The influence of global climate change on agricultural productivity is an essential issue of ongoing concern. The growth and development of wheat, maize, and rice are influenced by elevated atmospheric CO₂ concentrations, increased temperatures, and seasonal rainfall patterns. However, due to differences in research methodologies (e.g., crop models, climate models, and climate scenarios), there is uncertainty in the existing studies regarding the magnitude and direction of future climate change impacts on crop yields. In order to completely assess the possible consequences of climate change and adaptation measures on crop production and to analyze the associated uncertainties, a database of future crop yield changes was developed using 68 published studies (including 1842 samples). A local polynomial approach was used with the full dataset to investigate the response of crop yield changes to variations in maximum and minimum temperatures, mean temperature, precipitation, and CO₂ concentrations. Then, a linear mixed-effects regression model was utilized with the limited dataset to explore the quantitative relationships between them. It was found that maximum temperature, precipitation, adaptation measure, study area, and climate model had significant effects on changes in crop yield. Crop yield will decline by 4.21% for each 1 °C rise in maximum temperature and increase by 0.43% for each 1% rise in precipitation. While higher CO₂ concentrations and suitable management strategies could mitigate the negative effects of warming temperatures, crop yield with adaptation measures increased by 64.09% compared to crop yield without adaptation measures. Moreover, the uncertainty of simulations can be decreased by using numerous climate models. The results may be utilized to guide policy regarding the influence of climate change and to promote the creation of adaptation plans that will increase crop systems’ resilience in the future.

Fertilizer application is the basis for ensuring high yield, high quality and high efficiency of farmland. In order to meet the demand for food with the increasing of population, the application of nitrogen fertilizer will be further increased, which will lead to problems such as N₂O emission and nitrogen loss from farmland, it will easily deteriorate the soil and water environment of farmland, and will not conducive to the sustainable development of modern agriculture. However, optimizing fertilizer management is an important way to solve this problem. While, due to the differences in the study conditions (geographical location, environmental conditions, experimental design, etc.), leading to the results obtained in the literatures about the N₂O emission with different nitrogen fertilizer application strategies have significant differences, which requiring further comprehensive quantitative analysis. Therefore, we analyzed the effects of nitrogen fertilizer application strategies (different fertilizer types and fertilizer application rates) on N₂O emissions from the fields (rice, wheat and maize) based on the Meta-analysis using 67 published studies (including 1289 comparisons). For the three crops, inorganic fertilizer application significantly increased on-farm N₂O emissions by 19.7–101.05% for all three; and organic fertilizer increased N₂O emissions by 28.16% and 69.44% in wheat and maize fields, respectively, but the application of organic fertilizer in rice field significantly reduced N₂O emissions by 58.1%. The results showed that overall, the application of inorganic fertilizers resulted in higher N₂O emissions from farmland compared to the application of organic fertilizers. In addition, in this study, the average annual temperature, annual precipitation, soil type, pH, soil total nitrogen content, soil organic carbon content, and soil bulk weight were used as the main influencing factors of N₂O emission under nitrogen fertilizer strategies, and the results of the study can provide a reference for the development of integrated management measures to control greenhouse gas emissions from agricultural soils.

Global climate change is intensifying and extreme weather events are occurring frequently, with far-reaching impacts on agricultural production. The Songnen Plain, as an important maize production region in China, faces challenges posed by climate change. This study aims to explore the effects of climate extremes on maize yield and provide a scientific basis for the adaptation of agriculture to climate change in this region. The study focuses on the spatial and temporal evolution characteristics of climate extremes during the maize reproductive period from 1988 to 2020 in the Songnen Plain and their impacts on maize yield. Daily temperature and precipitation data from 11 meteorological stations were selected and combined with maize yield information to assess the spatial and temporal trends of extreme climate indices using statistical methods such as the moving average and Mann–Kendall (M-K) mutation test. Pearson correlation analysis and a random forest algorithm were also used to quantify the degree of influence of extreme climate on maize yield. The results showed that (1) the extreme heat and humidity indices (TN90p, TX90p, CWD, R95p, R10, and SDII) tended to increase, while the cold indices (TN10p, TX10p) and the drought indices (CDD) showed a decreasing trend, suggesting that the climate of the Songnen Plain region tends to be warmer and more humid. (2) The cold indices in the extreme temperature indices showed a spatial pattern of being higher in the north and lower in the south and lower in the west and higher in the east, while the warm indices were the opposite, and the extreme precipitation indices showed a spatial pattern of being higher in the east and lower in the west. (3). Both maize yield and trend yield showed a significant upward trend. Maize meteorological yield showed a fluctuating downward trend within the range of −1.64~0.79 t/hm². During the 33 years, there were three climatic abundance years, two climatic failure years, and the rest of the years were normal years. (4) The cold index TN10p and warm indices TN90p and CWD were significantly correlated with maize yield, in which TN90p had the highest degree of positive correlation with yield, and in the comprehensive analysis, the importance of extreme climatic events on maize yield was in the order of TN90p, TN10p, and CWD. This study demonstrates the impact of extreme climate indices on maize yield in the Songnen Plain, providing a scientific basis for local agricultural management and decision-making, helping to formulate response strategies to mitigate the negative effects of extreme climate, ensure food security, and promote sustainable agricultural development.

With global warming driving an increase in extreme precipitation, the ensuing disasters present an unsustainable scenario for humanity. Consequently, understanding the characteristics of extreme precipitation has become paramount. Analyzing observational data from 1961 to 2020 across 29 meteorological stations in Heilongjiang Province, China, we employed kriging interpolation, the trend-free pre-whitening Mann–Kendall (TFPW–MK) method, and linear trend analysis. These methods allowed us to effectively assess the spatiotemporal features of extreme precipitation. Furthermore, Pearson’s correlation analysis explored the relationship between extreme precipitation indices (EPIs) and geographic factors, while the geodetector quantified the impacts of climate teleconnections. The results revealed the following: (1) There has been a clear trend in increasing extreme precipitation over the last few decades, particularly in the indices of wet day precipitation (PRCPTOT), very wet day precipitation (R95P), and extremely wet day precipitation (R99P), with regional mean trends of 10.4 mm/decade, 5.7 mm/decade, and 3.4 mm/decade, respectively. This spatial trend showed a decrease from south to north. (2) Significant upward trends were observed in both spring and winter for the maximum 1-day precipitation (RX1day) and the maximum 5-day precipitation (RX5day). (3) The latitude and longitude were significantly correlated with the most extreme precipitation indices, while elevation showed a weaker correlation. (4) Extreme precipitation exhibited a nonlinear response to large-scale climate teleconnections, with the combined influence of factors having a greater impact than individual factors. This research provides critical insights into the spatiotemporal dynamics of extreme precipitation, guiding the development of targeted strategies to mitigate risks and enhance resilience. It offers essential support for addressing regional climate challenges and promoting agricultural development in Heilongjiang Province.

Understanding the spatial patterns and driving mechanisms of net primary productivity (NPP) and precipitation utilization efficiency (PUE) is crucial for assessing ecosystem services. This study analyzed the variations in NPP and PUE in Heilongjiang Province from 2001 to 2020, using MOD17A3 NPP products and meteorological, topographic, and land use data. The distribution of the NPP and PUE of seven land use categories was determined in the study, namely, cropland, forest, grassland, water, barren, impervious and wetland. The multi-year spatial averages for NPP and PUE were 428.96 gC·m⁻²·a⁻¹ and 0.74 gC·m⁻²·mm⁻¹, respectively, with forests showing the highest values and barren lands the lowest. During the study period, 91.4% of the NPP increased at an average rate of 3.36 gC·m⁻²·a⁻¹, while PUE exhibited a polarized trend. Changes in land use, especially conversions involving cropland and forest, along with climatic factors such as rising precipitation and temperature, significantly influenced NPP and PUE dynamics. These findings provide a scientific basis for ecological restoration and the assessment of ecosystem function under changing climatic conditions.