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https://hub.ifdc.org/handle/20.500.14297/1583

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Browsing Publications by Author "Alex C. Ruane"

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    A Taxonomy-Based Approach to Shed Light on the Babel of Mathematical Models for Rice Simulation
    (2016) Roberto Confalonieri; Simone Bregaglio; Myriam Adam; Françoise Ruget; Tao Li; Toshihiro Hasegawa; Xinyou Yin; Yan Zhu; Kenneth J. Boote; Samuel Buis; Tamon Fumoto; Donald Gaydon; Tanguy Lafarge; Manuel Marcaida; Hiroshi Nakagawa; Alex C. Ruane; Balwinder-Singh; Upendra Singh; Liang Tang; Fulu Tao; Job Fugice; Hiroe Yoshida; Zhao Zhang; Lloyd T. Wilson; Jeff Baker; Yubin Yang; Yuji Masutomi; Daniel Wallach; Marco Acutis; Bas Bouman
    For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii)similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance.
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    Improving Rice Models for More Reliable Prediction of Responses of Rice Yield to CO2 and Temperature Elevation
    (2016) Tao Li; Xinyou Yin; Toshihiro Hasegawa; Ken Boote; Yan Zhu; Myriam Adam; Jeff Baker; Bas Bouman; Simone Bregaglio; Samuel Buis; Roberto Confalonieri; Job Fugice; Tamon Fumoto; Donald Gaydon; Soora Naresh Kumar; Tanguy Lafarge; Manuel Marcaida; Yuji Masutomi; Hitochi Nakagawa; DNL Pequeno; Alex C. Ruane ; Françoise Ruget; Upendra Singh; Liang Tang; Fulu Tao; Daniel Wallach; Lloyd T. Wilson; Yubin Yang; Hiroe Yoshida; Zhao Zhang; Jinyu Zhu
    Increased CO2 concentration and air temperature are two very important variables associated with global warming and climate change. Assessing the putative impacts of these factors on rice production is crucial for global food security due to rice being the staple food for more than half of the world population. Rice crop models are useful for predicting rice productivity under climate change. However, model predictions have uncertainties arisen due to the inaccurate inputs and the varying capabilities of models to capture yield performance. A series of modeling activities were implemented by the AgMIP Rice Team (consisting of 16 rice models currently) to improve the model capability for reducing the uncertainties of model prediction.