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Poster communications

First Soybean Multi-model Sensitivity Analysis to CO 2 , Temperature, Water, and Nitrogen

Kritika Kothari 1 Montserrat Salmeron 1 Rafael Battisti 2 Kenneth Boote 3 Sotirios Archontoulis 4 Adriana Confalone 5 Julie Constantin Vianna Cuadra Sanatiago Philippe Debaeke Babacar Faye 6 Brian Grant 7 Gerrit Hoogen-Boom 8 Qi Jing 7 Laan Michael van der 10 9 Fernando Macena Fabio Marin 10 Alireza Nehbandani 11 Claas Nendel Purcell Larry 12 Qian Budong 7 Alex Ruane Evandro da Silva Ward Smith Afshin Soltani Amit Srivastava Aparecido Viera Nilson
1 University of Kentucky
2 UFG - Universidade Federal de Goiás [Goiânia]
3 AGRICULTURAL AND BIOLOGICAL ENGINEERING UNIVERSITY OF FLORIDA GAINESVILLE USA
4 Department of Agronomy [Kansas State University]
5 Facultad de Agronomía [Buenos Aires]
6 INRES - Institute of Crop Science and Resource Conservation
7 Canadian National Collection of Insects, Arachnids, and Nematodes, Science & Technology Branch, Agriculture and Agri-Food Canada, Ottawa, ON, K1A 0C6
8 Agricultural and Biological Engineering
9 University of Pretoria [South Africa]
10 USP - University of São Paulo
11 URAC - Grassland Ecosystems Research Group, Agronomy Unit
12 BES - Bio-Medical and Environmental Sciences
Abstract : Crop model responses to changes in climatic and management factors could be highly variable due to variation in model coding and prior calibration with limited observed data, thus introducing high uncertainty in food projections. Coordinated multi-model comparison studies play a crucial role in evaluating model uncertainty and improving existing models. The present study is the first model intercomparison effort on a legume, soybean (Glycine max L. (Merr.)), with special emphasis on the crop N processes. Previous multi-model studies in cereal grain crops identified the value of model ensembles to reduce uncertainties in yield projections (Bassu et al., 2014; Martre et al., 2015). Interestingly, model responses to changes in temperature and atmospheric carbon dioxide concentration [CO 2 ] were not affected by the level of observed data available for calibration (Bassu et al., 2014). We propose to test: 1) whether soybean multi-model ensembles are better estimators of yield, crop growth and grain nitrogen concentration (grain N%) than individual models; and 2) whether the level of observed data available during calibration influences model responses to variation in climatic and N fertilization factors ([CO 2 ], Temperature, Water, and Nitrogen; CTWN sensitivity analysis).
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https://hal.inria.fr/hal-02950318
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Submitted on : Sunday, September 27, 2020 - 6:04:40 PM
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IRSTEA | INRA | TDS-MACS | INRAE | ICROPM2020-SESSION1 | ICROPM2020 | ICROPM2020-SESSION2 | ICROPM2020-SESSION3 | ICROPM2020-SESSION4 | ICROPM2020-SESSION5 | ICROPM2020-SESSION6 | ICROPM2020-SESSION7

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Kritika Kothari, Montserrat Salmeron, Rafael Battisti, Kenneth Boote, Sotirios Archontoulis, et al.. First Soybean Multi-model Sensitivity Analysis to CO 2 , Temperature, Water, and Nitrogen. ICROPM2020: Second International Crop Modelling Symposium , Feb 2020, Montpellier, France. ⟨hal-02950318⟩

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