The increasing pressure on land resources for food and energy production along with efforts to maintain natural systems necessitates the development of compatible land uses that maximize the co-benefits of multiple ecosystem services. One such land sharing opportunity is the restoration and management of native grassland vegetation beneath ground-mounted solar energy facilities, which can both protect biodiversity and restore related ecosystem services. In this paper, the researchers applied the InVEST modeling framework to investigate the potential response of four ecosystem services (carbon storage, pollinator supply, sediment retention, and water retention) to native grassland habitat restoration at 30 solar facilities across the Midwest United States. Compared to pre-solar agricultural land uses, solar-native grassland habitat produced a 3-fold increase in pollinator supply and a 65% increase in carbon storage potential. The researchers also observed increases in sediment and water retention of over 95% and 19%, respectively. They applied these results to project the potential benefits of adoption of native grassland management practices in current and future solar energy buildout scenarios. Their study demonstrates how multifunctional land uses in agriculture-dominated landscapes may improve the provision of a variety of ecosystem services and improve the landscape compatibility of renewable energy and food production. These findings may be used to build cooperative relationships between the solar industry and surrounding communities to better integrate solar energy into agricultural landscapes.

Solar electricity from solar parks in rural areas are cost effective and can be deployed fast therefore play an important role in the energy transition. The optimal design of a solar park is largely affected by income scheme, electricity transport capacity, and land lease costs. Important design parameters for utility-scale solar parks that may affect landscape, biodiversity, and soil quality are ground coverage ratio, size, and tilt of the PV tables. Particularly, low tilt PV at high coverage reduces the amount of sunlight on the ground strongly and leads to deterioration of the soil quality over the typical 25-year lifetime. In contrast, vertical PV or an agri-PV designed fairly high above the ground leads to more and homogeneous ground irradiance; these designs are favored for pastures and croplands. In general, the amount and distribution of ground irradiance and precipitation will strongly affect which crops can grow below and between the PV tables and whether this supports the associated food chain. As agrivoltaics is the direct competition between photosynthesis and photovoltaics. Understanding when, where and how much light reaches the ground is key to relate the agri-PV solar park design to the expected agricultural and electricity yields. We have shown that by increasing the minimum height of the system, decreasing the size of the PV tables and decreasing the coverage ratio, the ground irradiance increases, in particular around the gaps between the tables. The most direct way of increasing the lowest irradiance in a solar park design is to use semi-transparent PV panels, such as the commercially available bifacial glass-glass modules. In conclusion: we have shown that we can achieve similar ground irradiance levels in an east- and west-facing design with 77% ground coverage ratio as is achieved by a south-facing design at 53% coverage.

The use of renewable energy in modern greenhouse management is important to achieve efficient and sustainable food supplies for a world with increasing population. This study assessed the performance of a blind-type shading regulator that can automatically rotate semi-transparent photovoltaic (PV) blades installed on the greenhouse roof in response to sunlight variation. The PV blind oriented parallel to the roof partially blocked intense sunlight penetration into the greenhouse, but it transmitted sunlight during cloudy time by turning the blind bearing to be perpendicular to the roof. A stable irradiation environment is therefore producible in the greenhouse under variable sky conditions. Annual operations demonstrated that the blinds’ own generated electrical energy can sustain PV blind operation and produce surplus electrical energy. The PV blind electricity generation and sunlight availability for crops below the PV blind roof were calculated based on a mathematical model developed using theoretical sunlight parameters and the experimentally obtained PV blind system parameters. Assuming cloudless skies and threshold irradiance for blind rotation set at 500 W m−2, 13.0 and 12.3kWh m−2 yr−1 surplus electrical energy can be generated, respectively, by north–south and east–west oriented model greenhouses. Cloudy skies reduce surplus electrical energy production by 50%, but PV blinds can supply greenhouse electrical energy demands partially or completely, depending on the degree of greenhouse electrification. Below the PV blinds, 8–10 MJ m−2 day−1 of insolation is expected to irradiate crops under actual sky conditions. This insolation is sufficient to cultivate major horticultural crops. Regulating the threshold irradiance level for PV blind turning can control the sunlight apportionment ratio for cultivation and electricity generation, thereby enabling sustainable energy–food dual production in a greenhouse.

Wireless sensor networks (WSNs) can be reliable tools in agricultural management. In this work, a low cost, low power consumption, and simple wireless sensing system dedicated for agricultural environments is presented. The system is applicable to small to medium sized fields, located anywhere with cellular network coverage, even in isolated rural areas. The novelty of the developed system lies in the fact that it uses a dummy device as Coordinator which through simple but advanced programming can receive, process, and send data packets from all End-notes to the cloud via a 4G cellular network. Furthermore, it is energy independent, using solar energy harvesting panels, making it feasible to operate in remote, isolated fields. A star topology was followed for the sake of simplification, low energy demands and increased network reliability. The developed system was tested and evaluated in laboratory and real field environment with satisfactory operation in terms of independence, and operational reliability concerning packet losses, communication range (>250m covering fields up to 37ha), energy autonomy, and uninterrupted operation. The network can support up to seven nodes in a 30 min data acquisition cycle. These results confirmed the potential of this system to serve as a viable option for monitoring environmental, soil, and crop parameters.

Global energy consumption and costs have increased exponentially in recent years, accelerating the search for viable, profitable, and sustainable alternatives. Renewable energy is currently one of the most suitable alternatives. The high variability of meteorological conditions (irradiance, ambient temperature, and wind speed) requires the development of complex and accurate management models for the optimal performance of photovoltaic systems. The simplification of photovoltaic models can be useful in the sizing of photovoltaic systems, but not for their management in real time. To solve this problem, we developed the I-Solar model, which considers all the elements that comprise the photovoltaic system, the meteorological conditions, and the energy demand. We have validated it on a solar pumping system, but it can be applied to any other system. The I-Solar model was compared with a simplified model and a machine learning model calibrated in a high-power and complex photovoltaic pumping system located in Albacete, Spain. The results show that the I-Solar model estimates the generated power with a relative error of 7.5%, while the relative error of machine learning models was 5.8%. However, models based on machine learning are specific to the system evaluated, while the I-Solar model can be applied to any system.

The concept of combining photovoltaics (PV) with agriculture (agrivoltaics or APV) is being explored across the globe and has established field trials in countries including, but not limited to, Germany, Vietnam, Italy, France, Japan and Chile. An agrivoltaic system involves positioning solar panels directly above or near active agricultural land to provide some form of shading to the crops and to generate electricity from the solar array. The usefulness of this concept is seen when considering the abundance of land that becomes available to the PV market if that land can be shared with the agricultural sector. For instance, consider that in 2016 Australia used 372 million hectares of land for agriculture, of which 8.3% was designated cropland. Therefore, even if some proportion of this cropland (say an 8th) are retrofitted with overhanging PV systems, Australia’s effective solar generation area would increase by roughly four million hectares. This would greatly enhance the renewable energy sectors ability to satisfy baseload energy requirements of the national grid. At first glance the concept of shading plants seems counterintuitive to the perception that cropland should be without obstructions. However, agrivoltaics recognises that crops do not require every hour of sunlight to photosynthesise. Consequently, the solar energy resource can effectively be shared with photovoltaic technology to increase the productivity of the land without greatly decreasing the yield of the crop, and in some cases, increasing crop yield. This is achieved by spacing the rows of solar panels in such a way that the shadows caused by the panels still permit crops to photosynthesise sufficiently in addition to reducing heat related stress caused by the environment. As such, this study aims to review existing literature about agrivoltaics and use experimentation to explore if the advantages they provide are great enough to justify their introduction into Australian agriculture. A key parameter for this study is land productivity that is measured using “land equivalent ratio” (LER) which is a combination of crop yield (measured in kilograms) and energy production (measured in watt-hours).

Achieving optimal yield and quality at harvest depends on the grower’s ability to avoid abiotic stresses (water, light, and temperature). This task has usually been satisfied through the implementation of adequate horticultural practices. In the context of clean energy transition and global climate change, growers nowadays have the possibility to grow their crops under solar panels, which modify the micro-environment of the crops. Being able to anticipate the behavior of plants under these new micro-environmental conditions would help growers adapt their horticultural practices. For electricity producers, in the context of dynamic agrivoltaic systems, anticipating the crop status is useful to choose a solar panels steering policy that maximizes electricity production while ensuring favorable environmental conditions for the crop to grow. To help electricity producers and growers estimate a crop status under panels, we developed a decision support system (DSS) called crop_sim. As of now, it can be used to monitor two types of perennial crops: grapevines and apple trees. crop_sim produces three indicators of the crop status: predawn water potential, canopy temperature and carbon production. Besides providing information on the crop status, the DSS incorporates an expert system which indicates the best time and the amount of irrigation to maintain a desired water status under the new micro-environmental conditions. This paper first focuses on the description of crop_sim and the usefulness of the three indicators. Then, a case study is presented. Our results show that, in a mature vineyard, with a typical panel steering policy conservative on crop yield, growers could save 13% of water compared to an open-field reference. Experimental data pertaining to apple trees, grapevines, tomatoes, and maize are being collected. They will be used to adapt the model to tomato and maize, evaluate it and make it robust enough to bring to market. Further improvements of the crop_sim model may be required to finely reproduce observations in the field. A full validation of the model is expected when all data from the experiments will be available. The DSS will evolve depending on the requirements of the agrivoltaics community and may incorporate additional plant indicators and new expert system rules.

In this paper, a novel UGV (unmanned ground vehicle) for precision agriculture, named “Agri.q,” is presented. The Agri.q has a multiple degrees of freedom positioning mechanism and it is equipped with a robotic arm and vision sensors, which allow to challenge irregular terrains and to perform precision field operations with perception. In particular, the integration of a 7 DOFs (degrees of freedom) manipulator and a mobile frame results in a reconfigurable workspace, which opens to samples collection and inspection in non-structured environments. Moreover, Agri.q mounts an orientable landing platform for drones which is made of solar panels, enabling multi-robot strategies and solar power storage, with a view to sustainable energy. In fact, the device will assume a central role in a more complex automated system for agriculture, that includes the use of UAV (unmanned aerial vehicle) and UGV for coordinated field monitoring and servicing. The platform is covered with photovoltaic panels useful to recharge a 12 V battery that gives power to the system. A positioning device permits to orient the platform so to keep it horizontal above the ground or to optimize the sunlight collection process. A seven DOFs manipulator is mounted on the rover and can be placed in different positions on the UGV depending on the task that it has to perform. The solution adopted to mount the robotic arm on the rover is discussed and indexes to evaluate the performances of the mounting system in terms of augmentation of the robotic workspace are presented. The mechatronic system architecture is also reported and finally the first prototype is shown in different meaningful configurations. Future studies will consider the integration of vision sensors on the prototype and will analyze Agri.q02 capabilities in a real vineyard scenario. Moreover, the software package for the autonomous guide and manipulation tasks will be developed.

The transition to using clean, affordable, and reliable electrical energy is critical for enhancing human opportunities and capabilities. In the United States, many states and localities are engaging in this transition despite the lack of ambitious federal policy support. This research builds on the theoretical framework of the multilevel perspective (MLP) of sociotechnical transitions as well as the concept of energy justice to investigate potential pathways to 100 percent renewable energy (RE) for electricity provision in the U.S. This research seeks to answer the question: what are the technical, policy, and perceptual pathways, barriers, and opportunities for just transition to 100% renewable electricity in the U.S., at a state and local levels? In this dissertation, an analysis of factors contributing to RE transition in communities across the country is developed. Results from this are used to make further analysis and recommendations to research undertaken specifically in the context of Michigan’s Western Upper Peninsula (WUP). This dissertation demonstrates that research on achieving a just energy transition requires transdisciplinary approaches that integrate social sciences, engineering, and natural sciences and multiple ways of knowing from scientists, practitioners, and diverse community perspectives. This research provides tools for decision makers at all levels of government, local stakeholders, citizens, and the academic world in understanding what matters for success in a just transition to 100% RE in the U.S.

The rapid increase of photovoltaic installations highlights the potential of agrivoltaic systems. These dual-land use systems mitigate land use conflicts for places with limited open space and moreover, show the potential as an added value in crop- and livestock cultivation. The many different names and interacting possibilities between agriculture and PV make it difficult and confusing for stakeholders to compare and benchmark existing installations as well as propose and set new (EU) legislation schemes. This work proposes a standardized classification (including names) of agrivoltaic systems, which is usable worldwide. The classification is based on the application, system, the farming type, PV structure and flexibility. These elements makes it possible to describe and categorize each existing agrivoltaic installation properly. This work suggests to mention each sub-category (for example: static stilted orchard agrovoltaic system) in future research papers or documents to order to better compare (rangevoltaic <=> agrovoltaic) and benchmark new installed installations. When comparing agrivoltaics, the use of the proposed seven KPIs will help to make meaningful comparisons and grounded decisions in case of possible new installations.