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Assessing the accuracy and robustness of a process-based model for coffee agroforestry systems in Central America

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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 coffee-growing farms. Two sub-models were improved: 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.

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Acknowledgements

This study was part of the PCP Agroforestry Systems with Perennial Crops, a scientific partnership platform led by CATIE and CIRAD in Central America. We greatly acknowledge CATIE and its partners and CIRAD for facilitating the data required for the calibration of this model, and the farm owners for granting us access to their farms and records. We also thank the reviewers of the original manuscript whose comments have led to considerable improvements in the presentation.

Funding

This study was funded by the Caf’Adapt project, Fontagro/RF-1027. It was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) with the support from CGIAR Fund Donors and through bilateral funding agreements. For details, please visit https://ccafs.cgiar.org/donors. Coffee-Flux Observatory, Llano Bonito and CATIE agroforestry trial were also supported by SOERE F-ORE-T from Ecofor, Allenvi and the French national research infrastructure ANAEE-F (http://www.anaee-france.fr/fr/), the European project CAFNET (EuropAid/121998/C/G), the Ecosfix project (ANR-2010-STRA-003-01), the CIRAD-SAFSE project, the MACACC project (ANR-13-AGRO-0005) and the CATIE-PROCAGICA-IICA-UE project. MvO acknowledges support from BBSRC through GCRF project SEACAF (BB/S01490X/1).

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Authors and Affiliations

  1. CATIE, Centro Agronómico Tropical de Investigación y Enseñanza, Turrialba, 30501, Costa Rica

    Oriana Ovalle-Rivera, Elias de Melo Virginio Filho & Bruno Rapidel

  2. UK Centre for Ecology & Hydrology (CEH-Edinburgh), Bush Estate, Penicuik, EH26 0QB, UK

    Marcel Van Oijen

  3. International Center for Tropical Agriculture (CIAT), Cali, Colombia

    Oriana Ovalle-Rivera & Peter Läderach

  4. CIRAD UMR Eco&Sols, BP1386, CP18524, Dakar, Senegal

    Olivier Roupsard

  5. Eco&Sols, CIRAD, INRAE, IRD, Institut Agro, Univ Montpellier, Montpellier, France

    Olivier Roupsard

  6. LMI IESOL, Centre IRD-ISRA de Bel Air, BP1386, CP18524, Dakar, Senegal

    Olivier Roupsard

  7. CATIE, Centro Agronómico Tropical de Investigación y Enseñanza, Managua, Nicaragua

    Mirna Barrios

  8. CIRAD, UMR SYSTEM, 30501, Turrialba, Costa Rica

    Bruno Rapidel

  9. SYSTEM, CIHEAM-IAMM, CIRAD, INRAE, Institut Agro, Univ Montpellier, Montpellier, France

    Bruno Rapidel

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  1. Oriana Ovalle-Rivera
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  2. Marcel Van Oijen
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  3. Peter Läderach
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  4. Olivier Roupsard
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  5. Elias de Melo Virginio Filho
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  6. Mirna Barrios
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  7. Bruno Rapidel
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Correspondence to Marcel Van Oijen.

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The coffee agroforestry model CAF2014 can be downloaded from https://doi.org/10.5281/zenodo.3608877.

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Ovalle-Rivera, O., Van Oijen, M., Läderach, P. et al. Assessing the accuracy and robustness of a process-based model for coffee agroforestry systems in Central America. Agroforest Syst 94, 2033–2051 (2020). https://doi.org/10.1007/s10457-020-00521-6

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