Search

Climate change is expected to increase the frequency and severity of climate extremes, which will negatively impact crop production. As one of the main food and feed crops, maize is also vulnerable to extreme climate events. In order to accurately and comprehensively assess the future climate risk to maize, it is urgent to project and evaluate the stress of extreme climate related maize production under future climate scenarios. In this study, we comprehensively evaluated the spatio-temporal changes in the frequency and intensity of six extreme climate indices (ECIs) across China's maize harvest region by using a multi-model ensemble method, and examined the capability of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to capture these variations. We found that the Independence Weight Mean (IWM) ensemble results calculated by multiple Global Climate Models (GCMs) with bias correction could better reproduce each ECI. The results indicated that heat stress for maize showed consistent increase trends under four future climate scenarios in the 21st century. The intensity and frequency of the three extreme temperature indices in 2080s were significantly higher than these in 2040s, and in the high emission scenario were significantly higher than these in the low emission scenario. The three extreme precipitation indices changed slightly in the future, but the spatial changes were more significant. Therefore, with the uncertainty of climate change and the differences of climate characteristics in different regions, the optimization of specific management measures should be considered in combination with the specific conditions of future local climate change. [ABSTRACT FROM AUTHOR]

The warming slowdown observed between 1998 and 2012 has raised concerns in recent years. To examine the temporal and spatial variations in annual mean temperature (Tmp) as well as 12 extreme temperature indices (ETIs), and to assess the presence of a warming slowdown in North China (NC), we analyzed homogenized daily observational datasets from 79 meteorological stations spanning 1960 to 2020. Additionally, we investigated the influences of 78 atmospheric circulation indices (ACIs) on ETIs during the period of warming slowdown. To compare temperature changes, the study area was divided into three parts based on topographic conditions: Areas I, II, and III. The results revealed significant warming trends in Tmp and the 12 ETIs from 1960 to 2020. Comparing the time frames of 1960–1998, 2012–2020, and 1998–2012, both Tmp and the 12 ETIs displayed a cooling trend in the latter period, confirming the existence of a warming slowdown in NC. Notably, indices derived from daily maximum temperature exhibited higher cooling rates during 1998–2012, with winter contributing most significantly to the cooling trend among the four seasons. The most pronounced warming slowdown was observed in Area I, followed by Area III and Area II. Furthermore, our attribution analysis of ACIs concerning the temperature change indicated that the Asia Polar Vortex Area Index may have had the greatest influence on ETIs from 1960 to 2016. Moreover, the weakening of the Tibet Plateau Index Band and the Asian Latitudinal Circulation Index, and the strengthening of the Eurasian Latitudinal Circulation Index, were closely associated with ETIs during the warming slowdown period in NC. Through this research, we aim to deepen our understanding of climate change in NC and offer a valuable reference for the sustainable development of its natural ecology and social economy. [ABSTRACT FROM AUTHOR]

Therefore, crop mechanism models play an important role in assessing the effects of climate change on crop production and proposing effective coping strategies. Climate change and its impacts on agricultural production and food security are a significant source of public concern around the world. The paper by Shayanmehr et al. [[13]] constructed a new integrated climate-hydrological-economic model to assess the impact of future climate change on water resources and crop production. [Extracted from the article]

The accuracy prediction for the crop yield is conducive to the food security in regions and/or nations. To some extent, the prediction model for crop yields combining the crop mechanism model with statistical regression model (SRM) can improve the timeliness and robustness of the final yield prediction. In this study, the accumulated biomass (AB) simulated by the Agricultural Production Systems sIMulator (APSIM) model and multiple climate indices (e.g., climate suitability indices and extreme climate indices) were incorporated into SRM to predict the wheat yield in the North China Plain (NCP). The results showed that the prediction model based on the random forest (RF) algorithm outperformed the prediction models using other regression algorithms. The prediction for the wheat yield at SM (the period from the start of grain filling to the milky stage) based on RF can obtain a higher accuracy (r = 0.86, RMSE = 683 kg ha−1 and MAE = 498 kg ha−1). With the progression of wheat growth, the performances of yield prediction models improved gradually. The prediction of yield at FS (the period from flowering to the start of grain filling) can achieve higher precision and a longer lead time, which can be viewed as the optimum period providing the decent performance of the yield prediction and about one month's lead time. In addition, the precision of the predicted yield for the irrigated sites was higher than that for the rainfed sites. The APSIM-simulated AB had an importance of above 30% for the last three prediction events, including FIF event (the period from floral initiation to flowering), FS event (the period from flowering to the start of grain filling) and SM event (the period from the start of grain filling to the milky stage), which ranked first in the prediction model. The climate suitability indices, with a higher rank for every prediction event, played an important role in the prediction model. The winter wheat yield in the NCP was seriously affected by the low temperature events before flowering, the high temperature events after flowering and water stress. We hope that the prediction model can be used to develop adaptation strategies to mitigate the negative effects of climate change on crop productivity and provide the data support for food security. [ABSTRACT FROM AUTHOR]