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description Publicationkeyboard_double_arrow_right Article , Journal 2011 ItalyPublisher:Institute of Experimental Botany Authors: ARENA, CARMEN; VITALE, LUCA; A. Virzo De Santo;handle: 11588/426299 , 20.500.14243/25472
This work aimed to evaluate if gas exchange and PSII photochemical activity in maize are affected by different irradiance levels during short-term exposure to elevated CO2. For this purpose gas exchange and chlorophyll a fluorescence were measured on maize plants grown at ambient CO2 concentration (control CO2) and exposed for 4 h to short-term treatments at 800 ?mol(CO2) mol-1 (high CO2) at a photosynthetic photon flux density (PPFD) of either 1,000 ?mol m-2 s-1 (control light) or 1,900 ?mol m-2 s-1 (high light). At control light, high-CO2 leaves showed a significant decrease of net photosynthetic rate (PN) and a rise in the ratio of intercellular to ambient CO2 concentration (Ci/Ca) and water-use efficiency (WUE) compared to control CO2 leaves. No difference between CO2 concentrations for PSII effective photochemistry (?PSII), photochemical quenching (qp) and nonphotochemical quenching (NPQ) was detected. Under high light, high-CO2 leaves did not differ in PN, Ci/Ca, ?PSII and NPQ, but showed an increase of WUE. These results suggest that at control light photosynthetic apparatus is negatively affected by high CO2 concentration in terms of carbon gain by limitations in photosynthetic dark reaction rather than in photochemistry. At high light, the elevated CO2 concentration did not promote an increase of photosynthesis and photochemistry but only an improvement of water balance due to increased WUE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type , Review , Preprint 2021Embargo end date: 01 Jan 2022 United Kingdom, Norway, United Kingdom, Norway, Italy, United Kingdom, Italy, Italy, Spain, Qatar, United Kingdom, Denmark, Italy, Denmark, Italy, Italy, Germany, Netherlands, Finland, Italy, Sweden, Netherlands, Germany, Netherlands, Spain, Spain, Netherlands, Spain, Italy, Lithuania, Germany, Norway, Spain, Italy, Germany, Norway, Netherlands, Germany, United Kingdom, Italy, United Kingdom, Italy, Netherlands, Switzerland, Netherlands, Spain, Italy, Belgium, Spain, Netherlands, Spain, Lithuania, France, Germany, Sweden, United States, Belgium, Germany, Italy, Italy, Netherlands, Germany, Netherlands, Qatar, United Kingdom, United KingdomPublisher:Wiley Funded by:EC | eLTER PLUS, EC | LEAP-AGRI, ARC | Discovery Early Career Re... +32 projectsEC| eLTER PLUS ,EC| LEAP-AGRI ,ARC| Discovery Early Career Researcher Award - Grant ID: DE180100570 ,EC| DESIRA ,ANR| ASICS ,SNSF| ICOS-CH Phase 2 ,NSF| Integrating species traits into species pools: A multi-scale approach to understanding community assembly ,EC| SUPER-G ,AKA| Atmosphere and Climate Competence Center (ACCC) ,UKRI| E3 - Edinburgh Earth and Environment - Doctoral Training Partnership ,UKRI| Climate as a driver of shrub expansion and tundra greening ,UKRI| SCORE: Supply Chain Optimisation for demand Response Efficiency ,EC| FORMICA ,RSF| The anatomical and physiological response of Scots pine xylem formation to variable water availability ,SNSF| Climate change impacts on biodiversity: From macro- to microclimate ,EC| AIAS ,NSERC ,RCN| Effects of herbivory and warming on tundra plant communities ,RCN| The role of Functional group interactions in mediating climate change impacts on the Carbon dynamics and Biodiversity of alpine ecosystems ,EC| AfricanBioServices ,EC| ECLAIRE ,ARC| Discovery Early Career Researcher Award - Grant ID: DE140101611 ,NWO| Specialists at work: how decomposers break down plant litter ,EC| PERMTHAW ,EC| ICOS ,EC| NICH ,SNSF| How does forest microclimate affect biodiversity dynamics? ,DFG| EarthShape: Earth Surface Shaping by Biota ,RCN| The effect of snow depth and snow melt timing on arctic terrestrial ecosystems. ,EC| SustainSAHEL ,UKRI| Forecasting the impacts of drought on human-modified tropical forests by integrating models with data ,UKRI| UK Status, Change and Projections of the Environment (UK-SCaPE) ,ANR| IMPRINT ,DFG| German Centre for Integrative Biodiversity Research - iDiv ,ANR| ODYSSEEWinkler, Manuela; Plichta, Roman; Buysse, Pauline; Lohila, Annalea; Spicher, Fabien; Boeckx, Pascal; Wild, Jan; Feigenwinter, Iris; Olejnik, Janusz; Risch, Anita; Khuroo, Anzar; Lynn, Joshua; di Cella, Umberto; Schmidt, Marius; Urbaniak, Marek; Marchesini, Luca; Govaert, Sanne; Uogintas, Domas; Assis, Rafael; Medinets, Volodymyr; Abdalaze, Otar; Varlagin, Andrej; Dolezal, Jiri; Myers, Jonathan; Randall, Krystal; Bauters, Marijn; Jimenez, Juan; Stoll, Stefan; Petraglia, Alessandro; Mazzolari, Ana; Ogaya, Romà; Tyystjärvi, Vilna; Hammerle, Albin; Wipf, Sonja; Lorite, Juan; Fanin, Nicolas; Benavides, Juan; Scholten, Thomas; Yu, Zicheng; Veen, G.; Treier, Urs; Candan, Onur; Bell, Michael; Hörtnagl, Lukas; Siebicke, Lukas; Vives-Ingla, Maria; Eugster, Werner; Grelle, Achim; Stemkovski, Michael; Theurillat, Jean-Paul; Matula, Radim; Dorrepaal, Ellen; Steinbrecher, Rainer; Alatalo, Juha; Fenu, Giuseppe; Arzac, Alberto; Homeier, Jürgen; Porro, Francesco; Robinson, Sharon; Ghosn, Dany; Haugum, Siri; Ziemblińska, Klaudia; Camargo, José; Zhao, Peng; Niittynen, Pekka; Liljebladh, Bengt; Normand, Signe; Dias, Arildo; Larson, Christian; Peichl, Matthias; Collier, Laura; Myers-Smith, Isla; Zong, Shengwei; Kašpar, Vít; Cooper, Elisabeth; Haider, Sylvia; von Oppen, Jonathan; Cutini, Maurizio; Benito-Alonso, José-Luis; Luoto, Miska; Klemedtsson, Leif; Higgens, Rebecca; Zhang, Jian; Speed, James; Nijs, Ivan; Macek, Martin; Steinwandter, Michael; Poyatos, Rafael; Niedrist, Georg; Curasi, Salvatore; Yang, Yan; Dengler, Jürgen; Géron, Charly; de Pablo, Miguel; Xenakis, Georgios; Kreyling, Juergen; Forte, Tai; Bailey, Joseph; Knohl, Alexander; Goulding, Keith; Wilkinson, Matthew; Kljun, Natascha; Roupsard, Olivier; Stiegler, Christian; Verbruggen, Erik; Wingate, Lisa; Lamprecht, Andrea; Hamid, Maroof; Rossi, Graziano; Descombes, Patrice; Hrbacek, Filip; Bjornsdottir, Katrin; Poulenard, Jérôme; Meeussen, Camille; Guénard, Benoit; Venn, Susanna; Dimarco, Romina; Man, Matěj; Scharnweber, Tobias; Chown, Steven; Pio, Casimiro; Way, Robert; Erickson, Todd; Fernández-Pascual, Eduardo; Pușcaș, Mihai; Orsenigo, Simone; Di Musciano, Michele; Enquist, Brian; Newling, Emily; Tagesson, Torbern; Kemppinen, Julia; Serra-Diaz, Josep; Gottschall, Felix; Schuchardt, Max; Pitacco, Andrea; Jump, Alistair; Exton, Dan; Carnicer, Jofre; Aschero, Valeria; Urban, Anastasiya; Daskalova, Gergana; Santos, Cinthya; Goeckede, Mathias; Bruna, Josef; Andrews, Christopher; Jónsdóttir, Ingibjörg; Casanova-Katny, Angélica; Moriana-Armendariz, Mikel; Ewers, Robert; Pärtel, Meelis; Sagot, Clotilde; Herbst, Mathias; De Frenne, Pieter; Milbau, Ann; Gobin, Anne; Alexander, Jake; Kopecký, Martin; Buchmann, Nina; Kotowska, Martyna; Puchalka, Radoslaw; Penuelas, Josep; Gigauri, Khatuna; Prokushkin, Anatoly; Moiseev, Pavel; Jentsch, Anke; Klisz, Marcin; Barrio, Isabel; Ammann, Christof; Panov, Alexey; Van Geel, Maarten; Finckh, Manfred; Vaccari, Francesco; Erschbamer, Brigitta; Backes, Amanda; Robroek, Bjorn; Campoe, Otávio; Ahmadian, Negar; Boike, Julia; Thomas, Haydn; Pastor, Ada; Smith, Stuart; Pauli, Harald; Kollár, Jozef; de Cássia Guimarães Mesquita, Rita; Michaletz, Sean; Fuentes-Lillo, Eduardo; Urban, Josef; Greenwood, Sarah; Lens, Luc; Van de Vondel, Stijn; Vitale, Luca; Remmele, Sabine; Naujokaitis-Lewis, Ilona; Meusburger, Katrin; Cremonese, Edoardo; Barros, Agustina; Bokhorst, Stef; Svátek, Martin; Allonsius, Camille; Høye, Toke;doi: 10.1111/gcb.16060 , 10.32942/osf.io/pksqw , 10.3929/ethz-b-000523670 , 10.5445/ir/1000143688 , 10.21256/zhaw-24832 , 10.17863/cam.81331
pmid: 34967074
pmc: PMC9303923
handle: https://repository.ubn.ru.nl/handle/2066/286285 , 1871.1/b0fc7fdf-22e3-45ef-8d70-30d75b7f5fba , 20.500.11755/f67625de-3e1f-4112-899d-3dae951cfbfc , 11250/2986065 , 11250/2983746 , 10852/91639 , 10037/24329 , 10037/28344 , 20.500.14243/445619 , 10261/358672 , 2066/286285 , 10481/73202 , 10576/30034 , 11250/2979811 , 10067/1859610151162165141 , 10651/64961 , 1983/7aa6df09-efc2-4f70-8bec-268ab675f242 , 11590/476830 , 10449/74200 , 11584/332967 , 11695/119970 , 11697/178559 , 1854/LU-8743335 , 10017/50911 , 11381/2931752 , 11571/1450206 , 10044/1/107406 , 1893/33794 , 10900/135817
doi: 10.1111/gcb.16060 , 10.32942/osf.io/pksqw , 10.3929/ethz-b-000523670 , 10.5445/ir/1000143688 , 10.21256/zhaw-24832 , 10.17863/cam.81331
pmid: 34967074
pmc: PMC9303923
handle: https://repository.ubn.ru.nl/handle/2066/286285 , 1871.1/b0fc7fdf-22e3-45ef-8d70-30d75b7f5fba , 20.500.11755/f67625de-3e1f-4112-899d-3dae951cfbfc , 11250/2986065 , 11250/2983746 , 10852/91639 , 10037/24329 , 10037/28344 , 20.500.14243/445619 , 10261/358672 , 2066/286285 , 10481/73202 , 10576/30034 , 11250/2979811 , 10067/1859610151162165141 , 10651/64961 , 1983/7aa6df09-efc2-4f70-8bec-268ab675f242 , 11590/476830 , 10449/74200 , 11584/332967 , 11695/119970 , 11697/178559 , 1854/LU-8743335 , 10017/50911 , 11381/2931752 , 11571/1450206 , 10044/1/107406 , 1893/33794 , 10900/135817
AbstractResearch in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1‐km2resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1‐km2pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse‐grained air temperature estimates from ERA5‐Land (an atmospheric reanalysis by the European Centre for Medium‐Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome‐specific offsets emphasize that the projected impacts of climate and climate change on near‐surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil‐related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.
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For further information contact us at helpdesk@openaire.eu188 citations 188 popularity Top 1% influence Top 10% impulse Top 0.1% Powered by BIP!
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Peer-reviewedData sources: Bergen Open Research Archive - UiBInstitut National de la Recherche Agronomique: ProdINRAArticle . 2022License: CC BY NCData sources: Bielefeld Academic Search Engine (BASE)University of Bristol: Bristol ResearchArticle . 2022Data sources: Bielefeld Academic Search Engine (BASE)University of Copenhagen: ResearchArticle . 2022Data sources: Bielefeld Academic Search Engine (BASE)Archivio della Ricerca - Università degli Studi Roma TreArticle . 2022Data sources: Archivio della Ricerca - Università degli Studi Roma TreEcoEvoRxiv PreprintsPreprint . 2021Full-Text: https://ecoevorxiv.org/pksqw/downloadData sources: EcoEvoRxiv PreprintsGlobal Change BiologyReview . 2021Repositorio Institucional Universidad de GranadaArticle . 2022License: CC BY NCData sources: Repositorio Institucional Universidad de GranadaRepositorio Institucional de la Universidad de OviedoArticle . 2022License: CC BY NCData sources: Repositorio Institucional de la Universidad de OviedoGhent University Academic BibliographyArticle . 2022Data sources: Ghent University Academic BibliographyQatar University: QU Institutional RepositoryArticleData sources: Bielefeld Academic Search Engine (BASE)IRIS UNIPV (Università degli studi di Pavia)Article . 2022Data sources: Bielefeld Academic Search Engine (BASE)Archivio della ricerca dell'Università di Parma (CINECA IRIS)Article . 2022Data sources: Bielefeld Academic Search Engine (BASE)Eberhard Karls University Tübingen: Publication SystemArticle . 2022Data sources: Bielefeld Academic Search Engine (BASE)All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1111/gcb.16060&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2024 ItalyPublisher:MDPI AG GABBRIELLI, MARA; Allegrezza, Marina; RAGAGLINI, GIORGIO; Manco, Antonio; Vitale, Luca; PEREGO, ALESSIA;handle: 20.500.14243/452894 , 2434/1030155
Modeling approaches have emerged to address uncertainties arising from N2O emissions variability, representing a powerful methodology to investigate the two emitting processes (i.e., nitrification and denitrification) and to represent the interconnected dynamics among soil, atmosphere, and crops. This work offers an extensive overview of the widely used models simulating N2O under different cropping systems and management practices. We selected process-based models, prioritizing those with well-documented algorithms found in recently published scientific articles or having published source codes. We reviewed and compared the algorithms employed to simulate N2O emissions, adopting a unified symbol system. The selected models (APSIM, ARMOSA, CERES-EGC, CROPSYST, CoupModel, DAYCENT, DNDC, DSSAT, EPIC, SPACSYS, and STICS) were categorized by the approaches used to model nitrification and denitrification processes, discriminating between implicit or explicit consideration of the microbial pool and according to the formalization of the main environmental drivers of these processes (soil nitrogen concentration, temperature, moisture, and acidity). Models’ setting and performance assessments were also discussed. From the appraisal of these approaches, it emerged that soil chemical–physical properties and weather conditions are the main drivers of N cycling and the consequent gaseous emissions.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Embargo end date: 01 Jan 2020 Australia, United Kingdom, France, Spain, United States, Czech Republic, Russian Federation, Italy, France, Germany, Russian Federation, France, Italy, Australia, Germany, Belgium, United Kingdom, Switzerland, Czech Republic, Italy, United KingdomPublisher:Wiley Publicly fundedFunded by:EC | FORMICA, RSF | The anatomical and physio..., DFG +13 projectsEC| FORMICA ,RSF| The anatomical and physiological response of Scots pine xylem formation to variable water availability ,DFG ,EC| ICOS ,DFG| German Centre for Integrative Biodiversity Research - iDiv ,ANR| ODYSSEE ,NSF| Collaborative Research: ABI Development: Symbiota2: Enabling greater collaboration and flexibility for mobilizing biodiversity data ,SNSF| How does forest microclimate affect biodiversity dynamics? ,EC| AfricanBioServices ,UKRI| E3 - Edinburgh Earth and Environment - Doctoral Training Partnership ,SNSF| Lif3web: The present and future spatial structure of tri-trophic networks ,ANR| IMPRINT ,RCN| Disentangling the impacts of herbivory and climate on ecological dynamics ,NSF| MSB-ECA: Phylogenetically-informed modeling of the regional context of community assembly ,UKRI| Climate as a driver of shrub expansion and tundra greening ,EC| SUPER-GHarald Pauli; Josef Urban; Josef Urban; Sonia Merinero; Pieter De Frenne; Josefine Walz; Bente J. Graae; Michael B. Ashcroft; Michael B. Ashcroft; Tim Seipel; Ian Klupar; Ilya M. D. Maclean; Juan J. Jiménez; Jonas Schmeddes; Lucia Hederová; James D. M. Speed; Amanda Ratier Backes; Christian Rossi; Christian Rossi; Christian Rossi; Alessandro Petraglia; Isla H. Myers-Smith; Adrian V. Rocha; Pallieter De Smedt; Ellen Dorrepaal; Martin Macek; Pieter Vangansbeke; Miska Luoto; Nicoletta Cannone; Luca Vitale; José Luis Benito Alonso; Josef Brůna; Jan Wild; Marko Smiljanic; Edmund W. Basham; Eduardo Fuentes-Lillo; Eduardo Fuentes-Lillo; C. Johan Dahlberg; Sergiy Medinets; Keith W. Larson; Ann Milbau; Pekka Niittynen; Koenraad Van Meerbeek; Juha Aalto; Juha Aalto; Loïc Pellissier; Meelis Pärtel; Tudor-Mihai Ursu; Rafael A. García; Rafael A. García; Lore T. Verryckt; Laurenz M. Teuber; Kristoffer Hylander; Shengwei Zong; Shyam S. Phartyal; Shyam S. Phartyal; Agustina Barros; Valeria Aschero; Valeria Aschero; Rebecca A. Senior; Michael Stemkovski; Jonas J. Lembrechts; Joseph Okello; Joseph Okello; Jan Altman; Romina D. Dimarco; Julia Kemppinen; Pavel Dan Turtureanu; Dany Ghosn; Lukas Siebicke; Andrew D. Thomas; Zuzana Sitková; Sonja Wipf; Olivier Roupsard; Sanne Govaert; Robert G. Björk; Christian D. Larson; Fatih Fazlioglu; M. Rosa Fernández Calzado; Jörg G. Stephan; Jiri Dolezal; Jiri Dolezal; Michele Carbognani; Aud H. Halbritter; Mihai Pușcaș; David H. Klinges; Juergen Kreyling; Mats P. Björkman; Florian Zellweger; Esther R. Frei; Marijn Bauters; Camille Pitteloud; Jozef Kollár; Gergana N. Daskalova; Miguel Portillo-Estrada; Robert Kanka; Ana Clara Mazzolari; William D. Pearse; William D. Pearse; Elizabeth G. Simpson; Martin Svátek; Stuart W. Smith; Stuart W. Smith; Martin A. Nuñez; Jhonatan Sallo Bravo; Onur Candan; Mana Gharun; Austin Koontz; Simone Cesarz; T'Ai Gladys Whittingham Forte; George Kazakis; Joseph J. Bailey; Zhaochen Zhang; Nico Eisenhauer; Volodymyr I. Medinets; Jonathan Lenoir; Juan Lorite; Radim Matula; Lena Muffler; Lena Muffler; Aníbal Pauchard; Aníbal Pauchard; Pascal Boeckx; Maaike Y. Bader; Robert Weigel; Marek Čiliak; Kamil Láska; Brett R. Scheffers; Camille Meeussen; Benjamin Blonder; Benjamin Blonder; Felix Gottschall; Ronja E. M. Wedegärtner; Francesco Malfasi; Jonas Ardö; Roman Plichta; Pascal Vittoz; Mario Trouillier; Julia Boike; Peter Barančok; Christian Rixen; Lisa J. Rew; Andrej Varlagin; Valter Di Cecco; Ivan Nijs; Jan Dick; Charly Geron; Charly Geron; Bernard Heinesch; Patrice Descombes; Mauro Guglielmin; Angela Stanisci; Filip Hrbáček; Martin Wilmking; Jian Zhang; Krystal Randall; Katja Tielbörger; Peter Haase; Peter Haase; Alistair S. Jump; Rafaella Canessa; Masahito Ueyama; Matěj Man; František Máliš; Marcello Tomaselli; Stef Haesen; Salvatore R. Curasi; Sylvia Haider; Andrea Lamprecht; Miguel Ángel de Pablo; Haydn J.D. Thomas; Nina Buchmann; Manuela Winkler; Klaus Steinbauer; Toke T. Høye; Fernando Moyano; Miroslav Svoboda; Christopher Andrews; Martin Kopecký; Martin Kopecký; Rebecca Finger Higgens; Hans J. De Boeck; Jürgen Homeier; Juha M. Alatalo; Ben Somers; Khatuna Gigauri; Andrej Palaj; Thomas Scholten; Mia Vedel Sørensen; Edoardo Cremonese; Liesbeth van den Brink;pmid: 32311220
handle: 20.500.14243/370921 , 1854/LU-8681704 , 11381/2880120 , 1893/31042 , 10900/106894
pmid: 32311220
handle: 20.500.14243/370921 , 1854/LU-8681704 , 11381/2880120 , 1893/31042 , 10900/106894
AbstractCurrent analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long‐term average thermal conditions at coarse spatial resolutions only. Hence, many climate‐forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold‐air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free‐air temperatures, microclimatic ground and near‐surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near‐surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.
NERC Open Research A... arrow_drop_down CIRAD: HAL (Agricultural Research for Development)Article . 2020Full-Text: https://hal.science/hal-03003135Data sources: Bielefeld Academic Search Engine (BASE)Archivio della ricerca dell'Università di Parma (CINECA IRIS)Article . 2020Full-Text: https://hdl.handle.net/11381/2880120Data sources: Bielefeld Academic Search Engine (BASE)University of California: eScholarshipArticle . 2020Full-Text: https://escholarship.org/uc/item/41n2d8c6Data sources: Bielefeld Academic Search Engine (BASE)Publikationenserver der Georg-August-Universität GöttingenArticle . 2021Institut National de la Recherche Agronomique: ProdINRAArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)Recolector de Ciencia Abierta, RECOLECTAArticle . 2020Data sources: Recolector de Ciencia Abierta, RECOLECTARepository of the Czech Academy of SciencesArticle . 2020Data sources: Repository of the Czech Academy of SciencesGlobal Change BiologyArticle . 2020 . Peer-reviewedLicense: Wiley Online Library User AgreementData sources: CrossrefeScholarship - University of CaliforniaArticle . 2020Data sources: eScholarship - University of CaliforniaGhent University Academic BibliographyArticle . 2020Data sources: Ghent University Academic BibliographyUniversitätsbibliographie, Universität Duisburg-EssenArticle . 2020Data sources: Universitätsbibliographie, Universität Duisburg-EssenSiberian Federal University: Archiv Elektronnych SFUArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)University of Wollongong, Australia: Research OnlineArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)University of Stirling: Stirling Digital Research RepositoryArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)Archivio della ricerca dell'Università di Parma (CINECA IRIS)Article . 2020Data sources: Bielefeld Academic Search Engine (BASE)Eberhard Karls University Tübingen: Publication SystemArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)Natural Environment Research Council: NERC Open Research ArchiveArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1111/gcb.15123&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 148 citations 148 popularity Top 1% influence Top 10% impulse Top 0.1% Powered by BIP!
more_vert NERC Open Research A... arrow_drop_down CIRAD: HAL (Agricultural Research for Development)Article . 2020Full-Text: https://hal.science/hal-03003135Data sources: Bielefeld Academic Search Engine (BASE)Archivio della ricerca dell'Università di Parma (CINECA IRIS)Article . 2020Full-Text: https://hdl.handle.net/11381/2880120Data sources: Bielefeld Academic Search Engine (BASE)University of California: eScholarshipArticle . 2020Full-Text: https://escholarship.org/uc/item/41n2d8c6Data sources: Bielefeld Academic Search Engine (BASE)Publikationenserver der Georg-August-Universität GöttingenArticle . 2021Institut National de la Recherche Agronomique: ProdINRAArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)Recolector de Ciencia Abierta, RECOLECTAArticle . 2020Data sources: Recolector de Ciencia Abierta, RECOLECTARepository of the Czech Academy of SciencesArticle . 2020Data sources: Repository of the Czech Academy of SciencesGlobal Change BiologyArticle . 2020 . Peer-reviewedLicense: Wiley Online Library User AgreementData sources: CrossrefeScholarship - University of CaliforniaArticle . 2020Data sources: eScholarship - University of CaliforniaGhent University Academic BibliographyArticle . 2020Data sources: Ghent University Academic BibliographyUniversitätsbibliographie, Universität Duisburg-EssenArticle . 2020Data sources: Universitätsbibliographie, Universität Duisburg-EssenSiberian Federal University: Archiv Elektronnych SFUArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)University of Wollongong, Australia: Research OnlineArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)University of Stirling: Stirling Digital Research RepositoryArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)Archivio della ricerca dell'Università di Parma (CINECA IRIS)Article . 2020Data sources: Bielefeld Academic Search Engine (BASE)Eberhard Karls University Tübingen: Publication SystemArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)Natural Environment Research Council: NERC Open Research ArchiveArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1111/gcb.15123&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022 ItalyPublisher:Springer Science and Business Media LLC Authors: Zenone T; Vitale L; Famulari D; Magliulo V;handle: 20.500.14243/416230
AbstractBackgroundThe evaporative fraction (EF) represents an important biophysical parameter reflecting the distribution of surface available energy. In this study, we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning (ML) classes of algorithms: the linear regression (LR), regression tree (RT), support vector machine (SVM), ensembles of tree (ETs) and Gaussian process regression (GPR) to predict the EF at daily time step. The adopted methodology consisted of three main steps that include: (i) selection of the EF predictors; (ii) comparison of the different classes of ML; (iii) application, cross-validation of the selected ML algorithms and comparison with the observed data.ResultsOur results indicate that SVM and GPR were the best classes of ML at predicting the EF, with a total of four different algorithms: cubic SVM, medium Gaussian SVM, the Matern 5/2 GPR, and the rational quadratic GPR. The comparison between observed and predicted EF in all four algorithms, during the training phase, were within the 95% confidence interval: theR2value between observed and predicted EF was 0.76 (RMSE 0.05) for the medium Gaussian SVM, 0.99 (RMSE 0.01) for the rational quadratic GPR, 0.94 (RMSE 0.02) for the Matern 5/2 GPR, and 0.83 (RMSE 0.05) for the cubic SVM algorithms. Similar results were obtained during the testing phase. The results of the cross-validation analysis indicate that theR2values obtained between all iterations for each of the four adopted ML algorithms were basically constant, confirming the ability of ML as a tool to predict EF.ConclusionML algorithms represent a valid alternative able to predict the EF especially when remote sensing data are not available, or the sky conditions are not suitable. The application to different geographical areas, or crops, requires further development of the model based on different data sources of soils, climate, and cropping systems.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 1 citations 1 popularity Top 10% influence Average impulse Average Powered by BIP!
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2015 ItalyPublisher:Copernicus Publ., Göttingen , Germania Funded by:EC | GHG EUROPE, EC | ICOSEC| GHG EUROPE ,EC| ICOSXiuchen Wu; Nicolas Vuichard; Philippe Ciais; Nicolas Viovy; Nathalie de NobletDucoudré; Xuhui Wang; Vincenzo Magliulo; Martin Wattenbach; Luca Vitale; Paul Di Tommasi; Eddy J Moors; Wilma Jans; Jan Elbers; Eric Ceschia; Tiphaine Tallec; Christian Bernhofer; Thomas Grünwald; Christine Moureaux; Tanguy Manise; Anne Ligne; Pierre Cellier; Benjamin Loubet; Nicolas Mascher;handle: 20.500.14243/314737 , 20.500.14243/295284
The responses of crop plants to changing climate and CO2 could have large effects on food production, and impact carbon, water and energy fluxes, causing feedbacks to climate. To simulate the responses of temperate crops to changing climate and CO2, accounting for the specific phenology of crops mediated by management practice, we develop a process-oriented terrestrial biogeochemical model ORCHIDEE-CROP, which integrates a generic crop phenology and harvest module and a very simple parameterization of nitrogen fertilization, into the DGVM ORCHIDEEv196, in order to simulate biophysical and biogeochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but it is tested here for maize and winter wheat, with the phenological parameterizations of two European varieties. We evaluate this new model against eddy covariance and biometric measurements at 7 winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are Net Ecosystem Exchange (NEE), latent heat and sensible heat fluxes. Site additional measurements of leaf area index (LAI), aboveground biomass and yield are used as well. Evaluation results reveal that ORCHIDEE-CROP reproduces the observed timing of crop development stages and the amplitude of pertaining LAI changes in contrast to ORCHIDEEv196, that by default applies to crops the same phenology of grass. A near-halving of the root mean square error of LAI from 2.38±0.77 m2 m-2 to 1.08±0.34 m2 m-2 is obtained between ORCHIDEEv196 and ORCHIDEE-CROP across the 7 study sites. Improved crop phenology and carbon allocation lead to a general good match between modelled and observed aboveground biomass [with a normalized root mean squared error (NRMSE) of 11.0%-54.2%], crop yield, as well as of carbon and energy fluxes with NRMSE of ~9.0-20.1% and ~9.4-22.3% for NEE, and sensible and latent heat fluxes, respectively. The model data misfits for energy fluxes are within uncertainties of the measurements, which show an incomplete energy balance closure within the range 80.6-86.3%. The remaining discrepancies between modelled and observed LAI and other variables at specific sites are partly attributable to unrealistic representation of human management. In addition, ORCHIDEE-CROP is shown to have the ability to capture the spatial gradients of both biogeochemical (gross primary productivity and NEE) and biophysical (sensible and latent heat fluxes) variables across the sites in Europe, an important requirement for future spatially explicit simulations. Further improvement of the model with an explicit parameterization of nutrition dynamics and management, is expected to improve its predictive ability to simulate croplands in an Earth System Model
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description Publicationkeyboard_double_arrow_right Article , Journal 2011 ItalyPublisher:Institute of Experimental Botany Authors: ARENA, CARMEN; VITALE, LUCA; A. Virzo De Santo;handle: 11588/426299 , 20.500.14243/25472
This work aimed to evaluate if gas exchange and PSII photochemical activity in maize are affected by different irradiance levels during short-term exposure to elevated CO2. For this purpose gas exchange and chlorophyll a fluorescence were measured on maize plants grown at ambient CO2 concentration (control CO2) and exposed for 4 h to short-term treatments at 800 ?mol(CO2) mol-1 (high CO2) at a photosynthetic photon flux density (PPFD) of either 1,000 ?mol m-2 s-1 (control light) or 1,900 ?mol m-2 s-1 (high light). At control light, high-CO2 leaves showed a significant decrease of net photosynthetic rate (PN) and a rise in the ratio of intercellular to ambient CO2 concentration (Ci/Ca) and water-use efficiency (WUE) compared to control CO2 leaves. No difference between CO2 concentrations for PSII effective photochemistry (?PSII), photochemical quenching (qp) and nonphotochemical quenching (NPQ) was detected. Under high light, high-CO2 leaves did not differ in PN, Ci/Ca, ?PSII and NPQ, but showed an increase of WUE. These results suggest that at control light photosynthetic apparatus is negatively affected by high CO2 concentration in terms of carbon gain by limitations in photosynthetic dark reaction rather than in photochemistry. At high light, the elevated CO2 concentration did not promote an increase of photosynthesis and photochemistry but only an improvement of water balance due to increased WUE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type , Review , Preprint 2021Embargo end date: 01 Jan 2022 United Kingdom, Norway, United Kingdom, Norway, Italy, United Kingdom, Italy, Italy, Spain, Qatar, United Kingdom, Denmark, Italy, Denmark, Italy, Italy, Germany, Netherlands, Finland, Italy, Sweden, Netherlands, Germany, Netherlands, Spain, Spain, Netherlands, Spain, Italy, Lithuania, Germany, Norway, Spain, Italy, Germany, Norway, Netherlands, Germany, United Kingdom, Italy, United Kingdom, Italy, Netherlands, Switzerland, Netherlands, Spain, Italy, Belgium, Spain, Netherlands, Spain, Lithuania, France, Germany, Sweden, United States, Belgium, Germany, Italy, Italy, Netherlands, Germany, Netherlands, Qatar, United Kingdom, United KingdomPublisher:Wiley Funded by:EC | eLTER PLUS, EC | LEAP-AGRI, ARC | Discovery Early Career Re... +32 projectsEC| eLTER PLUS ,EC| LEAP-AGRI ,ARC| Discovery Early Career Researcher Award - Grant ID: DE180100570 ,EC| DESIRA ,ANR| ASICS ,SNSF| ICOS-CH Phase 2 ,NSF| Integrating species traits into species pools: A multi-scale approach to understanding community assembly ,EC| SUPER-G ,AKA| Atmosphere and Climate Competence Center (ACCC) ,UKRI| E3 - Edinburgh Earth and Environment - Doctoral Training Partnership ,UKRI| Climate as a driver of shrub expansion and tundra greening ,UKRI| SCORE: Supply Chain Optimisation for demand Response Efficiency ,EC| FORMICA ,RSF| The anatomical and physiological response of Scots pine xylem formation to variable water availability ,SNSF| Climate change impacts on biodiversity: From macro- to microclimate ,EC| AIAS ,NSERC ,RCN| Effects of herbivory and warming on tundra plant communities ,RCN| The role of Functional group interactions in mediating climate change impacts on the Carbon dynamics and Biodiversity of alpine ecosystems ,EC| AfricanBioServices ,EC| ECLAIRE ,ARC| Discovery Early Career Researcher Award - Grant ID: DE140101611 ,NWO| Specialists at work: how decomposers break down plant litter ,EC| PERMTHAW ,EC| ICOS ,EC| NICH ,SNSF| How does forest microclimate affect biodiversity dynamics? ,DFG| EarthShape: Earth Surface Shaping by Biota ,RCN| The effect of snow depth and snow melt timing on arctic terrestrial ecosystems. ,EC| SustainSAHEL ,UKRI| Forecasting the impacts of drought on human-modified tropical forests by integrating models with data ,UKRI| UK Status, Change and Projections of the Environment (UK-SCaPE) ,ANR| IMPRINT ,DFG| German Centre for Integrative Biodiversity Research - iDiv ,ANR| ODYSSEEWinkler, Manuela; Plichta, Roman; Buysse, Pauline; Lohila, Annalea; Spicher, Fabien; Boeckx, Pascal; Wild, Jan; Feigenwinter, Iris; Olejnik, Janusz; Risch, Anita; Khuroo, Anzar; Lynn, Joshua; di Cella, Umberto; Schmidt, Marius; Urbaniak, Marek; Marchesini, Luca; Govaert, Sanne; Uogintas, Domas; Assis, Rafael; Medinets, Volodymyr; Abdalaze, Otar; Varlagin, Andrej; Dolezal, Jiri; Myers, Jonathan; Randall, Krystal; Bauters, Marijn; Jimenez, Juan; Stoll, Stefan; Petraglia, Alessandro; Mazzolari, Ana; Ogaya, Romà; Tyystjärvi, Vilna; Hammerle, Albin; Wipf, Sonja; Lorite, Juan; Fanin, Nicolas; Benavides, Juan; Scholten, Thomas; Yu, Zicheng; Veen, G.; Treier, Urs; Candan, Onur; Bell, Michael; Hörtnagl, Lukas; Siebicke, Lukas; Vives-Ingla, Maria; Eugster, Werner; Grelle, Achim; Stemkovski, Michael; Theurillat, Jean-Paul; Matula, Radim; Dorrepaal, Ellen; Steinbrecher, Rainer; Alatalo, Juha; Fenu, Giuseppe; Arzac, Alberto; Homeier, Jürgen; Porro, Francesco; Robinson, Sharon; Ghosn, Dany; Haugum, Siri; Ziemblińska, Klaudia; Camargo, José; Zhao, Peng; Niittynen, Pekka; Liljebladh, Bengt; Normand, Signe; Dias, Arildo; Larson, Christian; Peichl, Matthias; Collier, Laura; Myers-Smith, Isla; Zong, Shengwei; Kašpar, Vít; Cooper, Elisabeth; Haider, Sylvia; von Oppen, Jonathan; Cutini, Maurizio; Benito-Alonso, José-Luis; Luoto, Miska; Klemedtsson, Leif; Higgens, Rebecca; Zhang, Jian; Speed, James; Nijs, Ivan; Macek, Martin; Steinwandter, Michael; Poyatos, Rafael; Niedrist, Georg; Curasi, Salvatore; Yang, Yan; Dengler, Jürgen; Géron, Charly; de Pablo, Miguel; Xenakis, Georgios; Kreyling, Juergen; Forte, Tai; Bailey, Joseph; Knohl, Alexander; Goulding, Keith; Wilkinson, Matthew; Kljun, Natascha; Roupsard, Olivier; Stiegler, Christian; Verbruggen, Erik; Wingate, Lisa; Lamprecht, Andrea; Hamid, Maroof; Rossi, Graziano; Descombes, Patrice; Hrbacek, Filip; Bjornsdottir, Katrin; Poulenard, Jérôme; Meeussen, Camille; Guénard, Benoit; Venn, Susanna; Dimarco, Romina; Man, Matěj; Scharnweber, Tobias; Chown, Steven; Pio, Casimiro; Way, Robert; Erickson, Todd; Fernández-Pascual, Eduardo; Pușcaș, Mihai; Orsenigo, Simone; Di Musciano, Michele; Enquist, Brian; Newling, Emily; Tagesson, Torbern; Kemppinen, Julia; Serra-Diaz, Josep; Gottschall, Felix; Schuchardt, Max; Pitacco, Andrea; Jump, Alistair; Exton, Dan; Carnicer, Jofre; Aschero, Valeria; Urban, Anastasiya; Daskalova, Gergana; Santos, Cinthya; Goeckede, Mathias; Bruna, Josef; Andrews, Christopher; Jónsdóttir, Ingibjörg; Casanova-Katny, Angélica; Moriana-Armendariz, Mikel; Ewers, Robert; Pärtel, Meelis; Sagot, Clotilde; Herbst, Mathias; De Frenne, Pieter; Milbau, Ann; Gobin, Anne; Alexander, Jake; Kopecký, Martin; Buchmann, Nina; Kotowska, Martyna; Puchalka, Radoslaw; Penuelas, Josep; Gigauri, Khatuna; Prokushkin, Anatoly; Moiseev, Pavel; Jentsch, Anke; Klisz, Marcin; Barrio, Isabel; Ammann, Christof; Panov, Alexey; Van Geel, Maarten; Finckh, Manfred; Vaccari, Francesco; Erschbamer, Brigitta; Backes, Amanda; Robroek, Bjorn; Campoe, Otávio; Ahmadian, Negar; Boike, Julia; Thomas, Haydn; Pastor, Ada; Smith, Stuart; Pauli, Harald; Kollár, Jozef; de Cássia Guimarães Mesquita, Rita; Michaletz, Sean; Fuentes-Lillo, Eduardo; Urban, Josef; Greenwood, Sarah; Lens, Luc; Van de Vondel, Stijn; Vitale, Luca; Remmele, Sabine; Naujokaitis-Lewis, Ilona; Meusburger, Katrin; Cremonese, Edoardo; Barros, Agustina; Bokhorst, Stef; Svátek, Martin; Allonsius, Camille; Høye, Toke;doi: 10.1111/gcb.16060 , 10.32942/osf.io/pksqw , 10.3929/ethz-b-000523670 , 10.5445/ir/1000143688 , 10.21256/zhaw-24832 , 10.17863/cam.81331
pmid: 34967074
pmc: PMC9303923
handle: https://repository.ubn.ru.nl/handle/2066/286285 , 1871.1/b0fc7fdf-22e3-45ef-8d70-30d75b7f5fba , 20.500.11755/f67625de-3e1f-4112-899d-3dae951cfbfc , 11250/2986065 , 11250/2983746 , 10852/91639 , 10037/24329 , 10037/28344 , 20.500.14243/445619 , 10261/358672 , 2066/286285 , 10481/73202 , 10576/30034 , 11250/2979811 , 10067/1859610151162165141 , 10651/64961 , 1983/7aa6df09-efc2-4f70-8bec-268ab675f242 , 11590/476830 , 10449/74200 , 11584/332967 , 11695/119970 , 11697/178559 , 1854/LU-8743335 , 10017/50911 , 11381/2931752 , 11571/1450206 , 10044/1/107406 , 1893/33794 , 10900/135817
doi: 10.1111/gcb.16060 , 10.32942/osf.io/pksqw , 10.3929/ethz-b-000523670 , 10.5445/ir/1000143688 , 10.21256/zhaw-24832 , 10.17863/cam.81331
pmid: 34967074
pmc: PMC9303923
handle: https://repository.ubn.ru.nl/handle/2066/286285 , 1871.1/b0fc7fdf-22e3-45ef-8d70-30d75b7f5fba , 20.500.11755/f67625de-3e1f-4112-899d-3dae951cfbfc , 11250/2986065 , 11250/2983746 , 10852/91639 , 10037/24329 , 10037/28344 , 20.500.14243/445619 , 10261/358672 , 2066/286285 , 10481/73202 , 10576/30034 , 11250/2979811 , 10067/1859610151162165141 , 10651/64961 , 1983/7aa6df09-efc2-4f70-8bec-268ab675f242 , 11590/476830 , 10449/74200 , 11584/332967 , 11695/119970 , 11697/178559 , 1854/LU-8743335 , 10017/50911 , 11381/2931752 , 11571/1450206 , 10044/1/107406 , 1893/33794 , 10900/135817
AbstractResearch in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1‐km2resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1‐km2pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse‐grained air temperature estimates from ERA5‐Land (an atmospheric reanalysis by the European Centre for Medium‐Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome‐specific offsets emphasize that the projected impacts of climate and climate change on near‐surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil‐related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2024 ItalyPublisher:MDPI AG GABBRIELLI, MARA; Allegrezza, Marina; RAGAGLINI, GIORGIO; Manco, Antonio; Vitale, Luca; PEREGO, ALESSIA;handle: 20.500.14243/452894 , 2434/1030155
Modeling approaches have emerged to address uncertainties arising from N2O emissions variability, representing a powerful methodology to investigate the two emitting processes (i.e., nitrification and denitrification) and to represent the interconnected dynamics among soil, atmosphere, and crops. This work offers an extensive overview of the widely used models simulating N2O under different cropping systems and management practices. We selected process-based models, prioritizing those with well-documented algorithms found in recently published scientific articles or having published source codes. We reviewed and compared the algorithms employed to simulate N2O emissions, adopting a unified symbol system. The selected models (APSIM, ARMOSA, CERES-EGC, CROPSYST, CoupModel, DAYCENT, DNDC, DSSAT, EPIC, SPACSYS, and STICS) were categorized by the approaches used to model nitrification and denitrification processes, discriminating between implicit or explicit consideration of the microbial pool and according to the formalization of the main environmental drivers of these processes (soil nitrogen concentration, temperature, moisture, and acidity). Models’ setting and performance assessments were also discussed. From the appraisal of these approaches, it emerged that soil chemical–physical properties and weather conditions are the main drivers of N cycling and the consequent gaseous emissions.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Embargo end date: 01 Jan 2020 Australia, United Kingdom, France, Spain, United States, Czech Republic, Russian Federation, Italy, France, Germany, Russian Federation, France, Italy, Australia, Germany, Belgium, United Kingdom, Switzerland, Czech Republic, Italy, United KingdomPublisher:Wiley Publicly fundedFunded by:EC | FORMICA, RSF | The anatomical and physio..., DFG +13 projectsEC| FORMICA ,RSF| The anatomical and physiological response of Scots pine xylem formation to variable water availability ,DFG ,EC| ICOS ,DFG| German Centre for Integrative Biodiversity Research - iDiv ,ANR| ODYSSEE ,NSF| Collaborative Research: ABI Development: Symbiota2: Enabling greater collaboration and flexibility for mobilizing biodiversity data ,SNSF| How does forest microclimate affect biodiversity dynamics? ,EC| AfricanBioServices ,UKRI| E3 - Edinburgh Earth and Environment - Doctoral Training Partnership ,SNSF| Lif3web: The present and future spatial structure of tri-trophic networks ,ANR| IMPRINT ,RCN| Disentangling the impacts of herbivory and climate on ecological dynamics ,NSF| MSB-ECA: Phylogenetically-informed modeling of the regional context of community assembly ,UKRI| Climate as a driver of shrub expansion and tundra greening ,EC| SUPER-GHarald Pauli; Josef Urban; Josef Urban; Sonia Merinero; Pieter De Frenne; Josefine Walz; Bente J. Graae; Michael B. Ashcroft; Michael B. Ashcroft; Tim Seipel; Ian Klupar; Ilya M. D. Maclean; Juan J. Jiménez; Jonas Schmeddes; Lucia Hederová; James D. M. Speed; Amanda Ratier Backes; Christian Rossi; Christian Rossi; Christian Rossi; Alessandro Petraglia; Isla H. Myers-Smith; Adrian V. Rocha; Pallieter De Smedt; Ellen Dorrepaal; Martin Macek; Pieter Vangansbeke; Miska Luoto; Nicoletta Cannone; Luca Vitale; José Luis Benito Alonso; Josef Brůna; Jan Wild; Marko Smiljanic; Edmund W. Basham; Eduardo Fuentes-Lillo; Eduardo Fuentes-Lillo; C. Johan Dahlberg; Sergiy Medinets; Keith W. Larson; Ann Milbau; Pekka Niittynen; Koenraad Van Meerbeek; Juha Aalto; Juha Aalto; Loïc Pellissier; Meelis Pärtel; Tudor-Mihai Ursu; Rafael A. García; Rafael A. García; Lore T. Verryckt; Laurenz M. Teuber; Kristoffer Hylander; Shengwei Zong; Shyam S. Phartyal; Shyam S. Phartyal; Agustina Barros; Valeria Aschero; Valeria Aschero; Rebecca A. Senior; Michael Stemkovski; Jonas J. Lembrechts; Joseph Okello; Joseph Okello; Jan Altman; Romina D. Dimarco; Julia Kemppinen; Pavel Dan Turtureanu; Dany Ghosn; Lukas Siebicke; Andrew D. Thomas; Zuzana Sitková; Sonja Wipf; Olivier Roupsard; Sanne Govaert; Robert G. Björk; Christian D. Larson; Fatih Fazlioglu; M. Rosa Fernández Calzado; Jörg G. Stephan; Jiri Dolezal; Jiri Dolezal; Michele Carbognani; Aud H. Halbritter; Mihai Pușcaș; David H. Klinges; Juergen Kreyling; Mats P. Björkman; Florian Zellweger; Esther R. Frei; Marijn Bauters; Camille Pitteloud; Jozef Kollár; Gergana N. Daskalova; Miguel Portillo-Estrada; Robert Kanka; Ana Clara Mazzolari; William D. Pearse; William D. Pearse; Elizabeth G. Simpson; Martin Svátek; Stuart W. Smith; Stuart W. Smith; Martin A. Nuñez; Jhonatan Sallo Bravo; Onur Candan; Mana Gharun; Austin Koontz; Simone Cesarz; T'Ai Gladys Whittingham Forte; George Kazakis; Joseph J. Bailey; Zhaochen Zhang; Nico Eisenhauer; Volodymyr I. Medinets; Jonathan Lenoir; Juan Lorite; Radim Matula; Lena Muffler; Lena Muffler; Aníbal Pauchard; Aníbal Pauchard; Pascal Boeckx; Maaike Y. Bader; Robert Weigel; Marek Čiliak; Kamil Láska; Brett R. Scheffers; Camille Meeussen; Benjamin Blonder; Benjamin Blonder; Felix Gottschall; Ronja E. M. Wedegärtner; Francesco Malfasi; Jonas Ardö; Roman Plichta; Pascal Vittoz; Mario Trouillier; Julia Boike; Peter Barančok; Christian Rixen; Lisa J. Rew; Andrej Varlagin; Valter Di Cecco; Ivan Nijs; Jan Dick; Charly Geron; Charly Geron; Bernard Heinesch; Patrice Descombes; Mauro Guglielmin; Angela Stanisci; Filip Hrbáček; Martin Wilmking; Jian Zhang; Krystal Randall; Katja Tielbörger; Peter Haase; Peter Haase; Alistair S. Jump; Rafaella Canessa; Masahito Ueyama; Matěj Man; František Máliš; Marcello Tomaselli; Stef Haesen; Salvatore R. Curasi; Sylvia Haider; Andrea Lamprecht; Miguel Ángel de Pablo; Haydn J.D. Thomas; Nina Buchmann; Manuela Winkler; Klaus Steinbauer; Toke T. Høye; Fernando Moyano; Miroslav Svoboda; Christopher Andrews; Martin Kopecký; Martin Kopecký; Rebecca Finger Higgens; Hans J. De Boeck; Jürgen Homeier; Juha M. Alatalo; Ben Somers; Khatuna Gigauri; Andrej Palaj; Thomas Scholten; Mia Vedel Sørensen; Edoardo Cremonese; Liesbeth van den Brink;pmid: 32311220
handle: 20.500.14243/370921 , 1854/LU-8681704 , 11381/2880120 , 1893/31042 , 10900/106894
pmid: 32311220
handle: 20.500.14243/370921 , 1854/LU-8681704 , 11381/2880120 , 1893/31042 , 10900/106894
AbstractCurrent analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long‐term average thermal conditions at coarse spatial resolutions only. Hence, many climate‐forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold‐air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free‐air temperatures, microclimatic ground and near‐surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near‐surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.
NERC Open Research A... arrow_drop_down CIRAD: HAL (Agricultural Research for Development)Article . 2020Full-Text: https://hal.science/hal-03003135Data sources: Bielefeld Academic Search Engine (BASE)Archivio della ricerca dell'Università di Parma (CINECA IRIS)Article . 2020Full-Text: https://hdl.handle.net/11381/2880120Data sources: Bielefeld Academic Search Engine (BASE)University of California: eScholarshipArticle . 2020Full-Text: https://escholarship.org/uc/item/41n2d8c6Data sources: Bielefeld Academic Search Engine (BASE)Publikationenserver der Georg-August-Universität GöttingenArticle . 2021Institut National de la Recherche Agronomique: ProdINRAArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)Recolector de Ciencia Abierta, RECOLECTAArticle . 2020Data sources: Recolector de Ciencia Abierta, RECOLECTARepository of the Czech Academy of SciencesArticle . 2020Data sources: Repository of the Czech Academy of SciencesGlobal Change BiologyArticle . 2020 . Peer-reviewedLicense: Wiley Online Library User AgreementData sources: CrossrefeScholarship - University of CaliforniaArticle . 2020Data sources: eScholarship - University of CaliforniaGhent University Academic BibliographyArticle . 2020Data sources: Ghent University Academic BibliographyUniversitätsbibliographie, Universität Duisburg-EssenArticle . 2020Data sources: Universitätsbibliographie, Universität Duisburg-EssenSiberian Federal University: Archiv Elektronnych SFUArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)University of Wollongong, Australia: Research OnlineArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)University of Stirling: Stirling Digital Research RepositoryArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)Archivio della ricerca dell'Università di Parma (CINECA IRIS)Article . 2020Data sources: Bielefeld Academic Search Engine (BASE)Eberhard Karls University Tübingen: Publication SystemArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)Natural Environment Research Council: NERC Open Research ArchiveArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1111/gcb.15123&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 148 citations 148 popularity Top 1% influence Top 10% impulse Top 0.1% Powered by BIP!
more_vert NERC Open Research A... arrow_drop_down CIRAD: HAL (Agricultural Research for Development)Article . 2020Full-Text: https://hal.science/hal-03003135Data sources: Bielefeld Academic Search Engine (BASE)Archivio della ricerca dell'Università di Parma (CINECA IRIS)Article . 2020Full-Text: https://hdl.handle.net/11381/2880120Data sources: Bielefeld Academic Search Engine (BASE)University of California: eScholarshipArticle . 2020Full-Text: https://escholarship.org/uc/item/41n2d8c6Data sources: Bielefeld Academic Search Engine (BASE)Publikationenserver der Georg-August-Universität GöttingenArticle . 2021Institut National de la Recherche Agronomique: ProdINRAArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)Recolector de Ciencia Abierta, RECOLECTAArticle . 2020Data sources: Recolector de Ciencia Abierta, RECOLECTARepository of the Czech Academy of SciencesArticle . 2020Data sources: Repository of the Czech Academy of SciencesGlobal Change BiologyArticle . 2020 . Peer-reviewedLicense: Wiley Online Library User AgreementData sources: CrossrefeScholarship - University of CaliforniaArticle . 2020Data sources: eScholarship - University of CaliforniaGhent University Academic BibliographyArticle . 2020Data sources: Ghent University Academic BibliographyUniversitätsbibliographie, Universität Duisburg-EssenArticle . 2020Data sources: Universitätsbibliographie, Universität Duisburg-EssenSiberian Federal University: Archiv Elektronnych SFUArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)University of Wollongong, Australia: Research OnlineArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)University of Stirling: Stirling Digital Research RepositoryArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)Archivio della ricerca dell'Università di Parma (CINECA IRIS)Article . 2020Data sources: Bielefeld Academic Search Engine (BASE)Eberhard Karls University Tübingen: Publication SystemArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)Natural Environment Research Council: NERC Open Research ArchiveArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1111/gcb.15123&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022 ItalyPublisher:Springer Science and Business Media LLC Authors: Zenone T; Vitale L; Famulari D; Magliulo V;handle: 20.500.14243/416230
AbstractBackgroundThe evaporative fraction (EF) represents an important biophysical parameter reflecting the distribution of surface available energy. In this study, we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning (ML) classes of algorithms: the linear regression (LR), regression tree (RT), support vector machine (SVM), ensembles of tree (ETs) and Gaussian process regression (GPR) to predict the EF at daily time step. The adopted methodology consisted of three main steps that include: (i) selection of the EF predictors; (ii) comparison of the different classes of ML; (iii) application, cross-validation of the selected ML algorithms and comparison with the observed data.ResultsOur results indicate that SVM and GPR were the best classes of ML at predicting the EF, with a total of four different algorithms: cubic SVM, medium Gaussian SVM, the Matern 5/2 GPR, and the rational quadratic GPR. The comparison between observed and predicted EF in all four algorithms, during the training phase, were within the 95% confidence interval: theR2value between observed and predicted EF was 0.76 (RMSE 0.05) for the medium Gaussian SVM, 0.99 (RMSE 0.01) for the rational quadratic GPR, 0.94 (RMSE 0.02) for the Matern 5/2 GPR, and 0.83 (RMSE 0.05) for the cubic SVM algorithms. Similar results were obtained during the testing phase. The results of the cross-validation analysis indicate that theR2values obtained between all iterations for each of the four adopted ML algorithms were basically constant, confirming the ability of ML as a tool to predict EF.ConclusionML algorithms represent a valid alternative able to predict the EF especially when remote sensing data are not available, or the sky conditions are not suitable. The application to different geographical areas, or crops, requires further development of the model based on different data sources of soils, climate, and cropping systems.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 1 citations 1 popularity Top 10% influence Average impulse Average Powered by BIP!
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2015 ItalyPublisher:Copernicus Publ., Göttingen , Germania Funded by:EC | GHG EUROPE, EC | ICOSEC| GHG EUROPE ,EC| ICOSXiuchen Wu; Nicolas Vuichard; Philippe Ciais; Nicolas Viovy; Nathalie de NobletDucoudré; Xuhui Wang; Vincenzo Magliulo; Martin Wattenbach; Luca Vitale; Paul Di Tommasi; Eddy J Moors; Wilma Jans; Jan Elbers; Eric Ceschia; Tiphaine Tallec; Christian Bernhofer; Thomas Grünwald; Christine Moureaux; Tanguy Manise; Anne Ligne; Pierre Cellier; Benjamin Loubet; Nicolas Mascher;handle: 20.500.14243/314737 , 20.500.14243/295284
The responses of crop plants to changing climate and CO2 could have large effects on food production, and impact carbon, water and energy fluxes, causing feedbacks to climate. To simulate the responses of temperate crops to changing climate and CO2, accounting for the specific phenology of crops mediated by management practice, we develop a process-oriented terrestrial biogeochemical model ORCHIDEE-CROP, which integrates a generic crop phenology and harvest module and a very simple parameterization of nitrogen fertilization, into the DGVM ORCHIDEEv196, in order to simulate biophysical and biogeochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but it is tested here for maize and winter wheat, with the phenological parameterizations of two European varieties. We evaluate this new model against eddy covariance and biometric measurements at 7 winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are Net Ecosystem Exchange (NEE), latent heat and sensible heat fluxes. Site additional measurements of leaf area index (LAI), aboveground biomass and yield are used as well. Evaluation results reveal that ORCHIDEE-CROP reproduces the observed timing of crop development stages and the amplitude of pertaining LAI changes in contrast to ORCHIDEEv196, that by default applies to crops the same phenology of grass. A near-halving of the root mean square error of LAI from 2.38±0.77 m2 m-2 to 1.08±0.34 m2 m-2 is obtained between ORCHIDEEv196 and ORCHIDEE-CROP across the 7 study sites. Improved crop phenology and carbon allocation lead to a general good match between modelled and observed aboveground biomass [with a normalized root mean squared error (NRMSE) of 11.0%-54.2%], crop yield, as well as of carbon and energy fluxes with NRMSE of ~9.0-20.1% and ~9.4-22.3% for NEE, and sensible and latent heat fluxes, respectively. The model data misfits for energy fluxes are within uncertainties of the measurements, which show an incomplete energy balance closure within the range 80.6-86.3%. The remaining discrepancies between modelled and observed LAI and other variables at specific sites are partly attributable to unrealistic representation of human management. In addition, ORCHIDEE-CROP is shown to have the ability to capture the spatial gradients of both biogeochemical (gross primary productivity and NEE) and biophysical (sensible and latent heat fluxes) variables across the sites in Europe, an important requirement for future spatially explicit simulations. Further improvement of the model with an explicit parameterization of nutrition dynamics and management, is expected to improve its predictive ability to simulate croplands in an Earth System Model
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