TY - JOUR A1 - Kothari, Kritika A1 - Battisti, Rafael A1 - Boote, Kenneth J. A1 - Archontoulis, Sotirios A1 - Confalone, Adriana A1 - Constantin, Julie A1 - Cuadra, Santiago A1 - Debaeke, Philippe A1 - Faye, Babacar A1 - Grant, Brian A1 - Hoogenboom, Gerrit A1 - Jing, Qi A1 - van der Laan, Michael A1 - Macena da Silva, Fernando Antonio A1 - Marin, Fabio R. A1 - Nehbandani, Alireza A1 - Nendel, Claas A1 - Purcell, Larry C. A1 - Qian, Budong A1 - Ruane, Alex C. A1 - Schoving, Celine A1 - Silva, Evandro H. F. M. A1 - Smith, Ward A1 - Soltani, Afshin A1 - Srivastava, Amit A1 - Vieira, Nilson A. A1 - Slone, Stacey A1 - Salmeron, Montserrat T1 - Are soybean models ready for climate change food impact assessments? JF - European journal of agronomy : the official journal of the European Society for Agronomy N2 - An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 degrees C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 degrees C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble mean yield responses to temperature and [CO2] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual models. KW - Agricultural Model Inter-comparison and Improvement Project (AgMIP); KW - Model ensemble KW - Model calibration KW - Temperature KW - Atmospheric CO2 KW - concentration KW - Legume model Y1 - 2022 U6 - https://doi.org/10.1016/j.eja.2022.126482 SN - 1161-0301 SN - 1873-7331 VL - 135 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Liu, Qi A1 - Kämpf, Horst A1 - Bussert, Robert A1 - Krauze, Patryk A1 - Horn, Fabian A1 - Nickschick, Tobias A1 - Plessen, Birgit A1 - Wagner, Dirk A1 - Alawi, Mashal T1 - Influence of CO2 degassing on the microbial community in a dry mofette field in Hartoušov, Czech Republic (Western Eger Rift) JF - Frontiers in Microbiology N2 - The Cheb Basin (CZ) is a shallow Neogene intracontinental basin filled with fluvial and lacustrine sediments that is located in the western part of the Eger Rift. The basin is situated in a seismically active area and is characterized by diffuse degassing of mantle-derived CO2 in mofette fields. The Hartousov mofette field shows a daily CO2 flux of 23-97 tons of CO2 released over an area of 0.35 km(2) and a soil gas concentration of up to 100% CO2. The present study aims to explore the geo-bio interactions provoked by the influence of elevated CO2 concentrations on the geochemistry and microbial community of soils and sediments. To sample the strata, two 3-m cores were recovered. One core stems from the center of the degassing structure, whereas the other core was taken 8 m from the ENE and served as an undisturbed reference site. The sites were compared regarding their geochemical features, microbial abundances, and microbial community structures. The mofette site is characterized by a low pH and high TOC/sulfate contents. Striking differences in the microbial community highlight the substantial impact of elevated CO2 concentrations and their associated side effects on microbial processes. The abundance of microbes did not show a typical decrease with depth, indicating that the uprising CO2-rich fluid provides sufficient substrate for chemolithoautotrophic anaerobic microorganisms. Illumine MiSeq sequencing of the 16S rRNA genes and multivariate statistics reveals that the pH strongly influences microbial composition and explains around 38.7% of the variance at the mofette site and 22.4% of the variance between the mofette site and the undisturbed reference site. Accordingly, acidophilic microorganisms (e.g., OTUs assigned to Acidobacteriaceae and Acidithiobacillus) displayed a much higher relative abundance at the mofette site than at the reference site. The microbial community at the mofette site is characterized by a high relative abundance of methanogens and taxa involved in sulfur cycling. The present study provides intriguing insights into microbial life and geo-bio interactions in an active seismic region dominated by emanating mantle-derived CO2-rich fluids, and thereby builds the basis for further studies, e.g., focusing on the functional repertoire of the communities. However, it remains open if the observed patterns can be generalized for different time-points or sites. KW - geo–bio interaction KW - elevated CO2 KW - concentration KW - paleo-sediment KW - deep biosphere KW - acidophilic microorganisms KW - Acidobacteriaceae KW - Acidithiobacillus KW - Acidothermus Y1 - 2018 U6 - https://doi.org/10.3389/fmicb.2018.02787 SN - 1664-302X VL - 9 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Blanchard, Gilles A1 - Zadorozhnyi, Oleksandr T1 - Concentration of weakly dependent Banach-valued sums and applications to statistical learning methods JF - Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability N2 - We obtain a Bernstein-type inequality for sums of Banach-valued random variables satisfying a weak dependence assumption of general type and under certain smoothness assumptions of the underlying Banach norm. We use this inequality in order to investigate in the asymptotical regime the error upper bounds for the broad family of spectral regularization methods for reproducing kernel decision rules, when trained on a sample coming from a tau-mixing process. KW - Banach-valued process KW - Bernstein inequality KW - concentration KW - spectral regularization KW - weak dependence Y1 - 2019 U6 - https://doi.org/10.3150/18-BEJ1095 SN - 1350-7265 SN - 1573-9759 VL - 25 IS - 4B SP - 3421 EP - 3458 PB - International Statistical Institute CY - Voorburg ER -