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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Adaptive Cropping Systems Laboratory » Research » Publications at this Location » Publication #328665

Research Project: Development and Application of Mechanistic Process-Driven Crop Models for Assessing Effects and Adapting Agriculture to Climate Changes

Location: Adaptive Cropping Systems Laboratory

Title: A potato model intercomparison across varying climates and productivity levels

Author
item Fleisher, David
item CONDORI, BRUNO - International Potato Center
item QUIROZ, ROBERTO - International Potato Center
item Alva, Ashok
item ASSENG, SENTHOLD - University Of Florida
item BARREDA, CAROLINA - International Potato Center
item BINDI, MARCO - University Of Florence
item BOOTE, K - University Of Florida
item FERRISE, ROBERTO - University Of Florence
item FRANKE, ANGELINUS - University Of The Free State
item GOVINDAKRISHNAN, P - Central Potato Research Institute
item HARAHAGAZWE, DIEUDONNE - International Potato Center
item HOOGENBOOM, GERRIT - University Of Florida
item KUMAR, SOORA - Central Plantation Crops Research Institute
item MERANTE, PAOLO - University Of Florence
item NENDEL, CLAAS - Institute Of Landscape Systems Analysis, Leibniz Centre For Agricultural Landscape Research
item OLESEN, JORGEN - Aarhus University
item PARKER, PHILLIP - Institute Of Landscape Systems Analysis, Leibniz Centre For Agricultural Landscape Research
item RAES, DIRK - Leuven University
item RAYMUNDO, RUBI - University Of Florida
item RUANE, ALEX - Nasa Goddard Institute For Space Studies
item STOCKLE, CLAUDIO - Washington State University
item SUPIT, IWAN - Wageningen University
item VANUYTRECHT, ELINE - Leuven University
item WOLF, JOOST - Wageningen University
item WOLI, PREM - Washington State University

Submitted to: Global Change Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/28/2016
Publication Date: 8/19/2016
Citation: Fleisher, D.H., Condori, B., Quiroz, R., Alva, A.K., Asseng, S., Barreda, C., Bindi, M., Boote, K.J., Ferrise, R., Franke, A.C., Govindakrishnan, P.M., Harahagazwe, D., Hoogenboom, G., Kumar, S.N., Merante, P., Nendel, C., Olesen, J., Parker, P., Raes, D., Raymundo, R.M., Ruane, A.C., Stockle, C., Supit, I., Vanuytrecht, E., Wolf, J., Woli, P. 2016. A potato model intercomparison across varying climates and productivity levels. Global Change Biology. 23(3):1258-1281. doi.org/10.1111/gcb.13411.

Interpretive Summary: Many different mathematical models are being used to predict the effects of climate change on agriculture. Sometimes model predictions differ from each other because they use different types of equations and algorithms. These differences decrease our confidence in the reliability and accuracy of the models. Potato is the 3rd most important food crop in the world based on total yield, and many different crop models have been developed to simulate its production. This research focused on comparing the predictions to current and future weather patterns from eight of these models at different potato production locations across the world. Differences in model predictions were shown to be larger when potato was produced in low-yielding regions in the world as compared to high-yielding regions. Models agreed more closely with each other when they were initially calibrated with observed yield data in addition to management information at each region. The study showed that between 3 and 5 potato models would be needed so that the average simulations for potato yield had the same variability of those grown in a farmer’s field. These results will support efforts of researchers, policy planners, crop consultants, and scientists to understand, assess, and adapt to effects of climate change on potato production.

Technical Abstract: A potato crop multi-model assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low- (Chinoli, Bolivia and Gisozi, Burundi) and high- (Jyndevad, Denmark and Washington, United States) input management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with inter-annual variability, and predictions for all agronomic variables were significantly different from one model to another (p < 0.001). Uncertainty averaged 15% higher for low- versus high- input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET respectively as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-parts per million of C, declined 4.6% per degree Celsius, and declined 2% for every 10% decrease in rainfall (for non-irrigated sites). Differences in predictions due to model representation of light utilization were significant (p < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.