Dry season temperature and rainy season precipitation significantly affect the spatio-temporal pattern of rubber plantation phenology in Yunnan province

Hongyan Lai; Bangqian Chen; Xiong Yin; Guizhen Wang; Xincheng Wang; Ting Yun; Guoyu Lan; Zhixiang Wu; Chuan Yang; Weili Kou

doi:10.3389/fpls.2023.1283315

Dry season temperature and rainy season precipitation significantly affect the spatio-temporal pattern of rubber plantation phenology in Yunnan province

Front Plant Sci. 2023 Dec 14:14:1283315. doi: 10.3389/fpls.2023.1283315. eCollection 2023.

Authors

Hongyan Lai^{1

2}, Bangqian Chen², Xiong Yin³, Guizhen Wang², Xincheng Wang^{2

3}, Ting Yun³, Guoyu Lan², Zhixiang Wu², Chuan Yang², Weili Kou¹

Affiliations

¹ College of Forestry, Southwest Forestry University, Kunming, China.
² Hainan Danzhou Agro-ecosystem National Observation and Research Station, State Key Laboratory Incubation Base for Cultivation & Physiology of Tropical Crops, Rubber Research Institute (RRI), Chinese Academy of Tropical Agricultural Sciences (CATAS), Haikou, China.
³ Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, China.

Abstract

The ongoing global warming trajectory poses extensive challenges to plant ecosystems, with rubber plantations particularly vulnerable due to their influence on not only the longevity of the growth cycle and rubber yield, but also the complex interplay of carbon, water, and energy exchanges between the forest canopy and atmosphere. However, the response mechanism of phenology in rubber plantations to climate change remains unclear. This study concentrates on sub-optimal environment rubber plantations in Yunnan province, Southwest China. Utilizing the Google Earth Engine (GEE) cloud platform, multi-source remote sensing images were synthesized at 8-day intervals with a spatial resolution of 30-meters. The Normalized Difference Vegetation Index (NDVI) time series was reconstructed using the Savitzky-Golay (S-G) filter, coupled with the application of the seasonal amplitude method to extract three crucial phenological indicators, namely the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS). Linear regression method, Pearson correlation coefficient, multiple stepwise regression analysis were used to extract of the phenology trend and find the relationship between SOS, EOS and climate factors. The findings demonstrated that 1) the phenology of rubber plantations has undergone dynamic changes over the past two decades. Specifically, the SOS advanced by 9.4 days per decade (R² = 0.42, p< 0.01), whereas the EOS was delayed by 3.8 days per decade (R² = 0.35, p< 0.01). Additionally, the LOS was extended by 13.2 days per decade (R² = 0.55, p< 0.01); 2) rubber phenology demonstrated a notable sensitivity to temperature fluctuations during the dry season and precipitation patterns during the rainy season. The SOS advanced 2.0 days (r =-0.19, p< 0.01) and the EOS advanced 2.8 days (r =-0.35, p< 0.01) for every 1°C increase in the cool-dry season. Whereas a 100 mm increase in rainy season precipitation caused the SOS to be delayed by 2.0 days (r = 0.24, p< 0.01), a 100 mm increase in hot-dry season precipitation caused the EOS to be advanced by 7.0 days (r =-0.28, p< 0.01); 3) rubber phenology displayed a legacy effect of preseason climate variations. Changes in temperature during the fourth preseason month and precipitation during the fourth and eleventh preseason months are predominantly responsible for the variation in SOS. Meanwhile, temperature changes during the second, fourth, and ninth preseason months are primarily responsible for the variation in EOS. The study aims to enhance our understanding of how rubber plantations respond to climate change in sub-optimal environments and provide valuable insights for sustainable rubber production management in the face of changing environmental conditions.

Keywords: GEE; S-G filter; global warming; legacy effect; phenology change; rubber plantations.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by National Natural Science Foundation of China (42071418, 32260391, 31760181), Natural Science Foundation of Hainan Province (422CXTD527), Central Public-interest Scientific Institution Basal Research Fund (1630022023007, RRIKLOF202301), and Earmarked Fund for China Agriculture Research System (CARS-33).