ABSTRACT
Climate change affects the growth of regional vegetation; both trees and grain crops often change concurrently, such that the annual radial growth of trees shows consistent inter-annual variations with the total grain crop productivity. However, it remains unclear whether they exhibit concurrent responses to climate factors, and that there lacks of the study on long-term high-resolution variations of grain crops productivity. This paper employs a Pinus massoniana tree-ring series from the Tongbai Mountains to analyze the correlations between tree-ring chronology, local climate data and grain productivity indicators (i.e., total sown grain areas (TSA), total grain outputs (TGO), and average grain yield per hectare (YPH)) of Henan Province in central China. The results indicate that temperature in March and August was the main limiting factor on tree growth, and the best concurrent relationship with tree growth was TSA, which has a correlation of 0.747 (p < 0.001) during 1959–2020. Therefore, a 124-year TSA series in Henan Province was reconstructed using tree-ring data from the Tongbai Mountains, which reveals there were two distinct low periods of total grain sown area in the 1920 s-1930 s and 1980 s-2000 s. There are significant cycles of about 2.57a (p < 0.01), 2.89a (p < 0.05), and 10.95a (p < 0.1), indicating that vegetation growth might be affected by large-scale climate forcing, such as ENSO (2-7a cycle) and sunspot activity (11a cycle). Overall, this study outlines a new approach to understand long-term changes in grain production, which is conducive to grain management and socioeconomic sustainability.
Jianfeng Peng a,b,* , Kunyu Peng c , Jinbao Li d,e , Meng Peng a , Yameng Liu a , Xiaoxu Wei a , Jinkuan Li a , Xuan Li a , Jiayue Cui a , Jiaxin Li a
a College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
b The Key Laboratory of Earth System Observation and Simulation of Henan Province, Kaifeng 475004, China
c College of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China
d Department of Geography, University of Hong Kong, Hong Kong Special Administrative Region
e HKU Shenzhen Institute of Research and Innovation, Shenzhen 518057, China
https://doi.org/10.1016/j.ecolind.2023.110608
