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Article

Agri-PV (Agrivoltaics) in Developing Countries: Advancing Sustainable Farming to Address the Water–Energy–Food Nexus

by
Kedar Mehta
*,
Meeth Jeetendra Shah
and
Wilfried Zörner
Institute of new Energy Systems, Technische Hochschule, 85049 Ingolstadt, Germany
*
Author to whom correspondence should be addressed.
Energies 2024, 17(17), 4440; https://doi.org/10.3390/en17174440
Submission received: 20 July 2024 / Revised: 29 August 2024 / Accepted: 2 September 2024 / Published: 4 September 2024
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
Browse Figures
Graphical abstract
"> Figure 1
<p>(<b>a</b>) The connections and various important factors in the W-E-F nexus in Central Asia and (<b>b</b>) W-E-F nexus index of Central Asian countries, based on [<a href="#B19-energies-17-04440" class="html-bibr">19</a>].</p> "> Figure 2
<p>Cotton farming conditions due to increase in hot temperatures in Uzbekistan. (<b>a</b>) Typical cotton farm in Uzbekistan and (<b>b</b>) burnt cotton balls due to increased temperature (photo credit: author).</p> "> Figure 3
<p>Visual spectrum and demonstration of PAR flow.</p> "> Figure 4
<p>The research design of the presented research.</p> "> Figure 5
<p>Major agricultural regions in Uzbekistan selected for the presented study (highlighted colors).</p> "> Figure 6
<p>Modelling and simulation approach to identify suitable configuration based on number of modules required according to tilt angle and respective PAR.</p> "> Figure 7
<p>(<b>a</b>) Distance between the modules and the rows is equal to 1 m; (<b>b</b>) distance between the modules and the rows is equal to 2 m.</p> "> Figure 8
<p>(<b>a</b>) Distance between modules is 1 m, and distance between rows is equal to 3 m; (<b>b</b>) distance between modules is 1 m, and distance between rows is equal to 6 m.</p> "> Figure 9
<p>(<b>a</b>) Distance between modules is 2 m, and distance between rows is equal to 4 m; (<b>b</b>) distance between modules is 2 m, and distance between rows is equal to 7 m.</p> "> Figure 10
<p>(<b>a</b>) Design of Agri-PV system at 15 ° tilt angle; (<b>b</b>) design of Agri-PV system at 40 ° tilt angle.</p> "> Figure 11
<p>Selected Agri-PV configuration based on simulation and optimization results.</p> "> Figure 12
<p>Visual effectiveness of Agri-PV on water, energy, and food network (Own illustration).</p> ">
Versions Notes

Abstract

:
The escalating demand for water, energy, and food, coupled with the imperative for sustainable development, necessitates innovative solutions to address the complex interdependencies within the water–energy–food nexus. In this context, agriculture and photovoltaics (Agri-PV or Agri–voltaics) systems have emerged as a promising approach to promoting sustainable agricultural practices while enhancing energy efficiency and food production. However, limited research, especially on the technical aspects of Agri-PV, has resulted in a knowledge gap regarding how to model and determine the suitability of Agri-PV for different crops based on local conditions. This study presents a novel approach to modeling and simulating Agri-PV systems for various major crops in developing countries, using Uzbekistan as a case study. It provides a blueprint for selecting suitable Agri-PV systems. The research investigates the technical feasibility of Agri-PV technology tailored to Uzbekistan’s agricultural landscape, with broader implications for Central Asia. Employing a systematic methodology, the study begins by selecting appropriate sites and crops for Agri-PV system testing, ensuring the relevance and applicability of the research findings to the local context. Using advanced software tools such as PVSyst, the study accurately calculates photosynthetically active radiation (PAR) values specific to selected crops, bridging a significant knowledge gap and providing empirical data essential for informed decision making. The methodology further incorporates an in-depth analysis of economic and technical considerations in selecting PV modules and inverters, enhancing the scientific accuracy of the study. By strategically modeling Agri-PV systems based on parameters like row density, module distance, and tilt angle, this research aims to optimize the integration of photovoltaic technology with agricultural practices in Uzbekistan. Moreover, this study helps to understand the impact of Agri-PV systems on the water–energy–food nexus, providing valuable insights into the potential benefits and challenges specific to the region. The study identifies the positive impact of Agri-PV on major crops and provides a suitable design and modeling approach for sustainable farming practices.

Graphical Abstract">

Graphical Abstract

1. Introduction

1.1. Background and Context

The water–energy–food (W-E-F) nexus is a conceptual framework that recognises the interconnections and interdependencies between water, energy, and food systems. These three sectors are closely linked, as water is essential for energy production and agricultural activities, energy is required for water treatment and distribution, and food production relies heavily on both water and energy inputs [1]. The nexus approach highlights the need for integrated management and governance across these sectors to ensure sustainable resource use and address the challenges posed by climate change, population growth, and economic development. Climate change is exacerbating the pressures on the W-E-F nexus, particularly in developing countries, where vulnerabilities are often higher due to limited resources, infrastructure deficiencies, and socioeconomic constraints [2,3,4].
Rising temperatures, altered precipitation patterns, and increased frequency and intensity of extreme weather events are impacting water availability, energy production, and agricultural yields, threatening food and energy security. The current literature identifies that the W-E-F nexus in developing countries is critical and is aggravated by rapid population growth, urbanization, and changing consumption patterns, which are driving up demand for water, energy, and food resources [4,5,6].
Hence, there is an immediate need to address the W-E-F nexus challenge. Addressing the W-E-F nexus challenge in developing countries requires a holistic and integrated approach that considers the interconnections between water, energy, and food systems, as well as the impacts of climate change [7,8,9]. Summing up, meeting the increasing demand for water, energy, and food while maintaining sustainable development is a complex issue that requires innovative solutions. One such solution is the integration of agriculture and photovoltaics (Agri-PV) systems. Agri-PV systems offer a promising solution to address the challenges of the water–energy–food nexus by promoting sustainable water use in agriculture, improving energy efficiency, and increasing food production [10].
Agri-PV technology provides a promising solution to effectively handle the global issues raised by the W-E-F nexus. This novel approach, which can address all three aspects of the W-E-F nexus, brings several benefits. Agri-PV systems can substantially improve crop productivity by optimizing the microclimate conditions for various crops due to the strategic placement of PV panels [11]. This setup provides shading, reducing temperature extremes, protecting crops from harsh radiation, and consequently enhancing soil moisture levels by reducing evaporation. All these lead to significant water savings for irrigation, promoting more efficient water use in agricultural practices [12]. Beyond addressing the W-E-F nexus, the promotion of Agri-PV also contributes to reaching the goal of net-zero energy emissions in agriculture. It also offers insights into next-generation agriculture and prepares farmers for forthcoming challenges.
To effectively implement the innovative and sustainable application of Agri-PV technology globally within the W-E-F nexus context, it is crucial to understand that different crops require specific microclimates and optimal solar irradiation levels. This knowledge helps tailor suitable crop specifications and system designs [13]. In that regard, the presented article discusses novel research about Agri-PV design for major crops from the various categories (i.e., cotton, wheat, rice, barley, and potatoes) and assesses the technical feasibility of its use with these major crops from developing countries by considering Uzbekistan, a Central Asian country.

1.2. Introducing a Special Focus on Central Asia

The global challenge of the W-E-F nexus is particularly acute in Central Asian countries [14,15]. This region contends with intricate interdependencies and significant challenges, including escalating demands for water, energy, and food due to population growth, economic expansion, and climate change [16].
Central Asia, comprised of five republics formerly part of the Soviet Union—Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan—exists within an arid and isolated environment with harsh climatic conditions. The region’s reliance on shared water resources is evident, with upstream countries such as Kyrgyzstan and Tajikistan leaning heavily on hydropower, while downstream nations like Kazakhstan, Turkmenistan, and Uzbekistan rely on the Amu Darya and Syr Darya rivers for agricultural purposes [17]. This has led to tensions, with upstream nations seeking to maximize hydropower generation conflicting with downstream countries prioritizing water for irrigation. The dissolution of Soviet-era resource-sharing agreements has exacerbated these conflicts. Moreover, climate change exacerbates the situation as upstream glaciers retreat and precipitation patterns shift, affecting water availability for energy and agriculture, potentially impacting hydropower generation and agricultural productivity [18].
Figure 1a illustrates the connections and various important factors of W-E-F nexus in Central Asia and Figure 1b represents W-E-F nexus index of Central Asian countries. The W-E-F nexus index is a quantitative measure derived from scientific methodologies aimed at evaluating the interconnections and interdependencies among water, energy, and food systems within a defined geographical area or system boundary.
Despite abundant natural energy resources in downstream countries, water stress poses a significant vulnerability for agriculture and food production in Central Asia. Uzbekistan, predominantly agricultural and with the largest and youngest population in the region, faces substantial challenges in this regard. As a double-landlocked country, Uzbekistan relies heavily on upstream regions for water supply, with only 20% of its water resources internally sourced. However, upstream regions prioritize electricity generation and export, exacerbating water supply issues for agriculture. Uzbekistan, which consumes about 90% of its water in agriculture, stands as the highest agricultural water consumer in Central Asia [20]. Despite inefficient irrigation methods stemming from outdated infrastructure and suboptimal practices, irrigated agriculture remains vital due to the arid and semi-arid climate of the region.

1.3. The Need to Introduce Agri-PV in Uzbekistan

Uzbekistan stands as one of the world’s most energy and resource-intensive nations, heavily reliant on natural gas and experiencing a continuous growth in gas imports. The country’s increasing population exacerbates its natural gas dependency, leading to higher emissions. Currently, 90% of Uzbekistan’s power is generated from natural gas, resulting in frequent blackouts during winter due to decreased gas pipeline pressure [21]. In response, some regions have turned to coal for household heating due to supply insecurity [22]. Despite most of the population having access to electricity, Uzbekistan experiences irregular and frequent blackouts. Approximately 1500 rural settlements with 1.5 million people lack adequate connection to the central power grid, leading to power supply insecurity [23]. It has been reported that 36% of firms in Uzbekistan face power outages an average of 1.9 times per month [24]. The Uzbekistani government is aiming to address this issue by exploring renewable energy development in rural areas, prioritizing electricity generation closer to these communities [25].
Agriculture accounts for 25% of Uzbekistan’s GDP, making it a crucial sector after energy. However, Uzbekistan faces severe climate change impacts, particularly affecting agriculture, due to rising temperatures and water scarcity in Central Asia. These changes destabilize agricultural production, posing a significant threat to the country’s food security [26]. Since 2021, Uzbekistan has witnessed crop failures and extensive water shortages attributed to climate change effects [27]. In the summer, some regions have reported temperatures exceeding 45 °C [28], leading to increased soil evaporation and subsequent soil salinity issues. Already, 50% of irrigated areas in Uzbekistan suffer from soil salinity problems [29]. With temperatures rising, precipitation decreasing, and glaciers retreating, Uzbekistan is poised to become one of the world’s most water-stressed countries [30].
The primary crop, cotton, faces challenges due to heat stress and burns caused by rising temperatures. Cotton is particularly sensitive to climate change; higher temperatures increase water demands, especially during the blooming stage (Copernicus; Schlubach, analyzed meteorological data; Intergovernmental Panel of Climate Change). According to studies, temperatures above 32 °C negatively affect cotton yields, and further temperature increases could render cotton cultivation economically unviable in Uzbekistan. Meanwhile, 60% of women engage in cotton harvesting, with 76% residing in rural areas, making the potential loss of cotton cultivation a significant economic concern [31].
Figure 2 shows on-site images illustrating the impact of excessive temperatures on cotton crops. In addition to cotton, other major crops such as wheat, barley, rice, and potatoes are also suffering from similar issues.
As Agri-PV is a relatively new technology, scientific evidence relating to its modelling, its effects on various crops, and its impact on water savings is rather limited. Currently, scientific insights into this technology are primarily acquired from a limited number of installations. With very few systems installed globally, mostly situated in European countries, China, and the USA, the comprehensive analysis remains at an introductory level, heavily reliant on theoretical data [32,33,34]. Therefore, there is a noticeable gap in extensive, empirical research on this subject. Due to limited studies, existing results showing the impact of PV panel shading on various crop yields have been inconsistent. So far, the effects of Agri-PV have been simulated on only a select number of crops such as tomatoes, potatoes, cucumbers, kiwifruits, lettuce, and corn to test suitability. The impact on other major and common crops remains unclear [32,35]. Also, most of the research has been carried out for selected countries. Most developing countries are unexplored in the research.
Photosynthetic active radiation (PAR) is crucial for crop production as it represents the spectrum of sunlight (400–700 nm) that plants use for photosynthesis. Adequate PAR is essential for optimal growth, development, and yield, as it drives the conversion of light energy into chemical energy [36]. Insufficient PAR can limit photosynthesis, reducing crop productivity. Conversely, excessive PAR can cause damage and stress to plants. Properly managed PAR ensures that crops receive sufficient light for photosynthesis, leading to higher yields and better-quality produce, while also supporting sustainable farming practices and enhancing the efficiency of Agri-PV systems (Figure 3). However, as mentioned earlier, due to limited system installations and a lack of scientific knowledge, the required PAR for individual crops with suitable Agri-PV design has not been thoroughly investigated [37].
Light quality, defined by the specific wavelengths emitted by the sun, significantly impacts plant growth. PAR serves as the energy source for photosynthetic processes, affecting growth and development. Insufficient or excessive light can adversely affect crop yield by influencing photosynthesis.
The light saturation point (LSP) represents the maximum light intensity at which photosynthesis plateaus, beyond which further increases do not enhance photosynthetic rates. Quantifying PAR is vital for optimizing agricultural practices. While direct PAR measurement remains challenging, studies have established a conversion factor known as the quanta-to-energy ratio. By converting PAR from quantum units to energy units, appropriate irradiation levels for specific crops can be determined. For instance, the light saturation points for cotton range between 1000 and 1600 μmol/m2/s [38].
Achieving suitable irradiation levels is crucial for cotton growth and yield optimization, underscoring the importance of appropriately configuring Agri-PV systems to enhance crop productivity and system effectiveness.
Especially in the case of Uzbekistan/Central Asia, there exists a gap in research dedicated to assessing the technical feasibility of Agri-PV. This gap impedes our understanding of the potential of Agri-PV for associated crop types and its benefits for the water–energy–food nexus. At the same time, there is an immediate need to identify a solution that helps to preserve food losses and enhance the energy situation. In response to this need, this research paper aims to explore the benefits of integrating photovoltaic systems with agricultural practices and how they contribute to resolving the water–energy–food nexus, considering Uzbekistan as a case study area. Specifically, this paper examines the challenges in the water–energy–food nexus that can be addressed through Agri-PV systems, sustainable water uses in agriculture through Agri-PV, and the benefits of integrating photovoltaic systems with agricultural practices in terms of energy efficiency and food production.

2. Research Design

2.1. Research Question

By considering the existing gap in the research and the need for scientific contribution, the presented research article aims to investigate the technical feasibility of Agri-PV technology for major crops in Uzbekistan. The success of the Agri-PV system depends upon the balance between energy and agriculture. To achieve this goal, the following research questions are raised and answered:
  • What are the optimal configurations for integrating photovoltaic systems with different crop types in Uzbekistan’s agricultural landscape?
  • How do Agri-PV systems impact crop yield, water usage, and energy efficiency in general for Uzbekistan/developing countries?

2.2. Research Methodology

Referring to Figure 4, the methodology was structured in three different stages.
Stage 1 began with the selection of appropriate sites and crops for conducting Agri-PV system performance tests. Subsequently, the PAR values of the chosen crops were calculated using PVSyst 7.4 software [39]. This software, developed by PVsyst, was utilized for designing and simulating Agri-PV models to determine the PAR values.
After defining the simulation period, the critical Stage 2 was developed. Here, the feasibility of the designed Agri-PV system with its associated crops was determined. The first step in this phase involved the careful selection of PV modules and inverters, considering both technical specifications and economic factors. Subsequently, a suitable modelling approach was formulated based on parameters such as row distance, module distance, and tilt angle of the PV modules, to ascertain their suitability.
After simulating the Agri-PV model, the PAR values obtained were compared against predefined threshold values. In cases where the simulation output deviated from the threshold values, optimization of the model was undertaken until the desired threshold values were attained.
Stage 3 was initiated once the Agri-PV model achieved the designated PAR range for the selected crops. This stage involved the sizing and dimensioning of the land. Additionally, the sizing of the chosen PV modules and inverters for the land area was conducted, followed in the concluding phase of this research work by an analysis and discussion of the electricity generated.

2.3. Novelty of the Research

The scientific novelty of this research is underscored by its systematic methodology tailored to the unique agricultural context of Uzbekistan and Central Asia. This study employed a comprehensive approach, beginning with the selection of suitable sites and crops for Agri-PV system testing, a critical step in ensuring the relevance and applicability of the research findings to the local agricultural landscape. Furthermore, the utilization of advanced software tools such as PVSyst for designing and simulating Agri-PV models added scientific rigor to the assessment of technical feasibility.
By accurately calculating the PAR values specific to the selected crops in Uzbekistan, this research bridges a significant knowledge gap and provides empirical data essential for informed decision making. In this regard, the presented study is the first attempt in the case of Uzbekistan to check the feasibility of the Agri-PV technology for primary crops. The methodology also incorporated an in-depth analysis of technical considerations involved in selecting PV modules and inverters, further enhancing the scientific rigor of the study.
By strategically modelling Agri-PV systems based on parameters like row distance, module distance, and tilt angle, this research aims to optimize the integration of photovoltaic technology with agricultural practices in the region. By integrating scientific principles with practical applications, this research contributes to advancing knowledge in the field of sustainable agriculture and renewable energy, with direct implications for addressing pressing environmental and socio-economic challenges in Uzbekistan and Central Asia.

3. Modelling and Assessing Agri-PV System Performance

3.1. Case Study Area and Crop Selection

This study explored the performance of an Agri-PV system across primary crop-producing regions in Uzbekistan. The primary focus lies on assessing the system’s efficacy amidst varying weather conditions experienced across different latitudes in these regions. The primary crops selected for examination included not only cotton but also wheat, rice, potato, and barley, which collectively constitute a significant portion of Uzbekistan’s agricultural output. Figure 5 underscores the widespread cultivation of these primary crops across Uzbekistan, illustrating the importance and relevance of investigating their performance in the context of Agri-PV systems.
Through systematic data collection and analysis, this study aimed to identify patterns, trends, and best practices that can optimize the integration of photovoltaic technology with agricultural practices across different regions.
For instance, the Kashkadarya region in the southeast stands out as the leading producer of cotton, followed by the Bukhara and Khorezm regions, each contributing approximately 11% and 9% of the total cotton production, respectively. Furthermore, these regions also play a vital role in the cultivation of wheat, barley, rice, and potato, further emphasizing the multifaceted agricultural landscape of Uzbekistan [40].

3.2. Calculation of Photosynthetic Active Radiation (PAR)

Based on the extensive literature review, the required PAR for each primary crop in Uzbekistan was calculated. These are the targeted values the designed Agri-PV system must attain to demonstrate its compatibility with the selected crops.
In the case of an overhead Agri-PV system, wherein the entire land area is covered with PV modules at a certain elevation, shading appears as a critical concern. Full-density arrangement of PV modules results in substantial shading over the crops, thereby limiting the amount of light intensity reaching them. This obstruction of the photosynthesis process directly impacts crop yield. Consequently, comprehending the light intensity incident on the crops becomes imperative, particularly for the overhead Agri-PV system type. The primary objective of the novel PAR modelling presented in this section was to conduct shading analysis and examine the behavior of the light intensity reaching the crops. While several PV software tools facilitate shading analysis, they typically provide results based on irradiation hitting the surface of the PV modules rather than on the ground. Additionally, the absence of specialized software for designing and simulating Agri-PV systems poses a challenge. In this regard, the presented calculated environment in the panel below is a very novel aspect. The modelling is explained consequently.
Upon designing and simulating a PV system in the PVSyst software, an Microsoft Excel 2016 sheet with hourly values was generated, consisting of direct and diffuse irradiations along with their shading factors experienced in the field due to the presence of PV modules [41].
There is no functionality for simulating an Agri-PV system in PVSyst, but there is an opportunity to construct an Agri-PV system in the “Construction/Perspective” window and simulate the model to obtain the desired shading factors. These shading factors were then extracted and substituted in the following equation, based on Guleed and Farid [41].
P A R   t o t a l = D i r   R   ∗   D i r F + D i f f r   ∗   D i i f f   ∗   P A R F
P A R t o t a l = photosynthetically active radiation (W/m2).
D i r R = the horizontal beam irradiation captured directly from the sun on a flat horizontal plane (W/m2) [39].
D i f f r = the amount of radiation received per unit area by a surface that has spread in the atmosphere because of clouds, dust, etc., also termed “horizontal diffuse irradiation” (W/m2) [39].
D i r F = the near-shading factor on the beam, also termed “direct factor”(%), which is the ratio of the shaded area to the total sensitive area [42].
D i i f f = the near-shading factor on the sky, termed as a diffuse factor (%). It is the integral of the shading factor of the overall sky directions seen by the plane of the array. PVSyst calculates this factor considering the diffuse as “isotropic,” which means that irrespective of the direction in space, the received irradiance is identical and constant over the year, thus independent of the sun’s position but dependent on characteristics of the geometry of the system itself [42].
P A R F (PAR factor) = based on a relationship with global horizontal irradiation, falling in the range of 45% to 50%. This factor is considered as no direct measurement of PAR exists [43].
As per Monteith and Reifsnyder [44], considering a PAR factor of 50% proves to be a practical approximation for solar applications, including in the context of this study. This factor aids in estimating the total irradiation reaching the Earth’s surface. However, it generates PAR values hitting the PV modules, which diverges from the focus of this research. Instead, our study concentrated on acquiring PAR values reaching the cotton crops on the ground, beneath the photovoltaic modules, rather than solely on the modules themselves.
According to the World Meteorological Organization, the majority of the energy reaching the Earth’s surface is absorbed, with only a small fraction being reflected back into space. Specifically, the Earth’s system, comprising the atmosphere and surface, absorbs 70% of the incoming solar energy, while 30% is reflected. Hence, our study adopted a PAR factor of 0.7, corresponding to 70% of the total irradiation hitting the crops on the ground. Based on this novel model approach, the PAR values were obtained and are presented in Table 1. Table 1 presents the light saturation points for various crops, sourced from the literature. These values served as the basis for calculating the corresponding PAR values using Equation (1), as mentioned earlier. The effectiveness of an Agri-PV system is gauged by its ability to attain the PAR values specified for the target crops. For instance, the PAR values for cotton range between 217 W/m2 and 348 W/m2. This indicates that cotton crops situated beneath the photovoltaic modules should receive a minimum of 217 W/m2 and a maximum of 348 W/m2 of irradiance for optimal growth and development through photosynthesis. Deviating from this desired range, either below or above, could lead to a decrease in yield and adversely impact the quality of cotton balls.

3.3. Selection of Type of Module Technology and Inverter

The PVSyst database provides a comprehensive array of module technologies and inverters from various manufacturers [39]. Among the plethora of module options available in the market, this study opted for monocrystalline modules due to several advantageous factors. These modules are readily accessible, boast higher efficiency levels, come at competitive prices, and exhibit a lower temperature coefficient. Notably, monocrystalline modules outperform polycrystalline and thin-film technologies, particularly in extreme temperature conditions [50]. This aspect holds significant importance as the effectiveness of PV modules under hot temperatures is crucial.
While bifacial modules have the potential to generate more electricity by harnessing the albedo effect, their suitability depends on specific installation scenarios where the rear side of the solar array can capture reflected sunlight [51]. However, in this context, where the ground is primarily covered with cotton crops, the albedo effect is minimized. Furthermore, bifacial modules entail a relatively complex design and incur higher installation costs due to additional layers of glass and backsheet [52].
Therefore, considering factors such as affordability, availability, efficiency, and simplicity in mounting structures, mono facial monocrystalline modules from Trina Solar, with a capacity of 550 Wp, were chosen for energy output.
Additionally, an SMA inverter with a capacity of 110 kWac was selected to facilitate the conversion of DC power from photovoltaics to AC power output. The determination of the optimal number of modules and the appropriate inverter was made based on land availability considerations in the “System” parameter section of the PVSyst software.

4. Simulation and Modelling Approach

During the literature review, various configurations were assessed to determine the most suitable type of Agri-PV system. It was observed that existing case studies for cotton predominantly featured overhead Agri-PV systems, indicating utilization of the entire land beneath the PV modules for crop cultivation. These systems ranged in size from 7.2 kWp to 50 MWp, demonstrating scalability across different land sizes. The capacity of an Agri-PV system within a plot is influenced by the arrangement of PV modules, with row distance and module distance being key parameters.
For instance, in Amrol, Gujarat, row distance and module distance were unequal, whereas in Junagadh, Gujarat, they were equal, resulting in a checkerboard configuration. Willockx et al. [53] noted that using a checkerboard arrangement led to homogeneous radiation distribution and higher PAR results compared with a straight-line arrangement. The case studies also indicate that the typical elevation of PV modules is approximately 3 m. According to DIN SPEC 91434 [54], if the height of PV modules falls below 2.10 m, the land beneath them is deemed unsuitable for agriculture. Furthermore, research by Guleed and Farid [41] suggests that the standard elevation height for an overhead Agri-PV system ranges from 3 m to 5 m. Additionally, findings from a study conducted by Charline DOS SANTOS [55] emphasize that the height of PV modules should not surpass 5 m.
Based on the literature review findings, this study opted to design an overhead Agri-PV system at a fixed height of 4 m, employing a checkerboard arrangement of PV modules to ensure uniform radial distribution.
Initially, the system was designed for a smaller land area of 600 m2, with scalability planned based on land availability. The orientation of the Agri-PV system was to be set at an azimuth of 0° to maximize electricity generation, given its southward direction. Due to the absence of standardized guidelines for tilt angle, row distance, and module distance for overhead Agri-PV systems, this study designed and simulated various checkerboard configurations to identify the most efficient setup for cotton farms in Uzbekistan. For a detailed outline of the methodology used in modelling the Agri-PV system, please refer to Figure 6.
The initial simulation was run only for cotton crops. The region selected for the simulation was Kashkadarya, as it is the largest cotton-producing region of Uzbekistan. The fixed parameters throughout the modelling approach were the height (4 m), azimuth (0°), and land area (600 m2). In contrast, the variable parameters were the module distance, row distance, and tilt angle within the checkerboard arrangement of the PV modules. The aim was to simulate the Agri-PV system in PVSyst to obtain the shading factors and substitute these into Equation (1), to compare the simulated PAR values with the threshold PAR values of the selected crops.
Referring to Figure 6, the modelling and simulation approach was divided into two scenarios. The first scenario was the “Constant Tilt Angle,” indicating that the tilt angle was constant along with other fixed parameters.
For the initial analysis, the tilt angle was kept constant at 30°; this was chosen by running a short test on PVGIS software [56]. PVGIS is an open-source tool that allows quick irradiation and yield analysis, providing high-level estimations in seconds. In this study, we analyzed the performance of a 1 kWp grid-connected PV system in Kashkadarya at tilt angles of 25°, 30°, and 45°, with an azimuth of 0°, representing a southward orientation. The yields obtained were 1533 kWh/kWp for 25°, 1543 kWh/kWp for 30°, and 1519 kWh/kWp for 45°. Since the yield at a 30° tilt angle was comparatively higher, this angle was chosen as the constant value to perform Scenario 1 in PVSyst:
  • Case 1 was when the distance between the modules was equal to the distance between the rows of the modules. These varied from 1 m to 3 m at an equal interval of 1 m;
  • Case 2 was when the distance between the modules was kept constant at 1 m and only the row distance was varied;
  • Case 3 was when the distance between the modules was kept constant at 2 m and only the row distance is varied.
The idea was to create multiple permutations and combinations of the module and row densities to simulate several configurations and analyze the behavior of the PAR values for the changing densities. The next step was to select the most suitable configuration from Scenario 1 concerning the PAR values. The simulated PAR values had to match the threshold PAR values of the cotton crops (refer to Table 1). This selected configuration was then further analyzed in the next step.
In Scenario 2, titled “Variable Tilt Angle,” the selected configuration maintained constant module distance, row distance, and fixed parameters. The sole variable parameter was the tilt angle. The objective was to observe the behavior of photosynthetically active radiation (PAR) values concerning the varying tilt angle within the Agri-PV system. The tilt angle was adjusted from 10° to 45° at intervals of 5°. The PAR outcomes obtained from Scenario 2 were then compared to the threshold PAR values of the cotton crop. Consequently, this comparison facilitated the determination of the most suitable Agri-PV configuration for cotton farms in Kashkadarya.

4.1. Scenario 1: Constant Tilt Angle

In this scenario, along with height (4 m) and azimuth (0°), the tilt angle was assumed to be constant at 30°. The varying parameters were module distance and distance between the rows.
  • Case 1
In Case 1, the module distance and row distance were initially set equal to each other; both densities began at 1 m and were subsequently increased in increments of 1 m, up to a maximum of 3 m.
Figure 7a showcases a land area of 600 m2 depicted in green, featuring overhead-type PV modules arranged in a checkerboard pattern represented in blue. Both module distance and row distance were set at 1 m, with modules positioned 4 m above ground level in the south direction, totalling 90 modules. Conversely, Figure 7b illustrates the same checkerboard arrangement, but with a module distance and row distance of 2 m.
A comparison between Figure 7a,b reveals a reduction in the number of modules as distance increases. In this scenario, the module count decreased significantly from 90 to 56. However, increasing the distance allowed more sunlight to penetrate the PV modules and reach the crops. Similarly, in subsequent cases, PV modules were arranged based on a 3 m module and row distance. These configurations underwent simulation to derive irradiation and shading parameters, which, upon substitution into Equation (1), yielded the PAR values.
  • Case 2
In this scenario, while maintaining a height of 4 m, azimuth of 0°, and a constant module distance of 1 m, the tilt angle was set at 30°, with row densities ranging from 2 m to 7 m in increments of 1 m. The objective was to observe the impact of varying row densities on PAR values while keeping the module distance constant.
Starting with a module distance of 1 m and a row distance of 2 m, this combination was tested, continuing with incremental increases in row distance up to 7 m. These combinations were explored to understand their effects on PAR values.
For instance, in Figure 8a, with a module distance of 1 m and a row distance of 3 m, the number of modules is 54. Conversely, Figure 8b depicts a scenario where the module distance remains constant at 1 m while the row distance increases to 6 m, resulting in a significant reduction in the number of modules to 36. Various configurations were simulated to derive PAR values, which were then compared against the threshold PAR range for the crops.
  • Case 3
In Case 3, similar to Case 2, the module distance remained constant at 2 m while the row densities varied from 3 m to 7 m in increments of 1 m. This analysis aimed to assess the impact of different row densities while maintaining a consistent module distance. Starting with a module distance of 2 m and a row distance of 3 m, the combinations were explored, with row distance gradually increasing up to 7 m. These configurations were evaluated to understand their effects on the system. For instance, in Figure 9a, with a constant module distance of 2 m and a row distance of 4 m, there are 35 modules. Similarly, Figure 9b illustrates a scenario where the module distance remains constant at 2 m while the row distance increases to 7 m, resulting in a reduction in the number of modules to 28. These combinations were among the described configurations tested.
After simulating the configurations in Scenario 1, the PAR values obtained were compared against the threshold PAR values for the crops. The configuration that achieved the desired PAR range without compromising the number of modules was selected for further analysis in Scenario 2.

4.2. Scenario 2: Variable Tilt Angle

At this stage, the selected configuration from Scenario 1 underwent further analysis. In this scenario, the height (4 m), azimuth (0°), module distance, and row distance (based on the selected configuration) remained constant. The sole variable parameter in this scenario was the tilt angle, which ranged from 10° to 45° in increments of 5°. The objective was to observe how PAR values changed with different tilt angles. Below are illustrations exemplifying this scenario. For instance, Figure 10a,b depict the overhead Agri-PV system arranged in a checkerboard pattern with varying tilt angles, such as 15° and 40°, respectively.

5. Results and Discussion

In this section, the results obtained from simulating both scenarios discussed previously are presented. The primary objective of Scenario 1 was to identify the optimal configuration that satisfied the crop’s PAR criteria by varying both module distance and row distance. This optimal configuration was then subjected to further analysis in Scenario 2, where the focus shifted to varying tilt angles. By examining the impact of different tilt angles, the aim was to determine the most suitable Agri-PV system for crops in Uzbekistan, considering Kashkadarya as an example region. The months of June, July, and August were considered as the simulation period, as they represent the peak growing season for the crops in Uzbekistan.
The analysis considered both the energy production efficiency and the agricultural viability of the system, ensuring that the selected configuration would not only maximize energy output but also support optimal crop growth conditions. The findings from these simulations are crucial for developing a comprehensive understanding of how Agri-PV systems can be effectively integrated into agricultural practices in this region, thereby contributing to both sustainable energy production and enhanced agricultural productivity.

5.1. Scenario 1: Constant Tilt Angle

Table 2 provides the photosynthetically active radiation (PAR) values for the Kashkadarya region across different configurations outlined in Case 1, Case 2, and Case 3 from Scenario 1. The simulations considered June, July, and August, which constitute the growing season based on the crop calendar in Uzbekistan. During these months, cotton crop seeds are sown, and this season is considered crucial for the crop’s growth and development. Consequently, the yield depends on the respective months’ weather conditions. Harvesting typically occurs in September and October. Therefore, analysing the PAR values during the crop’s mid-season, specifically the development phase, is crucial for understanding performance. The objective was to select a configuration that aligned with the PAR range for cotton crops (217 W/m2–348 W/m2) while maintaining the maximum number of modules.
Case 1: In this case, module distance and row distance were set equal. Results indicated that densities of 1 m and 2 m would not reach the target PAR range of 217 W/m2–348 W/m2 for cotton crops. When both densities were set to 3 m, the PAR values fell within the target range, but the number of modules was significantly reduced. Although this configuration meets the PAR requirements, the reduction in module count would lead to lower electricity output. Therefore, these configurations were not considered for further analysis due to the imbalance between agricultural and energy outputs.
Case 2: For this case, module distance was set to 1 m while row distance varied from 2 m to 7 m in 1 m increments. None of these configurations reached the target PAR range for cotton crops. As a result, these configurations were not pursued further in the study.
Case 3: In this case, module distance was set to 2 m, and row distance varied from 3 m to 7 m in 1 m increments. When the row distance was 3 m or 4 m, the PAR values were below the target range. However, increasing the row distance to 5 m, 6 m, and 7 m resulted in PAR values that aligned with the target range of 217 W/m2–348 W/m2. Specifically, with a module distance of 2 m and a row distance of 5 m, the required number of PV modules is 35. For row densities of 6 m or 7 m, the number of modules decreases to 28 without a significant increase in PAR values. Consequently, this study selected a configuration with a module distance of 2 m and a row distance of 5 m for further analysis in Scenario 2, focusing on varying tilt angles.
This approach ensured a balanced optimization between maximizing PAR for cotton crop growth and maintaining a sufficient number of PV modules for energy production. Summarizing PAR values for June, July, and August with a fixed module distance of 1 m and varying row distance revealed a clear trend: as distance increased, so did the PAR values. Similarly, when module distance remained constant at 2 m and the row distance varied, the pattern persisted: higher distance correlated with higher PAR values.

5.2. Scenario 2: Variable Tilt Angle

The chosen configuration from Scenario 1 (module distance = 2 m; row distance = 5 m) underwent further examination concerning varying tilt angles. Tilt angles were adjusted in 5° increments from 10° to 45°. Table 3 illustrates the simulated PAR values for cotton crops in the Kashkadarya region. Notably, at a 45° tilt angle, the simulated PAR values align with the threshold PAR range for cotton crops. Given that the minimum PAR value required for cotton crop photosynthesis is 217 W/m2, the overhead Agri-PV configuration with a module distance of 2 m, row distance of 5 m, and a 45° tilt angle achieves the desired PAR range. Consequently, Case 8 from the table was identified as the most efficient configuration for cotton farms in Kashkadarya, Uzbekistan, closely approximating the PAR range of cotton crops with 35 modules within a 600 m2 plot area.

5.3. Selected Configuration Based in Parametric Study

Below, Table 4 presents a comprehensive overview of the specifically tailored Agri-PV configuration intended for cotton farms in the Kashkadarya region, ensuring optimized performance. Additionally, Figure 11 provides a detailed 3D sketch illustrating the geometric arrangement of the system.

5.4. Implementation of the Chosen Agri-PV Configuration across Different Regions

The preceding analysis has exclusively focused on cotton crops within the Kashkadarya region. While the Agri-PV system has proven successful in cultivating cotton in Kashkadarya, this study’s scope, as outlined in Section 3.1, extends to assessing the system’s applicability across Uzbekistan’s major cotton-producing regions. Furthermore, as articulated in Section 3.2, this study aims to evaluate the potential for growing Uzbekistan’s top five primary cultivated crops within the Agri-PV system’s framework. This section therefore describes simulations of the Agri-PV system across various regions mirroring Kashkadarya, using PVSyst.
In this simulation, the only variable parameter was the location. By specifying the desired location in PVSyst and re-running the Agri-PV model with the parameters outlined in Table 4, simulated PAR values were derived by substituting shading factors into Equation (1). These simulated PAR values are presented in Table 5.
By comparing these simulated values with the threshold values for the respective crops, as outlined in Table 3, the feasibility of integrating Agri-PV with these crops was evaluated.
It is worth noting that the Meteonorm and PVGIS databases for Uzbekistan offer limited precise synthetic and typical meteorological year (TMY) data specific to corresponding latitudes. Consequently, they provided values based on the nearest available data.
Table 5 illustrates simulated PAR values across various regions of Uzbekistan. Cotton, wheat, and barley, known for their preference for sunlight, exhibit promising potential for successful cultivation in Kashkadarya, Bukhara, Khorezm, Jizzakh, and Tashkent. The simulated PAR values for these regions aligned closely with the threshold PAR values for these crops, indicating favourable conditions for growth.
Conversely, in Ferghana Valley, Surkhandarya, Samarkand, and Karakalpakstan, the combination of cotton, wheat, and barley with an Agri-PV system is less optimal due to comparatively lower irradiation levels, resulting in reduced PAR output. In these regions, the presence of photovoltaic modules would impede the crops’ photosynthesis process, potentially hampering their growth and development. However, in such scenarios, cultivating rice and potatoes in conjunction with an Agri-PV system emerges as a viable alternative. These crops require less sunlight for photosynthesis, making them better suited to the conditions where PAR output may be limited by the presence of photovoltaic modules.

6. Outlook and Challenges

By comparing these simulated values with the threshold values for the respective crops, as outlined in the previous section, the feasibility of integrating Agri-PV with these crops was evaluated. Based on the research results and thorough investigation, it can be concluded that Agri-PV is one of the most suitable tools for addressing the W-E-F nexus while mitigating the effects of climate change. Without Agri-PV, there is high consumption of water due to high temperatures, no electricity production, and reduced crop production or crop failure due to high temperatures. In contrast, with Agri-PV, there is optimized water usage due to moderated temperatures, electricity production from solar panels, and improved crop production due to reduced heat stress, as demonstrated in Figure 12.
Looking forward, the adoption of Agri-PV systems could revolutionize agricultural practices and energy production, offering a sustainable solution to some of the most pressing global challenges. Future research should focus on refining the technology, improving efficiency, and making Agri-PV systems more cost-effective and accessible to farmers worldwide. Additionally, policies supporting the integration of Agri-PV in farming communities, alongside investments in infrastructure and training, will be crucial for widespread implementation. By continuing to explore and expand the potential of Agri-PV, we can create resilient agricultural systems that not only withstand the impacts of climate change but also contribute to a sustainable and secure energy future. However, the implementation of Agri-PV in developing countries faces several challenges. High initial costs and lack of access to financing can be significant barriers for small-scale farmers. Additionally, there may be limited technical expertise and infrastructure to support the installation and maintenance of Agri-PV systems [57,58]. Cultural resistance to adopting new technologies and insufficient government support further complicate the situation. Overcoming these obstacles requires coordinated efforts to provide financial incentives, build local capacity, and create favorable policy environments that encourage the adoption of Agri-PV in these regions [59].

7. Conclusions and Future Scope

This study highlights the transformative potential of Agri-PV systems in developing countries, specifically focusing on staple crops like cotton, wheat, rice, barley, and potatoes. Using Uzbekistan as a case study, simulations reveal that regions such as Kashkadarya, Bukhara, Khorezm, Jizzakh, and Tashkent exhibit favorable conditions for integrating cotton, wheat, and barley with Agri-PV systems. In these areas, the photosynthetically active radiation (PAR) values align closely with the crops’ sunlight requirements, indicating optimal growth potential. Conversely, regions like Ferghana Valley, Surkhandarya, Samarkand, and Karakalpakstan show lower irradiation levels, making these regions less suitable for cotton, wheat, and barley under Agri-PV systems. However, in these areas, rice and potatoes emerge as viable alternatives, as they require less sunlight for photosynthesis and can thrive even when PAR output is limited by the presence of photovoltaic modules.
The study provides significant insights into the technical feasibility of Agri-PV systems and their potential to enhance food production in Uzbekistan and other developing nations. However, several limitations must be addressed. The simulations were based on specific environmental and climatic conditions, which may not represent all scenarios across different regions. Additionally, the economic analysis did not fully account for long-term maintenance costs and the broader economic benefits of increased crop yields. The reliance on theoretical models also necessitates extensive field trials and real-world validation. Furthermore, the social and cultural aspects of adopting Agri-PV systems have not been deeply explored, and these could affect the practical implementation and acceptance of this technology.
Future research should focus on overcoming these limitations by incorporating comprehensive data, conducting empirical studies, and exploring a wider range of factors, including techno-economics, grid integration, and regulatory frameworks. Collaborative efforts among government bodies, landowners, agricultural stakeholders, and energy experts will be essential to successfully implement and optimize Agri-PV systems, ensuring they positively impact both agriculture and energy sectors in Uzbekistan and beyond.

Author Contributions

Conceptualization, K.M., M.J.S. and W.Z., methodology, K.M., M.J.S. and W.Z.; software, M.J.S. validation, K.M., M.J.S. and W.Z.; formal analysis, M.J.S.; investigation, M.J.S. and K.M.; resources, K.M. and W.Z.; writing—original draft preparation, M.J.S. and K.M.; writing—review and editing, K.M., M.J.S. and W.Z.; visualization, K.M. and M.J.S.; supervision, W.Z.; funding acquisition, K.M. and W.Z. All authors have read and agreed to the published version of the manuscript.

Funding

The research work was funded by the Federal Ministry of Education and Research (BMBF) of the Federal Republic of Germany within the LEAP-RE funding programme under the project “SWITCH” (Project ID 03SF0720A).

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Energies 17 04440 g001
Figure 1. (a) The connections and various important factors in the W-E-F nexus in Central Asia and (b) W-E-F nexus index of Central Asian countries, based on [19].
Figure 1. (a) The connections and various important factors in the W-E-F nexus in Central Asia and (b) W-E-F nexus index of Central Asian countries, based on [19].
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Figure 2. Cotton farming conditions due to increase in hot temperatures in Uzbekistan. (a) Typical cotton farm in Uzbekistan and (b) burnt cotton balls due to increased temperature (photo credit: author).
Figure 2. Cotton farming conditions due to increase in hot temperatures in Uzbekistan. (a) Typical cotton farm in Uzbekistan and (b) burnt cotton balls due to increased temperature (photo credit: author).
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Figure 3. Visual spectrum and demonstration of PAR flow.
Figure 3. Visual spectrum and demonstration of PAR flow.
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Figure 4. The research design of the presented research.
Figure 4. The research design of the presented research.
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Figure 5. Major agricultural regions in Uzbekistan selected for the presented study (highlighted colors).
Figure 5. Major agricultural regions in Uzbekistan selected for the presented study (highlighted colors).
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Figure 6. Modelling and simulation approach to identify suitable configuration based on number of modules required according to tilt angle and respective PAR.
Figure 6. Modelling and simulation approach to identify suitable configuration based on number of modules required according to tilt angle and respective PAR.
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Figure 7. (a) Distance between the modules and the rows is equal to 1 m; (b) distance between the modules and the rows is equal to 2 m.
Figure 7. (a) Distance between the modules and the rows is equal to 1 m; (b) distance between the modules and the rows is equal to 2 m.
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Figure 8. (a) Distance between modules is 1 m, and distance between rows is equal to 3 m; (b) distance between modules is 1 m, and distance between rows is equal to 6 m.
Figure 8. (a) Distance between modules is 1 m, and distance between rows is equal to 3 m; (b) distance between modules is 1 m, and distance between rows is equal to 6 m.
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Figure 9. (a) Distance between modules is 2 m, and distance between rows is equal to 4 m; (b) distance between modules is 2 m, and distance between rows is equal to 7 m.
Figure 9. (a) Distance between modules is 2 m, and distance between rows is equal to 4 m; (b) distance between modules is 2 m, and distance between rows is equal to 7 m.
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Figure 10. (a) Design of Agri-PV system at 15 ° tilt angle; (b) design of Agri-PV system at 40 ° tilt angle.
Figure 10. (a) Design of Agri-PV system at 15 ° tilt angle; (b) design of Agri-PV system at 40 ° tilt angle.
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Figure 11. Selected Agri-PV configuration based on simulation and optimization results.
Figure 11. Selected Agri-PV configuration based on simulation and optimization results.
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Figure 12. Visual effectiveness of Agri-PV on water, energy, and food network (Own illustration).
Figure 12. Visual effectiveness of Agri-PV on water, energy, and food network (Own illustration).
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Table 1. PAR values of primary cultivated crops of Uzbekistan, based on simulation study.
Table 1. PAR values of primary cultivated crops of Uzbekistan, based on simulation study.
Crop Type Crop Name Light Saturation Point
(μmol/m2/s)		PAR (W/m2)References
Fiber crop Cotton1000–1600217–438[45]
Cereal cropsWheat 1000–1800217–391[46]
Barley 1000–2000217–435[47]
Rice640–1025130–223[48]
Tuber cropPotato 400–50087–109[49]
Table 2. Scenario 1 results (Green = low PAR values, Red = high PAR values).
Table 2. Scenario 1 results (Green = low PAR values, Red = high PAR values).
Module Row Spacing (m)Module Spacing Density (m)PAR in June (W/m2)PAR in July(W/m2)PAR in August (W/m2)Number of ModulesModules per Row
Case 1Slot 111179.2179.6163.69010
Slot 2 22203204187.9568
Slot 333216.5217.6200.3306
Case 2Slot 121191.6192.4176.8728
Slot 2 31201.2201.2184.8546
Slot 341206.7207.6190.4455
Slot 451208.4209.6193.8455
Slot 561212.4213.4196.1364
Slot 671212.8213.9197.6364
Case 3Slot 132210211193.8426
Slot 2 42214.1215.2197.9355
Slot 352215.3216.6200.4355
Slot 462218.3219.4202.1284
Slot 572218.6219.8203.1284
Table 3. PAR values at varying tilt angles(Green= low PAR values, Red = high PAR values).
Table 3. PAR values at varying tilt angles(Green= low PAR values, Red = high PAR values).
Module Row Spacing (m)Module Spacing Density (m)Tilt Angle (°)PAR in June (W/m2)PAR in July(W/m2)PAR in August (W/m2)Number of Modules
Case 15210212.63214198.4335
Case 2 15213.86215.37199.84
Case 320214.23215.68199.91
Case 425214.71216.1200.09
Case 530215.3216.63200.39
Case 635215.98217.27200.78
Case 740216.75217.99201.27
Case 845217.59218.8201.84
Table 4. Specifications of the designed Agri-PV configuration.
Table 4. Specifications of the designed Agri-PV configuration.
Row distance between modules (m)5
Distance between modules (m)2
Tilt angle (°)45
Height (m)4
Modules’ arrangementCheckerboard
Module OrientationPortrait
Azimuth (°)0 (South)
Cotton plot area (m2)600
Required number of modules35
Table 5. Suitability of Agri-PV system across various crops across agricultural regions in Uzbekistan (Green = low PAR values, Red = high PAR values).
Table 5. Suitability of Agri-PV system across various crops across agricultural regions in Uzbekistan (Green = low PAR values, Red = high PAR values).
Name of the RegionAverage PAR (W/m2)Crops
JuneJulyAugustCottonWheatBarleyRicePotatoes
Bukhara220.50215.80200.80✔ ✔ ✔ ✔ ✔
Kashkadarya217.60218.80201.80✔ ✔ ✔ ✔ ✔
Khorezm213.50215.80193.30✔ ✔ ✔ ✔ ✔
Ferghana Valley196.10213.80191.90 ✔ ✔
Surkhandarya173.00218.40200.40 ✔ ✔
Jizzakh220.10213.10193.40✔ ✔ ✔ ✔ ✔
Samarkand203.50210.60197.10 ✔ ✔
Tashkent213.20210.60197.10✔ ✔ ✔ ✔ ✔
Karakalpakstan198.90200.00171.90 ✔ ✔
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Mehta, K.; Shah, M.J.; Zörner, W. Agri-PV (Agrivoltaics) in Developing Countries: Advancing Sustainable Farming to Address the Water–Energy–Food Nexus. Energies 2024, 17, 4440. https://doi.org/10.3390/en17174440

AMA Style

Mehta K, Shah MJ, Zörner W. Agri-PV (Agrivoltaics) in Developing Countries: Advancing Sustainable Farming to Address the Water–Energy–Food Nexus. Energies. 2024; 17(17):4440. https://doi.org/10.3390/en17174440

Chicago/Turabian Style

Mehta, Kedar, Meeth Jeetendra Shah, and Wilfried Zörner. 2024. "Agri-PV (Agrivoltaics) in Developing Countries: Advancing Sustainable Farming to Address the Water–Energy–Food Nexus" Energies 17, no. 17: 4440. https://doi.org/10.3390/en17174440

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