Search Results (144)

Agrivoltaics is an important pathway for promoting the coordinated development of clean energy production and agricultural utilization. However, the structural characteristics of flexible agrivoltaic (AV) systems may significantly alter field light and thermal conditions, while their effects on crop growth and yield formation remain unclear. To address this issue, a flexible AV system in Sihong County, Jiangsu Province, was selected as the study site, and continuous field monitoring combined with crop measurements was used to evaluate changes in microclimate, wheat physiological responses, and yield performance. The results showed that the flexible AV system significantly changed the field microclimate. During the wheat growing season, the monthly average solar radiation intensity under and between PV panels decreased by 62.0% and 56.9%, respectively, compared with that in the open field. The array also showed a certain thermal regulation effect, with heat preservation during the overwintering stage and cooling during the later growth stage. Shading reduced wheat net photosynthetic rate and stomatal conductance, but adaptive responses such as increased leaf area and chlorophyll content were observed. Wheat yield within the flexible AV system was significantly lower than that in the open field, with reductions of 43.4% and 47.2% in 2024 and 41.8% and 44.6% in 2025 for the areas under and between PV panels, respectively. Overall, light reduction under high coverage conditions remained the main factor limiting wheat yield. These results provide a theoretical basis for structural optimization and crop selection in flexible AV systems. Full article

Agrivoltaics (AV), the dual use of land for solar power generation and agricultural production, offers a sustainable strategy for optimising land resources. Overhead photovoltaic (PV) modules can change microclimatic conditions, thereby influencing soil moisture, soil temperature, and air temperature and humidity. This study assessed the impact of AV systems on air and soil conditions in a pilot apple orchard in Kressbronn im Bodensee (Bodenseekreis district, Baden-Württemberg, Germany). A semi-transparent PV system (40% transparency) was installed, and soil and air parameters were continuously monitored between August 2024 and August 2025. The results show that AV increased average soil moisture by 11.8% and decreased average soil temperature by 0.8 °C compared to the reference area from a one-year period (August 2024 up to August 2025). Air temperatures beneath the modules were +0.2 °C higher in the early morning but lower (−0.5 °C) at midday during the warmer season. Relative humidity beneath the PV modules showed a diurnal pattern, with higher values than the open reference during nighttime and early morning hours, and lower values during certain daytime windows, resulting in an overall mean difference of −2.2%. These findings confirm that AV installations substantially alter microclimatic conditions, with potential implications for orchard management and crop productivity. Full article

The rapid expansion of solar photovoltaics is intensifying competition for land and highlighting the need for scalable energy solutions that can be integrated into existing power systems without displacing agricultural activity. Once the technical and agronomic viability of agrivoltaic configurations has been demonstrated at field scale, a critical next step toward their market consolidation is the assessment of their deployment potential at regional scales from an energy systems and grid integration perspective. This study presents a GIS-based framework to evaluate the large-scale implementation of vertically integrated agrivoltaic systems, using vineyard landscapes in the Region of Murcia (southeastern Spain) as a representative case study. The analysis combines high-resolution land-use data, crop distribution, regulatory constraints on grid connection distances, and existing electrical infrastructure to quantify installable capacity, energy production, self-consumption potential, and grid accessibility. Results indicate that vertically mounted bifacial PV systems could reach up to 7.06 GWp, generating approximately 11.84 TWh/year, while revealing a pronounced spatial mismatch between optimal agrivoltaic production sites and current grid connection points. This distance-dependent distribution highlights the need for differentiated deployment strategies, balancing local self-consumption, grid reinforcement, and centralized injection. Beyond the specific case examined, the proposed approach provides a transferable framework for energy system planning, supporting grid-aware agrivoltaic deployment in diverse regions and regulatory contexts. Full article

Tracking agrivoltaic (TAV) systems represent a significant form of agrivoltaics, which optimize solar energy capture through the dynamic adjustment of photovoltaic (PV) panel tilt angles. However, there is limited research on the effects of TAV systems on the three-dimensional spatial distribution of the light environment within PV arrays and their impacts on agricultural production. Therefore, a comparative experiment was conducted between wheat production under a TAV system and traditional open-field cultivation. Solar radiation intensity sensors were deployed to continuously monitor the dynamic changes in solar radiation under and between the PV panels throughout the entire growth period. Simultaneously, a light environment model for the TAV system was constructed, and the photosynthetic parameters of wheat leaves, as well as yield, were measured. The results indicated that the light environment within the system exhibited significant gradient attenuation, with average light capture rates of 43.2% and 46.1% for the inter-panel and under-panel measurement points, respectively. The model results confirmed that the synergistic adjustment of panel tilt angle and solar altitude angle significantly affected the shading effects, leading to notable spatiotemporal heterogeneity in the light environment during the winter solstice, spring equinox, and summer solstice. This heterogeneity showed as regular variations in shadows and radiation, collectively forming a dynamic light–thermal environment that influences crop growth. Wheat yields under and between the panels decreased by 11.5% and 6.6%, respectively, compared to the open-field control, with yields of 4625.9 kg·hm⁻² and 4883.6 kg·hm⁻². Additionally, the photosynthetic characteristics of the leaves effectively reflected the yield differences. Overall, the comprehensive benefit assessment demonstrates that the TAV system can effectively mitigate the reduction in wheat yield in PV farmlands. This study provides a theoretical basis for optimizing the light environment in AV systems. Full article

Crop yield responses to reduced solar radiation are central to the design of agrivoltaic systems, yet crop-specific patterns and critical shading thresholds remain insufficiently characterized across diverse environments. This study evaluates yield responses across a global dataset of 546 observations from 66 studies, including agrivoltaic, shading, and agroforestry systems. Relative yield was analyzed in relation to reduction in solar radiation (RSR), crop type, and environmental variables using exploratory analysis, multiple linear regression, and tree-based ensemble models. Crop responses varied systematically across crop types. Fruits, berries, and fruity vegetables maintained or increased yield under lower shading levels, while forages, leafy vegetables, cereals, and tubers showed gradual declines, and maize and grain legumes exhibited the strongest sensitivity. Across models, yield responses were non-linear, with relatively stable yields at lower shading levels followed by accelerated declines beyond approximately 50–60% RSR. Climatic conditions further influenced these patterns, with crops in higher-radiation and warmer environments maintaining yields more effectively under partial shade. These findings demonstrate that crop yield responses depend on crop type, shading intensity, and environmental context, providing an agronomic basis for crop selection and agrivoltaic system design. Full article

Photovoltaic systems are usually considered technologies used exclusively for energy production. However, when examined more comprehensively, they may also provide environmental and agronomic benefits under specific system designs and crop–climate conditions. In agrivoltaic systems, the same area of land is used simultaneously for agricultural production and solar energy generation, creating opportunities for more efficient and sustainable resource use. Photovoltaic installations can alter the microclimate around crops and reduce key abiotic stress factors, such as heat stress and water loss, which often contribute to declines in crop yields. Thus, they may contribute to improved production stability and more efficient use of natural resources under certain conditions. Agrivoltaics can also be considered through a social ecology framework for adapting to new weather conditions. Its social dimension lies in the way agrivoltaic systems reshape land-use governance, influence farmer adoption and stakeholder participation, and affect how economic and environmental benefits are distributed within rural communities. This review goes beyond conventional assessments focused mainly on land-use efficiency by integrating microclimatic, agronomic, and socio-economic dimensions of agrivoltaic systems. It also identifies key research gaps, particularly regarding long-term and multi-site evidence, crop-specific system design, landscape-scale impacts, and socio-economic resilience. Overall, agrivoltaics can constitute a socio-ecological infrastructure that contributes to the mitigation of abiotic stress and the adaptation of agriculture to climate change. Full article

Although the economic importance of optimizing lighting systems and energy use in indoor farming is well known, there is a notable lack of studies focusing on economic trade-off analysis between agrivoltaic and grid-powered solutions integrated into light map analysis and revenue sensitivity assessments. To address this gap, this study investigates an indoor true vertical lettuce agrotunnel. Regional, seasonal, and market type sensitivity analyses were carried out on the prices and experimental yields to calculate the revenue from lettuce production. Technoeconomic analysis indicated that agrivoltaics outperformed the grid-only power supply by net present cost and levelized cost of electricity reductions of 13.2% and 44.2%, respectively. Photosynthetic photon flux density (PPFD) and daily light integral (DLI) were compared for the cases of 41–61 cm distances between the lights and the walls. With the 20 cm distance variations, 16.6–17.8% changes in the average PPFD were achievable without increasing energy consumption. In addition, annual revenue from lettuce green mix packs ranged from $61,735 to $86,164 USD for singles and from $83,603 to $116,685 USD for multiple plants per pot strategies, depending on the variety packs. Luxury and mid-range price categories kept the return on investment above 10% at all capital cost levels. The agrotunnel achieved the maximum specific yield of >70 kg/m²/year. Full article

The increasing competition for land between agriculture and electricity generation has driven the implementation agrivoltaic systems (AVSs) as a strategy aligned with Sustainable Development Goals 7 and 13. This study systematically analyzes how AVS design influences agricultural yield (AY), energy yield (EY), and overall sustainability. A systematic review was conducted following the PRISMA protocol, complemented by bibliometric analysis and an exploratory correlation analysis of design variables, productivity indicators, and environmental and economic metrics. From an initial set of 243 records, 79 studies published between 2018 and 2025 were included. The results identify general trends across heterogeneous studies, although these patterns should not be interpreted as universally applicable. Intermediate ground cover ratios (GCRs) (≈30–40%) are commonly associated with favorable trade-offs between AY and EY, often resulting in land equivalent ratios above 1.5 under specific conditions. Reported outcomes indicate that AVS can achieve increases in EY, improvements in water-use efficiency, reductions in CO₂ emissions, and competitive economic performance, although these results vary depending on crop type, climate, system configuration, and PV technology. Overall, the analysis highlights GCR as a key design parameter and underscores that AVS performance depends on multivariable and context-specific design rather than universally applicable thresholds, reinforcing its potential as a sustainable agri-energy solution. Full article

Water scarcity is a global phenomenon, and Pakistan is no exception to it. This study aims to assess the techno-economic efficiency of the irrigation system for guava orchard and melon crop in the Hafizabad District of Punjab province in Pakistan. The study has employed efficiency theory for a comparative analysis of modern and high-efficiency irrigation methods in contrast to old traditional methods of irrigation to estimate differentiating impacts on technical efficiency (TE), economic efficiency (EE), water productiveness, and crop yield. The mixed method approach is exercised on data collected from 108 stratified farmers (large, medium and smallholders) using structured surveys and qualitative insights. Beta-regression models using Cauchit link function are applied to translate determinants of TE/EE by taking into account predictor factors such as farming experience, operational costs and water productivity. Results show that solar irrigation systems have significantly better performance than the conventional system by having better TE and EE scores than conventional system performance. Farming experience and water productivity also have positive effects on efficiencies. Results also show that solar systems increase water productivity, lower costs and increase guava and melon productivity to a significant extent, which in turns aid in reducing the effects of salinity and evaporation in arid conditions. The overall finding supports and emphasizes solar’s supremacy for sustainable horticulture. Findings highlight the importance of incentivizing solar adaptation and agrivoltaic integration in Pakistan to ensure sustainable agriculture in water-stressed areas such as Punjab for food security and resource conservation for the production of guava and melons. Full article

Agrivoltaics (AV) has emerged as an integrated land-use innovation capable of simultaneously addressing food, energy, and water challenges, yet its systemic implications for farming system sustainability remain insufficiently synthesized. This review adopts a farming system dynamics perspective to examine how AV systems reorganize biophysical, ecological, and socio-economic interactions across agroecosystems. Drawing upon agroecological principles, pathways of sustainable intensification and ecological intensification, and resource-loop strategies in circular economy, we identify the key elements and cause-and-effect relationships that shape AV system performance. Evidence indicates that the co-location of photovoltaics (PV) structures and crop cultivation generates new system properties, altered light distribution, moderated microclimates, redistributed soil moisture, and diversified production functions that influence productivity, resource-use efficiency, ecological services, and farm resilience. Using causal loop analysis, we conceptualize four central feedback dynamics: (i) PV–crop trade-offs and spatial-sharing relationships; (ii) microclimate modifications and crop physiological responses; (iii) ecological performance and landscape-level interactions; and (iv) circularity loops connecting resource conservation, renewable-energy substitution, soil processes, and material flows. This feedback collectively determines eco-efficiency outcomes, including enhanced land-equivalent productivity, improved water-use efficiency, strengthened regulating services, and reductions in external energy dependence. At the farming-system scale, AV diversifies income streams and stabilizes yields under climatic variability, whereas at the landscape scale, it fosters multifunctionality by supporting regenerative resource flows and ecological resilience. Building on these insights, we propose an integrated framework that links agroecological elements with dynamic feedback structures to guide context-specific AV design, management, and governance. This system-oriented synthesis provides a foundation for future research and policy efforts aimed at optimizing AV as a circular, resilient, and sustainable farming system innovation. Full article

Agrivoltaic systems co-locate photovoltaic (PV) arrays and crops, offering land-use efficiency and potential microclimate benefits, yet they introduce new challenges for computer-vision-based crop monitoring. PV structures produce strong, spatially varying shadows, specular reflections, and periodic occlusions that confound visual cues for diagnosing crop diseases and abiotic stresses. Meanwhile, agrivoltaic deployments are often distributed across farms and operators, making centralized data collection impractical due to privacy, ownership, and regulatory concerns. This paper proposes PrivAgriVolt, a novel privacy-preserving learning framework for agrivoltaic crop issue recognition that explicitly models PV-induced illumination and enables collaborative training without sharing raw images. The core algorithm integrates (i) a PV-geometry-conditioned shadow normalization module that fuses estimated array layout and sun-angle priors into a shadow-aware appearance canonization network, reducing illumination-induced domain shift across times and sites; (ii) a federated contrastive stress learner that aligns stress semantics across farms via prototype-based contrastive objectives while remaining robust to heterogeneous sensors and crop stages; and (iii) an adaptive privacy layer that combines secure aggregation with budget-aware gradient perturbation and client-level clipping to provide formal privacy guarantees while preserving fine-grained diagnostic performance. Extensive experiments on real agricultural vision benchmarks and agrivoltaic shadow variants demonstrate that PrivAgriVolt improves stress recognition and segmentation under PV shading while maintaining strong privacy–utility trade-offs. Full article

Renewable energy technologies are becoming more and more relevant in a variety of engineering fields as a result of the move toward low-carbon, sustainable energy systems. Although research has historically concentrated on power generation, it now covers a broad range of applications, including precision agriculture, smart grids, energy storage, healthcare devices, and sustainable buildings. However, existing review studies are often limited to single disciplines or specific technologies, lacking a unified cross-disciplinary perspective that captures the interconnected nature of modern renewable energy systems. This gap motivates the need for a comprehensive review that bridges multiple engineering domains. This review provides a comprehensive synthesis of literature on renewable energy applications in electrical and electronics, computer, environmental, biomedical, architectural, and agricultural engineering. In electrical and electronics engineering, the use of renewable energy sources is largely based on the efficient generation of electricity from natural resources such as solar, wind, and ocean energy. Computer engineering contributes through artificial intelligence (AI), Internet of Things (IoT) architectures, digital twins, and cybersecurity solutions, optimizing energy management. Environmental engineering emphasizes life cycle assessment, carbon footprint reduction, and circular economy strategies. In biomedical engineering, energy harvesting and self-powered devices illustrate micro-scale applications of renewable energy. Architectural engineering integrates renewable systems through building-integrated photovoltaics, net-zero energy designs, and smart building management, while agricultural engineering uses solar-powered irrigation, biomass utilization, agrivoltaic systems, and other sustainable practices. To support a low-carbon future with integrated and sustainable engineering solutions, this study not only highlights innovations within individual fields but also showcases how different disciplines can connect and work together. Overall, the review offers a novel cross-disciplinary framework that advances the understanding of renewable energy systems beyond isolated applications and provides direction for future integrative research. Full article

Fiji, with its many islands and mountainous terrain, has only about 11% of its total land area (2000 km²) suitable for cultivation. Therefore, it aims to meet both energy and food production simultaneously through agricultural photovoltaic (APV) systems. This study proposed an optimal agricultural management of APV system to increase farm income and solve the problem of low vegetable production. The practice is planned based on the data from farmer surveys, field study, simulation analysis, and agricultural market analysis. Firstly, a farmer survey was conducted to gather data on the agricultural activities and income of local farmers. Based on the survey results, field studies with various vegetables were conducted in an APV system. In simulation, yields of lettuce, taro, long bean, and cucumber were estimated in the APV system with different cropping management techniques (planting schedule and plant density). With the average yields of lettuce, taro, long bean, and cucumber at highest plant densities being (72.4, 71.1, 3.9, and 10.8) Mg/ha, respectively, according to economic analysis, the highest gross margin was achieved in taro in the APV system. This study shows that the APV system can increase farmers’ annual household income by 1.19 to 1.38%, which represents a meaningful absolute gain given the low average income levels identified in the farm survey. Full article

Agrivoltaic systems offer a pathway to simultaneously produce food and electricity, yet their effectiveness depends on how photovoltaic configurations influence crop productivity under specific environmental conditions. This study evaluated land-use efficiency in an Andean–Amazon transition region using monofacial, bifacial, and semitransparent photovoltaic configurations integrated with a maize–bean intercrop. Land-use efficiency was quantified through the Land Equivalent Ratio (LER), combining agricultural yield and electrical energy production. All configurations achieved LER values above 1.0, confirming a clear advantage over separate land use. The semitransparent configuration showed the highest LER (1.95–1.99), followed by bifacial (1.66–1.90) and monofacial systems (1.51–1.72). LER variation was driven primarily by crop productivity rather than energy yield, while normalized photovoltaic performance remained stable across configurations. These results demonstrate that agrivoltaic performance is governed by system-level crop response, emphasizing the role of photovoltaic design in optimizing food–energy systems under tropical mountain conditions. Full article

The application of artificial intelligence (AI) and machine learning (ML) to the optimisation of agrivoltaic systems represents a promising frontier for enhancing dual land-use efficiency. Insights from the literature identify substantial opportunities for the transfer of mature AI methodologies from renewable energy and agriculture applications to the emerging field of agrivoltaics. Despite agrivoltaic systems achieving reported Land Equivalent Ratios (LERs) of between 1.2 and 1.6—corresponding to a 20 to 60% increase in combined energy and crop productivity per unit of land—the adoption of dynamic, real-time optimisation remains limited. Key research gaps include the absence of cross-domain learning architectures, the limited integration of economic considerations within optimisation frameworks, and the lack of adaptive, multi-temporal modelling approaches. This perspective paper proposes a research roadmap for the development of next-generation AI systems capable of simultaneously optimising energy generation and agricultural productivity, thereby supporting sustainable land-use transitions in integrated agri-energy landscapes. Full article