Meteorological Impacts on Rubber Tree Powdery Mildew and Projections of Its Future Spatiotemporal Pattern

Meteorological conditions play a crucial role in driving outbreaks of rubber tree powdery mildew (RTPM). As the climate warms and techniques improve, rubber cultivation is expanding to higher latitudes, and the changing climate increases the RTPM risk. Rubber plantations on Hainan Island, situated o...

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Published in:Agriculture (Basel) Vol. 14; no. 4; p. 619
Main Authors: Kong, Jiayan, Wu, Lan, Cao, Jiaxin, Cui, Wei, Nie, Tangzhe, An, Yinghe, Sun, Zhongyi
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.04.2024
Subjects:
agriculture
Airborne microorganisms
Algorithms
Atmospheric models
Bayesian analysis
Bayesian-optimized least-squares boosted trees ensemble
case studies
China
climate
Climate change
Climatic changes
Cultivation techniques
Emissions
Environmental impact
Epidemics
equations
Hainan Island
Hevea brasiliensis
least squares
Mathematical models
Mediation
Outbreaks
Plantations
Powdery mildew
prediction
Prediction models
rain
Rainfall
risk
Rubber
Rubber industry
Rubber trees
structural equation model
structural equation modeling
temperature
Tropical environments
China
Asia
Hainan Island
ISSN:2077-0472, 2077-0472
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Summary:Meteorological conditions play a crucial role in driving outbreaks of rubber tree powdery mildew (RTPM). As the climate warms and techniques improve, rubber cultivation is expanding to higher latitudes, and the changing climate increases the RTPM risk. Rubber plantations on Hainan Island, situated on the northern margin of the tropics, have been selected as a case study to explore the meteorological mechanisms behind RTPM outbreaks quantitatively using a structural equation model, and project current and future RTPM outbreak patterns under different climate change scenarios by building predictive models based on data-driven algorithms. The following results were obtained: (1) days with an average temperature above 20 °C and days with light rain were identified as key meteorological drivers of RTPM using structural equation modeling (R2 = 0.63); (2) the Bayesian-optimized least-squares boosted trees ensemble model accurately predicted the interannual variability in the historical RTPM disease index (R2 = 0.79); (3) currently, due to the increased area of rubber plantations in the central region of Hainan, there is a higher risk of RTPM; and (4) under future climate scenarios, RTPM shows a decreasing trend (at a moderate level), with oscillating and sporadic outbreaks primarily observed in the central and northwest regions. We attribute this to the projected warming and drying trends that are unfavorable for RTPM. Our study is expected to enhance the understanding of the impact of climate change on RTPM, provide a prediction tool, and underscore the significance of the climate-aware production and management of rubber.
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ISSN:2077-0472
2077-0472
DOI:10.3390/agriculture14040619