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1. Combining UAV and Sentinel-2 Imagery for Estimating Millet FCover in a Heterogeneous Agricultural Landscape of Senegal

Author

Ibrahima Diack, Serigne Mansour Diene, Leroux Louise, Diouf Abdoul Aziz, Heuclin Benjamin, Roupsard Olivier, Letourmy Philippe, Audebert Alain, Sarr Idrissa, and Diallo Moussa

Subjects
Drone, fraction of green vegetation cover (FCover), machine learning, satellite, smallholder agriculture, sub-Saharan Africa (SSA), Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

In recent decades, remote sensing has been shown to be useful for crop cover monitoring over smallholder agricultural landscapes, such as agroforestry parklands. However, the fraction of green vegetation cover (FCover) has received little attention. Indeed, the collection of FCover ground data representative of the within-field heterogeneity is time-consuming. Thus, this article aims to bridge this gap by proposing an original methodological framework combining FCover data derived from unmanned aerial vehicle (UAV) and Sentinel-2 (S2) images for estimating millet FCover at the landscape scale in an agroforestry parkland of the groundnut basin of Senegal during the 2021 and 2022 cropping seasons. UAV-based FCover was computed over a 3 m × 3 m grid using a thresholding approach for six dates over the cropping seasons and then used as ground observation for the upscaling of millet FCover at the landscape scale with S2 data. Various spectral vegetation indices and textural features were derived from S2, and several modeling approaches based on machine learning algorithms were benchmarked. Our results showed that the modeling approach using the full-time series in combination with a random forest algorithm was able to explain 73% (root mean square error = 12.13%) of the UAV-FCover variability after validation in 2021 and 2022. In addition, UAV images are suitable for consistent monitoring of millet FCover over heterogeneous agricultural landscapes by training S2 satellite images. To further check its robustness, this approach should be tested for different crops and practices across a variety of agricultural landscapes in sub-Saharan Africa.

Published
2024
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2. Calibrating the STICS soil-crop model to explore the impact of agroforestry parklands on millet growth

Author

Sow, Sidy, Senghor, Yolande, Sadio, Khardiatou, Vezy, Rémi, Roupsard, Olivier, Affholder, François, N’dienor, Moussa, Clermont-Dauphin, Cathy, Gaglo, Espoir Koudjo, Ba, Seydina, Tounkara, Adama, Balde, Alpha Bocar, Agbohessou, Yelognissè, Seghieri, Josiane, Sall, Saidou Nourou, Couedel, Antoine, Leroux, Louise, Jourdan, Christophe, Diaite, Diaminatou Sanogo, and Falconnier, Gatien N.

Published
2024
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6. SoilTemp: A global database of near‐surface temperature

Author

Lembrechts, Jonas J, Aalto, Juha, Ashcroft, Michael B, De Frenne, Pieter, Kopecký, Martin, Lenoir, Jonathan, Luoto, Miska, Maclean, Ilya MD, Roupsard, Olivier, Fuentes‐Lillo, Eduardo, García, Rafael A, Pellissier, Loïc, Pitteloud, Camille, Alatalo, Juha M, Smith, Stuart W, Björk, Robert G, Muffler, Lena, Backes, Amanda Ratier, Cesarz, Simone, Gottschall, Felix, Okello, Joseph, Urban, Josef, Plichta, Roman, Svátek, Martin, Phartyal, Shyam S, Wipf, Sonja, Eisenhauer, Nico, Pușcaș, Mihai, Turtureanu, Pavel D, Varlagin, Andrej, Dimarco, Romina D, Jump, Alistair S, Randall, Krystal, Dorrepaal, Ellen, Larson, Keith, Walz, Josefine, Vitale, Luca, Svoboda, Miroslav, Higgens, Rebecca Finger, Halbritter, Aud H, Curasi, Salvatore R, Klupar, Ian, Koontz, Austin, Pearse, William D, Simpson, Elizabeth, Stemkovski, Michael, Graae, Bente Jessen, Sørensen, Mia Vedel, Høye, Toke T, Calzado, M Rosa Fernández, Lorite, Juan, Carbognani, Michele, Tomaselli, Marcello, Forte, T'ai GW, Petraglia, Alessandro, Haesen, Stef, Somers, Ben, Van Meerbeek, Koenraad, Björkman, Mats P, Hylander, Kristoffer, Merinero, Sonia, Gharun, Mana, Buchmann, Nina, Dolezal, Jiri, Matula, Radim, Thomas, Andrew D, Bailey, Joseph J, Ghosn, Dany, Kazakis, George, Pablo, Miguel A, Kemppinen, Julia, Niittynen, Pekka, Rew, Lisa, Seipel, Tim, Larson, Christian, Speed, James DM, Ardö, Jonas, Cannone, Nicoletta, Guglielmin, Mauro, Malfasi, Francesco, Bader, Maaike Y, Canessa, Rafaella, Stanisci, Angela, Kreyling, Juergen, Schmeddes, Jonas, Teuber, Laurenz, Aschero, Valeria, Čiliak, Marek, Máliš, František, De Smedt, Pallieter, Govaert, Sanne, Meeussen, Camille, Vangansbeke, Pieter, Gigauri, Khatuna, Lamprecht, Andrea, Pauli, Harald, Steinbauer, Klaus, Winkler, Manuela, Ueyama, Masahito, and Nuñez, Martin A

Subjects
Climate Action, Climate Change, Ecosystem, Microclimate, Snow, Temperature, climate change, database, ecosystem processes, microclimate, soil climate, species distributions, temperature, topoclimate, Environmental Sciences, Biological Sciences, Ecology
Abstract

Current 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.

Published
2020

8. Eddy covariance measurements reveal a decreased carbon sequestration strength 2010–2022 in an African semiarid savanna.

Author

Wieckowski, Aleksander, Vestin, Patrik, Ardö, Jonas, Roupsard, Olivier, Ndiaye, Ousmane, Diatta, Ousmane, Ba, Seydina, Agbohessou, Yélognissè, Fensholt, Rasmus, Verbruggen, Wim, Gebremedhn, Haftay Hailu, and Tagesson, Torbern

Subjects
CARBON sequestration, GLOBAL warming, SOIL moisture, VAPOR pressure, RAINFALL
Abstract

Monitoring the changes of ecosystem functioning is pivotal for understanding the global carbon cycle. Despite its size and contribution to the global carbon cycle, Africa is largely understudied in regard to ongoing changes of its ecosystem functioning and their responses to climate change. One of the reasons is the lack of long‐term in situ data. Here, we use eddy covariance to quantify the net ecosystem exchange (NEE) and its components—gross primary production (GPP) and ecosystem respiration (Reco) for years 2010–2022 for a Sahelian semiarid savanna to study trends in the fluxes. Significant negative trends were found for NEE (12.7 ± 2.8 g C m2 year−1), GPP (39.6 ± 7.9 g C m2 year−1), and Reco (32.2 ± 8.9 g C m2 year−1). We found that NEE decreased by 60% over the study period, and this decrease was mainly caused by stronger negative trends in rainy season GPP than in Reco. Additionally, we observed strong increasing trends in vapor pressure deficit, but no trends in rainfall or soil water content. Thus, a proposed explanation for the decrease in carbon sink strength is increasing atmospheric dryness. The warming climate in the Sahel, coupled with increasing evaporative demand, may thus lead to decreased GPP levels across this biome, and lowering its CO2 sequestration. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Improving pearl millet yield estimation from UAV imagery in the semiarid agroforestry system of Senegal through textural indices and reflectance normalization

Author

Diene, Serigne Mansour, Diack, Ibrahima, Audebert, Alain, Roupsard, Olivier, Leroux, Louise, Diouf, Abdoul Aziz, Mbaye, Modou, Fernandez, Romain, Diallo, Moussa, Sarr, Idrissa, Diene, Serigne Mansour, Diack, Ibrahima, Audebert, Alain, Roupsard, Olivier, Leroux, Louise, Diouf, Abdoul Aziz, Mbaye, Modou, Fernandez, Romain, Diallo, Moussa, and Sarr, Idrissa

Abstract

Enhancing food security in the Sahel through nature-based solutions is urgent given population growth, resource scarcity and climate change. Traditional agroforestry parklands are a farmer- and nature-based widespread form of ecological intensification which randomly integrates trees into crop fields. While most studies estimating crop yields in agroforestry have been conducted in controlled experimental settings, few have addressed the inherent variability in such highly heterogeneous systems. Thus, the purpose of this study is to benefit from a UAV-based proxy-sensing and machine learning approach to address the variability of pearl millet grain yield, according to the distance to randomly distributed trees in a traditional agroforestry system dominated by Faidherbia albida (i.e. groundnut basin of Senegal). 21 vegetation indices (VIs), 32 normalized difference texture indices (NDTIs) derived from multispectral drone images, and normalization variables for radiative conditions were used with yield data collected in 15 plots (around 1 ha each) and subplots (15 m 2 each) displayed at 3 distances from the tree over five cropping seasons (2018 - 2022). In this context, the optimal phenological stage was determined for predicting pearl millet grain yield, which proved to be the pre-heading period. This period was used as the basis for our machine learning model training dataset in the subplots. Two models, Random Forest (RF) and Gradient Boosting Machine (GBM) were compared by combining VIs, NDTIs and normalization variables. GBM was the best-performing model, explaining 78% of observed pearl millet yield variability over five years in the subplots, with a RMSE of 16 g. m −2 . This study revealed that NDTIs calculated from red and green bands were more influential for yield estimation than those based on near-infrared. These results were subsequently used to predict yield in all plots, resulting in a mean relative error of 17.5% between yields estimated by the farmers

Published
2024

10. Innovations and policies based on 20 years of findings from a coffee agroforestry trial

Author

Virginio Filho, Elias de Melo, Somarriba, Eduardo, Cerda, Rolando, Casanoves, Fernando, Cordero, Carlos A., Avelino, Jacques, Roupsard, Olivier, Rapidel, Bruno, Vaast, Philippe, Harmand, Jean-Michel, Staver, Charles, Beer, John, Mora, Argenis, Morales, Víctor Hugo, Fonseca, Carlos, Vargas, Victor J., Ramírez, Luis Guillermo, Soto, Gabriela, Isaac, Marney E., Umaña, L.D., Fernandez, A.T., Romero, Luis Fernando, Gómez, René León, Caicedo, Carlos, Pico, Jimmy, Montagnini, Florencia, Haggar, Jeremy, Virginio Filho, Elias de Melo, Somarriba, Eduardo, Cerda, Rolando, Casanoves, Fernando, Cordero, Carlos A., Avelino, Jacques, Roupsard, Olivier, Rapidel, Bruno, Vaast, Philippe, Harmand, Jean-Michel, Staver, Charles, Beer, John, Mora, Argenis, Morales, Víctor Hugo, Fonseca, Carlos, Vargas, Victor J., Ramírez, Luis Guillermo, Soto, Gabriela, Isaac, Marney E., Umaña, L.D., Fernandez, A.T., Romero, Luis Fernando, Gómez, René León, Caicedo, Carlos, Pico, Jimmy, Montagnini, Florencia, and Haggar, Jeremy

Abstract

In 2000, CATIE began a long-term coffee agroforestry trial aimmg to research complex interactions among climate, soil, shade trees, managements, and coffee varieties, useful for the development of systems that enhance soil ecological restoration, biodiversity, and ecosystem services. The trial consists of combinations of shade and managements: six types of shade from associations of legume and timber trees, and full-sun systems, combined with four managements (high conventional (AC), moderate conventional (MC), intensive organic (MO) and low organic (BO)), resulting in >20 types of systems, in which traditional and improved coffee varieties are also being tested. More than 30 research centers and universities have been involved to produce >30 scientific papers, >40 theses, and >20 technical documents. The revision of the main fmdings indicates that full sun systems (AC, MC) produce high yields but with a trade-off on ecosystem services provision; while agroforestry systems with legume trees, especially MO and MC, reached both good productivity and provisioning ecosystem services (improving soil quality, carbon sequestration, regulation of pests ). Systems with only timber trees and BO collapsed. The economic analysis shows that most agroforestry systems are profitable, including those with MO or BO, with important lessons to reduce production costs. The most successful innovations related to soil improvements, shade and pest regulation, silvicultural mechanization, and carbon storage derived from the trial, were used as inputs to environment-focus policies, design of researchdevelopment projects and programs such as NAMA coffee. The results have been disseminated to more than 40,000 producers and hundreds of students through projects and relevant platforms such as PCP-AGROFORESTA, PROMECAFE, RECIPROCAFE, the Alianza de Mujeres en Café, PROCAGICA-IICA-UE, and coffee courses/diplomas. Thus, it is being demonstrated that diversification and good practices can contribut

Published
2024

11. Combining UAV and sentinel-2 imagery for estimating millet FCover in an heterogeneous agricultural landscape of Senegal

Author

Diack, Ibrahima, Diene, Serigne Mansour, Leroux, Louise, Diouf Abdoul, Aziz, Heuclin, Benjamin, Roupsard, Olivier, Letourmy, Philippe, Audebert, Alain, Sarr, Idrissa, Diallo, Moussa, Diack, Ibrahima, Diene, Serigne Mansour, Leroux, Louise, Diouf Abdoul, Aziz, Heuclin, Benjamin, Roupsard, Olivier, Letourmy, Philippe, Audebert, Alain, Sarr, Idrissa, and Diallo, Moussa

Abstract

In recent decades, remote sensing has been shown to be useful for crop cover monitoring over smallholder agricultural landscapes, such as agroforestry parklands. However, the fraction of green vegetation cover (FCover) has received little attention. Indeed, the collection of FCover ground data representative of the within-field heterogeneity is time-consuming. Thus, this article aims to bridge this gap by proposing an original methodological framework combining FCover data derived from unmanned aerial vehicle (UAV) and Sentinel-2 (S2) images for estimating millet FCover at the landscape scale in an agroforestry parkland of the groundnut basin of Senegal during the 2021 and 2022 cropping seasons. UAV-based FCover was computed over a 3 m × 3 m grid using a thresholding approach for six dates over the cropping seasons and then used as ground observation for the upscaling of millet FCover at the landscape scale with S2 data. Various spectral vegetation indices and textural features were derived from S2, and several modeling approaches based on machine learning algorithms were benchmarked. Our results showed that the modeling approach using the full-time series in combination with a random forest algorithm was able to explain 73% (root mean square error = 12.13%) of the UAV-FCover variability after validation in 2021 and 2022. In addition, UAV images are suitable for consistent monitoring of millet FCover over heterogeneous agricultural landscapes by training S2 satellite images. To further check its robustness, this approach should be tested for different crops and practices across a variety of agricultural landscapes in sub-Saharan Africa.

Published
2024

12. Modelling CO2 and N2O emissions from soils in silvopastoral systems of the West African Sahelian band.

Author

Agbohessou, Yélognissè, Delon, Claire, Grippa, Manuela, Mougin, Eric, Ngom, Daouda, Gaglo, Espoir Koudjo, Ndiaye, Ousmane, Salgado, Paulo, and Roupsard, Olivier

Subjects
SILVOPASTORAL systems, CARBON emissions, NITROGEN in soils, CARBON in soils, SOIL moisture
Abstract

Silvopastoral systems (SPSs) have been shown to improve ecosystem resilience and provide sustainable land management solutions in the Sahel. However, accurately estimating the contribution of Sahelian ecosystems to the overall greenhouse gas (GHG) balance is a challenge, in particular regarding the magnitude of carbon dioxide (CO 2) and nitrous oxide (N 2 O) emissions from soils. In this work, we spatialized and applied the process-based model Sahelian Transpiration Evaporation and Productivity – GENeral model of litter DEComposition – N 2 O (STEP–GENDEC-N 2 O) to investigate the magnitude and spatial and temporal patterns of herbaceous mass, as well as CO 2 and N 2 O emissions from soil (not net emissions) in Sahelian SPSs. Our results show that over the last decade (2012–2022), there was a heterogeneous spatial distribution of herbaceous mass production and of soil CO 2 and N 2 O emissions in Sahelian SPSs. Spatial variations in soil CO 2 emissions are primarily controlled by soil carbon content, temperature, herbaceous mass, and animal load, while soil nitrogen content, soil water content, and animal load are the main factors driving the spatial variations in N 2 O emissions from soil. The estimated CO 2 and N 2 O emissions from soil in Sahelian SPSs over the 2012–2022 period were equal to 58.79 ± 4.83 Tg CO 2 -C yr -1 (1 Tg = 10 12 g) and 21.59 ± 3.91 Gg N 2 O-N yr -1 (1 Gg = 10 9 g), respectively. These values are generally lower than estimates reported in the literature for tropical areas and croplands. Furthermore, our simulations indicated a significant annual rising trend of soil CO 2 and N 2 O emissions between 2012 and 2020 as herbaceous mass increased, making more C and N available for the nitrification, denitrification, and decomposition processes. By mapping soil CO 2 and N 2 O emissions, we provide crucial insights into the localization of emission hotspots in Sahelian SPSs, thereby offering valuable information that can be used to devise and implement effective strategies aimed at fostering carbon sequestration in the Sahel. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Global Surface Net-Radiation at 5 km from MODIS Terra

Author

Verma, Manish, Fisher, Joshua B, Mallick, Kaniska, Ryu, Youngryel, Kobayashi, Hideki, Guillaume, Alexandre, Moore, Gregory, Ramakrishnan, Lavanya, Hendrix, Valerie, Wolf, Sebastian, Sikka, Munish, Kiely, Gerard, Wohlfahrt, Georg, Gielen, Bert, Roupsard, Olivier, Toscano, Piero, Arain, Altaf, and Cescatti, Alessandro

Subjects
Earth Sciences, Geomatic Engineering, Atmospheric Sciences, Engineering, Affordable and Clean Energy, surface net-radiation, MODIS, FLUXNET, SURFRAD, modeling, validation, Classical Physics, Physical Geography and Environmental Geoscience, Atmospheric sciences, Physical geography and environmental geoscience, Geomatic engineering
Abstract

Reliable and fine resolution estimates of surface net-radiation are required for estimating latent and sensible heat fluxes between the land surface and the atmosphere. However, currently, fine resolution estimates of net-radiation are not available and consequently it is challenging to develop multi-year estimates of evapotranspiration at scales that can capture land surface heterogeneity and are relevant for policy and decision-making. We developed and evaluated a global net-radiation product at 5 km and 8-day resolution by combining mutually consistent atmosphere and land data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra. Comparison with net-radiation measurements from 154 globally distributed sites (414 site-years) from the FLUXNET and Surface Radiation budget network (SURFRAD) showed that the net-radiation product agreed well with measurements across seasons and climate types in the extratropics (Wilmott's index ranged from 0.74 for boreal to 0.63 for Mediterranean sites). Mean absolute deviation between the MODIS and measured net-radiation ranged from 38.0 ± 1.8 W.m-2 in boreal to 72.0 ± 4.1 W.m-2 in the tropical climates. The mean bias was small and constituted only 11%, 0.7%, 8.4%, 4.2%, 13.3%, and 5.4% of the mean absolute error in daytime net-radiation in boreal, Mediterranean, temperate-continental, temperate, semi-arid, and tropical climate, respectively. To assess the accuracy of the broader spatiotemporal patterns, we upscaled error-quantified MODIS net-radiation and compared it with the net-radiation estimates from the coarse spatial (1° x 1°) but high temporal resolution gridded net-radiation product from the Clouds and Earth's Radiant Energy System (CERES). Our estimates agreed closely with the net-radiation estimates from the CERES. Difference between the two was less than 10W.m-22 in 94% of the total land area. MODIS net-radiation product will be a valuable resource for the science community studying turbulent fluxes and energy budget at the Earth's surface.

Published
2016

15. The surface-atmosphere exchange of carbon dioxide in tropical rainforests: Sensitivity to environmental drivers and flux measurement methodology

Author

Fu, Zheng, Gerken, Tobias, Bromley, Gabriel, Araújo, Alessandro, Bonal, Damien, Burban, Benoît, Ficklin, Darren, Fuentes, Jose D., Goulden, Michael, Hirano, Takashi, Kosugi, Yoshiko, Liddell, Michael, Nicolini, Giacomo, Niu, Shuli, Roupsard, Olivier, Stefani, Paolo, Mi, Chunrong, Tofte, Zaddy, Xiao, Jingfeng, Valentini, Riccardo, Wolf, Sebastian, and Stoy, Paul C.

Published
2018
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18. Measuring and modelling energy partitioning in canopies of varying complexity using MAESPA model

Author

Vezy, Rémi, Christina, Mathias, Roupsard, Olivier, Nouvellon, Yann, Duursma, Remko, Medlyn, Belinda, Soma, Maxime, Charbonnier, Fabien, Blitz-Frayret, Céline, Stape, José-Luiz, Laclau, Jean-Paul, de Melo Virginio Filho, Elias, Bonnefond, Jean-Marc, Rapidel, Bruno, Do, Frédéric C., Rocheteau, Alain, Picart, Delphine, Borgonovo, Carlos, Loustau, Denis, and le Maire, Guerric

Published
2018
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21. Recent decline in the global land evapotranspiration trend due to limited moisture supply

Author

Jung, Martin, Reichstein, Markus, Ciais, Philippe, Seneviratne, Sonia I, Sheffield, Justin, Goulden, Michael L, Bonan, Gordon, Cescatti, Alessandro, Chen, Jiquan, de Jeu, Richard, Dolman, A Johannes, Eugster, Werner, Gerten, Dieter, Gianelle, Damiano, Gobron, Nadine, Heinke, Jens, Kimball, John, Law, Beverly E, Montagnani, Leonardo, Mu, Qiaozhen, Mueller, Brigitte, Oleson, Keith, Papale, Dario, Richardson, Andrew D, Roupsard, Olivier, Running, Steve, Tomelleri, Enrico, Viovy, Nicolas, Weber, Ulrich, Williams, Christopher, Wood, Eric, Zaehle, Sönke, and Zhang, Ke

Subjects
Climate Change Impacts and Adaptation, Environmental Sciences, Life on Land, Climate Action, Artificial Intelligence, Atmosphere, Fresh Water, Global Warming, History, 20th Century, History, 21st Century, Humidity, Plant Transpiration, Reproducibility of Results, Seasons, Soil, Uncertainty, Volatilization, Water Cycle, General Science & Technology
Abstract

More than half of the solar energy absorbed by land surfaces is currently used to evaporate water. Climate change is expected to intensify the hydrological cycle and to alter evapotranspiration, with implications for ecosystem services and feedback to regional and global climate. Evapotranspiration changes may already be under way, but direct observational constraints are lacking at the global scale. Until such evidence is available, changes in the water cycle on land−a key diagnostic criterion of the effects of climate change and variability−remain uncertain. Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm. In addition, we have assessed evapotranspiration variations over the same time period using an ensemble of process-based land-surface models. Our results suggest that global annual evapotranspiration increased on average by 7.1 ± 1.0 millimetres per year per decade from 1982 to 1997. After that, coincident with the last major El Niño event in 1998, the global evapotranspiration increase seems to have ceased until 2008. This change was driven primarily by moisture limitation in the Southern Hemisphere, particularly Africa and Australia. In these regions, microwave satellite observations indicate that soil moisture decreased from 1998 to 2008. Hence, increasing soil-moisture limitations on evapotranspiration largely explain the recent decline of the global land-evapotranspiration trend. Whether the changing behaviour of evapotranspiration is representative of natural climate variability or reflects a more permanent reorganization of the land water cycle is a key question for earth system science.

Published
2010

24. The land–atmosphere water flux in the tropics

Author

FISHER, JOSHUA B, MALHI, YADVINDER, BONAL, DAMIEN, DA ROCHA, HUMBERTO R, DE ARAÚJO, ALESSANDRO C, GAMO, MINORU, GOULDEN, MICHAEL L, HIRANO, TAKASHI, HUETE, ALFREDO R, KONDO, HIROAKI, KUMAGAI, TOMO'OMI, LOESCHER, HENRY W, MILLER, SCOTT, NOBRE, ANTONIO D, NOUVELLON, YANN, OBERBAUER, STEVEN F, PANUTHAI, SAMREONG, ROUPSARD, OLIVIER, SALESKA, SCOTT, TANAKA, KATSUNORI, TANAKA, NOBUAKI, TU, KEVIN P, and VON RANDOW, CELSO

Subjects
Earth Sciences, Atmospheric Sciences, Amazon, eddy covariance, evaporation, evapotranspiration, ISLSCP-II, LBA, model, remote sensing, tropical, Environmental Sciences, Biological Sciences, Ecology, Biological sciences, Earth sciences, Environmental sciences
Abstract

Tropical vegetation is a major source of global land surface evapotranspiration, and can thus play a major role in global hydrological cycles and global atmospheric circulation. Accurate prediction of tropical evapotranspiration is critical to our understanding of these processes under changing climate. We examined the controls on evapotranspiration in tropical vegetation at 21 pan-tropical eddy covariance sites, conducted a comprehensive and systematic evaluation of 13 evapotranspiration models at these sites, and assessed the ability to scale up model estimates of evapotranspiration for the test region of Amazonia. Net radiation was the strongest determinant of evapotranspiration (mean evaporative fraction was 0.72) and explained 87% of the variance in monthly evapotranspiration across the sites. Vapor pressure deficit was the strongest residual predictor (14%), followed by normalized difference vegetation index (9%), precipitation (6%) and wind speed (4%). The radiation-based evapotranspiration models performed best overall for three reasons: (1) the vegetation was largely decoupled from atmospheric turbulent transfer (calculated from Ω decoupling factor), especially at the wetter sites; (2) the resistance-based models were hindered by difficulty in consistently characterizing canopy (and stomatal) resistance in the highly diverse vegetation; (3) the temperature-based models inadequately captured the variability in tropical evapotranspiration. We evaluated the potential to predict regional evapotranspiration for one test region: Amazonia. We estimated an Amazonia-wide evapotranspiration of 1370 mm yr⁻¹, but this value is dependent on assumptions about energy balance closure for the tropical eddy covariance sites; a lower value (1096 mm yr⁻¹) is considered in discussion on the use of flux data to validate and interpolate models.

Published
2009

26. Spatio-temporal analysis of soil surface hydraulic properties in a semi-arid agroforestry system of the Senegalese groundnut basin

Author

Faye, Waly, primary, Orange, Didier, additional, Talla, Seydou, additional, Do, Frederic, additional, Jourdan, Christophe, additional, Roupsard, Olivier, additional, Faty, Abdoulaye, additional, Niang, Awa, additional, Kane, Alioune, additional, Di prima, Simone, additional, Angulo-Jaramillo, Raphael, additional, and Lassabatere, Laurent, additional

Published
2023
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27. Spatial-temporal variations of nitrous oxide emissions in coffee agroforestry systems in Costa Rica

Author

Bentzon-Tarp, Abeline, Helgadóttir, Diljá, Van den Meersche, Karel, Gay, Frédéric, Priemé, Anders, Roupsard, Olivier, Mages, Carolin, Elberling, Bo, Bentzon-Tarp, Abeline, Helgadóttir, Diljá, Van den Meersche, Karel, Gay, Frédéric, Priemé, Anders, Roupsard, Olivier, Mages, Carolin, and Elberling, Bo

Abstract

This study investigates spatial-temporal trends in N2O emissions from coffee production systems in Costa Rica with a focus on the effects of nitrogen fertilisation, topography and soil type. This is done by combining (i) multi-year continuous dynamic chamber measurements from sites with different fertilisation levels, (ii) static chamber measurements taken along a typical sloping coffee field and (iii) measurements from a laboratory incubation experiment with nutrient addition to different soil types. In the field and in the laboratory, additions included standard NPK fertiliser, ammonium nitrate (NH4NO3) as well as potassium nitrate (KNO3). Soils in a laboratory experiment were incubated under both drained and flooded conditions. Continuous measurements from automatic chambers show that annual N2O fluxes were dominated by bursts over few weeks following N-fertilisation with peak emissions up to 60 g N-N2O ha−1 day−1. A two-month slope experiment with static chambers after KNO3-fertilisation with 90 kg N ha−1 showed N2O significant differences between the highest daily emission rates from the top and the bottom of the slope (134 ± 20 g N-N2O ha−1 and 336 ± 104 g N-N2O ha−1, respectively) which can be explained by NO3- transport downhill and flooded conditions favouring denitrification at the bottom of the slope. Incubation experiments indicate that denitrification is the main process controlling N2O emissions but also that nitrification can result in low N2O emission rates under drained conditions. It can be concluded that the reported N2O emissions from the coffee agroforestry systems are generally low, but may be underestimated, as both poorly drained depressions functioning as N2O hotspots as well as temporal N2O bursts need to be taken into account., This study investigates spatial-temporal trends in N2O emissions from coffee production systems in Costa Rica with a focus on the effects of nitrogen fertilisation, topography and soil type. This is done by combining (i) multi-year continuous dynamic chamber measurements from sites with different fertilisation levels, (ii) static chamber measurements taken along a typical sloping coffee field and (iii) measurements from a laboratory incubation experiment with nutrient addition to different soil types. In the field and in the laboratory, additions included standard NPK fertiliser, ammonium nitrate (NH4NO3) as well as potassium nitrate (KNO3). Soils in a laboratory experiment were incubated under both drained and flooded conditions. Continuous measurements from automatic chambers show that annual N2O fluxes were dominated by bursts over few weeks following N-fertilisation with peak emissions up to 60 g N-N2O ha−1 day−1. A two-month slope experiment with static chambers after KNO3-fertilisation with 90 kg N ha−1 showed N2O significant differences between the highest daily emission rates from the top and the bottom of the slope (134 ± 20 g N-N2O ha−1 and 336 ± 104 g N-N2O ha−1, respectively) which can be explained by NO3- transport downhill and flooded conditions favouring denitrification at the bottom of the slope. Incubation experiments indicate that denitrification is the main process controlling N2O emissions but also that nitrification can result in low N2O emission rates under drained conditions. It can be concluded that the reported N2O emissions from the coffee agroforestry systems are generally low, but may be underestimated, as both poorly drained depressions functioning as N2O hotspots as well as temporal N2O bursts ne

Published
2023

28. Improving field-scale crop actual evapotranspiration monitoring with Sentinel-3, Sentinel-2, and Landsat data fusion

Author

European Space Agency, Agencia Estatal de Investigación (España), Ministerio de Ciencia e Innovación (España), European Commission, Fondation Total, LEAP-Agri, Nieto, Héctor [0000-0003-4250-6424], Guzinski, Radoslaw, Nieto, Héctor, Ramo Sánchez, Rubén, Sánchez, Juan Manuel, Jomaa, Ihab, Zitouna-Chebbi, Rim, Roupsard, Olivier, López-Urrea, Ramón, European Space Agency, Agencia Estatal de Investigación (España), Ministerio de Ciencia e Innovación (España), European Commission, Fondation Total, LEAP-Agri, Nieto, Héctor [0000-0003-4250-6424], Guzinski, Radoslaw, Nieto, Héctor, Ramo Sánchez, Rubén, Sánchez, Juan Manuel, Jomaa, Ihab, Zitouna-Chebbi, Rim, Roupsard, Olivier, and López-Urrea, Ramón

Abstract

One of the primary applications of satellite Land Surface Temperature (LST) observations lies in their utilization for modeling of actual evapotranspiration (ET) in agricultural crops, with the primary goals of monitoring and enhancing irrigation practices and improving crop water use productivity, as stipulated by Sustainable Development Goal (SDG) indicator 6.4.1. Evapotranspiration is a complex and dynamic process, both temporally and spatially, necessitating LST observations with high spatio-temporal resolution. Presently, none of the existing spaceborne thermal sensors can provide quasi-daily field-scale LST observations, prompting the development of methods for data fusion (thermal sharpening) of observations from various shortwave and thermal sensors to meet this spatio-temporal requirement. Previous research has demonstrated the effectiveness of combining shortwave-multispectral Sentinel-2 observations with thermal-infrared Sentinel-3 observations to derive daily, field-scale LST and ET estimates. However, these studies also highlighted limitations in capturing the distinct thermal contrast between cooler LST in irrigated agricultural areas and the hotter, adjacent dry regions. In this study, we aim to address this limitation by incorporating information on thermal spatial variability observed by Landsat satellites into the data fusion process, without being constrained by infrequent or cloudy Landsat thermal observations and while retaining the longwave radiance emission captured by the Sentinel-3 thermal sensor at its native resolution. Two approaches are evaluated, both individually and as a complementary combination, and validated against in situ LST measurements. The best performing approach, which leads to reduction in root mean square error of up to 1.5 K when compared to previous research, is subsequently used to estimate parcel-level actual evapotranspiration. The ET modeling process has also undergone various improvements regarding the gap-filling

Published
2023

31. Modelling CO2 and N2O emissions from soils in silvopastoral systems of the West-African Sahelian band.

Author

Agbohessou, Yélognissè, Delon, Claire, Grippa, Manuela, Mougin, Eric, Ngom, Daouda, Gaglo, Espoir Koudjo, Ndiaye, Ousmane, Salgado, Paulo, and Roupsard, Olivier

Subjects
SILVOPASTORAL systems, NITROGEN in soils, CARBON in soils, SOIL moisture, ECOLOGICAL resilience
Abstract

Silvopastoral systems (SPSs) have been shown to improve ecosystem resilience and provide sustainable land management solutions in the Sahel. However, accurately estimating the contribution of Sahelian ecosystems to the overall greenhouse gas (GHG) balance is a challenge, in particular the magnitude of carbon dioxide (CO2) and nitrous oxide (N2O) emissions from soils. In this work, we spatialized and applied the process-based model STEP-GENDEC-N2O to investigate the magnitude, spatial, and temporal patterns of herbaceous mass, as well as CO2 and N2O emissions from soil in Sahelian SPSs. Our results show that over the last decade (2012–2022), there was a heterogeneous spatial distribution of herbaceous mass production, as well as of soil CO2 and N2O emissions in Sahelian SPSs. Spatial variations in soil CO2 emissions are primarily controlled by soil carbon content, temperature, herbaceous mass, and animal load, while soil nitrogen content, soil water content, and animal load are the main factors driving the spatial variations in N2O emissions from soil. The estimated CO2 and N2O emissions from soil in Sahelian SPSs over the 2012–2022 period were equal to 58.79 ± 4.83 Tg CO2-C yr-1 (1 Tg = 1012 g) and 21.59 ± 3.91 Gg N2O-N yr-1 (1 Gg = 109 g), respectively. These values are generally lower than estimates reported in the literature for tropical areas and croplands. Furthermore, our simulations indicated a significant annual rising trend of soil CO2 and N2O emissions between 2012–2020 as herbaceous mass increases, making more C and N available for nitrification, denitrification and decomposition processes. By mapping soil CO2 and N2O emissions, we provide crucial insights into the localization of emission hotspots in Sahelian SPSs, thereby offering valuable information that can be used to devise and implement effective strategies aimed at fostering carbon sequestration in the Sahel. [ABSTRACT FROM AUTHOR]

Published
2023
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32. Assessment of the Faidherbia albida effect on millet yield using UAV images analysis and geostatistical techniques

Author

Diene, Serigne Mansour, primary, Fernandez, Romain, additional, Goze, Eric, additional, Diack, Ibrahima, additional, Faye, Marième, additional, Dabo, Al Housseynou, additional, Bousso, Pape Oumar Ba, additional, Audebert, Alain, additional, Roupsard, Olivier, additional, Leroux, Louise, additional, Mbaye, Modou, additional, Diouf, Abdou Aziz, additional, Diallo, Moussa, additional, and Sarr, Idrissa, additional

Published
2023
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33. Estimation of crop fractional cover (FCover) in smallholder farming systems using UAV and Sentinel-2 images : Case study of a Senegalese agroforestry parkland

Author

Diack, Ibrahima, primary, Leroux, Louise, additional, Heuclin, Benjamin, additional, Letourmy, Philippe, additional, Diene, Serigne Mansour, additional, Audebert, Alain, additional, Roupsard, Olivier, additional, Diouf, Abdoul Aziz, additional, Sarr, Idrissa, additional, and Diallo, Moussa, additional

Published
2023
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38. Is the Impact of Livestock on Greenhouse Gas Emissions Offset by the Presence of Trees in a Sahelian Silvopastoral System?

Author

Agbohessou, Yélognissè F., primary, Delon, Claire, additional, Mougin, Eric, additional, Grippa, Manuela, additional, Tagesson, Torbern, additional, Diedhiou, Moussa, additional, Ba, Seydina, additional, Ngom, Daouda, additional, Vezy, Rémi, additional, Ndiaye, Ousmane, additional, Assouma, Mohamed H., additional, and Roupsard, Olivier, additional

Published
2023
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41. Innovations and policies based on 20 years of findings from a coffee agroforestry trial

Author

Virginio Filho, Elias de Melo, Somarriba, Eduardo, Cerda, Rolando, Casanoves, Fernando, Cordero, Carlos A., Avelino, Jacques, Roupsard, Olivier, Rapidel, Bruno, Vaast, Philippe, Harmand, Jean-Michel, Staver, Charles, Beer, John, Mora, Argenis, Morales, Víctor Hugo, Fonseca, Carlos, Vargas, Victor J., Ramírez, Luis Guillermo, Soto, Gabriela, Isaac, Marney E., Umaña, L.D., Fernandez, A.T., Romero, Luis Fernando, Gómez, René León, Caicedo, Carlos, Pico, Jimmy, Montagnini, Florencia, and Haggar, Jeremy

Abstract

In 2000, CATIE began a long-term coffee agroforestry trial aimmg to research complex interactions among climate, soil, shade trees, managements, and coffee varieties, useful for the development of systems that enhance soil ecological restoration, biodiversity, and ecosystem services. The trial consists of combinations of shade and managements: six types of shade from associations of legume and timber trees, and full-sun systems, combined with four managements (high conventional (AC), moderate conventional (MC), intensive organic (MO) and low organic (BO)), resulting in >20 types of systems, in which traditional and improved coffee varieties are also being tested. More than 30 research centers and universities have been involved to produce >30 scientific papers, >40 theses, and >20 technical documents. The revision of the main fmdings indicates that full sun systems (AC, MC) produce high yields but with a trade-off on ecosystem services provision; while agroforestry systems with legume trees, especially MO and MC, reached both good productivity and provisioning ecosystem services (improving soil quality, carbon sequestration, regulation of pests ). Systems with only timber trees and BO collapsed. The economic analysis shows that most agroforestry systems are profitable, including those with MO or BO, with important lessons to reduce production costs. The most successful innovations related to soil improvements, shade and pest regulation, silvicultural mechanization, and carbon storage derived from the trial, were used as inputs to environment-focus policies, design of researchdevelopment projects and programs such as NAMA coffee. The results have been disseminated to more than 40,000 producers and hundreds of students through projects and relevant platforms such as PCP-AGROFORESTA, PROMECAFE, RECIPROCAFE, the Alianza de Mujeres en Café, PROCAGICA-IICA-UE, and coffee courses/diplomas. Thus, it is being demonstrated that diversification and good practices can contribute to adaptation and mitigation strategies in the coffee sector on which millions of families and their food systems depend.

Published
2023

42. Joint control of terrestrial gross primary productivity by plant phenology and physiology

Author

Xia, Jianyang, Niu, Shuli, Ciais, Philippe, Janssens, Ivan A., Chen, Jiquan, Ammann, Christof, Arain, Altaf, Blanken, Peter D., Cescatti, Alessandro, Bonal, Damien, Buchmann, Nina, Curtis, Peter S., Chen, Shiping, Dong, Jinwei, Flanagan, Lawrence B., Frankenberg, Christian, Georgiadis, Teodoro, Gough, Christopher M., Hui, Dafeng, Kiely, Gerard, Li, Jianwei, Lund, Magnus, Magliulo, Vincenzo, Marcolla, Barbara, Merbold, Lutz, Montagnani, Leonardo, Moors, Eddy J., Olesen, Jørgen E., Piao, Shilong, Raschi, Antonio, Roupsard, Olivier, Suyker, Andrew E., Urbaniak, Marek, Vaccari, Francesco P., Varlagin, Andrej, Vesala, Timo, Wilkinson, Matthew, Weng, Ensheng, Wohlfahrt, Georg, Yan, Liming, and Luo, Yiqi

Published
2015

46. Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database

Author

Yuan, Wenping, Cai, Wenwen, Xia, Jiangzhou, Chen, Jiquan, Liu, Shuguang, Dong, Wenjie, Merbold, Lutz, Law, Beverly, Arain, Altaf, Beringer, Jason, Bernhofer, Christian, Black, Andy, Blanken, Peter D., Cescatti, Alessandro, Chen, Yang, Francois, Louis, Gianelle, Damiano, Janssens, Ivan A., Jung, Martin, Kato, Tomomichi, Kiely, Gerard, Liu, Dan, Marcolla, Barbara, Montagnani, Leonardo, Raschi, Antonio, Roupsard, Olivier, Varlagin, Andrej, and Wohlfahrt, Georg

Published
2014
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47. Global maps of soil temperature

Author

Winkler, 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, Smiljanic, Marko, Hik, David, Canessa, Rafaella, van den Hoogen, Johan, Altman, Jan, Björkman, Mats, Cesarz, Simone, Blonder, Benjamin, Kazakis, George, Opedal, Øystein, Assmann, Jakob, Tanentzap, Andrew, Sidenko, Nikita, le Maire, Guerric, Ursu, Tudor-Mihai, Montagnani, Leonardo, Muffler, Lena, Hederová, Lucia, Rubtsov, Alexey, Pauchard, Aníbal, Tielbörger, Katja, Sørensen, Mia, Crowther, Thomas, Remmers, Wolfram, Pitteloud, Camille, Zyryanov, Viacheslav, Nilsson, Matts, Bazzichetto, Manuele, Sallo-Bravo, Jhonatan, Moiseev, Dmitry, Spasojevic, Marko, Haase, Peter, Pearse, William, Tutton, Rosamond, Fazlioglu, Fatih, Siqueira, David, Ardö, Jonas, Nardino, Marianna, Tomaselli, Marcello, Pavelka, Marian, García, Rafael, Nosetto, Marcelo, Bon, Matteo, Semenchuk, Philipp, Choler, Philippe, Scott, Tony, Halbritter, Aud, Dušek, Jiří, Mackenzie, Roy, Stanisci, Angela, Nouvellon, Yann, Kovács, Bence, Haesen, Stef, Veenendaal, Elmar, Juszczak, Radoslaw, Verheijen, Frank, de Andrade, Ana, Verbeeck, Hans, Bader, Maaike, RENAULT, David, Zimmermann, Reiner, Ferlian, Olga, Medinets, Sergiy, Walz, Josefine, Rossi, Christian, Rocha, Adrian, Lembrechts, Jonas, Jactel, Hervé, Brum, Barbara, Aartsma, Peter, Kobler, Johannes, Eisenhauer, Nico, Bjerke, Jarle, Pellissier, Loïc, Ueyama, Masahito, Manca, Giovanni, Bahalkeh, Khadijeh, Meysman, Filip, Niessner, Armin, Curtis, Robin, Six, Johan, Saccone, Patrick, Wang, Runxi, Ahrends, Antje, Okello, Joseph, Kolle, Olaf, Portillo-Estrada, Miguel, Laska, Kamil, Freeman, Erika, Di Cecco, Valter, Ashcroft, Michael, Steinbauer, Klaus, Della Chiesa, Stefano, van den Brink, Liesbeth, Herberich, Maximiliane, Loubet, Benjamin, Barančok, Peter, Hermanutz, Luise, Souza, Bartolomeu, Contador, Tamara, Zhang, Zhaochen, Aerts, Rien, Stephan, Jörg, Chojnicki, Bogdan, Manco, Antonio, Larson, Keith, Mondoni, Andrea, Palaj, Andrej, Schmeddes, Jonas, Hepenstrick, Daniel, Järveoja, Järvi, Manise, Tanguy, Barthel, Matti, Marciniak, Felipe, Weigel, Robert, Rixen, Christian, Turtureanu, Pavel, Hoffrén, Raúl, Iwata, Hiroki, Vittoz, Pascal, Wedegärtner, Ronja, Penczykowski, Rachel, Phartyal, Shyam, Sitková, Zuzana, Nagy, Laszlo, Ujházy, Karol, Heinesch, Bernard, Berauer, Bernd, Ogée, Jérôme, Malfasi, Francesco, Greise, Caroline, Helfter, Carole, Mosedale, Jonathan, Senior, Rebecca, Magliulo, Enzo, Nuñez, Martin, García, María, Wohlfahrt, Georg, Carbognani, Michele, Thomas, Andrew, Eklundh, Lars, Erfanian, Mohammad, Villar, Luis, Maier, Regine, Dahlberg, C., Guglielmin, Mauro, Jucker, Tommaso, Kelly, Julia, Olesen, Jørgen, Lang, Simone, Tanneberger, Franziska, Gharun, Mana, Jackowicz-Korczynski, Marcin, Convey, Peter, Aalto, Juha, Scheffers, Brett, Ujházyová, Mariana, Andres, Christian, Arriga, Nicola, Smith-Tripp, Sarah, Kanka, Róbert, Dick, Jan, Leihy, Rachel, Van Meerbeek, Koenraad, Maclean, Ilya, Vangansbeke, Pieter, Pampuch, Timo, Čiliak, Marek, Guillemot, Joannès, Sarneel, Judith, Souza, José, Svoboda, Miroslav, Björk, Robert, Merinero, Sonia, Zellweger, Florian, Simpson, Elizabeth, Cannone, Nicoletta, Abedi, Mehdi, Seipel, Tim, Klinges, David, Máliš, František, Basham, Edmund, Sewerniak, Piotr, Schwartz, Naomi, Trouillier, Mario, Vandvik, Vigdis, Shekhar, Ankit, Munoz-Rojas, Miriam, Nicklas, Lena, Goded, Ignacio, Manolaki, Paraskevi, Radujković, Dajana, Yu, Kailiang, Phoenix, Gareth, Cifuentes, Edgar, Seeber, Julia, Deronde, Bart, Lenoir, Jonathan, Frei, Esther, Wilmking, Martin, Hylander, Kristoffer, Graae, Bente, Calzado, M., Wang, Yifeng, Hampe, Arndt, Somers, Ben, Mörsdorf, Martin, Jastrzebowski, Szymon, Ejtehadi, Hamid, Terrestrial Ecology (TE), Universidad de Alcalá. Departamento de Geología, Geografía y Medio Ambiente, BioGeoClimate Modelling Lab, Department of Geosciences and Geography, Helsinki Institute of Sustainability Science (HELSUS), Institute for Atmospheric and Earth System Research (INAR), Universiteit Antwerpen = University of Antwerpen [Antwerpen], Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'Ecologie Alpine (LECA ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), LTSER Zone Atelier Alpes, Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecologie fonctionnelle et biogéochimie des sols et des agro-écosystèmes (UMR Eco&Sols), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Senckenberg Research Institute and Natural History Museum [Frankfurt], Senckenberg – Leibniz Institution for Biodiversity and Earth System Research - Senckenberg Gesellschaft für Naturforschung, Leibniz Association-Leibniz Association, Biodiversité, Gènes & Communautés (BioGeCo), Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Environnements, Dynamiques et Territoires de Montagne (EDYTEM), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), SILVA (SILVA), AgroParisTech-Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 (EDYSAN), Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), 12P1819N, Fonds Wetenschappelijk Onderzoek, ANR-10-LABX-0045,COTE,COntinental To coastal Ecosystems: evolution, adaptability and governance(2010), ANR-13-ISV7-0004,ODYSSEE,De nouvelles voies pour la modélisation des dynamiques d'assemblages d'espèces intégrant l'écologie et l'évolution: le cas des écosystèmes de montagne des Alpes et des Carpates(2013), ANR-20-EBI5-0004,ASICS,ASsessing and mitigating the effects of climate change and biological Invasions on the spatial redistribution of biodiversity in Cold environmentS(2020), ANR-19-CE32-0005,IMPRINT,IMpacts des PRocessus mIcroclimatiques sur la redistributioN de la biodiversiTé forestière en contexte de réchauffement du macroclimat(2019), European Project: 774124 , H2020,H2020-SFS-2017-2,SUPER-G (2018), European Project: 282910,EC:FP7:ENV,FP7-ENV-2011,ECLAIRE(2011), European Project: 641918,H2020,H2020-SC5-2014-two-stage,AfricanBioServices(2015), European Project: 678841,H2020,ERC-2015-STG,NICH(2016), European Project: 871128,eLTER PLUS (2020), European Project: 861974, H2020,SOCIETAL CHALLENGES - Food security, sustainable agriculture and forestry, marine, maritime and inland water research, and the bioeconomy,SustainSahel(2020), Lembrechts, Jonas J [0000-0002-1933-0750], van den Hoogen, Johan [0000-0001-6624-8461], Aalto, Juha [0000-0001-6819-4911], De Frenne, Pieter [0000-0002-8613-0943], Kemppinen, Julia [0000-0001-7521-7229], Kopecký, Martin [0000-0002-1018-9316], Luoto, Miska [0000-0001-6203-5143], Maclean, Ilya MD [0000-0001-8030-9136], Crowther, Thomas W [0000-0001-5674-8913], Bailey, Joseph J [0000-0002-9526-7095], Haesen, Stef [0000-0002-4491-4213], Klinges, David H [0000-0002-7900-9379], Niittynen, Pekka [0000-0002-7290-029X], Scheffers, Brett R [0000-0003-2423-3821], Van Meerbeek, Koenraad [0000-0002-9260-3815], Aartsma, Peter [0000-0001-5086-856X], Abdalaze, Otar [0000-0001-8140-0900], Abedi, Mehdi [0000-0002-1499-0119], Aerts, Rien [0000-0001-6694-0669], Ahmadian, Negar [0000-0002-7427-7198], Ahrends, Antje [0000-0002-5083-7760], Alatalo, Juha M [0000-0001-5084-850X], Alexander, Jake M [0000-0003-2226-7913], Allonsius, Camille Nina [0000-0003-2599-9941], Altman, Jan [0000-0003-4879-5773], Ammann, Christof [0000-0002-0783-5444], Andres, Christian [0000-0003-0576-6446], Andrews, Christopher [0000-0003-2428-272X], Ardö, Jonas [0000-0002-9318-0973], Arriga, Nicola [0000-0001-5321-3497], Arzac, Alberto [0000-0002-3361-5349], Aschero, Valeria [0000-0003-3865-4133], Assis, Rafael L [0000-0001-8468-6414], Assmann, Jakob Johann [0000-0002-3492-8419], Bader, Maaike Y [0000-0003-4300-7598], Bahalkeh, Khadijeh [0000-0003-1485-0316], Barančok, Peter [0000-0003-1171-2524], Barrio, Isabel C [0000-0002-8120-5248], Barros, Agustina [0000-0002-6810-2391], Basham, Edmund W [0000-0002-0167-7908], Bauters, Marijn [0000-0003-0978-6639], Bazzichetto, Manuele [0000-0002-9874-5064], Marchesini, Luca Belelli [0000-0001-8408-4675], Bell, Michael C [0000-0002-3401-7746], Benavides, Juan C [0000-0002-9694-2195], Benito Alonso, José Luis [0000-0003-1086-8834], Berauer, Bernd J [0000-0002-9472-1532], Bjerke, Jarle W [0000-0003-2721-1492], Björk, Robert G [0000-0001-7346-666X], Björkman, Mats P [0000-0001-5768-1976], Björnsdóttir, Katrin [0000-0001-7421-9441], Blonder, Benjamin [0000-0002-5061-2385], Boeckx, Pascal 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Subjects
0106 biological sciences, Zoology and botany: 480 [VDP], Q1, 01 natural sciences, Global map, SDG 13 - Climate Action, Soil temperature, Zone climatique, bepress|Physical Sciences and Mathematics|Environmental Sciences, bioclimatic variables, global maps, microclimate, near-surface temperatures, soil temperature, soil-dwelling organisms, temperature offset, weather stations, ComputingMilieux_MISCELLANEOUS, General Environmental Science, Global and Planetary Change, GB, Geology, PE&RC, 6. Clean water, Near-surface soil temperature, international, [SDE]Environmental Sciences, 551: Geologie und Hydrologie, Plantenecologie en Natuurbeheer, Température du sol, Near-surface temperature, Near-surface temperatures, Biologie, P40 - Météorologie et climatologie, bepress|Physical Sciences and Mathematics|Earth Sciences, MITIGATION, bepress|Life Sciences|Ecology and Evolutionary Biology, bepress|Physical Sciences and Mathematics|Oceanography and Atmospheric Sciences and Meteorology|Climate, Bioclimatic variables, Settore BIO/07 - ECOLOGIA, 577: Ökologie, Biology, Ecosystem, Ekologi, Changement climatique, Cartographie, Biology and Life Sciences, Microclimate, 15. Life on land, bepress|Physical Sciences and Mathematics|Environmental Sciences|Environmental Monitoring, Agriculture and Soil Science, 0401 agriculture, forestry, and fisheries, Temperature offset, Weather stations, Plan_S-Compliant-OA, Soil, bepress|Life Sciences, ddc:550, Geología, Ecology, Temperature, 04 agricultural and veterinary sciences, Biological Sciences, FOREST, Weather station, Variation saisonnière, Chemistry, Bioclimatologie, bepress|Physical Sciences and Mathematics, 1171 Geosciences, Technology and Engineering, Climate Change, Plant Ecology and Nature Conservation, MOISTURE, LITTER DECOMPOSITION, PERMAFROST, ddc:570, SUITABILITY, G1, bepress|Physical Sciences and Mathematics|Oceanography and Atmospheric Sciences and Meteorology, Global maps, VDP::Mathematics and natural scienses: 400::Zoology and botany: 480, Environmental Chemistry, Zoologiske og botaniske fag: 480 [VDP], Soil-dwelling organisms, Aquatic Ecology, P30 - Sciences et aménagement du sol, Bioclimatic variable, SNOW-COVER, bepress|Physical Sciences and Mathematics|Earth Sciences|Soil Science, Earth sciences, PLANT-RESPONSES, CLIMATIC CONTROLS, Soil-dwelling organism, 13. Climate action, Earth and Environmental Sciences, VDP::Matematikk og naturvitenskap: 400::Zoologiske og botaniske fag: 480, 040103 agronomy & agriculture, Réchauffement global, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Environmental Sciences, 010606 plant biology & botany
Abstract

JJL received funding from the Research Foundation Flanders (grant nr. 12P1819N). The project received funding from the Research Foundation Flanders (grants nrs, G018919N, W001919N). JVDH and TWC received funding from DOB Ecology. JA received funding from the University of Helsinki, Faculty of Science (MICROCLIM, grant nr. 7510145) and Academy of Finland Flagship (grant no. 337552). PDF, CM and PV received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ERC Starting Grant FORMICA 757833). JK received funding from the Arctic Interactions at the University of Oulu and Academy of Finland (318930, Profi 4), Maaja vesitekniikan tuki ry., Tiina and Antti Herlin Foundation, Nordenskiold Samfundet and Societas pro Fauna et Flora Fennica. MK received funding from the Czech Science Foundation (grant nr. 20-28119S) and the Czech Academy of Sciences (grant nr. RVO 67985939). TWC received funding from National Geographic Society grant no. 9480-14 and WW-240R-17. MA received funding from CISSC (program ICRP (grant nr:2397) and INSF (grant nr: 96005914). The Royal Botanic Garden Edinburgh is supported by the Scottish Government's Rural and Environment Science and Analytical Services Division. JMA received funding from the Funding Org. Qatar Petroleum (grant nr. QUEX-CAS-QP-RD-18/19). JMA received funding from the European Union's Horizon 2020 research and innovation program (grant no. 678841) and from the Swiss National Science Foundation (grant no. 31003A_176044). JA was supported by research grants LTAUSA19137 (program INTER-EXCELLENCE, subprogram INTER-ACTION) provided by Czech Ministry of Education, Youth and Sports and 20-05840Y of the Czech Science Foundation. AA was supported by the Ministry of Science and Higher Education of the Russian Federation (grant FSRZ-2020-0014). SN, UAT, JJA, and JvO received funding from the Independent Research Fund Denmark (7027-00133B). LvdB, KT, MYB and RC acknowledge funding from the German Research Foundation within the Priority Program SPP-1803 'EarthShape: Earth Surface Shaping by Biota' (grant TI 338/14-1&2 and BA 3843/6-1). PB was supported by grant project VEGA of the Ministry of Education of the Slovak Republic and the Slovak Academy of Sciences No. 2/0132/18. Forest Research received funding from the Forestry Commission (climate change research programme). JCB acknowledges the support of Universidad Javeriana. JLBA received funding from the Direccion General de Cambio Climatico del Gobierno de Aragon; JLBA acknowledges fieldwork assistance by Ana Acin, the Ordesa y Monte Perdido National Park, and the Servicio de Medio Ambiente de Soria de la Junta de Castilla y Leon. RGB and MPB received funding from BECC - Biodiversity and Ecosystem services in a Changing Climate. MPB received funding from The European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie Grant Agreement No. 657627 and The Swedish Research Council FORMAS - future research leaders No. 2016-01187. JB received funding from the Czech Academy of Sciences (grant nr. RVO 67985939). NB received funding from the SNF (grant numbers 40FA40_154245, 20FI21_148992, 20FI20_173691, 407340_172433) and from the EU (contract no. 774124). ICOS EU research infrastructure. EU FP7 NitroEurope. EU FP7 ECLAIRE. The authors from Biological Dynamics of Forest Fragments Project, PDBFF, Instituto Nacional de Pesquisas da Amazonia, Brazil were supported by the MCTI/CNPq/FNDCT - AcAo Transversal no68/2013 - Programa de Grande Escala da Biosfera-Atmosfera na Amazonia - LBA; Project 'Como as florestas da Amazonia Central respondem as variacoes climaticas? Efeitos sobre dinamica florestal e sinergia com a fragmentacAo florestal'. This is the study 829 of the BDFFP Technical Series. to The EUCFLUX Cooperative Research Program and Forest Science and Research Institute-IPEF. NC acknowledges funding by Stelvio National Park. JC was funded by the Spanish government grant CGL2016-78093-R. ANID-FONDECYT 1181745 AND INSTITUTO ANTARTICO CHILENO (INACH FR-0418). SC received funding from the German Research Foundation (grant no. DFG- FZT 118, 202548816). The National Science Foundation, Poland (grant no. UMO-2017/27/B/ST10/02228), within the framework of the 'Carbon dioxide uptake potential of sphagnum peatlands in the context of atmospheric optical parameters and climate changes' (KUSCO2) project. SLC received funding from the South African National Research Foundation and the Australian Research Council. FM, M, KU and MU received funding from Slovak Research and Development Agency (no. APVV-19-0319). Instituto Antartico Chileno (INACH_RT-48_16), Iniciativa Cientifica Milenio Nucleo Milenio de Salmonidos Invasores INVASAL, Institute of Ecology and Biodiversity (IEB), CONICYT PIA APOYO CCTE AFB170008. PC is supported by NERC core funding to the BAS 'Biodiversity, Evolution and Adaptation Team. EJC received funding from the Norwegian Research Council (grant number 230970). GND was supported by NERC E3 doctoral training partnership grant (NE/L002558/1) at the University of Edinburgh and the Carnegie Trust for the Universities of Scotland. Monitoring stations on Livingston Island, Antarctica, were funded by different research projects of the Gobern of Spain (PERMAPLANET CTM2009-10165-E; ANTARPERMA CTM2011-15565-E; PERMASNOW CTM2014-52021-R), and the PERMATHERMAL arrangement between the University of Alcala and the Spanish Polar Committee. GN received funding from the Autonomous Province of Bolzano (ITA). The infrastructure, part of the UK Environmental Change Network, was funded historically in part by ScotNature and NERC National Capability LTS-S: UK-SCAPE; NE/R016429/1). JD was supported by the Czech Science Foundation (GA17-19376S) and MSMT (LTAUSA18007). ED received funding from the Kempe Foundation (JCK-1112 and JCK-1822). The infrastructure was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme I (NPU I), grant number LO1415 and by the project for national infrastructure support CzeCOS/ICOS Reg. No. LM2015061. NE received funding from the German Research Foundation (DFG- FZT 118, 202548816). BE received funding from the GLORIA-EU project no EVK2-CT2000-00056, the Autonomous Province of Bolzano (ITA), from the Tiroler Wissenschaftsfonds and from the University of Innsbruck. RME was supported by funding to the SAFE Project from the Sime Darby Foundation. OF received funding from the German Research Foundation (DFG- FZT 118, 202548816). EFP was supported by the Jardin Botanico Atlantico (SV-20-GIJON-JBA). MF was funded by the German Federal Ministry of Education and Research (BMBF) in the context of The Future Okavango (Grant No. 01LL0912) and SASSCAL (01LG1201M; 01LG1201N) projects. EFL received funding from ANID PIA / BASAL FB210006. RAG received funding from Fondecyt 11170516, CONICYT PIA AFB170008 and ANID PIA / BASAL FB210006. MBG received funding from National Parks (DYNBIO, #1656/2015) and The Spanish Research Agency (VULBIMON, #CGL2017-90040-R). MG received funding from the Swiss National Science Foundation (ICOS-CH Phase 2 20FI20_173691). FG received funding from the German Research Foundation (DFG- FZT 118, 202548816). KG and TS received funding from the UK Biotechnology and Biological Research Council (grant = 206/D16053). SG was supported by the Research Foundation Flanders (FWO) (project G0H1517N). KJ and PH received funding from the EU Horizon2020 INFRAIA project eLTER-PLUS (871128), the project LTER-CWN (FFG, F&E Infrastrukturforderung, project number 858024) and the Austrian Climate Research Program (ACRP7 - CentForCSink - KR14AC7K11960). SH and ARB received funding through iDiv funded by the German Research Foundation (DFG- FZT 118, 202548816). LH received funding from the Czech Science Foundation (grant nr. 20-28119S) and the Czech Academy of Sciences (grant nr. RVO 67985939). MH received funding from the Baden-Wurttemberg Ministry of Science, Research and Arts via the project DRIeR (Drought impacts, processes and resilience: making the in-visible visible). LH received funding from International Polar Year, Weston Foundation, and ArcticNet. DH received funding from Natural Sciences and Engineering Council (Canada) (RGPIN-06691). TTH received funding from Independent Research Fund Denmark (grant no. 8021-00423B) and Villum Foundation (grant no. 17523). Ministry of Education, Youth and Sports of the Czech Republic (projects LM2015078, VAN2020/01 and CZ.02.1.01/0.0/0.0/16_013/0001708). KH, CG and CJD received funding from Bolin Centre for Climate Research, Stockholm University and from the Swedish research council Formas [grant n:o 2014-00530 to KH]. JJ received funding from the Funding Org. Swedish Forest Society Foundation (grant nr. 2018-485-Steg 2 2017) and Swedish Research Council FORMAS (grant nr. 2018-00792). AJ received funding from the German Federal Ministry of Education and Research BMBF (Grant Nr. FKZ 031B0516C SUSALPS) and the Oberfrankenstiftung (Grant Nr. OFS FP00237). ISJ received funding from the Energy Research Fund (NYR-11 - 2019, NYR-18 - 2020). TJ was supported by a UK NERC Independent Research Fellowship (grant number: NE/S01537X/1). RJ received funding from National Science Centre of Poland (grant number: 2016/21/B/ST10/02271) and Polish National Centre for Research and Development (grant number: Pol-Nor/203258/31/2013). VK received funding from the Czech Academy of Sciences (grant nr. RVO 67985939). AAK received funding from MoEFCC, Govt of India (AICOPTAX project F. No. 22018/12/2015/RE/Tax). NK received funding from FORMAS (grants nr. 2018-01781, 2018-02700, 2019-00836), VR, support from the research infrastructure ICOS-SE. BK received funding from the National Research, Development and Innovation Fund of Hungary (grant nr. K128441). Ministry of Education, Youth and Sports of the Czech Republic (projects LM2015078 and CZ.02.1.01/0.0/0.0/16_013/0001708). Project B1-RNM-163-UGR-18-Programa Operativo FEDER 2018, partially funded data collection. Norwegian Research Council (NORKLIMA grants #184912 and #244525) awarded to Vigdis Vandvik. MM received funding from the Czech Science Foundation (grant nr. 20-28119S) and the Czech Academy of Sciences (grant nr. RVO 67985939). Project CONICYT-PAI 79170119 and ANID-MPG 190029 awarded to Roy Mackenzie. This work was partly funded by project MIUR PON Cluster OT4CLIMA. RM received funding from the SNF project number 407340_172433. FM received funding from the Stelvio National Park. PM received funding from AIAS-COFUND fellowship programme supported by the Marie Skodowska- Curie actions under the European Union's Seventh Framework Pro-gramme for Research, Technological development and Demonstration (grant agreement no 609033) and the Aarhus University Research Foundation, Denmark. RM received funding from the Ministry of Education, Youth and Sports of the Czech Republic (project LTT17033). SM and VM received funding from EU FP6 NitroEurope (grant nr. 17841), EU FP7 ECLAIRE (grant nr. 282910), the Ministry of Education and Science of Ukraine (projects nr. 505, 550, 574, 602), GEF-UNEP funded "Toward INMS" project (grant nr. NEC05348) and ENI CBC BSB PONTOS (grant nr. BSB 889). The authors from Biological Dynamics of Forest Fragments Project, PDBFF, Instituto Nacional de Pesquisas da Amazonia, Brazil were supported by the MCTI/CNPq/FNDCT - AcAo Transversal no68/2013 - Programa de Grande Escala da Biosfera-Atmosfera na Amazonia - LBA; Project 'Como as florestas da Amazonia Central respondem as variacoes climaticas? Efeitos sobre dinamica florestal e sinergia com a fragmentacAo florestal'. FJRM was financially supported by the Netherlands Organization for Scientific Research (VICI grant 016.VICI.170.072) and Research Foundation Flanders (FWO-SBO grant S000619N). STM received funding from New Frontiers in Research Fund-Exploration (grant nr. NFRF-2018-02043) and NSERC Discovery. MMR received funding from the Australian Research Council Discovery Early Career Research Award (grant nr. DE180100570). JAM received funding from the National Science Foundation (DEB 1557094), International Center for Advanced Renewable Energy and Sustainability (I-CARES) at Washington University in St. Louis, ForestGEO, and Tyson Research Center. IM-S was funded by the UK Natural Environment Research Council through the ShrubTundra Project (NE/M016323/1). MBN received funding from FORMAS, VR, Kempe Foundations support from the research infrastructures ICOS and SITES. MDN received funding from CONICET (grant nr. PIP 112-201501-00609). Spanish Ministry of Science grant PID2019-110521GB-I00 and Catalan government grant 2017-1005. French National Research Agency (ANR) in the frame of the Cluster of Excellence COTE (project HydroBeech, ANR-10-LABX-45). VLIR-OUS, under the Institutional University Coorperation programme (IUC) with Mountains of the Moon University. Project LAS III 77/2017/B entitled: \"Estimation of net carbon dioxide fluxes exchanged between the forest ecosystem on post-agricultural land and between the tornado-damaged forest area and the atmosphere using spectroscopic and numerical methods\", source of funding: General Directorate of State Forests, Warsaw, Poland. Max Planck Society (Germany), RFBR, Krasnoyarsk Territory and Krasnoyarsk Regional Fund of Science, project number 20-45-242908. Estonian Research Council (PRG609), and the European Regional Development Fund (Centre of Excellence EcolChange). Canada-Denmark Arctic Research Station Early Career Scientist Exchange Program, from Polar knowledge Canada (POLAR) and the Danish Agency for Science and Higher Education. AP received funding from Fondecyt 1180205, CONICYT PIA AFB170008 and ANID PIA / BASAL FB210006. MP received funding from the Funding Org. Knut and Alice Wallenberg Foundation (grant nr. 2015.0047), and acknowledges funding from the Swedish Research Council (VR) with contributing research institutes to both the SITES and ICOS Sweden infrastructures. JP and RO were funded by the Spanish Ministry of Science grant PID2019-110521GB-I00, the fundacion Ramon Areces grant ELEMENTAL-CLIMATE, and the Catalan government grant 2017-1005. MPB received funding from the Svalbard Environmental Protection Fund (grant project number 15/128) and the Research Council of Norway (Arctic Field Grant, project number 269957). RP received funding from the Ministry of Education, Youth and Sports of the Czech Republic (grant INTER-TRANSFER nr. LTT20017). LTSER Zone Atelier Alpes; Federation FREE-Alpes. RP received funding from a Humboldt Fellowship for Experienced Researchers. Prokushkin AS and Zyryanov VI contribution has been supported by the RFBR grant #18-05-60203-Arktika. RPu received founding from the Polish National Science Centre (grant project number 2017/27/B/NZ8/00316). ODYSSEE project (ANR-13-ISV7-0004, PN-II-ID-JRP-RO-FR-2012). KR was supported through an Australian Government Research Training Program Scholarship. Fieldwork was supported by the Global Challenges program at the University of Wollongong, the ARC the Australian Antarctic Division and INACH. DR was funded by the project SUBANTECO IPEV 136 (French Polar Institute Paul-Emile Victor), Zone Atelier CNRS Antarctique et Terres Australes, SAD Region Bretagne (Project INFLICT), BiodivERsa 2019-2020 BioDivClim call 'ASICS' (ANR-20-EBI5-0004). SAR received funding from the Australian Research Council. NSF grant #1556772 to the University of Notre Dame. Pavia University (Italy). OR received funding from EU-LEAP-Agri (RAMSES II), EU-DESIRA (CASSECS), EU-H2020 (SustainSahel), AGROPOLIS and TOTAL Foundations (DSCATT), CGIAR (GLDC). AR was supported by the Russian Science Foundation (Grant 18-74-10048). Parc national des Ecrins. JS received funding from Vetenskapsradet grant nr (No: 2014-04270), ALTER-net multi-site grant, River LIFE project (LIFE08 NAT/S/000266), Flexpeil. Helmholtz Association long-term research program TERENO (Terrestrial Environmental Observatories). PS received funding from the Polish Ministry of Science and Higher Education (grant nr. N N305 304840). AS acknowledges funding by ETH Zurich project FEVER ETH-27 19-1. LSC received funding from NSERC Canada Graduate Scholarship (Doctoral) Program; LSC was also supported by ArcticNet-NCE (insert grant #). Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (141513/2017-9); FundacAo Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro (E26/200.84/2019). ZS received funding from the SRDA (grants nos. APVV-16-0325 and APVV-20-0365) and from the ERDF (grant no. ITMS 313011S735, CE LignoSilva). JS, MB and CA received funding from core budget of ETH Zurich. State excellence Program M-V \"WETSCAPES\". AfricanBioServices project funded by the EU Horizon 2020 grant number 641918. The authors from KIT/IMK-IFU acknowledge the funding received within the German Terrestrial Environmental Observatories (TERENO) research program of the Helmholtz Association and from the Bavarian Ministry of the Environment and Public Health (UGV06080204000). Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project number 192626868, in the framework of the collaborative German-Indonesian research project CRC 990 (SFB): 'EFForTS, Ecological and Socioeconomic Functions of Tropical Lowland Rainforest Transformation Systems (Sumatra, Indonesia)'. MS received funding from the Ministry of Education, Youth and Sports of the Czech Republic (grant nr. INTER-TRANSFER LTT19018). TT received funding from the Swedish National Space Board (SNSB Dnr 95/16) and the CASSECS project supported by the European Union. HJDT received funding from the UK Natural Environment Research Council (NERC doctoral training partnership grant NE/L002558/1). German Science Foundation (DFG) GraKo 2010 \"Response\". PDT received funding from the MEMOIRE project (PN-III-P1-1.1-PD2016-0925). Arctic Challenge for Sustainability II (ArCS II; JPMXD1420318865). JU received funding from Czech Science Foundation (grant nr. 21-11487S). TU received funding from the Romanian Ministry of Education and Research (CCCDI - UEFISCDI -project PN-III-P2-2.1-PED-2019-4924 and PN2019-2022/19270201-Ctr. 25N BIODIVERS 3-BIOSERV). AV acknowledge funding from RSF, project 21-14-00209. GFV received funding from the Dutch Research Council NWO (Veni grant, no. 863.14.013). Australian Research Council Discovery Early Career Research Award DE140101611. FGAV received funding from the Portuguese Science Foundation (FCT) under CEECIND/02509/2018, CESAM (UIDP/50017/2020+UIDB/50017/2020), FCT/MCTES through national funds, and the co-funding by the FEDER, within the PT2020 Partnership Agreement and Compete 2020. Ordesa y Monte Perdido National Park. MVI received funding from the Spanish Ministry of Science and Innovation through a doctoral grant (FPU17/05869). JW received funding from the Czech Science Foundation (grant nr. 20-28119S) and the Czech Academy of Sciences (grant nr. RVO 67985939). CR and SW received funding from the Swiss Federal Office for the Environment (FOEN) and the de Giacomi foundation. YY received funding from the National Natural Science Foundation of China (Grant no. 41861134039 and 41941015). ZY received funding from the National Natural Science Foundation of China (grant nr. 41877458). FZ received funding from the Swiss National Science Foundation (grant nr. 172198 and 193645). PZ received funding from the Funding Org. Knut and Alice Wallenberg Foundation (grant no. 2015.0047). JL received funding from (i) the Agence Nationale de la Recherche (ANR), under the framework of the young investigators (JCJC) funding instrument (ANR JCJC Grant project NoANR-19-CE32-0005-01: IMPRINT) (ii) the Centre National de la Recherche Scientifique (CNRS) (Defi INFINITI 2018: MORFO); and the Structure Federative de Recherche (SFR) Condorcet (FR CNRS 3417: CREUSE). Fieldwork in the Arctic got facilitated by funding from the EU INTERACT program. SN, UAT, JJA and JvO would like to thank the field team of the Vegetation Dynamics group for their efforts and hard work. We acknowledge Dominique Tristan for letting access to the field. For the logistic support the crew of INACH and Gabriel de Castilla Station team on Deception Island. We thank the Inuvialuit and Kluane First Nations for the opportunity to work on their land. MAdP acknowledges fieldwork assistance and logistics support to Unidad de Tecnologia Marina CSIC, and the crew of Juan Carlos I and Gabriel de Castilla Spanish Antarctic Stations, as well as to the different colleagues from UAH that helped on the instrument maintenance. ERF acknowledges fieldwork assistance by Martin Heggli. MBG acknowledges fieldwork and technical assistance by P Abadia, C Benede, P Bravo, J Gomez, M Grasa, R Jimenez, H Miranda, B Ponz, J Revilla and P Tejero and the Ordesa and Monte Perdido National Park staff. LH acknowledges field assistance by John Jacobs, Andrew Trant, Robert Way, Darroch Whitaker; we acknowledge the Inuit of Nunatsiavut, and the Co-management Board of Torngat Mountains National Park for their support of this project and acknowledge that the field research was conducted on their traditional lands. We thank our many bear guides, especially Boonie, Eli, Herman, John and Maria Merkuratsuk. AAK acknowledges field support of Akhtar Malik, Rameez Ahmad. Part of microclimatic records from Saxony was funded by the Saxon Switzerland National Park Administration. Tyson Research Center. JP acknowledges field support of Emmanuel Malet (Edytem) and Rangers of Reserves Naturelles de Haute-Savoie (ASTERS). Practical help: Roel H. Janssen, N. Huig, E. Bakker, Schools in the tepaseforsoket, Forskar fredag, Erik Herberg. The support by the Bavarian Forest National Park administration is highly appreciated. LvdB acknowledges CONAF and onsite support from the park rangers from PN Pan de Azucar, PN La Campana, PN Nahuelbuta and from communidad agricola Quebrada de Talca. JL and FS acknowledge Manuel Nicolas and all forest officers from the Office National des Forets (ONF) who are in charge of the RENECOFOR network and who provided help and local support for the installation and maintenance of temperature loggers in the field., Research 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-km2 resolution 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-km2 p ixels ( summarized f rom 8 519 u nique t emperature 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., FWO G018919N W001919N 12P1819N, DOB Ecology, University of Helsinki, Faculty of Science (MICROCLIM) 7510145, European Research Council (ERC) FORMICA 757833, Arctic Interactions at the University of Oulu, Academy of Finland 318930 337552, Maaja vesitekniikan tuki ry., Tiina and Antti Herlin Foundation, Nordenskiold Samfundet, Societas pro Fauna et Flora Fennica, Grant Agency of the Czech Republic 20-28119S 20-05840Y GA17-19376S 21-11487S, Czech Academy of Sciences RVO 67985939, National Geographic Society 9480-14 WW-240R-17, CISSC (program ICRP) 2397, Iran National Science Foundation (INSF) 96005914, Scottish Government's Rural and Environment Science and Analytical Services Division, Qatar Petroleum QUEX-CAS-QP-RD-18/19, European Union's Horizon 2020 research and innovation program 678841, Swiss National Science Foundation (SNSF), European Commission 172198 193645 31003A_176044, Ministry of Education, Youth & Sports - Czech Republic LTAUSA19137, Ministry of Science and Higher Education of the Russian Federation FSRZ-2020-0014, Independent Research Fund Denmark 8021-00423B 7027-00133B, German Research Foundation (DFG) DFG- FZT 118 202548816 TI 338/14-1 TI 338/14-2 BA 3843/6-1, grant project VEGA of the Ministry of Education of the Slovak Republic Slovak Academy of Sciences 2/0132/18, Forestry Commission, Universidad Javeriana, Direccion General de Cambio Climatico del Gobierno de Aragon, European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie Grant 657627 SNF 407340_172433 40FA40_154245 20FI21_148992 20FI20_173691, European Commission 17841 774124, MCTI/CNPq/FNDCT 68/2013, Project 'Como as florestas da Amazonia Central respondem as variacoes climaticas? Efeitos sobre dinamica florestal e sinergia com a fragmentacAo florestal', Spanish Government, European Commission CGL2016-78093-R, ANID-FONDECYT 1181745, National Science Foundation, Poland UMO-2017/27/B/ST10/02228, National Research Foundation - South Africa, Australian Research Council, Slovak Research and Development Agency APVV-19-0319, Instituto Antartico Chileno INACH_RT-48_16 INACH FR-0418, Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) PIA APOYO CCTE AFB170008 PIA AFB170008, UK Research & Innovation (UKRI), Natural Environment Research Council (NERC), Research Council of Norway, European Commission 230970, NERC E3 doctoral training partnership grant at the University of Edinburgh NE/L002558/1, Carnegie Trust for the Universities of Scotland, Gobern of Spain PERMAPLANET CTM2009-10165-E ANTARPERMA CTM2011-15565-E PERMASNOW CTM2014-52021-R, University of Alcala, Spanish Polar Committee, Autonomous Province of Bolzano (ITA), ScotNature, NERC National Capability LTS-S: UK-SCAPE NE/R016429/1, Ministry of Education, Youth & Sports - Czech Republic LTAUSA18007, Kempe Foundation JCK-1112 JCK-1822, Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme I (NPU I) LO1415, project for national infrastructure support CzeCOS/ICOS LM2015061 GLORIA-EU EVK2-CT2000-00056, Tiroler Wissenschaftsfonds, University of Innsbruck, Sime Darby Foundation, Jardin Botanico Atlantico SV-20-GIJON-JBA, Federal Ministry of Education & Research (BMBF) 01LL0912 01LG1201M 01LG1201N, Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 11170516 1180205, ANID PIA / BASAL FB210006, National Parks (DYNBIO) 1656/2015, Spanish Research Agency (VULBIMON) CGL2017-90040-R, Swiss National Science Foundation (SNSF) 20FI20_173691, Biotechnology and Biological Sciences Research Council (BBSRC) 206/D16053 FWO G0H1517N, EU Horizon2020 INFRAIA project eLTER-PLUS 871128, project LTER-CWN (FFG, F&E Infrastrukturforderung) 858024, Austrian Climate Research Program ACRP7 - CentForCSink - KR14AC7K11960, iDiv by the German Research Foundation DFG- FZT 118 202548816, Baden-Wurttemberg Ministry of Science, Research and Arts, Weston Foundation, ArcticNet, Natural Sciences and Engineering Research Council of Canada (NSERC) RGPIN-06691, Villum Foundation 17523, Ministry of Education, Youth & Sports - Czech Republic LM2015078 VAN2020/01 CZ.02.1.01/0.0/0.0/16_013/0001708 LTT17033 LTT20017 INTER-TRANSFER LTT19018, Bolin Centre for Climate Research, Stockholm University, Swedish Research Council Swedish Research Council Formas 2014-00530 2018-00792 2016-01187, Swedish Forest Society Foundation 2018-485-Steg 2 2017, Federal Ministry of Education & Research (BMBF) FKZ 031B0516C SUSALPS, Oberfrankenstiftung OFS FP00237, Energy Research Fund NYR-11 - 2019 NYR-18 - 2020, UK NERC Independent Research Fellowship NE/S01537X/1, National Science Centre, Poland 2016/21/B/ST10/02271, Polish National Centre for Research and Development Pol-Nor/203258/31/2013, MoEFCC, Govt of India (AICOPTAX project) 22018/12/2015/RE/Tax, Swedish Research Council Formas 2018-01781 2018-02700 2019-00836, research infrastructure ICOS-SE, National Research, Development and Innovation Fund of Hungary K128441, Programa Operativo FEDER 2018 B1-RNM-163-UGR-18, Norwegian Research Council (NORKLIMA grants) 184912 244525, CONICYT-PAI 79170119, ANID-MPG 190029, project MIUR PON Cluster OT4CLIMA, Stelvio National Park, AIAS-COFUND fellowship programme - Marie Skodowska- Curie actions under the European Union's Seventh Framework Pro-gramme for Research, Technological development and Demonstration 609033, Aarhus University Research Foundation, Denmark, EU FP6 NitroEurope 17841, EU FP7 ECLAIRE 282910, Ministry of Education and Science of Ukraine 505 550 574 602, GEF-UNEP NEC05348, ENI CBC BSB PONTOS BSB 889, Netherlands Organization for Scientific Research (NWO) 016.VICI.170.072, New Frontiers in Research Fund-Exploration NFRF-2018-02043, Natural Sciences and Engineering Research Council of Canada (NSERC), Australian Research Council DE180100570, National Science Foundation (NSF) DEB 1557094, International Center for Advanced Renewable Energy and Sustainability (I-CARES) at Washington University in St. Louis, Smithsonian Institution Smithsonian Tropical Research Institute, Tyson Research Center, UK Natural Environment Research Council through the ShrubTundra Project NE/M016323/1, Swedish Research Council Formas Swedish Research Council, Kempe Foundations - research infrastructure ICOS Kempe Foundations - research infrastructure SITES, Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) PIP 112-201501-00609, Spanish Government PID2019-110521GB-I00, Catalan government 2017-1005, French National Research Agency (ANR) ANR-10-LABX-45, General Directorate of State Forests, Warsaw, Poland, Max Planck Society, Russian Foundation for Basic Research (RFBR), Krasnoyarsk Territory Krasnoyarsk Regional Fund of Science 20-45-242908, Estonian Research Council PRG609, Knut & Alice Wallenberg Foundation 2015.0047, Swedish Research Council, fundacion Ramon Areces grant ELEMENTAL-CLIMATE, Svalbard Environmental Protection Fund 15/128, Research Council of Norway 269957, Humboldt Fellowship for Experienced Researchers, Russian Foundation for Basic Research (RFBR) 18-05-60203-Arktika, Polish National Science Centre 2017/27/B/NZ8/00316, ODYSSEE project (PN-II-ID-JRP-RO-FR-2012) ANR-13-ISV7-0004, Australian Government, Department of Industry, Innovation and Science, Global Challenges program at the University of Wollongong, ARC the Australian Antarctic Division, INACH, project SUBANTECO IPEV 136 (French Polar Institute Paul-Emile Victor), Zone Atelier CNRS Antarctique et Terres Australes, SAD Region Bretagne (Project INFLICT), BiodivERsa 2019-2020 BioDivClim call 'ASICS' ANR-20-EBI5-0004, National Science Foundation (NSF) 1556772, EU-LEAP-Agri (RAMSES II) EU-DESIRA (CASSECS) EU-H2020 (SustainSahel), AGROPOLIS, Total SA, CGIAR, Russian Science Foundation (RSF) 18-74-10048, Swedish Research Council 2014-04270, ALTER-net multi-site grant, River LIFE project LIFE08 NAT/S/000266, Flexpeil, Ministry of Science and Higher Education, Poland N N305 304840, ETH Zurich FEVER ETH-27 19-1, NSERC Canada Graduate Scholarship (Doctoral) Program, ArcticNet-NCE, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) 141513/2017-9, Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ) E26/200.84/2019, SRDA APVV-16-0325 APVV-20-0365, ERDF (CE LignoSilva) ITMS 313011S735, ETH Zurich, EU Horizon 2020 641918, German Terrestrial Environmental Observatories (TERENO) research program of the Helmholtz Association, Bavarian Ministry of the Environment and Public Health UGV06080204000 German Research Foundation (DFG) 192626868, Swedish National Space Board (SNSB) 95/16, CASSECS project by the European Union, Natural Environment Research Council (NERC) NE/L002558/1, MEMOIRE project PN-III-P1-1.1-PD2016-0925, Arctic Challenge for Sustainability II (ArCS II) JPMXD1420318865, Consiliul National al Cercetarii Stiintifice (CNCS), Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii (UEFISCDI) PN-III-P2-2.1-PED-2019-4924 PN2019-2022/19270201, 25N BIODIVERS 3-BIOSERV, Russian Science Foundation (RSF) 21-14-00209., Netherlands Organization for Scientific Research (NWO) 863.14.013, Australian Research Council DE140101611, Portuguese Foundation for Science and Technology CEECIND/02509/2018 CESAM UIDP/50017/2020+UIDB/50017/2020, Portuguese Foundation for Science and Technology European Commission, FEDER, within the PT2020 Partnership Agreement, Compete 2020, Spanish Government FPU17/05869, Swiss Federal Office for the Environment (FOEN), Giacomi foundation, National Natural Science Foundation of China (NSFC) 41861134039 41941015 41877458, French National Research Agency (ANR) ANR-19-CE32-0005-01 Centre National de la Recherche Scientifique (CNRS), Structure Federative de Recherche (SFR) Condorcet (FR CNRS 3417: CREUSE), EU INTERACT program, Inuit of Nunatsiavut, Co-management Board of Torngat Mountains National Park, Saxon Switzerland National Park Administration, Bavarian Forest National Park administration, BECC - Biodiversity and Ecosystem services in a Changing Climate, Research Foundation Flanders (FWO-SBO) S000619N

Published
2021

48. Oral presentation at World Congress on Agroforestry 2022 : Faidherbia albida transpiration and canopy conductance in a reference agroforestry system of West Africa

Author

Do, Frederic, Sarr, Mame, Diouf, Khalisse, Sow, Sidy, Diongue, Djim, Rocheteau, Alain, Diedhiou, Ibrahima, Seghieri, Josiane, Le Maire, Guerric, and Roupsard, Olivier

Subjects
west africa, sahel, agroforestry
Abstract

Faidherbia albida (FA) is an emblematic woody species of agroforestry systems in semi-arid Africa. It is phreatophyte and shows reverse phenology (leafless in rainy season) which avoids competition for soil water with associated crops. The root access to groundwater is assumed to prevent constraint on transpiration. However, the details of FA transpiration and canopy conductance under daily and seasonal conditions are poorly known. The study was conducted at Faidherbia-Flux1 station in the population-health-environment observatory of Niakhar, Senegal. Five mature trees were selected in the stand with a density of 6.9 tree ha-1. Sap flow and soil water content were continuously recorded over a complete year with meteorological variables and measurements of leaf area index and leaf water potential. Maximal transpiration estimated from sap flow peaked around 210 L tree-1 day-1 in the early-dry season (December) for an average tree (DBH of 48.5 cm and 260 m² of leaf area). The transpiration slowly decreased up to June before a sharp decrease following intense defoliation at the beginning of the rainy season. Predawn leaf water potential ranged between 0.25 in early-dry season and 0.45 MPa in mid-dry season, indicating a low water constraint. However, the reference canopy stomatal conductance per leaf area (at 1 kPa of air vapour pressure deficit, VPD) decreased from around 100 in early-dry season to 50 mmol m-2s-1 in mid-dry season suggesting a significant regulation. Moreover, in both cases, the canopy stomatal conductance was highly sensitive to VPD with a 50% decrease at 3 kPa. These results support influences of soil surface water and air dryness on transpiration. This study provides new bases for the modeling of transpiration and water uptake of FA trees. It is paving the way towards a more accurate water balance and a better assessment of FA parkland efficiency and sustainability. 1 Faidherbia

Published
2022
Full Text
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50. Global maps of soil temperature

Author

Lembrechts, Jonas J., van den Hoogen, Johan, Aalto, Juha, Ashcroft, Michael B., De Frenne, Pieter, Kemppinen, Julia, Kopecký, Martin, Luoto, Miska, Maclean, Ilya M. D., Crowther, Thomas W., Bailey, Joseph J., Haesen, Stef, Klinges, David H., Niittynen, Pekka, Scheffers, Brett R., Van Meerbeek, Koenraad, Aartsma, Peter, Abdalaze, Otar, Abedi, Mehdi, Aerts, Rien, Ahmadian, Negar, Ahrends, Antje, Alatalo, Juha M., Alexander, Jake M., Allonsius, Camille Nina, Altman, Jan, Ammann, Christof, Andres, Christian, Andrews, Christopher, Ardö, Jonas, Arriga, Nicola, Arzac, Alberto, Aschero, Valeria, Assis, Rafael L., Assmann, Jakob Johann, Bader, Maaike Y., Bahalkeh, Khadijeh, Barančok, Peter, Barrio, Isabel C., Barros, Agustina, Barthel, Matti, Basham, Edmund W., Bauters, Marijn, Bazzichetto, Manuele, Belelli Marchesini, Luca, Bell, Michael C., Benavides, Juan C., Benito Alonso, José Luis, Berauer, Bernd J., Bjerke, Jarle W., Björk, Robert G., Björkman, Mats P., Björnsdóttir, Katrin, Blonder, Benjamin, Boeckx, Pascal, Boike, Julia, Bokhorst, Stef, Brum, Bárbara N. S., Brůna, Josef, Buchmann, Nina, Buysse, Pauline, Camargo, Jose Luis C., Campoe, Otavio, Candan, Onur, Canessa, Rafaella, Cannone, Nicoletta, Carbognani, Michele, Carnicer, Jofre, Casanova-Katny, Angélica, Cesarz, Simone, Chojnicki, Bogdan, Choler, Philippe, Chown, Steven L., Cifuentes, Edgar F., Čiliak, Marek, Contador, Tamara, Convey, Peter, Cooper, Elisabeth J., Cremonese, Eodardo, Curasi, Salvatore R., Curtis, Robin, Cutini, Maurizio, Dahlberg, C. Johan, Daskalova, Gergana N., de Pablo, Miguel Angel, Della Chiesa, Stefano, Dengler, Jürgen, Deronde, Bart, Di Cecco, Valter, Di Musciano, Michele, Dick, Jan, Dimarco, Romina D., Dolezal, Jiri, Dorrepaal, Ellen, Dusek, Jiri, Eisenhauer, Nico, Eklundh, Lars, Erickson, Todd E., Erschbamer, Brigitta, Eugster, Werner, Ewers, Robert M., Exton, Dan A., Fanin, Nicolas, Fazlioglu, Fatih, Feigenwinter, Iris, Fenu, Giuseppe, Ferlian, Olga, Fernández Calzado, M. Rosa, Fernández-Pascual, Eduardo, Finckh, Manfred, Finger Higgens, Rebecca, Forte, T'ai G. W., Freeman, Erika C., Frei, Esther R., Fuentes-Lillo, Eduardo, García, Rafael A., García, María B., Géron, Charly, Gharun, Mana, Ghosn, Dany, Gigauri, Khatuna, Gobin, Anne, Goded, Ignacio, Goeckede, Mathias, Gottschall, Felix, Goulding, Keith, Govaert, Sanne, Jessen Graae, Bente, Greenwood, Sarah, Greiser, Caroline, Grelle, Achim, Guénard, Benoït, Guglielmin, Mauro, Guillemot, Joannès, Haase, Peter, Haider, Sylvia, Halbritter, Aud H., Hamid, Maroof, Hammerle, Albin, Hampe, Arndt, Haugum, Siri V., Hederová, Lucia, Heinesch, Bernard, Helfters, Carole, Hepenstrick, Daniel, Herberich, Maximiliane, Herbst, Mathias, Hermanutz, Luise, Hik, David S., Hoffrén, Raúl, Homeier, Jürgen, Hörtnagl, Lukas, Hoye, Toke T., Hrbacek, Filip, Hylander, Kristoffer, Iwata, Hiroki, Jackowicz-Korczynski, Marcin, Jactel, Hervé, Järveoja, Järvi, Jastrzębowski, Szymon, Jentsch, Anke, Jiménez, Juan J., Jónsdóttir, Ingibjörg S., Jucker, Tommaso, Jump, Alistair S., Juszczak, Radoslaw, Kanka, Robert, Kašpar, Vít, Kazakis, George, Kelly, Julia, Khuroo, Anzar A., Klemedtsson, Leif, Klisz, Marcin, Kljun, Natascha, Knohl, Alexander, Kobler, Johannes, Kollár, Jozef, Kotowska, Martyna M., Kovács, Bence, Kreyling, Juergen, Lamprecht, Andrea, Lang, Simone I., Larson, Christian, Larson, Keith, Laska, Kamil, Le Maire, Guerric, Leihy, Rachel I., Lens, Luc, Liljebladh, Bengt, Lohila, Annalea, Lorite, Juan, Loubet, Benjamin, Lynn, Joshua, Macek, Martin, Mackenzie, Roy, Magliulo, Enzo, Maier, Regine, Malfasi, Francesco, Malis, František, Man, Matěj, Manca, Giovanni, Manco, Antonio, Manise, Tanguy, Manolaki, Paraskevi, Marciniak, Felipe, Matula, Radim, Mazzolari, Ana Clara, Medinets, Volodymyr, Meeussen, Camille, Merinero, Sonia, de Cássia Guimarães Mesquita, Rita, Meusburger, Katrin, Meysman, Filip J.R., Michaletz, Sean T., Milbau, Ann, Moiseev, Dmitry, Moiseev, Pavel, Mondoni, Andrea, Monfries, Ruth, Montagnani, Leonardo, Moriana-Armendariz, Mikel, Morra di Cella, Umberto, Mörsdorf, Martin, Mosedale, Jonnathan R., Muffler, Lena, Muñoz-Rojas, Miriam, Myers, Jonnathan A., Myers-Smith, Isla H., Nagy, Laszlo, Nardino, Marianna, Naujokaitis-Lewis, Ilona, Newling, Emily, Nicklas, Lena, Niedrist, Georg, Niessner, Armin, Nilsson, Mats B., Normand, Signe, Nosetto, Marcelo, Nouvellon, Yann, Nunez, Martin, Ogaya, Romà, Ogée, Jérôme, Okello, Joseph, Olejnik, Janusz, Olesen, Jørgen Eivind, Opedal, Oystein H., Orsenigo, Simone, Palaj, Andrej, Pampuch, Timo, Panov, Alexey V., Pärtel, Meelis, Pastor, Ada, Pauchard, Anibal, Pauli, Harald, Pavelka, Marian, Pearse, William D., Peichl, Matthias, Pellissier, Loïc, Penczykowski, Rachel M., Peñuelas, Josep, Petit Bon, Matteo, Petraglia, Alessandro, Phartyal, Shyam S., Phoenix, Gareth K., Pio, Casimiro, Pitacco, Andrea, Pitteloud, Camille, Plichta, Roman, Porro, Francesco, Portillo-Estrada, Miguel, Poulenard, Jérôme, Poyatos, Rafael, Prokushkin, Anatoly S., Puchalka, Radoslaw, Puscas, Mihai, Radujković, Dajana, Randall, Krystal, Ratier Backes, Amanda, Remmele, Sabine, Remmers, Wolfram, Renault, David, Risch, Anita C., Rixen, Christian, Robinson, Sharon A., Robroek, Bjorn J.M., Rocha, Adrian V., Rossi, Christian, Rossi, Graziano, Roupsard, Olivier, et al., Lembrechts, Jonas J., van den Hoogen, Johan, Aalto, Juha, Ashcroft, Michael B., De Frenne, Pieter, Kemppinen, Julia, Kopecký, Martin, Luoto, Miska, Maclean, Ilya M. D., Crowther, Thomas W., Bailey, Joseph J., Haesen, Stef, Klinges, David H., Niittynen, Pekka, Scheffers, Brett R., Van Meerbeek, Koenraad, Aartsma, Peter, Abdalaze, Otar, Abedi, Mehdi, Aerts, Rien, Ahmadian, Negar, Ahrends, Antje, Alatalo, Juha M., Alexander, Jake M., Allonsius, Camille Nina, Altman, Jan, Ammann, Christof, Andres, Christian, Andrews, Christopher, Ardö, Jonas, Arriga, Nicola, Arzac, Alberto, Aschero, Valeria, Assis, Rafael L., Assmann, Jakob Johann, Bader, Maaike Y., Bahalkeh, Khadijeh, Barančok, Peter, Barrio, Isabel C., Barros, Agustina, Barthel, Matti, Basham, Edmund W., Bauters, Marijn, Bazzichetto, Manuele, Belelli Marchesini, Luca, Bell, Michael C., Benavides, Juan C., Benito Alonso, José Luis, Berauer, Bernd J., Bjerke, Jarle W., Björk, Robert G., Björkman, Mats P., Björnsdóttir, Katrin, Blonder, Benjamin, Boeckx, Pascal, Boike, Julia, Bokhorst, Stef, Brum, Bárbara N. S., Brůna, Josef, Buchmann, Nina, Buysse, Pauline, Camargo, Jose Luis C., Campoe, Otavio, Candan, Onur, Canessa, Rafaella, Cannone, Nicoletta, Carbognani, Michele, Carnicer, Jofre, Casanova-Katny, Angélica, Cesarz, Simone, Chojnicki, Bogdan, Choler, Philippe, Chown, Steven L., Cifuentes, Edgar F., Čiliak, Marek, Contador, Tamara, Convey, Peter, Cooper, Elisabeth J., Cremonese, Eodardo, Curasi, Salvatore R., Curtis, Robin, Cutini, Maurizio, Dahlberg, C. Johan, Daskalova, Gergana N., de Pablo, Miguel Angel, Della Chiesa, Stefano, Dengler, Jürgen, Deronde, Bart, Di Cecco, Valter, Di Musciano, Michele, Dick, Jan, Dimarco, Romina D., Dolezal, Jiri, Dorrepaal, Ellen, Dusek, Jiri, Eisenhauer, Nico, Eklundh, Lars, Erickson, Todd E., Erschbamer, Brigitta, Eugster, Werner, Ewers, Robert M., Exton, Dan A., Fanin, Nicolas, Fazlioglu, Fatih, Feigenwinter, Iris, Fenu, Giuseppe, Ferlian, Olga, Fernández Calzado, M. Rosa, Fernández-Pascual, Eduardo, Finckh, Manfred, Finger Higgens, Rebecca, Forte, T'ai G. W., Freeman, Erika C., Frei, Esther R., Fuentes-Lillo, Eduardo, García, Rafael A., García, María B., Géron, Charly, Gharun, Mana, Ghosn, Dany, Gigauri, Khatuna, Gobin, Anne, Goded, Ignacio, Goeckede, Mathias, Gottschall, Felix, Goulding, Keith, Govaert, Sanne, Jessen Graae, Bente, Greenwood, Sarah, Greiser, Caroline, Grelle, Achim, Guénard, Benoït, Guglielmin, Mauro, Guillemot, Joannès, Haase, Peter, Haider, Sylvia, Halbritter, Aud H., Hamid, Maroof, Hammerle, Albin, Hampe, Arndt, Haugum, Siri V., Hederová, Lucia, Heinesch, Bernard, Helfters, Carole, Hepenstrick, Daniel, Herberich, Maximiliane, Herbst, Mathias, Hermanutz, Luise, Hik, David S., Hoffrén, Raúl, Homeier, Jürgen, Hörtnagl, Lukas, Hoye, Toke T., Hrbacek, Filip, Hylander, Kristoffer, Iwata, Hiroki, Jackowicz-Korczynski, Marcin, Jactel, Hervé, Järveoja, Järvi, Jastrzębowski, Szymon, Jentsch, Anke, Jiménez, Juan J., Jónsdóttir, Ingibjörg S., Jucker, Tommaso, Jump, Alistair S., Juszczak, Radoslaw, Kanka, Robert, Kašpar, Vít, Kazakis, George, Kelly, Julia, Khuroo, Anzar A., Klemedtsson, Leif, Klisz, Marcin, Kljun, Natascha, Knohl, Alexander, Kobler, Johannes, Kollár, Jozef, Kotowska, Martyna M., Kovács, Bence, Kreyling, Juergen, Lamprecht, Andrea, Lang, Simone I., Larson, Christian, Larson, Keith, Laska, Kamil, Le Maire, Guerric, Leihy, Rachel I., Lens, Luc, Liljebladh, Bengt, Lohila, Annalea, Lorite, Juan, Loubet, Benjamin, Lynn, Joshua, Macek, Martin, Mackenzie, Roy, Magliulo, Enzo, Maier, Regine, Malfasi, Francesco, Malis, František, Man, Matěj, Manca, Giovanni, Manco, Antonio, Manise, Tanguy, Manolaki, Paraskevi, Marciniak, Felipe, Matula, Radim, Mazzolari, Ana Clara, Medinets, Volodymyr, Meeussen, Camille, Merinero, Sonia, de Cássia Guimarães Mesquita, Rita, Meusburger, Katrin, Meysman, Filip J.R., Michaletz, Sean T., Milbau, Ann, Moiseev, Dmitry, Moiseev, Pavel, Mondoni, Andrea, Monfries, Ruth, Montagnani, Leonardo, Moriana-Armendariz, Mikel, Morra di Cella, Umberto, Mörsdorf, Martin, Mosedale, Jonnathan R., Muffler, Lena, Muñoz-Rojas, Miriam, Myers, Jonnathan A., Myers-Smith, Isla H., Nagy, Laszlo, Nardino, Marianna, Naujokaitis-Lewis, Ilona, Newling, Emily, Nicklas, Lena, Niedrist, Georg, Niessner, Armin, Nilsson, Mats B., Normand, Signe, Nosetto, Marcelo, Nouvellon, Yann, Nunez, Martin, Ogaya, Romà, Ogée, Jérôme, Okello, Joseph, Olejnik, Janusz, Olesen, Jørgen Eivind, Opedal, Oystein H., Orsenigo, Simone, Palaj, Andrej, Pampuch, Timo, Panov, Alexey V., Pärtel, Meelis, Pastor, Ada, Pauchard, Anibal, Pauli, Harald, Pavelka, Marian, Pearse, William D., Peichl, Matthias, Pellissier, Loïc, Penczykowski, Rachel M., Peñuelas, Josep, Petit Bon, Matteo, Petraglia, Alessandro, Phartyal, Shyam S., Phoenix, Gareth K., Pio, Casimiro, Pitacco, Andrea, Pitteloud, Camille, Plichta, Roman, Porro, Francesco, Portillo-Estrada, Miguel, Poulenard, Jérôme, Poyatos, Rafael, Prokushkin, Anatoly S., Puchalka, Radoslaw, Puscas, Mihai, Radujković, Dajana, Randall, Krystal, Ratier Backes, Amanda, Remmele, Sabine, Remmers, Wolfram, Renault, David, Risch, Anita C., Rixen, Christian, Robinson, Sharon A., Robroek, Bjorn J.M., Rocha, Adrian V., Rossi, Christian, Rossi, Graziano, Roupsard, Olivier, and et al.

Abstract

Research 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-km2 resolution 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-km2 pixels (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.

Published
2022

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