Performance of a process-based model for predicting robusta coffee yield at the regional scale in Vietnam

Kouadio, Louis ORCID: https://orcid.org/0000-0001-9669-7807 and Tixier, Philippe and Byrareddy, Vivekananda and Marcussen, Torben and Mushtaq, Shahbaz and Rapidel, Bruno and Stone, Roger (2021) Performance of a process-based model for predicting robusta coffee yield at the regional scale in Vietnam. Ecological Modelling, 443:109469. pp. 1-12. ISSN 0304-3800

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Abstract

Reliable and timely prediction of robusta coffee (Coffea canephora Pierre ex A. Froehner) yield is pivotal to the profitability of the coffee industry worldwide. In this study we assess the performance of a simple process-based model for simulating and predicting robusta coffee yield at the regional scale in Vietnam. The model includes the key processes of coffee growth and development and simulates its response to variation in climate and potential water requirements throughout the growing season. The model was built and evaluated for the major Vietnamese robusta coffee-producing provinces Dak Lak, Dak Nong, Gia Lai, Kon Tum, and Lam Dong, using official provincial coffee yield data and climate station data for the 2001–2014 period, and field data collected during a 10-year (2008–2017) survey. Overall, good agreements were found between the observed and predicted coffee yields. Root mean square error (RMSE) and mean absolute percentage error (MAPE) values ranged from 0.24 to 0.33 t/ha, and 9% to 14%, respectively. Willmott's index of agreement (WI) was greater than or equal to 0.710 in model evaluation steps for three out of five provinces. The relatively low values of WI were found for provinces with relatively low inter-annual yield variability (i.e. Dak Lak and Dak Nong). Moreover, the model was successfully tested using remote sensing satellite and model-based gridded climate data: MAPE values were ≤ 12% and RMSE were ≤ 0.29 t/ha. Such evaluation is important for long-term coffee productivity studies in these regions where long-term climate stations data are not readily available. The simple process-based model presented in this study could serve as a basis for developing an integrated seasonal climate-robusta coffee yield forecasting system, which would offer substantial benefits to coffee growers and industry through better supply chain management and preparedness for extreme climate events, and increased profitability.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Applied Climate Sciences (1 Aug 2018 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Applied Climate Sciences (1 Aug 2018 -)
Date Deposited: 01 Feb 2021 05:45
Last Modified: 01 Feb 2021 05:45
Uncontrolled Keywords: Coffea canephora, Biophysical model, Climate variability, Climate risk management
Fields of Research (2008): 07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070103 Agricultural Production Systems Simulation
07 Agricultural and Veterinary Sciences > 0703 Crop and Pasture Production > 070302 Agronomy
Fields of Research (2020): 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300205 Agricultural production systems simulation
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3004 Crop and pasture production > 300403 Agronomy
Identification Number or DOI: https://doi.org/10.1016/j.ecolmodel.2021.109469
URI: http://eprints.usq.edu.au/id/eprint/41002

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