Probabilistic yield forecasting of robusta coffee at the farm scale using agroclimatic and remote sensing derived indices

Kouadio, Louis ORCID: https://orcid.org/0000-0001-9669-7807 and Byrareddy, Vivekananda M. and Sawadogo, Alidou and Newlands, Nathaniel K. (2021) Probabilistic yield forecasting of robusta coffee at the farm scale using agroclimatic and remote sensing derived indices. Agricultural and Forest Meteorology, 306:108449. pp. 1-12. ISSN 0168-1923

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Abstract

Timely and reliable coffee yield forecasts using agroclimatic information are pivotal to the success of agricultural climate risk management throughout the coffee value chain. The capability of statistical models to forecast coffee yields at different lead times during the growing season at the farm scale was assessed. Using data collected during a 10-year period (2008-2017) from 558 farmers across the four major coffee-producing provinces in Vietnam (Dak Lak, Dak Nong, Gia Lai, and Lam Dong), the models were built through a robust statistical modelling approach involving Bayesian and machine learning methods. Overall, coffee yields were estimated with reasonable accuracies across the four study provinces based on agroclimate variables, satellite-derived actual evapotranspiration, and crop and farm management information. Median values of prediction mean absolute percentage error (MAPE) ranged generally from 8% to 13%, and median root mean square errors (RMSE) between 295 kg/ha and 429 kg/ha. For forecasts at four to one month before harvest, errors did not vary markedly when comparing the median MAPE and RMSE values. For farms in Dak Lak, Dak Nong, and Lam Dong, the median forecasting MAPE and RMSE varied between 13% and 16% and between 420 kg/ha and 456 kg/ha, respectively. Using readily and freely available data, the modelling approach explored in this study appears flexible for an application to a larger number of coffee farms across the Vietnamese coffee-producing regions. Moreover, the study can serve as basis for developing a coffee yield predicting and forecasting system that will offer substantial benefits to the entire coffee industry through better supply chain management in coffee-producing countries worldwide.


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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: 11 May 2021 23:29
Last Modified: 11 May 2021 23:29
Uncontrolled Keywords: Coffea canephora; Crop yield forecasting; Remote sensing 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 > 0701 Agriculture, Land and Farm Management > 070105 Agricultural Systems Analysis and Modelling
Fields of Research (2020): 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300207 Agricultural systems analysis and modelling
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300205 Agricultural production systems simulation
Identification Number or DOI: https://doi.org/10.1016/j.agrformet.2021.108449
URI: http://eprints.usq.edu.au/id/eprint/41951

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