| Poster number |
Presenter |
Title |
| S1-P.01 |
Lethicia Magno |
Modelling Memory : do crop models need to become nostalgic? |
| S1-P.02 |
Danaë Rozendaal |
Crop growth models for tropical perennials: current advances and remaining challenges |
| S1-P.03 |
Yan Zhu |
Current rice models underestimate yield losses by short-term heat stresses |
| S1-P.04 |
A L C De Silva |
Variation in photosynthesis and transpiration efficiency of sugarcane at elevated atmospheric CO2 and temperature |
| S1-P.05 |
Kendall DeJonge |
Using crop coefficients and standardized evapotranspiration methods to evaluate crop model behavior |
| S1-P.06 |
Jean-Louis Durand |
Phenology of grasslands: a new model |
| S1-P.07 |
Florian Heinlein |
Modelling the transpiration of single maize plants using an explicit xylem flux model |
| S1-P.08 |
Panu Korhonen |
Root descriptions of crop simulation models - do they serve studies of climate-smart agriculture? |
| S1-P.09 |
Mukhtar Ahmed |
Modeling phenological responses of table grape cultivars |
| S1-P.10 |
Fety Andrianasolo |
Developing a mechanistic foliar stage model adapted to wheat diseases decision tools |
| S1-P.11 |
Fety Andrianasolo |
Predicting wheat yield and protein content at the plot scale with machine-learning and mechanistic models |
| S1-P.12 |
Ioannis Droutsas |
New modelling methodology for improving crop model performance under stress conditions |
| S1-P.13 |
Sylvain Edouard |
Analysis and modeling (STICS / L-egume) of crop growth under shading conditions in Agri-PV context |
| S1-P.14 |
Deborah Gaso |
Assimilating leaf area index into a simple crop model to predict soybean yield and maximum root depth at field scale |
| S1-P.15 |
Armen Kemanian |
What can crop modelers learn from machine learning models about corn, sorghum and soybean? |
| S1-P.16 |
Christoph Müller |
Potential yield simulated by Global Gridded Crop Models: what explains their difference |
| S1-P.17 |
Chinaza Onwuchekwa-Henry |
Potential for using low-cost spectral sensors to predict yield in small-scale rice fields in northwest Cambodia |
| S1-P.18 |
Simona Bassu |
Potential maize yields in a Mediterranean environment depend on conditions around flowering |
| S1-P.19 |
Rafael Battisti |
Performance of CSM-DSSAT-CROPGRO model for soybean plant density in low latitude in Brazil |
| S1-P.20 |
Martin Bednařík |
Potential and challenges of long term uninterrupted field crop rotations modelling: case study from Czech Republic |
| S1-P.21 |
Kurt-Christian Kersebaum |
From point to field scale: How consistent are agro-ecosystem models in terms of changes in soil texture? |
| S1-P.22 |
Bruce Kimball |
Prediction of Evapotranspiration and Yields of Maize: An Inter-comparison among 29 Maize Models and Future Plans |
| S1-P.23 |
Kritika Kothari |
First Soybean Multi-model Sensitivity Analysis to CO2, Temperature, Water, and Nitrogen |
| S1-P.24 |
Crystele Leauthaud |
Modelling floodplain grasslands to explore the impact of changing hydrological conditions on vegetation productivity |
| S1-P.25 |
Bing Liu |
Comparison of wheat simulation models for impacts of extreme temperature stress on grain quality |
| S1-P.26 |
Eva Pohanková |
Modelling of drought stress in field crops by crop growth model DAISY |
| S1-P.27 |
Elodie Ruelle |
Predicting grass growth: The MoSt GG model |
| S1-P.28 |
Hossein Zare |
Comparison of DSSAT wheat models performances with different regions and cultivars |
| S1-P.29 |
Mukhtar Ahmed |
APSIM Next Generation to Model Red Clover Under Nordic Climate |
| S1-P.30 |
Ahmad Banakar |
Extraction of FAO Growth Model in a Fuzzy Control Hydroponic Greenhouse |
| S1-P.31 |
Yuji Masutomi |
Development of a global crop growth simulation model for simulating long-term trends in rice yields: Global MATCRO-Rice |
| S1-P.32 |
João Vasco Silva |
Winter wheat development and growth in The Netherlands: Using a detailed field trial to update crop parameters in WOFOST |
| S1-P.33 |
Tamara ten Den |
The effect of potato cultivar differences on parameters in WOFOST |
| S1-P.34 |
Jingbo Zhen |
Modelling water and carbon balances of date palm trees under different salinity conditions |
| S1-P.35 |
Laura Delhez |
TADA, a mechanistic model for carbon, nitrogen and water cycle in cropland and grassland ecosystems |
