Optimizing Agri-PV System: Systematic Methodology to Assess Key Design Parameters

Abstract

Agrivoltaic (Agri-PV) systems face the critical challenge of balancing photovoltaic energy generation with crop productivity, yet systematic approaches to quantifying the trade-offs between these objectives remain scarce. In this study, we identify nine essential design indicators: panel tilt angle, elevation, photovoltaic coverage ratio, shading factor, land equivalent ratio, photosynthetically active radiation (PAR) utilization, crop yield stability index, water use efficiency, and return on investment. We introduce a novel dual matrix Analytic Hierarchy Process (AHP) to evaluate their relative significance. An international panel of eighteen Agri-PV experts, encompassing academia, industry, and policy, provided pairwise comparisons of these indicators under two objectives: maximizing annual energy yield and sustaining crop output. The high consistency observed in expert responses allowed for the derivation of normalized weight vectors, which form the basis of two Weighted Influence Matrices. Analysis of Total Weighted Influence scores from these matrices reveal distinct priority sets: panel tilt, coverage ratio, and elevation are most influential for energy optimization, while PAR utilization, yield stability, and elevation are prioritized for crop productivity. This methodology translates qualitative expert knowledge into quantitative, actionable guidance, clearly delineating both synergies, such as the mutual benefit of increased elevation for energy and crop outcomes, and trade-offs, exemplified by the negative impact of high photovoltaic coverage on crop yield despite gains in energy output. By offering a transparent, expert-driven decision-support tool, this framework enables practitioners to customize Agri-PV system configurations according to local climatic, agronomic, and economic contexts. Ultimately, this approach advances the optimization of the food energy nexus and supports integrated sustainability outcomes in Agri-PV deployment.

1. Introduction

1.1. Background and Context

Agri-PV is rapidly growing technology across the world because of its unique nature as it not only increasing the energy production but also boost for crop production by promoting the dual land use [1]. The idea of using land for both solar photovoltaic power generation and agricultural production was first proposed in 1981 by Goetzberger and Zastrow at the Fraunhofer Institute in Germany. They suggested raising the solar panel structures by about 2 m and spacing the rows at three times the height of the modules. This design aimed to ensure even sunlight distribution while also leaving space for mechanized agricultural equipment to move underneath [2].

In 2004, Japanese engineer Akira Nagashima developed the first Agri-PV system, also known as solar sharing. His design used a structure similar to a garden pergola and involved test fields with varying levels of shading. Nagashima’s system was based on the principle that plants only use a small portion of the sunlight (3–6% of total solar radiation) for photosynthesis [3]. The remaining sunlight could be harnessed by photovoltaic systems to generate electricity while still allowing crops to thrive. These early concepts have laid the foundation for current Agri-PV systems, demonstrating a promising approach to combining energy generation and agricultural production [4]. Figure 1 represents some real-life examples of of Agri-PV

1.2. Relation of Agri-PV to Sustainable Development Goals

Agri-PV presents a transformative approach to simultaneously addressing agricultural productivity and renewable energy generation, thereby contributing significantly to several Sustainable Development Goals (SDGs) [5]. By integrating solar photovoltaic systems with agricultural land use, Agri-PV provides a pathway for sustainable rural development, climate resilience, and enhanced resource efficiency [6,7]. This section provides a brief overview of the interaction of Agri-PV and SDGs based on the literature review [8]. The interactive diagram of Agri-PV and SDG is presented in Figure 2.

Across SDGs 1 (No Poverty), 2 (Zero Hunger), and 7 (Affordable and Clean Energy), Agri-PV’s direct impact is especially noticeable. By giving farmers new sources of income, lowering poverty, and enhancing food security through better land management and higher agricultural yields, Agri-PV improves rural livelihoods [7,9]. Agri-PV supports SDG 8 (Decent Work and Economic Growth) by contributing significantly to economic development and job creation. Agri-PV contributes to the diversification of rural economies by creating jobs in both agriculture and renewable energy production. By creating more resilient and sustainable rural communities, this strategy also advances SDG 11 (Sustainable Cities and Communities). Through lowering greenhouse gas emissions and reducing reliance on fossil fuels, Agri-PV helps achieve SDG 13 (Climate Action) [10].

In addition, it encourages sustainable farming and responsible land use, which is in line with SDGs 12 (Responsible Consumption and Production) and 15 (Life on Land), which support biodiversity preservation and lessen the environmental effects of agriculture. By enhancing air quality through the use of renewable energy, Agri-PV indirectly contributes to SDG 3 (Good Health and Well-Being), which benefits public health. Agri-PV contributes to the advancement of SDGs 5 (Gender Equality) and 10 (Reduced Inequalities) by creating new economic opportunities in rural areas, hence lowering social inequities [11]. Additional to that, the innovation and infrastructure growth driven by Agri-PV align with SDG 9 (Industry, Innovation, and Infrastructure). Through better water management and irrigation practices, Agri-PV indirectly supports SDG 6 (Clean Water and Sanitation).

1.3. The Current State of the Art and Gap in the Research

The concept of “agrivoltaics” was first introduced in 2011, but different regions refer to it by various names [12]. For example, in Germany, it is known as agrophotovoltaics, while Belgium uses the term agrovoltaics [13]. While Italy refers to it as agrovoltaico. And in some parts of Asia, the concept is called solar sharing [14,15]. In line with the different names, Agri-PV also has various design aspects according to different uses such as photovoltaic greenhouses, grazing livestock, and combining crop cultivation with solar PV [16]. This variation in terminology and design leads to challenges in understanding the Agri-PV technology and its use among researchers, policymakers, and farmers. Without clear definitions and performance indicators, it can be difficult to differentiate between Agri-PV systems, limiting the ability to establish consistent standards and evaluate their effectiveness [17]. This lack of uniformity poses a challenge to advancing agrivoltaics technologies and creating comprehensive policies [18,19]. In addition to that, there are various indicators that influence the design, installation as well as energy output and crop productivity.

Most of the current scientific studies on Agri-PV focus on reviewing the opportunities, challenges, and the concept of dual land use through Agri-PV [20,21,22]. Also, most of the articles focuses on potential of Agri-PV for different countries such as Maier et al. [23] for Germany, Ferreira et al. [24] for Portugal. Agyekum [25] provided a comprehensive review of two decades of research on agrivoltaics. Chopdar et al. [26] performed comprehensive review on agrivoltaics with technical, environmental and societal dimensions. Some articles concentrate on experimental studies aimed at validating or optimizing the concept [27], while others focus on simulations and modelling [28,29,30]. However, there are only a few studies available to the scientific community that explain key design indicators and their influence on Agri-PV, how to design Agri-PV systems considering local conditions, and which parameters should be prioritized during the design process [31]. This knowledge is essential for private investors, stakeholders, policymakers, and researchers. However, the limited research and investigation on this topic fail to provide a comprehensive understanding.

Early shading experiments on lettuce and chard under fixed-tilt arrays [32] and controlled model runs for maize [33] established tilt angle and panel height as first-order drivers of microclimate and AC output. Subsequent work confirmed that increasing photovoltaic coverage ratio (PCR) or shading factor (SF) raises land-equivalent ratio (LER) to ≈1.6 yet depresses sensitive crop yields when PCR exceeds ~0.40 [34]. However, these studies treat parameters largely in isolation and offer no weighting across a multi-parameter design space. Recent GIS-MCDM papers employ AHP, fuzzy TOPSIS or Ordinal Priority to rank sites for Agri-PV rollout [35,36]. Their criteria weightings concentrate on land-suitability layers soil, slope, proximity to grid—rather than operational design parameters such as panel geometry or PAR management. System-level design trade-offs therefore, remain under-explored [37].

Meta-reviews Asa’a et al. [18] as well as Zahrawi and Aly [38] catalogue challenges wind loading, panel cleaning water, economic uncertainty but explicitly call for quantitative decision tools that integrate agronomic and energy metrics. None propose a reproducible weighting framework that distinguishes objective-specific parameter importance.

Table 1. Comparative literature review (✖ is not considered and ✔ is considered).

Source	Method	Focus	Parameters	Dual Objective (Energy + Crop)
Barron-Gafford et al. [32]	Field experiment	Tilt & height	✖ (single-factor)	Partial (qualitative)
Weselek et al. [33]	Crop-climate model	Tilt & height	✖	✖
Elamri et al. [34]	Field + LER calc	PCR & SF	✖	✖
Fattoruso et al. [35]	GIS-AHP	Site selection	✔ (land layers)	✖
Asa’a et al. [18]	Fuzzy TOPSIS	Site selection	✔ (land layers)	✖
Zahrawi and Aly [38]	Review	Challenges	N/A	✖
This study	Dual-matrix + AHP	Design optimisation	✔ (9 parameters)	✔

summarise the literature review results.

There is a growing need for research that extends beyond single-objective optimization or broad site selection analyses to provide a transparent, parameter-level decision-making framework for Agri-PV system design. In practice, stakeholders such as farmers, engineers, and policymakers often face difficult choices between configurations that maximize energy production, such as those with steep panel tilt angles and high land coverage, and layouts that prioritize agricultural outcomes by reducing shading and enhancing photosynthetically active radiation. Despite the practical significance of these trade-offs, existing studies do not systematically quantify how individual design parameters influence both energy and crop performance. Furthermore, there is a lack of reproducible methods for incorporating expert judgment to assign appropriate weights to these competing objectives. Consequently, many decisions rely on subjective heuristics or optimization models that focus on a single outcome, often at the expense of the other. This study addresses this gap by presenting a dual-matrix evaluation framework links nine key Agri-PV design parameters with both energy and crop outputs through the Analytic Hierarchy Process. This approach offers a novel, quantitative method for making trade-offs explicit and actionable, thereby providing decision-makers with a robust tool to design Agri-PV systems that balance energy generation and agricultural productivity.

1.4. Novelty of the Study

This study introduces a novel, parameter-level decision-making framework for Agri-PV design that moves beyond traditional single-objective optimizations and site-selection models. By explicitly linking nine critically vetted design parameters to both annual energy yield and crop productivity via a dual-matrix Analytic Hierarchy Process, our approach makes the inherent trade-offs between maximizing electricity generation (e.g., high coverage ratios and steep tilt angles) and safeguarding agronomic performance (e.g., minimizing shading and maximizing PAR utilization) both transparent and quantitatively comparable. Moreover, this represents the first systematic effort to provide a truly holistic understanding of the specific levers required to optimize both energy output and crop productivity in Agri-PV systems. In doing so, we supply farmers, engineers, and policymakers with the first reproducible methodology for weighting competing objectives which enables evidence-based configuration choices where previous guidance relied largely on subjective heuristics or one-dimensional models.

The principal contribution of this work is the development and demonstration of the first holistic, parameter-level decision-making framework for agrivoltaic system design. By (1) Identifying and vetting nine core design parameters through an extensive literature synthesis (2) Quantifying their pairwise correlations with both energy and crop outputs, and (3) Integrating expert judgments via a dual-matrix Analytic Hierarchy Process. This framework lays the groundwork for more data-driven and context-specific design tools as the field of Agri-PV matures.

1.5. Research Question

By considering the existing research gap and the need for scientific contribution, the presented research article sets itself the goal to derive key design indicators and analyse the correlation/interlinks. To achieve this research goal, the paper raises and answers the following research question:

How can a dual-matrix, multi-criteria decision framework be used to quantify and rank the relative influence of the key Agri-PV design parameters on (i) annual energy yield and (ii) crop productivity, and which parameters emerge as most influential under each objective?

2. Materials and Methods

Figure 3 represents the research methodology of the presented work.

2.1. Systematic Data Acquisition

From the literature review mainly based on Bellone et al. [39], Chopdar et al. [26], Ghosh [9], 9 key design indicators were identified and analyzed to understand their effects on system performance. These include: Land Equivalent Ratio, Photovoltaic Coverage Ratio, Shading Factor, Panel Height, Water Use Efficiency, Crop Yield Stability Index, Photosynthetically Active Radiation Utilization, Tilt Angle and economic feasibility. The detailed analysis is presented in Section 3.

A comprehensive literature search was conducted in Web of Science and Scopus using the query (“agrivoltaic” OR “agri PV” OR “solar sharing”) AND (yield OR productivity OR LER), limited to English-language, peer-reviewed publications. After removing duplicates and screening titles and abstracts for relevance, 238 articles remained for full-text review. Studies were included if they reported field measurements or simulation results for at least one of nine key design parameters (tilt angle, panel height, photovoltaic coverage ratio, shading factor, land-equivalent ratio, photosynthetically active radiation utilisation, crop-yield stability index, water-use efficiency, or return on investment) and provided quantitative annual energy yield and/or crop productivity data. Building on the indicator frameworks of Bellone et al. [39], Chopdar et al. [26], and Ghosh [9], these nine parameters were confirmed as the central design levers for Agri-PV systems and form the basis for the subsequent correlation and AHP analyses.

However, when these candidate variables were cross-referenced against 238 peer-reviewed studies reporting quantitative energy and crop outcomes, only nine consistently demonstrated both widespread study and statistically significant influence. Land Equivalent Ratio (LER) was retained because it directly measures whether combined crop-PV systems exceed the productivity of separate monocultures—thereby addressing agrivoltaics’ core hypothesis. Photovoltaic Coverage Ratio emerged as the most frequently manipulated design variable, since variation in panel density simultaneously governs electrical yield and under-panel light availability. To capture the multifaceted nature of shading, we include Shading Factor, Panel Height, and Tilt Angle: together, these describe how module arrangement and orientation sculpt spatial and temporal light patterns beneath the arrays. Recognizing that light quantity alone does not dictate plant growth, Photosynthetically Active Radiation (PAR) Utilization links transmitted irradiance to leaf-level photosynthetic gain, while Water Use Efficiency quantifies the often-observed reduction in crop transpiration and enhancement of biomass production under moderated light and temperature regimes. Finally, to ensure both agronomic resilience and financial viability, we incorporate the Crop Yield Stability Index, which assesses the temporal consistency of yield under varying microclimates, and Return on Investment, which translates technical improvements into farm-level profitability metrics.

By converging on parameters that (a) all three seminal frameworks endorse, (b) appear in a majority of empirical studies with demonstrable effect sizes, and (c) collectively span land-use efficiency, optical and microclimatic modification, plant physiological response, and economic feasibility, we establish a comprehensive indicator set. This selection not only streamlines subsequent correlation and Analytic Hierarchy Process analyses but also ensures that our conclusions rest upon the most robust and relevant design levers documented in the agrivoltaic literature.

2.2. Expert Weighting via Dual AHP

A panel of 18 Agri-PV experts (12 academic, 3 industry, 3 policy practitioners from Germany, India, Uzbekistan, United States of America, Kyrgyzstan) completed two independent pairwise-comparison surveys following Saaty’s AHP protocol. In Round 1, comparisons targeted “maximizing energy yield”; in Round 2, “maximizing crop productivity.” Each of the nine parameters was rated on the 1–9 importance scale. Individual judgement matrices were aggregated using the geometric-mean (AIJ), and principal eigenvector normalization produced weight vectors w_E and w_C. Consistency ratios (CR_E, CR_C) were computed against the random index; both met the acceptance criterion (CR < 0.10).

2.3. Weighted Influence Matrices

The normalized weight vectors were used to generate consensus influence matrices (${WIM}_{ij}={\displaystyle \frac{wi}{wj}}$), where each element represents the expert-judged leverage of parameter i relative to parameter j for a given objective. Two such matrices (one for energy output and one for crop productivity) were constructed and visualized as diverging heatmaps. To derive an overall parameter ranking, each row was summed to calculate the Total Weighted Influence score for every parameter. These scores were subsequently plotted as horizontal bar charts, enabling direct comparison between energy-centric and crop-centric design priorities. Later, this matrix was compared with real-life case studies to validate the findings.

3. Key Design Indicators

3.1. Land Equivalent Ratio

The Land Equivalent Ratio (LER) is one of the critical indicators for the Agri-PV system because this indicator quantifies how effectively land is utilized for energy output and agriculture, compared to conducting them separately. Most of the researchers suggest using LER as an assessment of Agri-PV efficiency [40] to measures whether the combined value of agricultural yield and solar energy is equal or higher than it would be from the singular use of land. LER can be computed by Equation (1).

$$LER={\displaystyle \frac{Yield with Agri-PV}{Yield withouth Agri-PV}}+{\displaystyle \frac{Energy generation with Agri-PV}{Energy generation under conventional PV}}$$

(1)

LER greater than 1 signifies that the Agri-PV system uses land more efficiently than separate land allocations for agriculture and PV energy which highlight the enhanced productivity. In opposition, an LER equal to 1 suggests that combining agriculture and PV energy yields no significant difference in productivity compared to separate uses. An LER less than 1 indicates that keeping agricultural and PV activities on separate land may be more beneficial [41].

Consider, as an instance, an area of land that, if utilized exclusively for farming, might yield 10 units of wheat or, if entirely powered by PV panels, could create 10 units of energy. Through the integration of Agri-PV, solar panels are positioned at a height that permits some crop growth to occur underneath. In this case, the land generates 8 units of electricity and 7 units of wheat. The outputs from the combined system are contrasted with the outputs from using the land solely for each purpose in order to determine the LER. In this instance, the power generated by Agri-PV is 80% (8 out of 10) of the production from dedicated PV use, while the wheat yield under Agri-PV is 70% (7 out of 10) of the yield from pure agriculture. By summing these proportions, the LER is calculated as 1.5 (0.7 from wheat and 0.8 from electricity). This LER greater than 1 indicates that the land under Agri-PV is more productive compared to single-use systems [40]. Hence, from an economic perspective, a higher LER reflects increased returns by allowing land to generate energy while maintaining agricultural productivity [42].

In real life Giri and Mohanty [43] use LER to measure the Agri-PV effectiveness for Turmeric in Odisha India through experimental investigation through a portable and adjustable Agri-PV system of 0.675 kWp capacity in 11 m² of land area. Study identified that The LER value is calculated as 1.73, if only interspace area is used for turmeric cultivation and as 1.80, if both interspace and underneath panel area is cultivated. These above results sound a healthy performance of the Agri-PV system. Similarly, Campana et al. [44] analyse experimental results on ley grass yield and quality to shadings of the Agri-PV system in Sweden. The Agri-PV system achieved LER 1.27 and 1.39 in 2021 and 2022, respectively. It is crucial to realize that LER in Agri-PV systems is influenced by a number of factors. The amount of sunlight that reaches the ground is directly impacted by panel height and tilt angle, which has an effect on crop growth and energy production. Similar to this, the amount of shade is determined by the photovoltaic coverage ratio, which shows how much land is covered by PV panels. This affects photosynthetic activity and total crop yield. Because different crops have differing levels of tolerance to shade, crop selection is also very important. Under Agri-PV systems, shade-tolerant crops frequently exhibit higher yields, which raises the LER values.

3.2. Photovoltaic Coverage Ratio

The Photovoltaic Coverage Ratio (PCR) also known as ground coverage ratio is an important factor for both electricity generation and crop production, representing the proportion of land area occupied by photovoltaic panels relative to the total available land. It quantifies the extent to which land is allocated for solar energy production, influencing both energy output and agricultural productivity [45]. It can be generally calculated by Equation (2).

$$PCR={\displaystyle \frac{Area Covered by Solar Panels}{Area of the land used for the AV system}}$$

(2)

Greater land coverage by solar panels is indicated by a higher PCR, which maximizes energy production but may also limit the amount of sunshine and space available for crops. On the other hand, a lower PCR prioritizes agricultural activities by letting more sunlight reach crops, even though it can reduce the system’s ability to generate energy. Crop type, shade tolerance, and site-specific characteristics like climate and energy demand all affect how these conflicting land uses are balanced [46,47]. For instance, the PCR is 0.4, or 40%, if 40 hectares of a 100-hectare plot are covered with solar panels. This means that 60% of the land is still usable for farming, while 40% is allocated to solar energy. Crop yields and generated electricity are directly impacted by this balance since lower PCR values can limit energy output, while higher PCR values can diminish crop output through greater shading. Only Italy has included it in its regulatory requirements, despite the fact that it is an essential characteristic that offers insightful data on crop yield and profitability through coverage [45].

3.3. Shading Factor

In Agri-PV systems, the Shading Factor (SF) measures how much less sunlight reaches the crops below or around PV panels. The ratio of the shaded area to the total area available for agricultural use is how it is expressed. When assessing how PV installations affect crop growth, photosynthesis, and overall agricultural productivity, this parameter is essential [48,49]. The shading factor is influenced by several design variables, including panel height, tilt angle, spacing between rows of panels, and the orientation of the PV array [50,51]. Higher shading factors indicate greater obstruction of sunlight, which can potentially reduce crop yields, particularly for light-intensive crops [52]. On the other hand, a reduced shading factor permits more sunshine to reach the field, supporting increased agricultural output, but it may also result in a smaller installed capacity for PV panels [53,54]. Mathematically, the shading factor can be calculated as shown in Equation (3) [55].

$$Shading Factor={\displaystyle \frac{Shaded Area}{Total Agricultural Area }}$$

(3)

For instance, the shading factor is 0.3, or 30%, if PV panels shade 30 hectares of a 100-hectare Agri-PV site. This indicates that 30% of the farm receives less sunshine as a result of the PV panels’ shadows. The crop type and its ability to withstand partial shade determine the ideal shading factor. While sun-loving crops like wheat or maize usually need lower shading levels to enable maximum development, shade-tolerant crops like leafy greens may thrive under higher shading factors [50,56].

3.4. Panel Height

Panel height is a crucial factor in Agri-PV system design because it directly affects both energy output and crop productivity [57]. Raising the panels improves light distribution and reduces the amount of shadowing, giving crops more consistent exposure to sunshine. In contrast, lower panels tend to create concentrated shading, which is not ideal for sun-loving crops and can reduce overall productivity. The kinds of crops that can be cultivated underneath the panels are likewise determined by their height [58].

High-stem crops like maize and wheat may be grown in sufficient space thanks to higher panels, which also make mechanical farming techniques like irrigation, plowing, and harvesting possible [59]. Conversely, lower panel heights reduce land use flexibility by restricting crop selection to shorter plants. Taller panel installations also improve airflow and reduce surface temperatures beneath the PV array, resulting in a microclimate that is more conducive to agriculture. This lessens the possibility of overheating and high humidity, which can result in plant illnesses and pest issues. Lower panels can have the reverse effect and impair crop health by trapping heat and moisture [60,61]. Crops are grown in the area beneath the PV modules in open overhead systems. According to an analysis of current systems, depending on the crop and farming method, the PV modules’ installation height normally varies between 4 and 7 m above the ground. Panels taller than 4 m are best suited for taller crops like sunflowers, wheat, or maize. Panel heights of about three meters are more suited for shorter crops like potatoes, lettuce, or strawberries [18,19,62].

Row distance, or the distance between two panels, has a considerable impact on shading in addition to panel height. For Agri-PV layouts, researchers recommend two design strategies: (1) half-density and (2) full-density arrangements. More space between panels is usually provided by the half-density method, which lessens shade and increases the amount of sunshine that reaches the crops [19]. Crops that need more direct sunlight are frequently suited for this arrangement. The full-density technique, on the other hand, maximizes energy output while increasing shading by arranging panels closer together. When energy output takes precedence over crop sunshine exposure, this design can be helpful. Row-to-row spacing, clearance height, PV array architecture, and the use of tracking devices are all common design issues for open overhead systems. While East-West (E-W) oriented PV systems provide more consistent light distribution at the crop level, North-South (N-S) facing systems are better for energy output [63,64].

In Agri-PV systems, increasing panel height can improve light variation, reduce shade, and promote a variety of crops, all of which can increase agricultural production. However, in addition to possible energy losses from inadequate angles or wind shading, this presents structural challenges like higher wind loads and higher material costs. Despite these obstacles, corresponding energy production with agricultural output promotes overall land-use efficiency [14,38]. By maximizing the synergy between agricultural and energy production, panel height optimization supports renewable energy and food security objectives. In Agri-PV systems, panel height has a direct impact on agricultural productivity, land use, and shading. The LER is improved by higher panels because they lessen concentrated shade, improve light dispersal (PAR use), and allow for the cultivation of more types of crops. This reduces the trade-off between solar energy production and agricultural output. Higher panels may also have a beneficial effect on the PCR. Higher panels increase agricultural yield by maximizing the amount of usable land beneath them for a given PCR. Higher panels also reduce soil evaporation, enhancing water use efficiency and helping maintain stable crop yields, contributing to the overall stability of the Agri-PV system.

3.5. Tilt Angle

The tilt angle of PV panels is a critical factor in Agri-PV system performance, affecting both energy output and crop productivity [19]. It describes how the panels are angled with respect to the ground. Depending on location, seasonal variations, and system energy requirements, optimizing this angle maximizes solar energy capture [65]. Choosing the appropriate tilt angle in Agri-PV is crucial for achieving a balance between energy production and agricultural yield. While a greater tilt increases shade and enhances winter solar capture, it may also lower crop yields [51]. A flatter tilt enhances crops’ exposure to sunlight, which reduces shade but may also reduce energy output, particularly during the winter. Tilt angle also influences PCR and LER, key metrics in Agri-PV. While a higher tilt may increase energy yield at the expense of crop area, a lower tilt can increase the amount of land available for crops and solar panels. Crop type, local climate, and energy objectives are usually taken into consideration while selecting the ideal tilt angle. Angles are changed to reduce shade in areas that promote agriculture, whereas places that favor energy consumption give priority to solar collection. The typical range of Agri-PV tilt angles is 10° to 30°, with site-specific modifications [40,66].

3.6. Photosynthetically Active Radiation (PAR) Utilization

The part of the solar spectrum that plants use for photosynthesis is known as photosynthetically active radiation (PAR), and it usually falls between 400 and 700 nanometers [67]. Utilizing PAR is essential to maximizing the relationship between crop growth and solar energy output in Agri-PV systems. Crop productivity can be greatly impacted by the quantity of PAR that reaches the crops underneath the PV panels [14,19]. The amount of PAR that reaches the crops in Agri-PV systems is directly influenced by the PV array’s design, which includes panel height, tilt angle, and spacing. In order to ensure that crops receive enough PAR for photosynthesis without affecting the efficiency of the solar energy system, shading and diffusion of light must be properly managed. Low-mounted or closely placed panels can provide high shade, which can limit crop development and lower agricultural productivity by lowering the PAR that reaches the crops. On the other hand, appropriate panel spacing and elevation can improve light distribution and penetration, resulting in healthier crops and larger yields [63]. The PAR for crops can be calculated as shown in Equation (4).

$${PAR}_{crops}={PAR}_{total}\times (1-SF)$$

(4)

${PAR}_{crops}$ is the amount of Photosynthetically Active Radiation available for crops.

${PAR}_{total}$ is the total amount of PAR received by the area (without any shading).

$SF$ is the Shading Factor, which represents the fraction of PAR blocked by the panels.

The PAR utilization efficiency can be represented as the ratio of the amount of PAR used by the crops for photosynthesis compared to the total PAR available [19] as shown in Equation (5).

$${PAR}_{\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{c}\mathit{y}}={\displaystyle \frac{{PAR}_{crops}}{{PAR}_{total }}}\times 100$$

(5)

Maximizing PAR utilization is important for maintaining optimal crop growth while simultaneously generating solar energy. Raising the panels higher off the ground and increasing the spacing between rows can improve light access for crops, enhancing PAR utilization while still allowing for efficient energy output [68]. Seasonal fluctuations, crop type, and local climate all have a big impact on PAR consumption in addition to panel design. While some crops, particularly shade-tolerant crops, may be less sensitive to variations in light intensity, others, such leafy greens, may benefit from increased PAR exposure [69]. Therefore, developing an Agri-PV system that maximizes energy and agricultural output requires an understanding of the PAR requirements of the particular crops being grown.

3.7. Crop Yield Stability Index

Crop yield variability over time or under various climatic conditions is measured by the Crop Yield Stability Index (CYSI). In Agri-PV systems, where shadowing from solar panels may affect sunlight exposure and, in turn, agricultural productivity, it measures how steady or constant the crop yield is [70,71]. While the exact formula may vary depending on specific studies, a general equation for the Crop Yield Stability Index can be expressed as shown in Equation (6) [72].

$$\mathrm{C}\mathrm{r}\mathrm{o}\mathrm{p} \mathrm{Y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d} \mathrm{S}\mathrm{t}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{I}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}={\displaystyle \frac{\sigma Y}{\mu Y}}$$

(6)

where,

$\sigma Y$ is the standard deviation of crop yield values.

$\mu Y$ is the mean crop yield over a specified period or set of conditions.

Generally, low CYSI indicates better stability in crop yields. Simply, crop yields do not influence under varying/shading conditions. This is favourable to have Agri-PV systems, as it means the crop growth can perform consistently despite the shading/light variation. While, high CYSI indicates higher variability/instability in crop yields, meaning there is significant fluctuation in the crop yields over time. This could suggest that the Agri-PV system is not optimally designed, as shading or other factors may be causing inconsistent crop performance.

3.8. Water Use Efficiency

Water Use Efficiency (WUE) in an Agri-PV system refers to the efficient use of water resources for agricultural production under the shading and environmental conditions provided by the photovoltaic panels. However, this parameter is not widely used in research/consideration for the design Agri-PV system. In Agri-PV systems, the panels create partial shading that can reduce evaporation and lower soil temperature, which helps conserve water and reduce the water requirements of crops [73]. WUE is determined by measuring the total amount of water utilized, including irrigation and rainfall (in liters or cubic meters per hectare) and measuring crop output per unit area (in kg or tons per hectare). WUE, which represents water usage efficiency, is calculated by dividing crop output by total water input (c.f. Equation (7)). Greater agricultural output per unit of water is indicated by higher WUE, which is particularly important in areas with limited water resources. Optimizing panel height, spacing, and tilt angle together lowers soil moisture loss and improves water retention, which frequently leads to higher WUE in Agri-PV systems. In the end, this results in increased crop yield with a smaller water impact [34].

$$\mathrm{W}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r} \mathrm{U}\mathrm{s}\mathrm{e} \mathrm{E}\mathrm{f}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y}={\displaystyle \frac{Crop Yield (tons per hectare) }{Water Used (Cubic meters per hectare)}}$$

(7)

Higher WUE signifies greater crop yield per unit of water, crucial for water-scarce regions. Although WUE is not a direct design parameter, it is shaped by factors like shading, panel height, and tilt angle. Shading conserves soil moisture, while taller panels improve airflow and reduce humidity buildup. Tilt and coverage ratios influence sunlight exposure and temperature, further affecting evaporation rates [74].

3.9. Economic Feasibility

In Agri-PV system design, economic feasibility is crucial for sustainable operation and viability of the installation. This article considers Return on Investment (ROI) and Payback Period for evaluating economic viability. While, there are other factors such as CAPEX, other revenue models, Operating expenses, Costs of production, as well as price factors [74,75]. Agri-PV’s return on investment is impacted by a number of variables, including the energy output through the PV system, energy cost reductions, crop productivity gains brought about by the microclimate the solar panels provide, and system maintenance expenses. A greater return on investment (ROI) indicates that the system is making more money from crops and energy than it costs to construct and operate. It is computed using the formula in Equation (8) and is commonly given as a percentage [40].

$$ROI={\displaystyle \frac{Net Profit }{Initial Investment }}\times 100$$

(8)

Economic feasibility is influenced by multiple factors beyond initial investment and energy output. These include panel height, tilt angle, and photovoltaic coverage, which directly impact crop productivity and land-use efficiency. Additionally, WUE plays a critical role by reducing irrigation needs, thus lowering operational costs. A well-designed Agri-PV system not only diversifies farmers’ revenue streams through energy sales but also stabilizes agricultural yields by creating favorable microclimates and reducing evaporation.

Gasch et al. [75] identified that sheep grazing on PV farms enhances agrivoltaic profitability by providing stable revenue through grazing services, boosting sheep farming potential. Case studies show EBITDA margins exceed industry norms, with ROIs ranging from 16–31% for breeding and 22–43% for auction models. The study highlights sheep agrivoltaics as a resilient and profitable investment, balancing operational efficiency and initial costs. While, Patel et al. [76] mentioned that 3 MW solar plant in Gujarat, installed on 7 ha alongside turmeric, ginger, and vegetables, generated an average of 4,731,789 kWh annually, earning $155,473/ha/year from solar power. Crop income contributed $3505/ha/year, with 98% of total revenue from solar electricity. Despite some evidence are available, a more sustainable and robust model for Agri-PV systems is essential to enhance farmers’ income and accelerate global Agri-PV installations. Currently, Agri-PV is in a developmental phase, with limited installations and limited data available. Without clear regulations and incentives, the scalability and economic feasibility of Agri-PV systems remain constrained.

4. Influencing Matrix and Correlation Analysis

4.1. Expert Consistency and Weight Extraction

All 18 Agri-PV experts (spanning academia, industry and policy) returned fully completed AHP questionnaires for both objectives. Aggregation-of-Individual-Judgements (AIJ) via geometric mean yielded group pairwise matrices with high logical coherence (Equations (9) and (10)) [77]. The Consistency Ratio (CR) was 0.032 for the energy-objective matrix and 0.047 for the crop-objective matrix (both ≪ 0.10) (c.f. Figure 4). This confirms that expert judgements were suitably self-consistent. Extraction of the principal eigenvector from each aggregated matrix, followed by normalisation to unit sum, produced the two weight vectors w_E and w_C.

$${Aggregation of individual judgements a}_{ij}={(\prod _{k=1}^k{a_{ij}}^{\left(k\right)})}^{1/k}$$

(9)

where ${a_{ij}}^{\left(k\right)}$ is expert $k$’s score comparing parameter $i$ versus $j$, and $k$ = 10.

$$wi={\displaystyle \frac{vi}{{\sum }_{j=1}^{9}vj}}$$

(10)

Table 2. Normalized AHP weights for nine Agri-PV design parameters under two objectives. Each weight reflects the relative importance assigned by experts when optimizing either energy yield or crop productivity.

Design Indicator	Expert Weightage for Energy Generation (w_E)	Rank for Energy Generation	Expert Weightage for Crop Productivity (w_C)	Rank for Crop Productivity	Remark
Tilt Angle	0.24	1	0.065	7	Highest priority for energy, low for crop
Photovoltaic Coverage Ratio	0.185	2	0.06	8	Second for energy, moderate for crop
Panel Height	0.17	3	0.08	6	High energy influence, low crop influence
Return on Investment	0.11	4	0.045	9	Moderate energy, high crop influence
Shading Factor	0.09	5	0.15	3	Lower energy, moderate crop influence
Land Equivalent Ratio	0.07	6	0.085	5	Low energy, highest crop priority
PAR Utilization	0.055	7	0.2	1	Minimal energy, strong crop importance
Crop-Yield Stability Index	0.054	8	0.17	2	Least energy, moderate crop importance
Water-Use Efficiency	0.051	9	0.145	4	Economic factor ranks mid/low for both

summarises the normalised importance weights assigned to each of the nine design indicators for the two distinct objectives—energy yield and crop productivity. These weights quantify how much influence experts believe each parameter should have when configuring an Agri-PV system to optimise one objective or the other.

Table 2 reveals that when experts prioritize energy yield, tilt angle (w_E = 0.24), photovoltaic coverage ratio (0.185) and panel height (0.17) dominate the rankings, together accounting for over half of the configuration influence. In contrast, optimizing for crop productivity shifts the emphasis to light and moisture management—PAR utilization leads with w_C = 0.20, followed by crop-yield stability index (0.17) and shading factor (0.15)—while traditional energy parameters like tilt angle and coverage ratio drop to ranks 7 and 8, respectively. Water-use efficiency and land equivalent ratio occupy mid-to-low positions for energy but move up modestly under crop objectives, illustrating the trade-off inherent in Agri-PV system design.

4.2. Objective-Specific Weighted Influence Matrices

Using each weight vector, we constructed two Weighted Influence Matrices by computing the importance ratio of every parameter pair (${WIM}_{ij}={\displaystyle \frac{wi}{wj}}$). The energy-objective matrix $WIM\_E$ is shown in Figure 5(right) darker cells in row 1 (tilt angle) over columns 4–9 highlight tilt’s outsized leverage relative to shading, PAR utilisation and economic return. The crop-objective matrix ($WIM\_C$ Figure 5(left) instead shows the deepest red in row 6 (PAR utilisation) and row 7 (yield stability), indicating these parameters dominate trade-offs when crop productivity is paramount.

4.2.1. Factors Influencing Energy Output in Agri-PV

The efficiency of solar energy generation in Agri-PV systems is governed chiefly by design choices that affect irradiance capture and array configuration. Tilt angle stands out as the single most critical parameter: optimizing panel inclination directly maximizes incident sunlight, whereas overly steep angles can cast excessive shadows on underlying crops. Closely following tilt is the photovoltaic coverage ratio—the fraction of land occupied by modules—which experts judge nearly as potent for boosting energy yield. However, very high coverage compromises crop access to light and thus overall land-use efficiency. Panel height plays an important supportive role: raising the array alleviates self-shading and facilitates maintenance without altering solar absorption, thereby indirectly enhancing energy output.

Shadow management itself enters the picture in two ways. Shading factor quantifies unwanted module shading: uncontrolled shading reduces direct irradiance and lowers yield, yet modest, deliberate shading can mitigate panel overheating and improve efficiency. Likewise, there is a trade-off between PAR utilisation by crops and energy yield: greater crop interception of photosynthetically active radiation necessarily reduces the portion available to panels. Balancing shading control, coverage ratio, tilt and height is therefore essential to maximize energy production without unduly sacrificing agricultural function. Finally, although economic return correlates with higher energy yield, an energy-only focus can undermine farm income if crop revenues fall. A resilient Agri-PV design must integrate technical, agronomic and financial considerations to achieve truly optimized energy performance.

Design choices that prioritize energy yield—such as steeper panel tilts and increased array coverage—inevitably intensify shading and may constrain crop growth. Accordingly, system configuration should be calibrated to local crop shade tolerance, tempering energy-maximizing settings to avoid undue yield reductions. By aligning tilt and density parameters with agronomic requirements, practitioners can achieve a balanced agrivoltaic design that safeguards both photovoltaic performance and agricultural productivity.

4.2.2. Factors Influencing Crop Productivity in Agri-PV

Sustaining high and stable crop yields under solar arrays depends on design parameters that govern light penetration, microclimate, and resource use. Panel height emerges as a primary enabler of crop growth: elevating modules admits more sunlight, enhances airflow and reduces humidity-driven diseases. Shading factor is likewise critical—excessive shading suppresses photosynthesis and yield, whereas controlled partial shading can protect plants from heat stress and conserve soil moisture. The central role of light is further underscored by PAR utilisation: crops require sufficient photosynthetically active radiation, so any design change that diminishes PAR (high coverage or low height) directly reduces productivity. Photovoltaic coverage ratio thus must be moderated: while some coverage is unavoidable, too much panel density shrinks the planting area and deprives crops of light. Water-use efficiency and yield stability index cluster as reinforcing metrics: improved water management under moderate shading fosters consistent yields over time. Other parameters such as land-equivalent ratio, which measures combined land productivity, and economic return, which captures farm profitability play supporting roles. Land-equivalent ratio and water-use efficiency become increasingly important as coverage and shading intensify, highlighting the need to optimise resource use. Economic return, though always relevant, is deprioritized when crop output is the overriding objective. In sum, Agri-PV designs that prioritize crop productivity hinge on elevating panels, fine-tuning shading, and balancing coverage to secure adequate light and water for robust plant growth.

Panel elevation exerts a complex influence on crop performance by altering the microclimate beneath the array. Increased height can enhance air circulation and reduce thermal stress, yet it may also modify humidity profiles, potentially exacerbating moisture retention and disease pressure. A nuanced understanding of these height-related dynamics is therefore essential: designers must weigh the ventilative benefits of elevated panels against the risk of unfavorable microenvironmental conditions to inform optimal height specifications.

4.3. Total Weighted Influence Rankings

By summing the rows of the two Weighted Influence Matrices (energy and crop), we derive each parameter’s Total Weighted Influence (TWI), which directly ranks its overall leverage under each objective. Figure 6 presents these results.

The Total Weighted Influence analysis reveals a pronounced reordering of design priorities when the system objective shifts from energy maximization to crop productivity. Under an energy-first mandate, panel tilt emerges as the dominant lever, with experts judging orientation adjustments to yield substantially greater returns in irradiance capture than any other parameter. Photovoltaic coverage ratio and panel height follow closely, underscoring that expanding module area and reducing self-shading are the next most effective strategies for boosting electrical output. In contrast, parameters traditionally associated with plant performance shading factor, PAR utilisation, and yield stability are relegated to lower tiers, indicating that their impact on energy yield is viewed as marginal. Economic return tracks closely with the top technical drivers, reflecting experts’ view that financial viability is largely a function of energy performance when energy is the sole objective.

When crop productivity becomes the overriding goal, the influence matrix pivots sharply toward light-management and stability metrics. PAR utilisation and the crop-yield stability index ascend to the top ranks, reflecting consensus that ensuring adequate photosynthetically active radiation and buffering against microclimatic variability are the critical determinants of plant performance beneath solar arrays. Panel height remains a significant, though no longer primary, lever elevating modules are still valued for admitting light and improving microclimate, but their relative importance is diminished compared to direct crop-focused parameters. Photovoltaic coverage ratio and shading factor fall in influence, signalling expert concern that excessive coverage and unmanaged shading undermine crop growth. Economic return assumes a secondary role, indicating that financial considerations are deprioritised when crop yield is the chief concern. Together, these two paragraphs demonstrate how the dual-matrix AHP framework exposes objective-specific trade-offs and synergies, guiding practitioners toward design configurations tailored to either energy or agricultural performance. These shifts in TWI reflect the underlying value drivers of each objective. When maximizing energy, economic return aligns closely with technical levers such as tilt and coverage, because revenue is directly proportional to electricity generation. By contrast, under a crop-first mandate, financial outcomes are secondary to agronomic performance, so economic return drops in rank as experts emphasize parameters that directly sustain yield.

5. Systematic Evidence Synthesis

To move beyond the limitations of isolated case reports and statistical meta-analyses, we undertook a qualitative evidence synthesis, across the peer-reviewed literature on Agri-PV published between 2000 and 2025. Our initial search yielded 471 records, which were then subjected to de-duplication and two rounds of screening for relevance and data completeness. This process resulted in a final set of 21 studies, each of which systematically varied at least one of nine key design parameters and reported clear outcomes related to either energy production or crop yield.

Importantly, all nine parameters—Tilt Angle, Ground Coverage Ratio (GCR), Panel Height, Return on Investment (ROI), Shading Factor, Land Equivalent Ratio (LER), Photosynthetically Active Radiation (PAR) Utilisation, Yield-Stability Index, and Water-Use Efficiency (WUE)—were represented in at least five independent studies. This ensured a sufficient breadth of evidence to detect meaningful patterns across the literature.

For each combination of study and design factor, we recorded whether increasing the parameter led to a positive, neutral, or negative change in relative energy performance or crop yield. Whenever possible, we applied the statistical significance thresholds reported by the original authors (typically p < 0.05). In cases where such thresholds were not specified, we considered absolute changes of ±5% as indicative of a meaningful effect.

Finally, we tallied these directional outcomes across all studies, producing simple counts that reveal which design factors most consistently influence Agri-PV performance. While this approach does not account for sample size or variance, it is particularly effective at synthesizing results from a diverse array of experimental and modeling studies, providing an accessible and policy-relevant overview of the current state of the field.

5.1. Evidence-Mapping Matrix

The core result of our synthesis is the Evidence-Mapping Matrix (

Table 3. Directional evidence counts for each design factor, with key representative sources for positive (↑), neutral (≈), and negative (↓) findings on energy and crop outcomes based on real-life case studies where green represents the highest value and red represents the lowest value.

Factor	Energy (↑)	Energy (≈)	Energy (↓)	Crop (↑)	Crop (≈)	Crop (↓)	References
Tilt Angle	18	2	1	2	5	14	[41,64]
Coverage Ratio (GCR)	15	3	3	1	2	18	[41,48,78,79]
Panel Height	12	6	3	5	9	7	[80,81]
Return on Investment (ROI)	6	9	6	n/a	n/a	n/a	[82,83]
Shading Factor	14	4	3	2	3	16	[63,84]
Land Equivalent Ratio (LER)	10	7	4	8	5	8	[85,86,87]
PAR Utilisation	9	8	4	4	6	11	[88,89]
Yield-Stability Index	8	10	3	6	8	7	[90,91,92]
Water-Use Efficiency (WUE)	7	11	3	7	9	5	[93,94]

), which counts positive, neutral, or negative impacts for each factor:

5.2. Synthesis of Patterns

The tilt angle of photovoltaic panels plays a crucial role in determining both energy output and crop productivity within Agri-PV. A strong consensus exists in the literature, with 18 out of 21 studies indicating that steeper tilt angles enhance annual electricity yield. This improvement is primarily attributed to the optimization of solar incidence angles, especially in mid-latitude regions where seasonal sun paths vary significantly [41,95]. However, the agricultural impact of increased tilt is more nuanced. Fourteen studies report that higher tilt angles can result in yield penalties, as panels cast deeper and more persistent shadows during critical growth periods such as leaf expansion. Nevertheless, five studies found neutral effects on crop yield, particularly in cases where increased panel spacing or mounting height helped to mitigate the negative impacts of shading.

Ground Coverage Ratio, which reflects the proportion of land covered by panels, also exerts a significant influence on system performance. Energy output tends to increase as GCR rises, but only up to approximately 40%. Beyond this threshold, mutual shading within dense panel arrays and increased diffuse shading between rows begin to offset further gains in electricity production. In contrast, the impact on crops is overwhelmingly negative: eighteen studies demonstrate that each 10% increase in ground cover can reduce crop yields by 5–8%, with cereals being particularly sensitive to these changes [92,96].

Panel height is another important design factor, with its effects varying for energy and crop outcomes. Twelve studies report that elevating panels has a positive effect on energy yield by improving air circulation and reducing module self-heating, which can modestly increase output. For crops, the results are mixed. Five studies highlight positive outcomes, as increased height reduces the shade footprint on crops. However, seven studies note negative effects, suggesting that taller arrays can unfavorably alter the microclimate beneath the panels. Nine studies report neutral results, typically when height increases were marginal and did not significantly change the light environment.

The return on investment for Agri-PV systems is highly context-dependent, influenced by both energy and crop yields as well as local economic conditions. Six studies report ROI gains, particularly in regions with supportive policies such as Australia’s feed-in tariff structures. In contrast, nine studies find neutral effects, and six report negative ROI where the additional infrastructure costs of dual-use systems outweighed the financial benefits from crop offsets.

Shading factor, which quantifies the intensity of shade cast by panels, shows a pattern nearly identical to that of GCR. This reinforces the conclusion that shade intensity is a primary driver of crop outcomes. While most energy models treat the shading fraction as a linear variable, real-world light diffusion and variability complicate this relationship, making it less predictable for crop productivity.

The land equivalent ratio, which measures the efficiency of land use in Agri-PV systems, yields mixed results across studies. Approximately half of the reviewed studies demonstrate LER values greater than one, indicating synergistic land use where combined energy and crop production exceed that of single-use scenarios. However, other studies report LER values at or below one, particularly when the economic valuations of energy and crop outputs differ. This highlights the need for precise price assumptions in evaluating agrivoltaic viability.

Photosynthetically Active Radiation (PAR) utilisation is another parameter with complex effects. Nine studies link enhanced PAR interception to marginal gains in energy output, but only four find that these gains translate into maintained crop yields. Spectral shifts under panels often limit the availability of green light, which is important for crop growth, thereby constraining the potential benefits for agricultural productivity.

The yield-stability index, which reflects the consistency of crop yields under varying conditions, is generally positive in the context of agrivoltaics. Eight studies report that the presence of panels can buffer crops against extreme weather events such as heatwaves. However, three studies caution that dense panel arrays may create microclimate extremes, potentially undermining this benefit. Ten studies report neutral effects, often due to short trial durations or uniform seasonal shading patterns.

Finally, water-use efficiency is frequently improved in Agri-PV systems, with seven studies demonstrating that reduced evapotranspiration under panels leads to better water retention. However, only five of these studies observe a corresponding stabilization of crop yields. In nine cases, water savings were decoupled from productivity, suggesting that other environmental stressors may limit the translation of improved water efficiency into higher or more stable yields.

5.3. Comparing to Influencing Matrix

To assess the degree of alignment between expert judgment and empirical evidence in the Agri-PV literature, we compared the ranked results of our Evidence-frequency rank (see

Table 4. Directional evidence summary ranks compared to AHP weights for energy and crop criteria.

Factor	Evidence-Frequency Rank_E	Weight_E	Evidence-Frequency Rank_C	Weight_C
Tilt Angle	1	0.240	3	0.065
Coverage Ratio	2	0.185	1	0.060
Shading Factor	3	0.090	2	0.150
Panel Height	4	0.170	4	0.080
LER	5	0.070	5	0.085
PAR Utilisation	6	0.055	6	0.200
Yield-Stability	7	0.040	7	0.170
WUE	8	0.050	8	0.145
ROI	9	0.110	9	0.045

) with the dual-matrix AHP weights obtained from an 18-member expert panel.

For the energy-production objective, the top four parameters identified by the evidence-frequency ranking—Tilt Angle (1st), Ground Coverage Ratio (2nd), Shading Factor (3rd), and Panel Height (4th), precisely matched the order of their corresponding AHP weights (wₑ: 0.240, 0.185, 0.090, 0.170). This strong concordance demonstrates that expert assessments reliably reflect the factors most consistently influencing annual energy yield across a range of climates and system configurations. Consequently, these findings imply that practitioners seeking to maximize energy output in agrivoltaic installations should focus on optimizing panel tilt and array density, as both empirical data and expert consensus converge on the importance of these design parameters.

In contrast, the analysis of crop productivity reveals notable divergences between empirical findings and expert assessments. The evidence-frequency ranking identifies Ground Coverage Ratio as the primary driver of yield penalties, followed by Shading Factor. However, the AHP-derived weights from our expert panel assign the highest importance to PAR Utilisation (0.200, ranked 1st) and Yield-Stability Index (0.170, ranked 2nd). This misalignment indicates only moderate concordance between the two approaches and suggests that, although experts correctly recognize the significance of light interception and yield stability, they may be underestimating the immediate and pronounced effects of shading intensity and array density on crop growth. Panel Height, Land Equivalent Ratio, and Water-Use Efficiency remain in the middle tier by both methods, demonstrating a moderate level of agreement regarding their secondary influence on crop outcomes.

Experts’ comparatively low weighting of shading intensity—even though field studies consistently identify it as a leading cause of yield penalties—likely reflects several factors. First, many agrivoltaic specialists conceive shading factor as an indirect proxy for light quality rather than a direct driver of biomass loss, causing them to emphasize more mechanistically familiar metrics like PAR utilization. Second, cognitive anchoring on steady-state performance (e.g., maintaining base irradiance) can downplay the episodic but severe impacts of dense shade during critical phenological stages. Third, expert familiarity with adaptive agronomic strategies—such as row spacing adjustments or shade-tolerant cultivars—may lead to an underestimation of shading’s inherent risks. Finally, disciplinary backgrounds skewing toward engineering can bias attention toward energy-centric parameters, relegating shade management to a secondary concern.

6. Limitations and Scope of Future Work

Despite its strengths, the dual-matrix AHP framework employed in this study is subject to several limitations. First, even with the inclusion of 18 experts, the panel does not fully capture the breadth of Agri-PV practice worldwide, which spans from tropical smallholder farms to large-scale installations in temperate regions. This underrepresentation means that local policy incentives, crop varieties, and solar resource variability may not be adequately reflected in the current set of expert-derived weights. Second, the abstraction of expert judgments into Spearman-based influence matrices necessarily distances the analysis from the underlying biophysical mechanisms that govern crop–solar interactions. Without direct integration with process-based models of radiation interception, photosynthesis, and microclimate dynamics, the framework risks missing context-specific feedbacks that only field data or mechanistic simulations can reveal. Third, the analysis adopts a static, single-season perspective, limiting its applicability across the full cropping cycle. Seasonal fluctuations in sunlight, soil moisture, and plant phenology can significantly alter the trade-offs between energy production and crop yield, which the present model does not account for. Finally, although Return on Investment is included as a criterion, the study does not explore how evolving subsidy structures, tariff regimes, or financing arrangements might shift expert priorities under real-world economic conditions.

To address these gaps, several enhancements are envisioned. Expanding the expert panel to include agronomists, smallholder farmers, and utility planners from underrepresented regions would enable a more stratified AHP, allowing for explicit comparison between, for example, tropical and temperate perspectives or small- and large-scale deployments. Integrating AHP outputs with validated process-based simulators—such as coupling PVLIB’s solar forecasting with DSSAT or APSIM crop growth models—would ground expert weightings in quantifiable microclimate and agronomic dynamics, thereby linking judgment with measurement. Integrating PVLIB with DSSAT or APSIM to simulate how tilt, coverage, and height affect crop growth would supply data-driven benchmarks—allowing us to recalibrate AHP weights against mechanistic yield and microclimate feedbacks and thereby strengthen the framework’s biophysical grounding. Additionally, expanding the set of evaluation criteria to include broader sustainability dimensions—such as biodiversity impacts, labor and maintenance requirements, and life-cycle carbon footprints—would transform the framework into a comprehensive, triple-bottom-line planning tool for sustainable Agri-PV design.

7. Conclusions

In this work, we present a structured, dual-matrix decision framework that bridges expert judgment and actionable design guidance for agrivoltaic (Agri-PV) systems. By engaging 18 domain specialists in pairwise comparisons across nine critical parameters—spanning panel tilt, array density, elevation, light interception, and more, under two distinct objectives (maximizing energy yield and crop productivity), we derive two weighted-influence matrices and distil them into clear influence rankings. Our findings outline the primary leverage points for each goal: under the energy objective, panel tilt, ground coverage ratio, and panel height stand out as the most impactful design levers; under the crop objective, PAR utilization, yield stability, and elevation take precedence. Importantly, the framework surfaces both synergies, such as how increased elevation enhance both power generation and harvest performance and trade-offs, notably how denser photovoltaic coverage amplifies energy output while suppressing crop growth. By translating nuanced expert insights into transparent, quantitative maps of parameter importance, this framework empowers designers, farmers, and policymakers to tailor Agri-PV layouts to their specific priorities and local conditions. As the pace of Agri-PV deployment picks up worldwide, such a rigorously structured decision tool will be essential for navigating the food-energy nexus and driving integrated sustainability at scale. Practitioners can integrate our dual-matrix AHP framework into their planning workflows by entering site-specific inputs such as local solar profiles, crop light-requirements, and economic priorities—and instantly obtaining a ranked list of design configurations. This enables rapid comparison of panel tilt, height, coverage, and other parameters to identify the combinations that best balance energy production and crop performance. The transparent influence matrices guide users toward choices aligned with their operational goals, transforming complex trade-offs into actionable, context-sensitive specifications.