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This article examines the political dynamics of regulating decarbonised energy such as agrivoltaics and questions the intersectoral logics inherent in this energy. It examines the political dynamics of multi-level and multi-sector regulation of decarbonised energy such as agrivoltaics between the local and national levels and between the agricultural and energy sectors. It hypothesises that the political dynamics of agrivoltaics regulation are first and foremost multi-level: The national framework for agrivoltaics regulation is based on incentive instruments, in which the state attempts to develop a market to achieve its decarbonisation policy objectives via guaranteed feed-in tariffs, and also on an imprecise regulatory framework. At the same time, the regulation of agrivoltaic projects is a prerogative of the local level, paving the way for the development of diversified projects. Agrivoltaics also raises the question of the encounter between two historically well-established areas of public policy: energy decarbonisation policies and agrivoltaics. These strongly mobilise agriculture and farmers and call into question the traditional balances, institutions and sectoral divisions between the energy and agriculture sectors and their players.

Fundação de Amparo à Pesquisa do Estado de São Paulo http://dx.doi.org/10.13039/501100001807 Belgian Federal Science Policy Office http://dx.doi.org/10.13039/501100002749 Agence Nationale de la Recherche http://dx.doi.org/10.13039/501100001665 Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659 Svenska Forskningsrådet Formas http://dx.doi.org/10.13039/501100001862 Canada Research Chairs http://dx.doi.org/10.13039/501100001804 Austrian Science Fund http://dx.doi.org/10.13039/501100002428 Fondation BNP Paribas http://dx.doi.org/10.13039/501100008393 US NSF Biological Integration Institute program

The lack of revisit in long-term satellite time series, such as Landsat is an issue to assess ecosystems response to snow cover variations in mountains. A recent release of the Satellites Pour l'Observation de la Terre (SPOT) 1-5 satellite images collection by the SPOT World Heritage (SWH) program offers the opportunity to increase the temporal revisit of Landsat from 1986 to 2015 at 20 m resolution. However, spectral and radiometric limitations of these images hinder the application of well-established pixel-wise methods to extract the snow cover area. As a work-around, deep learning techniques, such as convolutional neural networks can incorporate both spectral and spatial information to classify every pixel as snow, cloud, or snow-free. However, the lack of reference data poses a challenge to the implementation of such data-driven approaches. Here, we develop an emulator of SPOT images, which takes as input Sentinel-2 images. As a result, an emulated SPOT image can be paired with a reference snow map generated from its source Sentinel-2 image to train a deep learning model able to process actual SPOT images. We follow this approach to train a U-Net and evaluate different training strategies. We apply the different models to classify actual SPOT images for which we have reference data for validation. The method yields high precision in detecting snow, with minimal false snow pixel identification. This is at the cost of overestimating cloud pixels around clouds and highly saturated areas. The results confirm the potential of this method to generate time series of snow cover maps using the SWH collection.

The influence of canopy structure on tropical tree growth has been scantly studied because of the difficulties making field measurements in these dense multi-layered ecosystems. The recent advent of unmanned aerial vehicles (UAVs), has made it easier to collect canopy data, so offering a way to gain a better understanding of forest productivity and thereby improve forest management. In this study, we assessed tree growth prediction using UAV-derived crown measurements as an alternative for field data. Four experimental 9 ha plots were sampled in two forest sites, Yoko in the Democratic Republic of the Congo and Loundoungou in the Republic of Congo. Field inventories were made between 2015 and 2020. For each tree, we computed the diameter increment (DBHI) using censuses and diameter-based competition indices (diameter-based CIs) using the first census. High-resolution orthoimages and digital surface models were acquired with UAVs in 2016 and 2018 in the two sites. They gave estimates of crown characteristics (size, relative elevation, shape) and crown-based competition indices (crown-based CIs). Co-recorded UAV and field measurements were obtained for 1558 trees. The diameter increment of these trees was then modelled using supervised component generalized linear regression, and 20 % of trees were kept for cross-validation. Combined field and UAV data predicted tree DBHI twice better than either taken separately. Diameter at breast height (DBH) and crown area (CA) were found to be complementary predictors. Crown-based CIs significantly improved predictions of models already containing DBH and CA. Adding diameter-based CIs to models containing DBH, CA, and crown-based CIs only marginally improved growth predictions, showing that tree competition can be well-described with UAV data. The model calibrated at one site predicted the growth at the other site well, suggesting that a general model could be devised for multiple sites. Growth variance was better explained in the site (Yoko) where the crown density was higher and the crown smaller. Further data are now needed from multiple sites with ranging stand structures and compositions to build a general model.

International audience; Increasing landscape heterogeneity has been suggested to be an important strategy to strengthen natural pest control in crops, especially through enhancing the amount of seminatural habitats. Increasing crop diversity is also a promising strategy to complement or replace seminatural habitat when seminatural habitat is scarce. However, their relative or possibly interactive effects on pest and weed infestation remain poorly investigated, and the role of different types of seminatural habitats has been understudied. Using an extensive sampling effort in 974 arable fields across 7 y, we evaluated the separate and interactive effects of crop diversity (seven arable crop types) and the amount of four types of seminatural habitats (meadows, hay, forests, and hedgerows) in the landscape on pest and weed control. Meadows and crop diversity, respectively, supported insect pest and weed control services in agricultural landscapes through a complementarity effect. Crop diversity increased weed seed predation rate (by 16%) and reduced weed infestation (by 6%), whereas long-term grasslands (to a much higher degree than hay or woody habitats) increased insect pest predation rates (by 23%) and reduced pest infestation (by 19%) in most arable crops. Our results demonstrate that diversification of the agricultural landscape requires long-term grasslands as well as improved crop diversity to ensure the delivery of efficient pest and weed control services.

AbstractTemporal crop diversification could reduce pesticide use by increasing the proportion of crops with low pesticide use (dilution effects) or enhancing the regulation of pests, weeds and diseases (regulation effects). Here, we use the French National DEPHY Network to compare pesticide use between 16 main crops (dilution effect) and to assess whether temporal crop taxonomic and functional diversification, as implemented in commercial farms specialized in arable field crops, could explain variability in total pesticide use within 16 main crops (regulation effect). The analyses are based on 14,556 crop observations belonging to 1334 contrasted cropping systems spanning the diversity of French climatic regions. We find that cropping systems with high temporal crop diversity generally include crops with low pesticide use. For several crops, total pesticide use is reduced under higher temporal crop functional diversity, temporal crop taxonomic diversity, or both. Higher cover crop frequency increases total pesticide use through an increase in herbicide use. Further studies are required to identify crop sequences that maximize regulation and dilution effects while achieving other facets of cropping system multiperformance.

International audience; This systematic literature review presents the latest advancements and insights about digital twin technology and robotics interfaced with extended reality in the context of Industry 4.0. As the extended reality technologies emerge, it results in an increasing overlap between digital twins and human-robot interactions in industrial settings, promoting collaboration between operators and cobots in manufacturing environments. The objective of this study is to serve as a valuable resource for researchers and practitioners working in the field of Industry 4.0. It aims to highlight the latest developments and innovations in the application of digital twins and robotics interfaced with extended reality technologies in manufacturing. By extracting data from relevant articles, it provides a comprehensive understanding of the current state-of-the-art in this field by: analyzing the favored extended reality interfaces for digital twin and robotics interactions; analyzing the digital twin and physical twin interaction; evaluating the digital twin application levels and pillars through extended reality interfacing; and introducing a new concept called augmented perception for creating new physical-digital interactions.