Integrating Solar Energy into German Vineyards: A Geospatial Framework for Identifying Agrivoltaic Potential

Abstract

This study quantifies where agrivoltaics can be feasibly implemented in German viticulture and presents a transparent GIS pre-screening framework for vineyards. Using plot-level cadastral data from Rhineland-Palatinate (54,078 ha planted area; covering 65% of Germany’s vineyard area), we integrate grid distance to the nearest connection point, slope, and planting year to identify technically and agronomically suitable sites. Under a 1000 m grid distance threshold, 44% of the planted area falls within potential connection range (15.6 GWp at 0.65 MWp per hectare). Across deployment scenarios, the feasible area spans 1.5–26.1% (0.52–9.17 GWp), with a balanced setting yielding 7.6% (2.68 GWp). We also conducted a one-at-a-time sensitivity analysis; the qualitative ranking of constraints is robust to a ±15% capacity-per-hectare band. The framework identifies plot-level feasibility under stated constraints and indicates where near-term implementation is most likely.

1. Introduction

Transforming the energy system to achieve climate neutrality is a key policy goal across the European Union [1]. To reach net-zero greenhouse gas emissions by 2050, the EU plans to significantly increase the share of renewable electricity [2]. This requires an expansion of renewable energy sources, especially wind and solar power. However, this may result in land-use conflicts—particularly when agricultural land is used for energy production—raising concerns about impacts on food security, natural habitats, and sustainable land management [3]. Although public support for renewable energy is generally high, local acceptance frequently diminishes when projects impact regions or culturally valued landscapes. Consequently, future solar expansion should balance technical feasibility with social and environmental considerations [4]. In light of these challenges, innovative land-use concepts that integrate energy production with existing land functions have emerged as a potential solution to address land-use conflicts [5]. Meanwhile, public acceptance is a key determinant of agrivoltaic deployment. Recent empirical evidence from Germany shows that agrivoltaics enjoys higher public acceptance than ground-mounted photovoltaics (PV) when agricultural production is maintained and landscape integration is addressed [5]. Perspectives from German nature conservation associations also indicate differentiated views on PV in agricultural landscapes and underscore the potential of dual-use systems to mitigate land-use conflicts compared with conventional ground-mounted PV [4]. Such perspectives underline the broader challenge of integrating renewable energy into agricultural landscapes in ways that maintain cultivation while addressing environmental and societal concerns. Agrivoltaic systems encompass a diverse range of technical designs, from elevated structures above crops to vertical bifacial installations integrated along crop rows. These systems vary in their degree of shading, energy yield, and structural complexity, creating trade-offs between crop productivity, installation costs, and energy generation efficiency [6]. By elevating or spatially arranging PV modules, agrivoltaic systems maintain agricultural activity while offering potential co-benefits such as reduced evapotranspiration and protection from hail or sunburn [7]. Agrivoltaics is thus increasingly viewed not only as a tool for farm diversification, but also as a means to enhance land-use efficiency and climate resilience [8]. Germany provides a promising context for assessing agrivoltaics in viticulture. With approximately 101,000 ha of vineyards, representing about 3% of the European vineyard surface, the country combines significant viticultural landscapes with ambitious renewable energy targets [9]. Recent policy initiatives, including the Federal PV Strategy, explicitly encourage the deployment of dual-use systems, proposing regulatory and financial incentives for demonstration projects [10]. These factors make Germany a suitable test case for exploring the feasibility of agrivoltaics in viticulture.

Within this context, viticulture has emerged as a particularly promising application for agrivoltaics. The impacts of climate change on viticulture and possible adaptation strategies have been the subject of extensive discussion in recent scientific literature [11]. Climate change is expected to affect grape composition and wine quality, with growing risks of drought, sunburn, and low acidity levels of the must [12,13]. Agrivoltaics could offer a promising solution to buffer climatic extremes and serve additionally as a protective installation against hail [14,15,16]. However, current scientific evidence on viticultural performance under elevated agrivoltaic systems remains scarce [17], and despite some promising benefits, the implementation of agrivoltaics in viticulture remains limited in scale [18]. One key challenge is identifying use cases and sites that meet cultivation and profitability requirements [19]. Additionally, factors such as the slope gradient and varietal sensitivity to shading are critical for assessing the suitability of agrivoltaics in vineyards. Moreover, technical constraints—such as proximity to electricity infrastructure or topographic limitations—must be considered to assess feasibility [19]. Several previous studies have focused on assessing the spatial potential of agrivoltaics on a national or regional level [20,21,22]. Current literature has also incorporated infrastructural factors such as grid connection availability at a national scale, indicating a regulatory potential of between 5600 and 7900 GWp in Germany [23]. In general, spatial suitability analyses for renewable energy deployment rely on different approaches, depending on the purpose of the investigation. Deterministic GIS-overlay methods are widely used for transparent and replicable pre-screening, while multi-criteria decision-making analyses (MCDA) add weighting schemes to prioritize sites; a recent study introduced an MCDA framework for agrivoltaics [23]. Such frameworks offer valuable prioritization insights but depend on normative assumptions and expert judgment.

Existing national assessments of agrivoltaics largely quantify theoretical potentials across crops and scales, but offer limited guidance on how dual-use systems would be integrated into specific production systems. German viticulture is a valuable case study for integrating solar electricity into productive landscapes, promoting climate-neutral and resilient agricultural systems: it faces increasing heat, drought, and weather extremes while relying on mechanized operations and stable vineyard structures. In this setting, agrivoltaics is considered an adaptation option to influence the microclimate without interrupting cultivation. However, what is missing is a plot-level, crop-specific pre-screening that makes interconnection feasibility explicit and combines it with agronomic constraints.

To address this gap, we present the first vineyard-specific, plot-level geospatial pre-screening for agrivoltaics in Germany. The framework applies a deterministic overlay that integrates agronomic and infrastructural constraints into a transparent, reproducible analysis. The intent is not to optimize economics or model crop response, but to establish a decision-oriented baseline that narrows large areas to implementable options, thereby supporting climate-resilient deployment strategies in viticulture. Accordingly, the study pursues two objectives: first, to quantify the share of planted vineyard area in Rhineland-Palatinate that remains suitable for agrivoltaics once agronomic and infrastructural constraints are applied; and second, to examine how policy-relevant thresholds translate into distinct suitability scenarios and sensitivity ranges. In doing so, by translating generic potentials into plot-resolved, infrastructure-aware options, the study helps to progress from the concept stage and initial pilot sites to broader implementation in German viticulture.

2. Materials and Methods

2.1. The Study Area

The study area covers all vineyard plots within the federal state of Rhineland-Palatinate, located in western Germany between approximately 49.0° and 51° N latitude and 6.0° to 8.5° E longitude. With approximately 65,000 hectares (ha) of vineyards, Rhineland-Palatinate accounts for over 65% of Germany’s total winegrowing area and includes the wine regions Rheinhessen, Palatinate, Moselle, Nahe, Ahr, and Middle Rhine (ordered by descending area size) [24]. Vineyards account for approximately 9% of the utilized agricultural area in Rhineland-Palatinate, providing a meaningful test case for national agrivoltaic strategies [25]. Vineyards in Rhineland-Palatinate are heterogeneous in terms of training systems, planting densities, and row orientations, all of which may influence the integration of agrivoltaics. The predominant training system in German viticulture is the vertical shoot positioning trellis using a wire frame, which facilitates mechanization and represents the current standard in vineyard management. Agrivoltaic designs typically focus on this system, as it provides a practical framework for integrating elevated PV structures. However, spatially explicit datasets for these parameters are not available at the regional scale, and their inclusion would therefore require strong assumptions with limited reproducibility. For this reason, our analysis focuses on constraints that can be derived from harmonized data sources, such as slope, planting year, and grid proximity.

Viticulture in Rhineland-Palatinate is characterized by a wide range of site conditions, including steep slopes along river valleys, such as the Moselle and Rhine, as well as flatter regions such as the Pfalz region. The spatial distribution of vineyard areas in Rhineland-Palatinate is shown in Figure 1. Vineyards shape the region’s landscape, and slopes can exceed 30% in traditional terraced winegrowing regions. This topographic diversity is a key factor in the spatial assessment of agrivoltaic systems, as slope inclination influences both the technical feasibility of PV installations and their potential effects on vine growth and management practices. The choice of trellis system, grape variety, and planting density varies substantially across the region, reflecting both historical practices and site-specific requirements. While these parameters are not directly modeled in the spatial analysis, they underline the heterogeneity of vineyard systems in Rhineland-Palatinate and may inform future site-specific evaluations of agrivoltaic integration.

Average annual global horizontal irradiation (GHI) in Rhineland-Palatinate ranges from approximately 1070 to 1200 kWh/m², placing the region among the higher irradiance zones in Germany [26]. Rhineland-Palatinate itself is characterized by a strongly differentiated regional climate. Rheinhessen, the German Wine Route region (Deutsche Weinstraße), and the Moselle Valley are among the warmest winegrowing areas in Germany, located in the southwestern part of the country near the Rhine River. In contrast, the nearby low mountain ranges of the Hunsrück, Eifel, and Westerwald experience a much harsher climate with lower temperatures and higher precipitation [27]. Rheinhessen is also known as one of the driest regions in Germany, making it particularly suitable for viticulture, but potentially also more vulnerable to the impacts of climate change, such as prolonged droughts and heat stress. Rhineland-Palatinate was chosen as the study region because it contains the majority of Germany’s vineyard area and encompasses a broad spectrum of cultivation settings, from steep-slope viticulture to mechanized flatland sites. This diversity makes the region a meaningful case study for exploring agrivoltaic suitability in viticulture.

2.2. The Methodological Framework

The assessment of agrivoltaic potential in German viticulture is based on a geospatial evaluation framework that integrates plot-level vineyard data with publicly accessible electricity infrastructure data. The methodological focus lies on identifying grid-accessible areas that are suitable for the deployment of agrivoltaic systems. Spatial modeling and data preprocessing were carried out using QGIS (version 3.34.12) in combination with Python (version 3.12.7) [28]. All processing steps and algorithm identifiers are listed in

Table A1. Documentation of the reproducible main steps (geometry validation, grid access points, reprojection, distance-based plot classification, Natura 2000 exclusion).

Step	What Was Done	Input Data	Key Parameter	Output	Purpose
1	Project setup and coordinate system	—	Target CRS for all distance operations: ETRS_1989_UTM_Zone_32N	Common metric coordinate basis	Ensures all buffers/proximity calculations are in meters (consistent, comparable distances).
2	Geometry validity check	Rhineland-Palatinate boundary (WFS vermkv:landesgrenze_rlp), Natura 2000 polygons (Natura2000_end2023.gpkg)	qgis:checkvalidity (default settings)	Valid (cleaned) geometries	Reduces topology errors and prevents failures in overlay/clip operations.
3	Clip Natura 2000 to study area	Natura 2000 polygons + RLP boundary	native:clip/native:intersection	Natura 2000 polygons restricted to RLP	Guarantees protected-area layers match the study extent; avoids cross-border artefacts.
4	Consolidate potential grid-access points	powergrid_2024-11-25.gpkg point layers: substation, switch, minor_distribution, distribution, connection, generator_renewable (filtered to source = ‘wind’)	Sequence of native:union operations	Consolidated multipoint dataset of potential grid-access locations	Unifies all relevant grid POIs into a single, robust candidate set for proximity analysis.
5	Reproject consolidated points to metric CRS	Consolidated grid-access points (from Step 4)	native:reprojectlayer: ETRS_1989_UTM_Zone_32N	Grid-access points in uniform projected system	Required so distance thresholds are interpreted in meters.
6	Classify vineyard plots by proximity to grid-access points	Vineyard plots (aggregated cadastral layer) + consolidated grid-access points	native:selectwithindistance; thresholds used in reporting: 100 m, 500 m, 1000 m	Selection per distance band	Produces the proximity classes cited in the main text (e.g., the 1 km figure).
7	Flag/exclude plots with protected-area overlap	Vineyard plots + Natura 2000 (RLP-clipped)	native:selectbylocation; predicates incl. intersects/within/touches	Plots flagged or excluded if intersecting Natura 2000	Reflects ecological/regulatory constraints in the site selection and sensitivity analysis.

. All datasets were aligned using the ETRS_1989_UTM_Zone_32N as a uniform projected coordinate system.

The primary input dataset consists of official viticultural cadastral data provided by the Agricultural Chamber of Rhineland-Palatinate [29]. The dataset comprises spatially explicit vineyard polygons, representing the actual land use for wine production in Rhineland-Palatinate. These plots serve as the geospatial foundation for all subsequent analyses.

In the first stage of the framework, shown in Figure 2, these vineyard plots were intersected with a community-curated electricity grid map of Germany [30]. This national infrastructure dataset includes over 1.29 million georeferenced points, representing various components of the electricity distribution and transmission network. This dataset was filtered and classified to identify relevant grid connection features, such as substations, distribution cabinets, and grid connection points close to wind turbines. These points served as potential grid access locations for agrivoltaic deployment and were retained for further analysis. We then calculated the minimum Euclidean distances in meters to the nearest grid connection point and divided the plots into distance bands.

The dataset integrates official cadastral and viticultural records with topographic and infrastructural information. The result is a customizable analysis dependent on the underlying research hypotheses. The criteria shown in

Table 1. Overview of selected input parameters used to evaluate the suitability of vineyard plots for agrivoltaics.

Parameter	Unit/Scale	Relevance	Data Source
Slope	%	Limits technical feasibility, affects machinery use	Cadastral data
Plot size	ha	Total plot size, including landscape elements	Cadastral data
Planted area	ha	Planted area that is used for viticulture	Cadastral data
Grid connection proximity	Distance (m)	Determines connection cost and feasibility	Open Street Map (2024-11-25)
Vine age	Year	Indicates replanting cycles and investment timing	Cadastral data
Land use	Land use entry	Active viticultural management	Cadastral data
Restricted area (Natura 2000)	ha	Excluded due to environmental protection	European Environment Agency
Grape variety	Variety name	Determines shading tolerance and suitability	Cadastral data
Solar irradiance (GHI)	kWh/m2/year	Indicates PV energy potential	Global Solar Atlas (2.12)

are particularly relevant for determining the practical and viticultural suitability of agrivoltaics in vineyards. To ensure methodological transparency, key constraints—such as slope gradient and planting year—were operationalized as discrete filters in the spatial framework (see Table 1 for parameter definitions and Tables 2–6 for their application in the results). These include the following:

• Planted area: The analysis uses the cadastral vineyard area to assess agrivoltaic potential. This reflects current regulatory frameworks, which require that agricultural productivity (a minimum required yield per hectare) must be maintained for land to be recognized as ‘agrivoltaic’. Installing PV modules over the whole property area would compromise the agricultural function of the site and violate the intended crop-integrated character of agrivoltaic systems. ‘Planted area’ denotes the actively cultivated vineyard area within a cadastral plot.
• Slope: Vineyards in Germany are commonly classified according to their slope gradient [31]. German law distinguishes between hillside locations and flat sites. Steep-slope vineyards are defined as those with a slope of more than 30%. This classification is widely used in viticultural practice, as slope has direct implications for vineyard management, mechanization, microclimate, and, in this context, the technical feasibility of elevated agrivoltaic systems. Due to technical considerations, particularly the increasing construction complexity on steeper terrain, we differentiated vineyard plots by slope gradient into three classes: 0–5% (low), 5–15% (moderate), and >15% (high). Slopes above 15% typically entail significant additional construction effort and may require adapted mounting structures or specialized installation techniques [20].
• Plot size: Plot size influences both grid integration requirements and economic viability, as larger systems may benefit from economies of scale but place greater demands on infrastructure. Following the approach proposed by Rösch et al. (2025), systems are differentiated by a threshold of 2.5 hectares, as German building law allows simplified approval procedures for agrivoltaic installations below this size [20,21].
• Restricted area: As part of the spatial filtering process, areas designated under the Natura 2000 network were excluded from the analysis. Although viticulture exists within some Natura 2000 sites, the installation of agrivoltaic systems in these protected areas is highly unlikely due to regulatory restrictions and potential conflicts with conservation objectives [22]. Construction may be legally permitted in individual cases under building law. However, new agrivoltaic installations in these areas are excluded from public funding under the German Renewable Energy Sources Act (EEG), § 37 (3), further limiting their practical feasibility [32].
• Vine age: This parameter enables the identification of either young vineyards that may require adapted agrivoltaic designs, or older vineyards approaching the end of their productive lifecycle, which may be more compatible with structural retrofitting. To account for the age of the vineyard plots, the planting year was used to calculate the vine age as of the 2025 growing season. Based on viticultural lifecycle stages and practical decision-making horizons, vine age was grouped into five discrete classes: 0–5 years, 6–10 years, 11–15 years, 16–20 years, and more than 20 years. This categorization serves as a proxy and addresses an assessment of potential age-related constraints or opportunities for agrivoltaics implementation.
• Grape variety: Based on ongoing field trials, selected grape varieties can be filtered. For this study, the most common grape varieties in Germany are categorized into white and red grape varieties. Given the significant diversity of grape varieties cultivated in Germany, the 12 most commonly planted varieties were selected. In our framework, grape variety is treated as a descriptive variable that can be filtered to reflect the distribution of grape varieties. However, this does not imply any inference about varietal shade tolerance, which is beyond the scope of the present analysis.

Through the integration of spatial grid proximity and vineyard-level characteristics, the model supports targeted identification of sites with high agrivoltaic potential. The framework is modular and can be refined or regionally adapted. To represent uncertainty in agronomic and infrastructural factors, we defined a set of spatial scenarios that combine slope, planting year, grape variety, and grid-distance filters to reflect restrictive, balanced, and inclusive planning assumptions. In addition, we conducted a one-at-a-time sensitivity analysis (OAT) on key parameters (slope, planting year, grid distance) to assess the robustness of results to individual assumptions. The scenario settings and one-at-a-time sensitivity are used to analyze threshold effects and trade-offs, eliminating the need for normative weights. On this basis, a differentiated picture of agrivoltaic potential in viticulture emerges. The following section presents the results of the study and reports scenario outcomes and sensitivity results, highlighting areas that remain technically and agronomically feasible under varying grid-access thresholds.

3. Results

The spatial assessment of agrivoltaic suitability in German viticulture highlights the strong influence of site-specific agricultural characteristics and proximity to grid infrastructure. Based on cadastral vineyard data for Rhineland-Palatinate, approximately 65,000 ha are classified as vineyard area. However, after excluding non-cultivated areas, the actively cultivated vineyard area is reduced to 54,078 ha, which serves as the basis for further analysis. This area will be referred to as the ‘planted area’ hereafter. To avoid overestimating potentials that resemble pseudo-agrivoltaic or ground-mounted PV, the analysis is restricted to the planted vineyard area. Assessing the total designated area would require a structural comparison with actual planting patterns, which is beyond the scope of this study.

3.1. Integration of Grid Infrastructure Data

One of the main limiting factors for the implementation of agrivoltaic systems is the availability of nearby grid connection points. Figure 3 illustrates the spatial distribution of vineyard plots in relation to potential grid infrastructure. To assess the influence of grid proximity on spatial suitability, a stepwise filtering was applied using maximum distance thresholds of ≤1500 m, ≤1000 m, ≤750 m, ≤500 m, ≤250 m, and ≤100 m from the nearest grid infrastructure [23]. The analysis for the total planted area shows that within a 250 m radius of a grid connection point, we computed 2155 ha of vineyard area that are technically favorable; at 500 m distance, the potential area increases to 7578 ha, and at 1000 m, the area further expands to 24,031 ha, although the cost of grid integration increases considerably.

These findings emphasize the importance of existing infrastructure for potential analysis of agrivoltaics, a factor that is often overlooked in broader potential assessments. Rather than aiming to categorize the overall area potential by connection distance, the use of distance thresholds serves to highlight those plots with the most favorable infrastructural conditions—thereby supporting the targeted identification of highly suitable locations. To estimate the installable capacity, literature-based reference values were used and cross-validated with empirical data from the agrivoltaics research facility in Geisenheim. The actual energy yield per hectare depends on various factors, including the type of PV module, the system design, and site-specific characteristics such as annual solar irradiation. Importantly, the system design itself is influenced by the cultivated crop. Given the crop-specific system design and empirical values, an average installable capacity of 0.65 MWp/ha is assumed for the present calculation [23].

The stepwise application of distance-to-grid thresholds reveals a reduction in suitable vineyard area for agrivoltaic deployment, as shown in Table 2. While the total planted area amounts to approximately 54,000 ha, only 44% remains within 1000 m of a potential grid connection point, corresponding to an installable capacity of about 15.6 GWp. Our findings should be read against broader land-use assessments: Although 44% is numerically close to the lower bound reported in a previous study (46–75% of technical potential remaining after soft restrictions), the metrics are not directly comparable. Recent work quantifies the regulatory downscaling of national, cross-crop technical potential, whereas our 44% measures the grid-proximate share of planted vineyard area before any agronomic screening [23]. Studies that omit explicit grid proximity consequently report larger potentials because the interconnection constraint is not binding in their geospatial setup [20]. In the following sections, we introduce crop-specific filters (slope, vine age, grape variety) that translate this infrastructure screening into viticulture-specific feasibility. Their quantitative effects are reported in Section 3.2.

The computed potential decreases with stricter thresholds, leaving only about 1% of the area within 100 m of the grid, and reducing the installable capacity to just 0.26 GWp. The results highlight grid proximity as a major limiting factor for spatial feasibility, particularly at shorter connection distances.

Table 2. Vineyard area and agrivoltaic potential at varying grid-distance thresholds.

Distance to Grid Connection Point	Planted Area	Share of Total Planted Area (%)	Installable Capacity (GWp)
All	54,078.0	100%	37.85
≤1500 m grid distance	37,964.7	70%	24.68
≤1000 m grid distance	24,031.5	44%	15.62
≤750 m grid distance	15,416.7	29%	10.02
≤500 m grid distance	7578.4	14%	4.93
≤250 m grid distance	2155.3	4%	1.40
≤100 m grid distance	405.1	1%	0.26

Table 2. Vineyard area and agrivoltaic potential at varying grid-distance thresholds.

Distance to Grid Connection Point	Planted Area	Share of Total Planted Area (%)	Installable Capacity (GWp)
All	54,078.0	100%	37.85
≤1500 m grid distance	37,964.7	70%	24.68
≤1000 m grid distance	24,031.5	44%	15.62
≤750 m grid distance	15,416.7	29%	10.02
≤500 m grid distance	7578.4	14%	4.93
≤250 m grid distance	2155.3	4%	1.40
≤100 m grid distance	405.1	1%	0.26

3.2. Filtering by Agronomic Criteria

Next, slope categories were introduced to examine the calculated area potential in more detail. As previously noted, slope presents technical challenges for both the construction of the agrivoltaic structure and the mechanization of viticulture.

Table 3. Stepwise reduction of suitable vineyard area and corresponding installable agrivoltaic capacity (GWp) based on distance-to-grid filters. Plots lacking slope information are classified as NA (not available).

Slope (%)	Planted Area (ha)
0–5%	19,352.7
5–10%	15,705.2
10–15%	7874.3
15–20%	3849.4
more than 20%	6135.6
NA	1160.8
Overall	54,078.0

provides a breakdown of the remaining vineyard area by slope classes. The majority of the area is located on gentle slopes (0–10%), which are generally more compatible with mechanized viticulture and the installation of elevated agrivoltaic systems. Steeper slopes contribute only a minor share to the total potential.

The next analytical stage applied plot size as a filter criterion, as shown in

Table 4. Distribution of vineyard plots by plot size and slope class. Plot sizes are categorized as smaller than 2.5 ha and equal to or larger than 2.5 ha. Slope classes include 0–5%, 5–10%, 10–15%, 15–20%, more than 20%, and plots without slope data (NA). The totals in each column represent the sum of all slope classes per category.

Slope (%)	Plot Size < 2.5 ha	Plot Size ≥ 2.5 ha	Total
0–5%	19,235.99	116.7	19,352.7
5–10%	15,652.3	52.9	15,705.2
10–15%	7868.7	5.6	7874.3
15–20%	3846.8	2.6	3849.4
more than 20%	6113.2	22.4	6135.6
NA	1153.1	7.7	1160.8
Total	53,870.1	207.9	54,078.0

. In order to assess planning and regulatory implications, vineyard plots were categorized by size, using a threshold of 2.5 hectares. This distinction is relevant as German building law permits simplified approval procedures for activities below this threshold. The distribution of plot sizes reveals that the vast majority of vineyard area is composed of plots smaller than 2.5 ha, while only a minor share exceeds this limit.

The grape varieties were selected from the dataset according to the size of their cultivation area in order to identify the top 12 grape varieties in Germany. In

Table 5. Distribution of vineyard area by grape variety category.

Grape Variety Category	Planted Area (ha)	Share of Total Planted Area (%)
White	33,261.5	62%
Red	8451.6	16%
Other	12,364.9	23%
Total	54,078.0	100%

, a distinction was made between white and red varieties. The most commonly cultivated white grape varieties are White Riesling, Ruländer, Grüner Silvaner, Müller-Thurgau, White Burgundy, Chardonnay, Kerner, Bacchus, and Scheurebe. The red varieties are dominated by Dornfelder, Portugieser, and Blauer Spätburgunder. This categorization forms the basis for further evaluations, particularly with regard to suitability for agrivoltaics.

Table 6. Distribution of vineyard area by planting year category. Plots lacking vine age information are classified as NA (not available).

Year Category	Planted Area (ha)	Share of Total Planted Area (%)
0–5 years	7279.6	13%
6–10 years	6964.5	13%
11–15 years	6608.7	12%
16–20 years	6628.9	12%
more than 20 years	24,851.1	46%
NA	1755.3	3%
Overall	54,078.0	100%

shows the distribution of vineyard area by planting year category. Nearly half of the total planted area (48%) consists of vineyards older than 20 years, while only 14% was planted within the past five years. The remaining area is relatively evenly distributed among the intermediate age categories. This age structure provides important context for assessing the technical and temporal suitability of vineyards for agrivoltaic deployment. Linking the installation of an agrivoltaic system to the replanting of a vineyard is supported by several practical considerations, particularly in terms of structural integration.

When agronomic factors—such as planting year and slope—and infrastructural constraints like grid proximity are combined, the cumulative effect leads to a substantial reduction in the theoretical agrivoltaic potential. In the analysis applying a 1000 m distance threshold to the nearest grid connection point, only 44% of the total vineyard area remains technically and infrastructurally feasible for dual land use. To further explore the spatial suitability of vineyards for agrivoltaic deployment, four scenarios were developed, each based on a combination of relevant biophysical and infrastructural criteria (see

Table 7. Vineyard area and installable agrivoltaic capacity under different suitability scenarios.

Scenario	Planted Area (ha)	Share of Total Area (%)	Installable Capacity (GWp) 1
High Priority	844.2	1.6%	0.55
Balanced Feasibility	4126.9	7.6%	2.68
Extended Potential	14,111.1	26.1%	9.17
In-Field Integration	792.4	1.5%	0.52

¹ Average installable capacity is calculated based on a factor of 0.65 MWp per hectare.

). These include slope, planting year, grape variety, and distance to the nearest grid connection point. In all scenarios, vineyards located in Natura 2000 protected areas were excluded. In Rhineland-Palatinate, excluding Natura 2000 sites led to a reduction in technically suitable vineyard area by 1127.5 ha (−7.4%) under the extended scenario with a 1000 m grid-distance threshold. The scenarios range from restrictive to inclusive, reflecting different strategic perspectives, from high-efficiency implementation to integrative land use.

The ‘High Priority’ scenario employs the most stringent filter set, selecting vineyard plots with slopes of 0–10%, a planting age exceeding 15 years, and a grid connection within 250 m. Grape variety is not restricted, since replanting with a suitable shade-tolerant grape variety is highly likely. The ‘Balanced Feasibility’ scenario expands the slope tolerance to 15%, reduces the minimum planting age to over 10 years, and increases the allowable grid distance to 500 m. This combination reflects a more practical compromise between technical feasibility and land availability. The ‘Extended Potential’ scenario is the most inclusive, allowing slopes up to 20%, including all vineyards older than 10 years, and extending the maximum grid distance to 1000 m. While this setting increases area potential, it includes locations with lower technical or economic viability.

In contrast, the ‘In-Field Integration Scenario’ targets vineyard plots where agrivoltaics could be introduced without requiring the removal and replanting of the vineyard. Therefore, only plots with young vines (<10 years) are selected, under the assumption that a recent planting allows structural adaptation to elevated PV systems. The scenario further restricts the analysis to gentle slopes (0–10%), white grape varieties with a relevant share in the German vine variety profile, and a maximum distance of 500 m to the grid. Only white grape varieties are included, based on the hypothesis that, under generally moderate climatic conditions, shading effects from agrivoltaic structures are more likely to cause undesired ripening delays in red varieties than in white ones. This scenario illustrates the potential for harmonizing existing vineyards with energy infrastructure.

The results indicate strong sensitivity of agrivoltaic potential to scenario criteria. While the ‘Extended Potential’ scenario represents a substantial area (28%), the restrictive scenarios (‘High Priority’ and ‘In-Field Integration’) demonstrate that very few plots (<2%) simultaneously fulfill multiple suitability conditions. To separately assess the influence of individual assumptions behind these scenario differences, we conducted a one-at-a-time (OAT) sensitivity analysis relative to a neutral baseline (≤15% slope; ≤500 m grid; no age/variety filters), which is chosen to balance technical constructability and typical grid accessibility while avoiding unvalidated agronomic assumptions (see

Table 8. Sensitivity analysis of vineyard suitability under different scenarios and one-at-a-time (OAT) parameter variations. The table reports vineyard area (ha), the photovoltaic capacity potential at 0.65 MWp ha⁻¹, as well as a sensitivity range of this parameter of 15%.

Scenario	Planted Area (ha)	Installable Capacity (GWp, 0.65 Factor)	Capacity Range (GWp, ±15%)
Baseline	5836.26	3.79	3.22–4.36
OAT—Age ≥ 10 years	4126.92	2.68	2.28–3.08
OAT—Age ≥ 15 years	3388.86	2.20	1.87–2.53
OAT—Grid ≤ 250 m	1774.7	1.15	0.98–1.33
OAT—Grid ≤ 1000 m	18,404.83	11.96	10.17–13.76
OAT—Variety (White)	2796.71	1.82	1.55–2.09
High Priority	844.2	0.55	0.47–0.63
Balanced Feasibility	4126.9	2.68	2.28–3.08
Extended Potential	14,111.1	9.17	7.80–10.55
In-Field Integration	792.4	0.52	0.44–0.59

).

The grid-distance threshold is the dominant lever: expanding from ≤500 m to ≤1000 m increases the feasible planted area from 10.79% to 34.03% (capacity increasing from 3.79 to 11.96 GWp), whereas tightening to ≤250 m reduces it to 3.28% (1.15 GWp). Vine age exerts a marked but smaller effect: ≥10 years lowers feasibility to 7.63% (2.68 GWp) and ≥15 years to 6.27% (2.20 GWp). Capacity bands (±15%) do not change these rankings (e.g., 0.98–1.33 GWp at ≤250 m in comparison to 10.17–13.76 GWp at ≤1000 m), indicating robustness to plausible variation in the capacity-per-hectare assumption. Taken together, these results imply that grid access is the primary regional constraint (favoring corridors near existing connection points), vine renewal cycles could represent a material factor shaping the near-term retrofit potential of vineyards, and varietal filtering substantially narrows the feasible area when applied conservatively. Accordingly, these results support a viticulture-specific siting approach, in which grid proximity, slope, and vine age timing are explicitly operationalized into plot-level feasibility assessments.

4. Discussion

4.1. Discussion of Results in Relation to Existing Research

To further contextualize these findings and illustrate the implications of varying assumptions, four distinct suitability scenarios were developed. These scenarios differ in their constraints regarding slope, grid proximity, vine age, and grape variety, thus providing strategic perspectives for targeted implementation of agrivoltaics in vineyards. The results fulfill the study’s objectives by quantifying the share of feasible vineyard plots under crop- and infrastructure-specific constraints and by showing how alternative parameter thresholds reshape suitability ranges into relevant insights for viticulture. The scenarios presented demonstrate how sensitive the agrivoltaic potential of vineyards is to varying technical, agronomic, and infrastructural constraints. The findings also indicate that national-scale agrivoltaic models tend to overestimate potentials. Crop-specific, tailored assessments are required that account for structural and temporal constraints rather than relying on generalized land assumptions. The restrictive High Priority scenario identifies only about 1.6% of vineyard area as suitable, representing optimal pilot sites with gentle slopes, close grid proximity, and older vines ready for renewal. In contrast, the broadest Extended Potential scenario includes around 26% of vineyard area, but incorporates challenging conditions like steeper slopes and higher grid-connection costs, potentially reducing practical viability. The Balanced Feasibility scenario (7.6%) provides a strategic compromise with moderate site constraints. Lastly, the specialized In-Field Integration scenario (1.5%) highlights opportunities in recently planted vineyards with gentle slopes and specific grape varieties, enabling agrivoltaic integration without vineyard replanting.

The findings of this study show that the spatial distribution of technically feasible plots is highly uneven (see Figure 3). Suitable plots tend to cluster in flatter regions with existing infrastructure—often near already developed energy locations such as wind power sites. This spatial pattern suggests that planning procedures for agrivoltaics in viticulture should adopt targeted, location-specific strategies, making use of existing grid corridors and suitable topographic conditions. By applying spatial filters step by step—slope, plot size, and proximity to grid infrastructure—it becomes clear that only a small subset of vineyard plots meets all relevant suitability criteria simultaneously. The theoretical area potential is substantially reduced by the intersection of agricultural criteria and infrastructural constraints.

Grid access, in particular, emerges as a critical limiting factor [33]. Despite the overall viticultural area in Rhineland-Palatinate, the results show that only a small fraction of plots lie within 100 to 500 m of potential grid connection points. Given the cost and challenges associated with grid connection—ranging in Germany from EUR 80 to EUR 260 per linear meter—this constraint can have an important impact on the overall economic feasibility of the systems [34]. The process of connecting renewable energy projects to the grid is becoming a critical limiting factor in the expansion of renewable energy [35,36]. Strub et al. (2024) argue that grid connection costs should be considered in economic evaluations of agrivoltaic systems, as these costs significantly impact the feasibility of specific business models [19]. Even if a low value were applied to the cost per meter, a site around 1000 m away from a feed-in point could be economically unviable without further grid expansion. This makes areas near transformer stations and wind turbines attractive for grid connection [37]. However, further investigation is required to validate these potential feed-in points technically.

Slope is a decisive factor in vineyard suitability for agrivoltaics. While steep slopes—often associated with high-quality, south-facing vineyard sites—may benefit from agrivoltaics as a climate adaptation strategy, they also pose significant limitations in terms of mechanizability and technical feasibility [20,23,38,39]. The implementation of agrivoltaics on such terrain is particularly challenging, as the spatial configuration of existing trellis systems often conflicts with the structural requirements of elevated PV installations. These incompatibilities can restrict the positioning of support columns and the alignment of PV rows, which in turn further limits the potential for mechanized operations under the solar infrastructure. In addition to topographic constraints, the age of the vineyard also plays a key role in determining the feasibility of agrivoltaics. High-mounted systems are typically installed in conjunction with new vineyard establishments, allowing for coordinated planning between trellis and PV infrastructure. In such cases, the investment time horizon for the PV system (typically 20–25 years) can align well with the productive lifespan of the vineyard, offering favorable conditions for long-term integration. In contrast, retrofitting existing vineyards—especially on sloped terrain—is often economically and structurally unfeasible. Young vineyards (<5 years), although biologically suitable, are economically impractical to replant solely for agrivoltaics. Very old vineyards (>20 years) may be near replacement, but often do not justify the long-term infrastructure investment. Mid-aged vineyards (6–15 years) typically lack both structural flexibility and an immediate replanting prospect, making them less favorable for agrivoltaic deployment.

Slope and vine age thus represent critical spatial and temporal constraints in agrivoltaics planning and should be considered jointly in suitability assessments. This confirms the central hypothesis of this study: that multiple site-specific factors must be integrated to realistically evaluate agrivoltaic potential in viticulture. While recent national-scale studies have begun to incorporate parameters such as legal restrictions and proximity to grid infrastructure [20,23], the present approach highlights the added value of a crop-specific perspective. Such crop-specific analyses—particularly in viticulture—enable the integration of cultivation-related variables such as slope variability, mechanization constraints, or varietal suitability. These parameters are essential for assessing not only technical feasibility, but also the potential role of agrivoltaics as a climate adaptation strategy. The present study illustrates how such factors can be operationalized in a spatial framework, thereby complementing broader national models with crop-specific relevance.

4.2. Critical Reflection on Methodological Robustness

While the study demonstrates a refined plot-level approach for identifying suitable areas for agrivoltaics in viticulture, several limitations affect the robustness and generalizability of the results. A key constraint lies in the quality and completeness of input data, particularly with respect to grid infrastructure and terrain modeling. The proximity analysis to potential grid connection points was based on an open, community-curated infrastructure dataset [30]. Although this dataset includes a wide range of infrastructure types—including substations, transformer stations, and generation plants—it does not provide official confirmation of connectivity or capacity. As grid data information is usually held by distribution system operators and is not publicly accessible, the derived proximity values should be considered approximations that may not always reflect actual technical feasibility. This introduces a level of uncertainty regarding the actual feasibility and cost of grid connection. The use of such proxy data, however, reflects a growing practice in spatial energy planning and enables early-stage analysis in the absence of verified alternatives [23,40]. To improve data reliability, future analyses—especially at the regional or municipal level—should involve grid operators directly to validate connection points and assess grid capacity. This would allow for more precise techno-economic modeling and increase the practical relevance of site suitability assessments.

In terms of topographic analysis, the study used a high-resolution digital elevation model for the state of Rhineland-Palatinate [29]. Potentially, a finer spatial resolution of this dataset may explain the distinct slope variability observed across vineyard plots. In contrast, many national-scale assessments rely on different raster data, which tend to smooth local terrain features and potentially underestimate actual slope conditions in hillside vineyards. Consequently, the higher sensitivity in slope classification observed here reflects both the specific topography of the region and the resolution of the underlying data. This highlights the importance of data resolution in spatial agrivoltaics modeling, particularly when evaluating complex, heterogeneous landscapes such as winegrowing areas. While our regional screening excludes Natura 2000 areas, we did not apply generic buffer zones because exclusion criteria are habitat- and site-specific and determined by the authorities on a case-by-case basis. Consequently, the estimates of suitability produced are conservative for protected areas, but may overstate feasibility in nearby transition zones. In cases where jurisdiction-specific guidance is available, the integration of parametric buffers into future iterations of the framework may be considered.

Beyond terrain and infrastructure, the influence of shading on viticulture needs to be better understood [41]. Nevertheless, our approach was able to consider plot-specific grape variety information. However, the suitability of a grape variety for agrivoltaics is multifactorial and should initially be discussed more broadly as part of a separate methodological approach with transparent scoring. While parameters such as slope and plot size serve as indicators for mechanization and accessibility, the physiological and enological effects of altered microclimates under PV structures remain poorly understood. These quality-related aspects are particularly relevant in viticulture, where market value is strongly tied to a set of different customer preferences [42]. Their exclusion from the spatial model reflects a general limitation of current agrivoltaics assessments and points to the need for further interdisciplinary integration between geospatial analysis and crop physiology. Finally, the model does not simulate energy yield, shading geometry, or investment returns. As such, it should be viewed as a spatial pre-screening approach—identifying technically and infrastructurally suitable sites—but not as a substitute for detailed project-level design or economic viability analysis. The strength of the approach lies in its ability to narrow down large agricultural areas to focused subspaces for more in-depth assessment.

4.3. Practical Implications

The results of this study provide valuable insights for relevant stakeholders seeking to assess the feasibility of agrivoltaic systems in viticulture. By identifying technically and infrastructurally suitable areas at the plot level, the spatial analysis supports a more targeted site selection process, which is especially important given the tight spatial, legal, and operational constraints in Rhineland-Palatinate. The strong influence of grid accessibility on suitability outcomes underscores the need to integrate connection cost considerations into early planning stages. Considering the current grid connection costs, which depend on terrain, project size, and technical conditions, proximity to existing infrastructure may play a decisive economic role. Consequently, the observed clustering of suitable plots in flatter areas with established grid access opens up the possibility of regional corridor planning for agrivoltaics in viticulture. Rather than attempting uniform implementation across entire wine regions, planning efforts may benefit from focusing on pre-qualified sub-areas with high spatial coherence and cost-efficiency potential. In line with broader aims to combine low-carbon energy supply with robust agricultural production, a prioritized expansion of further pilot sites within these corridors can accelerate learning under real operating conditions. Such pilots should apply consistent siting criteria and transparent evaluation to generate evidence on operational performance and vineyard management, thereby supporting scalable decision-making that advances both mitigation and adaptation goals.

From the perspective of vineyard owners, the results can inform long-term strategic decisions—such as aligning the installation of PV structures with vineyard planting cycles, which often occur after 20–35 years in fertile soils [43]. As previous research suggests (e.g., Strub et al., 2024), co-timed replanting and agrivoltaic construction may reduce disruption and allow for optimal row alignment under the system [19]. Particularly in permanent cropping systems, where planting structures remain in place for decades, coordinated planning becomes essential. On the other hand, the associated replanting increases initial investment costs [44]. This, in turn, makes the ‘planted year’ factor relevant in practice, as winegrowers may plan agrivoltaic deployment when renewal is foreseeable and cost bundling is feasible. In addition, the results highlight the relevance of participatory approaches in site assessment: by utilizing citizen-curated infrastructure data, this work reflects current trends in participatory spatial planning and research [45]. While such datasets require ongoing validation, early collaboration with grid operators and local stakeholders can verify connection options and address concerns related to landscape integration and public acceptance. Such approaches, which draw on local resources and expertise, have also been called for by researchers for a considerable period [23]. In this way, corridor-focused pilots and renewal-aligned projects can translate spatial pre-screening into implementable projects that support climate-neutral energy generation while enhancing the resilience of viticultural systems.

4.4. Future Research Needs

While the present study advances the spatial assessment of agrivoltaic potential in viticulture by applying a crop-specific GIS approach, several areas for further research emerge. First, there is a need for long-term empirical data on the physiological and qualitative effects of elevated PV structures on grapevines and different grape varieties. Relevant performance indicators for such trials could include soil moisture, monitoring of downy and powdery mildew incidence, yield, grape acidity, and sugar content, as well as operational aspects such as labor requirements and the degree of mechanization under agrivoltaic conditions. In the coming years, further results from existing agrivoltaics research installations are expected. Due to the perennial nature of viticulture, however, reliable data on grape quality will only become meaningful after several years. Moreover, study designs differ significantly in some cases, which calls for cautious interpretation and requires critical review when publishing results. As market value in viticulture is contingent on both subjective and objective quality traits, future research will provide more precise answers [46,47]. These will be based on multi-year field trials with robust study designs and comprehensive monitoring of both yield and quality factors. Second, grid infrastructure datasets remain a limiting factor in many spatial assessments. While open, community-curated sources represent a valuable starting point, they lack validation and often omit critical attributes such as injection capacity or upgrade requirements. To improve planning accuracy and avoid costly redesigns during project development, future studies should aim to cooperate directly with distribution system operators to obtain verified, location-specific data and assess technical hosting capacity as an additional spatial constraint. Third, the investigation of economic parameters remains underdeveloped. Beyond simplified proxies such as grid connection distance, a more comprehensive understanding of economic conditions is required—particularly with regard to regional variation in capital expenditures (CAPEX) and operational expenditures (OPEX) for different system types. These include, for instance, the higher CAPEX of elevated systems in viticulture compared to ground-mounted PV, as well as increased OPEX due to more complex maintenance and crop-specific system management. Further relevant factors include permitting requirements, substation capacities, and regional differences in land lease prices. Identifying such economic hotspots and constraints is crucial to assessing the real-world feasibility of agrivoltaic deployment and to enabling robust comparisons across land uses. This highlights the need for follow-up economic studies that complement spatial analyses. Finally, scaling the presented approach beyond a single federal state would allow for a comparative analysis across all winegrowing regions in Germany and beyond. A major opportunity, however, lies in transferring the approach to other countries with relevant viticultural areas. This would require the availability of standardized, high-resolution cadastral and agronomic data across jurisdictions. Collaborative frameworks for data harmonization and methodological transferability would significantly advance the evidence base for agrivoltaics in viticulture integration at national and international levels.

5. Conclusions

This study demonstrates that the suitability of agrivoltaic systems for use in viticulture depends on the combined impact of technical, infrastructural, and agronomic factors. Although vineyards are widespread in the Rhineland-Palatinate region, only a subset of plots meets key criteria such as moderate slope and adequate grid accessibility. Suitable sites tend to be found in flatter subregions with existing energy infrastructure, highlighting the need for location-specific, spatially differentiated planning. Grid accessibility is the most influential limiting factor for feasibility, while steep-slope vineyards are difficult to integrate structurally due to constraints relating to mechanization and anchoring. These findings support the core premise that realistic assessments must incorporate multiple site-specific constraints. The framework provides a practical screening tool at the plot level that can be used to complement broader national assessments by introducing crop-specific criteria relevant to viticulture. Using open, community-curated infrastructure data enables scalable, early-stage screening in settings with limited data, while making underlying assumptions transparent. Overall, combining geospatial analysis with viticultural knowledge can inform more targeted, context-sensitive strategies for integrating agrivoltaics into winegrowing landscapes. In conclusion, the study fulfils its two objectives by quantifying the share of vineyard area that remains feasible under crop-specific and infrastructural constraints and by demonstrating how alternative parameter thresholds reshape suitability outcomes by clarifying where and under which conditions agrivoltaics can realistically be integrated into German viticulture.