WARM 2 is the new Cassandra environment to run rice simulations using the WARM model. The idea behind its design was to provide users with a friendly and powerful virtual rice field.

Compared to WARM 1:
- it allows users to buid their own WARM according to specific needs by including or excluding modules for the simulation of specific processes and by choosing - for most of them - from alternate approaches;
- it allows the simulation of more processes (e.g., agrochemical fate, grain quality, transplanting) and it received updates in most of its algorithms.

WARM 2 allows to simulate the following processes:
- rice growth and development (hourly or daily time step) (Confalonieri et al., 2009a,b);
- soil hydrology (cascading with travel time or Richiards' equation);
- diseases (blast and brown spot);
- agrochemicals fate;
- damages due to extreme weather events (spikelet sterility due to heat and cold shoks, lodging);
- pre-harvest grain quality (amylose, protein and starch contents, viscosity profile, chalkiness, cracking, head rice yield) (Cappelli et al., 2014);
- floodwater effect on vertical thermal profile (Confalonieri et al., 2005);
- transplanting (manual and mechanical) (Pagani et al., 2014).
The application allows running automatic calibration (standard and genetic simplex) and the assimilation of exogenous leaf area index data. Moreover, it has an integrated tool for the generation of climate change scenarios.

WARM 2 is one of the AgMIP rice models (Li et al., 2014), and was designed by following a component based architecture under the MS .NET framework.

Product Manager: Roberto Confalonieri
Application file: request a download link providing your email below.

European Commission, Joint Research Centre, AGRI4CAST
CRA - Consiglio per la Ricerca e la Sperimentazione in Agricoltura (Italy)

2016 - Ecological Modelling, 320, 366-371
A model to simulate the dynamics of carbohydrate remobilization during rice grain filling.
Stella, T., Bregaglio, S., Confalonieri, R.

2015 - Global Change Biology, 21, 1328-1341
Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions.
Li, T., Hasegawa, T., Yin, X., Zhu, Y., Boote, K., Adam, M., Bregaglio, S., Buis, S., Confalonieri, R., Fumoto, T., Gaydon, D., Marcaida, M., Nakagawa, H., Oriol, P., Ruane, A.C., Ruget, F., Singh, B., Singh, U., Tang, L., Tao, F., Wilkens, P., Yoshida, H., Zhang, Z., Bouman, B.

2014 - Computers and Electronics in Agriculture, 104, 18-24
A software component implementing a library of models for the simulation of pre-harvest rice grain quality.
Cappelli, G., Bregaglio, S., Romani, M., Feccia, S., Confalonieri, R.

2014 - European Journal of Agronomy, 59, 78-85
Evaluation of WARM for different establishment techniques in Jiangsu (China).
Pagani, V., Francone, C., ZhiMingWang, Qiu, L., Bregaglio, S., Acutis, M., Confalonieri, R.

2014 - Crop Science, 54, 2294-2302
Impact of agro-management practices on rice elongation: analysis and modelling.
Confalonieri, R., Stella, T., Dominoni, P., Frasso, N., Consolati, G., Bertoglio, M., Bianchi, E., Bortone, L., Cairo, V., Cappelli, G., Cozzaglio, G., Fattorossi, G., Garbelli, A., D'Incecco, P., Marazzi, A., Marescotti, M.E., Marziali, F., Maserati, S., Mazza, M., Mottadelli, G., Negrini, G., Nutini, F., Orasen, G., Pacca, L., Pinnetti, M., Pirotta, M., Porta, R., Riva, A., Scaramelli, A., Sessa

2012 - Ecological Modelling, 225, 159-166
Quantifying plasticity in simulation models.
Confalonieri, R., Bregaglio, S., Acutis, M.

CNR - Consiglio Nazionale delle Ricerche
Distretto Agricolo delle Risaie Lomelline
Yonsei University
PATFRUT Soc.Coop.Agricola
Hellenic Agricultural Organization "DEMETER"
Ministerio de Agricultura y Ganadería - Ecuador
University of Nigeria
Universidad de Talca
Politecnico di Milano
University of Manchester
Universidad Sergio Arboleda
University of Melbourne

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