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Impacts of heat stress on leaf area index and growth duration of winter wheat in the North China Plain
- Chen, Yi, Zhang, Zhao, Tao, Fulu, Palosuo, Taru, Rötter, Reimund P.
- Field crops research 2018 v.222 pp. 230-237
- climate change, crop production, farmers, food security, heat stress, heat sums, leaf area index, leaves, monitoring, prediction, remote sensing, simulation models, temperature, winter wheat, China
- Impact of high temperature stress on crop growth and productivity is one key concern with respect to crop production and food security under climate change. Due to the complexity and diversity of crop characteristics and farmers’ management practices, as well as the difficulties in quantifying those agronomic management practices at reasonable temporal and spatial scales, crop responses to heat stress at a regional scale have not been properly assessed yet. In this study, we used remote-sensing data to investigate the responses of growth duration and leaf area index (LAI) of winter wheat to extreme high temperature during reproductive growing stage in the North China Plain from 2001 to 2008. Growing degree days above 0°C (GDD) from heading to maturity was used to represent average temperature of growing environment, and the extreme temperature (>34°C) degree days (EDD) was used as an indicator for heat stress. We detected statistically significant shortening of reproductive growing duration due to increase in GDD and EDD at both site and regional scales. We also found acceleration of leaf senescence under warmer environment, as well as considerable damages to leaf area by extremely high temperatures according to LAI values from remote-sensing data. Our results present the explicit patterns of crop responses to heat stress at different spatial scales and periods, indicating the complexity of the impacts of extreme events. Moreover, we highlighted that exposure, vulnerability and adaptation all should be considered in evaluating the impacts of extreme events. In addition, our findings suggest great potential for improving regional crop growth monitoring and yield prediction through assimilating remote-sensing data into mechanistic crop simulation models.