Integrating seasonal climate forecasts with Robusta coffee model across the agricultural landscapes of Vietnam

PhD Thesis


Byrareddy, Vivekananda Mittahalli. 2020. Integrating seasonal climate forecasts with Robusta coffee model across the agricultural landscapes of Vietnam. PhD Thesis Doctor of Philosophy. University of Southern Queensland. https://doi.org/10.26192/k1v4-0907
Title

Integrating seasonal climate forecasts with Robusta coffee model across the agricultural landscapes of Vietnam

TypePhD Thesis
Authors
AuthorByrareddy, Vivekananda Mittahalli
SupervisorStone, Roger
Mushtaq, Shahbaz
Kouadio, Louis
Institution of OriginUniversity of Southern Queensland
Qualification NameDoctor of Philosophy
Number of Pages177
Year2020
Digital Object Identifier (DOI)https://doi.org/10.26192/k1v4-0907
Abstract

Seasonal climate variations, extreme climatic events, particularly drought, and management practices such as fertiliser application and irrigation are among the factors affecting substantially Robusta coffee (Coffea canephora) production. In Vietnam, which is the world's largest Robusta coffee-producing country, the enhanced climate variability in recent decades significantly affected coffee production. Improved seasonal climate forecasts (SCF) integrated to Robusta coffee models are critical to reducing climate risk and capitalising on the opportunities. However, the methods to harness such integrated forecasting systems at the required temporal and spatial scales remain very much unknown or underutilised in Vietnam.

This Ph.D. project research aimed at addressing this vital question by developing an integrated SCF-Robusta coffee production model for yield forecasting, capable of simulating reliably Robusta coffee growth and predict yield under different environmental conditions and management practices at the regional scale. Specifically, we aimed to (1) characterise the fertiliser and irrigation management practices across the study area (the Central Highlands region of Vietnam) and develop empirical relationships between yields, fertiliser and irrigation; (2) assess drought impacts on coffee yield and profit, and the effectiveness of the mitigation strategies; (3) investigate the improvement of the simplified biophysical Robusta coffee model, the USQ-Robusta coffee model, through the integration of fertiliser and irrigation components; and (4) test the capability of integrating SCF and the modified version of the USQ-Robusta coffee model to forecast probabilistic coffee yields at sufficient lead times.

Farms data including management practices and coffee yield were collected randomly across the four major Vietnamese Robusta coffee-producing provinces (Dak Lak, Dak Nong, Gia Lai and Lam Dong) from 558 farmers over ten years (2008–2017). Climate data were retrieved from the US National Aeronautics and Space Administration’s Prediction Of Worlwide Energy Resources website. SCF derived from five selected prediction systems were sourced from the Climate Change Service website and from the University of Southern Queensland-Centre for Applied Climate Sciences (USQ-CACS)’s seasonal climate forecasting system.

Four types of chemical (urea, blended NPK, superphosphate and potassium chloride) and two types of natural (organic compost and lime) fertilisers were used routinely across the study provinces. The overuse of chemical fertilisers (double the recommended rates of N:P:K at 192:88:261 kg ha-1) did not result in higher yields in the majority of cases. The analysis of irrigation practices showed that, with adequate management practices, substantial water savings can be achieved: up to 26% reduction annually from the current levels (909 - 1818 L plant-1). With irrigation being typical in coffee farming, the majority of surveyed farmers adopted mulching in drought years and were best rewarded compared to their counterparts who did not (10% increase in gross margins on average). Our study also revealed that while drought reduced Robusta coffee yield by 6.5% on average, its impacts on gross margins were noticeable, with a 22% decline on average from levels achieved in average-rainfall-condition years. Improving the impacts of water stress on yield throughout the growth season in the USQ-Robusta coffee model resulted in better model performance. The existing water stress component was modified based on crop water requirements derived from the CROPWAT model. Prediction errors were reduced: up to 21% decrease of RMSE. Further, the probabilistic yield forecasting using SCF for May to November 2019 showed satisfactory results generally, with the median yield variance ranging from 0.26 to 0.29 t ha-1.

Improved management of fertiliser and irrigation, and crop diversification are options to enhance the profitability of coffee farming systems. This research knowledge could serve as a decision support tool for farmers and the coffee industry in business planning and climate risk management.

KeywordsRobusta coffee, seasonal climate forecasts, biophysical model, irrigation, fertiliser, drought adaptation strategy
ANZSRC Field of Research 2020370201. Climate change processes
300210. Sustainable agricultural development
300207. Agricultural systems analysis and modelling
300403. Agronomy
300201. Agricultural hydrology
300499. Crop and pasture production not elsewhere classified
Byline AffiliationsCentre for Applied Climate Sciences
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