Assessing the accuracy and robustness of a process-based model for coffee agroforestry systems in Central America
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Assessing the accuracy and robustness of a process-based model for coffee agroforestry systems in Central America Oriana Ovalle-Rivera . Marcel Van Oijen . Peter La¨derach . Olivier Roupsard . Elias de Melo Virginio Filho . Mirna Barrios . Bruno Rapidel
Received: 7 February 2020 / Accepted: 30 June 2020 Springer Nature B.V. 2020
Abstract Coffee is often grown in production systems associated with shade trees that provide different ecosystem services. Management, weather and soil conditions are spatially variable production factors. CAF2007 is a dynamic model for coffee agroforestry systems that takes these factors as inputs and simulates the processes underlying berry production at the field scale. There remain, however, uncertainties about process rates that need to be reduced through calibration. Bayesian statistics using Markov chain Monte
Carlo algorithms is increasingly used for calibration of parameter-rich models. However, very few studies have employed multi-site calibration, which aims to reduce parameter uncertainties using data from multiple sites simultaneously. The main objectives of this study were to calibrate the coffee agroforestry model using data gathered in long-term experiments in Costa Rica and Nicaragua, and to test the calibrated model against independent data from commercial coffeegrowing farms. Two sub-models were improved:
O. Ovalle-Rivera E. de Melo Virginio Filho B. Rapidel CATIE, Centro Agrono´mico Tropical de Investigacio´n y Ensen˜anza, Turrialba 30501, Costa Rica
M. Barrios CATIE, Centro Agrono´mico Tropical de Investigacio´n y Ensen˜anza, Managua, Nicaragua
O. Ovalle-Rivera P. La¨derach International Center for Tropical Agriculture (CIAT), Cali, Colombia
B. Rapidel CIRAD, UMR SYSTEM, 30501 Turrialba, Costa Rica
M. Van Oijen (&) UK Centre for Ecology & Hydrology (CEH-Edinburgh), Bush Estate, Penicuik EH26 0QB, UK e-mail: [email protected]
B. Rapidel SYSTEM, CIHEAM-IAMM, CIRAD, INRAE, Institut Agro, Univ Montpellier, Montpellier, France
O. Roupsard CIRAD UMR Eco&Sols, BP1386, CP18524 Dakar, Senegal O. Roupsard Eco&Sols, CIRAD, INRAE, IRD, Institut Agro, Univ Montpellier, Montpellier, France O. Roupsard LMI IESOL, Centre IRD-ISRA de Bel Air, BP1386, CP18524 Dakar, Senegal
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calculation of flowering date and the modelling of biennial production patterns. The modified model, referred to as CAF2014, can be downloaded at https:// doi.org/10.5281/zenodo.3608877. Calibration improved model performance (higher R2, lower RMSE) for Turrialba (Costa Rica) and Masatepe (Nicaragua), including when all experiments were pooled together. Multi-site and single-site Bayesian calibration led to similar RMSE. Validation on new data from coffee-growing farms revealed that both calibration methods improved simulation of yield and its bienniality. The thus improved model was used to test the effect of N fertilizer and shade in different locations on coffee yield. Keywords Agroforestry systems Bayesian calibratio
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