating farmer adoption through financial incentives:

To encourage the adoption of solar-powered smart irrigation systems, policymakers should implement targeted subsidy programs and financial incentives. Ensuring affordability through microfinance schemes, low-interest loans, and government grants can accelerate the transition toward sustainable agricultural practices.

2.

Capacity building and technical training:

Maximizing the efficiency of smart irrigation systems requires technical expertise. Therefore, large-scale training programs, agricultural extension services, and digital literacy initiatives should be established to equip farmers with the necessary skills for system operation and maintenance. These efforts will prevent technology abandonment and ensure long-term efficiency.

3.

Enhancing renewable energy infrastructure:

Expanding decentralized solar energy grids, storage facilities, and maintenance support networks is essential to improving energy accessibility for farmers in remote areas. By reducing dependency on non-renewable energy sources, such policies will strengthen economic resilience and promote environmental sustainability.

4.

Strengthening multi-stakeholder collaboration:

A coordinated approach involving policymakers, agricultural experts, renewable energy specialists, and local farming communities is vital for addressing the region’s unique water and energy challenges. Developing context-specific, evidence-based solutions through multi-stakeholder engagement will ensure the effective integration of solar-powered smart irrigation systems into existing agricultural frameworks, enhancing food security and climate resilience in arid regions.

Limitations and future directions:

This study, while providing valuable insights into the adoption and impact of solar-powered smart irrigation systems in arid regions, has several limitations that should be addressed in future research.

One key limitation is the cross-sectional design, which restricts the ability to capture long-term effects and trends over time. Future research should employ longitudinal studies to track the adoption and impact of solar-powered irrigation systems across multiple farming seasons. This would provide a more comprehensive understanding of the sustainability and evolving benefits of these technologies.

Additionally, this study relies on self-reported data from farmers, which may be subject to biases such as overreporting benefits or underreporting challenges. Future studies should integrate objective data collection methods, such as remote sensing, field-based monitoring, or precision agriculture tools, to validate and complement self-reported information.

Another limitation is the study’s focus on the Cholistan Desert, which may limit the generalizability of the findings to other arid or semi-arid regions with varying socio-economic and environmental conditions. Future research should conduct comparative analyses with other regions facing similar water scarcity and agricultural challenges, such as other deserts or drought-prone areas in South Asia, Africa, and the Middle East.

Furthermore, while this study demonstrates the positive impact of solar-powered irrigation on crop yield and resource efficiency, financial constraints remain a major barrier to widespread adoption. Future research should include cost–benefit analyses to assess the financial feasibility for smallholder farmers, considering factors such as initial investment, operational costs, potential subsidies, and long-term savings. This would help policymakers and development agencies design targeted financial support programs to enhance adoption.

Finally, future research could also explore the social and behavioral factors influencing adoption, including farmer perceptions, willingness to invest in new technologies, and the role of agricultural extension services. Addressing these aspects would provide a more holistic understanding of how to effectively scale up smart irrigation solutions in arid regions.