| Abstract: | The black soil region of northeast China is a vital food base and is one of the most sensitive regions to climate change in China. However, the characteristics of the crop phenological response and the integrated impact of climate and phenological changes on agricultural productivity in the region under the background of climate change are not clear. The future agricultural risk assessment has been insufficiently quantified and the existing risk level formulation lacks a sound basis. Based on remote sensing products, climate data, and model simulations, this study integrated a logistic function fitting curvature derivation, multiple linear regression, and scenario simulation to investigate crop phenology dynamics and their climate response characteristics in the black soil region. Additionally, the compound effects of climate and phenology changes on agricultural production and possible future risks were identified. The key results were as follows: (1) From 2000 to 2017, 29.76% of the black soil region of northeast China experienced a significant delay in the start of the growing season (SOS) and 16.71% of the total area displayed a trend for the end of the growing season (EOS) to arrive earlier. The time lagged effects of the SOS in terms of the crop response to climatic factors were site and climatic parameter dependent. The influence of temperature was widespread and its effect had a longer lag time in general; (2) Both climatic and phenological changes have had a significant effect on the inter-annual variability of crop production, and the predictive ability of both increased by 70.23%, while the predictive area expanded by 85.04%, as compared to that of climate change in the same period of the growing season; (3) Under the RCP8.5 scenario, there was a risk that the future crop yield would decrease in the north and increase in the south, and the risk area was constantly expanding. With a 2.0℃ rise in global temperature, the crop yield of the southern Songnen black soil sub-region would reduce by almost 10%. This finding will improve our understanding of the mechanisms underlying climate change and vegetation productivity dynamics, and is also helpful in the promotion of the risk management of agrometeorological disasters. |
