Taking maize production in Shanxi province as the research object, studied the dynamic change of carbon footprint of maize planting system in Shanxi province was studied from 2010 to 2019 based on life cycle assessment method and analyzed its composition, which would provide a certain theoretical basis for low-carbon and cleaner production of maize as well as the smooth increase of grain production in Shanxi province. The results showed that: (1) from 2010 to 2019, the average carbon footprint of maize production in Shanxi province was 1959.14kg CO₂-eq·ha⁻¹ and 0.36kg CO₂-eq·kg⁻¹ per unit area and per unit yield, respectively. The carbon footprint per unit area, yield and production value generally showed an increasing trend and then a decreasing trend, with a large fluctuation from year to year. (2) Fertilizer production(50.08%), field N2O emission(27.39%) and oil consumption of agricultural machinery(6.89%) were the main sources of carbon footprint of maize in Shanxi province. If only the greenhouse gas emissions from field production were considered, the contribution of field N₂O emissions due to fertilizer application and fuel consumption of agricultural machinery to the carbon footprint of field production reached 67.47% and 16.66%. (3) Scientific fertilization, chemical fertilizer reduction and organic substitution were still the key approaches to reduce the carbon footprint of maize. In summary, while reducing the carbon footprint of maize, reducing maize production costs and formulating emission reduction measures according to local conditions are the keys to balance maize productivity, ecological environment and economic benefits of maize.

Agriculture represents a significant contributor to non-carbon dioxide (CO₂) greenhouse gas (GHG) emissions in China. Effectively managing non-CO₂ GHG emissions from agriculture is crucial for achieving China's "dual-carbon" targets, providing valuable insights into guiding the green transformation of agriculture and promoting sustainable development. Utilizing statistical data on crop yield, fertilizer input, and paddy sowing area in the Xinjiang Uygur Autonomous Region from 2000 to 2020, the authors applied the IPCC coefficient method to assess total agricultural non-CO₂ GHG emissions. The Logarithmic Logarithmic Mean Divisia Index (LMDI) model was used to analyze the drivers of non-CO₂ GHG emissions from agricultural sources. Additionally, a Monte Carlo simulation, coupled with scenario analysis, was conducted to predict the emission trends of agricultural non-CO₂ GHG emissions in Xinjiang during the study period. The findings revealed that: (1) agricultural non-CO₂ GHG emissions in Xinjiang exhibited a fluctuating upward trend, experiencing a 34.43% increase, with animal husbandry identified as the primary emission source. (2) Key contributors to agricultural non-CO₂ GHG emissions were identified as the level of agricultural economic development and urbanization, contributing to emissions of 4211.74×10⁴tCO₂eq and 1016.08×10⁴tCO₂eq, respectively. Conversely, the efficiency of Agricultural non-CO₂ GHG emission intensity, rural population, and the agricultural structure acted as inhibitors, contributing to emission reductions of 4163.36×10⁴tCO₂eq, 224.84×10⁴tCO₂eq and 130.64×10⁴tCO₂eq , respectively, during the period from 2000 to 2020. (3) Agricultural non-CO₂ GHG emissions exhibited an increasing trend in both baseline and planning scenarios. However, the growth rate in the planning scenario surpassed that in the baseline scenario, and a slowdown in the growth rate is anticipated in the low-carbon scenario, with the potential for negative growth by 2035. (4) To effectively control non-CO₂ GHG emissions from agriculture, there is a need to reinforce the implementation of emission reduction policies, continually enhance the efficiency of agricultural production, improve incentive mechanisms for emission reduction, and encourage technological breakthroughs. These measures are essential to guide the green transformation of Xinjiang's agriculture and foster its sustainable development.

The aim of this study was to investigate the relationship between soil temperature and meteorological factors in the Huaibei region and to make predictions about future soil temperature. To achieve this, authors analyzed 58 years of daily measured data from the Wudaogou Hydrological Experiment Station, spanning from 1986 to 2021. The data included soil temperature readings from 9 soil layers at depths of 0-320cm, as well as measurements of 8 meteorological factors: mean air temperature, water surface evaporation, sunshine hours, precipitation, mean wind speed at 1.5m height, mean air relative humidity, absolute humidity, and water vapor pressure difference. The study analyzed the correlations between soil temperature and meteorological factors. The data of the eight meteorological factors were downscaled using Principal Component Analysis. Finally, a PCA−BP soil temperature prediction model was constructed using the extracted principal components as input. The model was compared with a BP neural network model that used only mean air temperature as input. The results indicate that (1) soil temperature is influenced not only by mean air temperature but also by meteorological factors such as water surface evaporation, absolute humidity, and water vapor pressure difference. The influence of meteorological factors on soil temperature decreases with increasing soil depth. The response of deep soil temperature to meteorological factors exhibits a time lag, which increases with soil depth. (2) The accuracy of the PCA−BP soil temperature model in predicting soil temperature at depths of 0-80cm after the 1^st, 3^rd, and 7^th day in the future was high, with an R² value greater than 0.79. The accuracy of the PCA−BP soil temperature model in predicting deep soil temperature increased as the number of prediction days increased due to the lag effect of soil temperature response to meteorological factors. (3) Adding new meteorological factors as inputs can enhance the accuracy of the soil temperature prediction model. The PCA−BP model outperformed the BP model, which only used the mean air temperature as input, in predicting the soil temperature after the 3^rd and 7^th predicted days.

Net Primary Productivity (NPP) is the net carbon sequestration of terrestrial vegetation, which is of great importance for the study of global carbon estimation. The rocky mountainous areas of North China is a typical arid and semi-arid climatic zone, and the accurate estimation of the NPP and its variation characteristics in this area is of great significance for the construction of China's ecological forestry engineering. Based on the climate data from 1980 to 2020 from Henan Xiaolangdi Forest Ecosystem National Observation and Research Station, this paper used 5 climate productivity models, including the Miami model, Thornthwaite Memorial model, Chikugo model, Zhu Zhihui model and Zhou Guangsheng model, to estimate and analyze the changing trend of NPP. The random forest algorithm was used to explore the influencing factors of NPP, and the regional MODIS NPP data was used as the standard to evaluate the suitable climate productivity model for estimating NPP in this area. The results showed that: (1) the annual average temperature, annual average maximum temperature, annual average minimum temperature, and annual precipitation in the rocky mountainous areas of North China showed an upward trend, and the rates were 0.05℃y⁻¹,0.04℃y⁻¹,0.05℃y⁻¹ and 1.58mmy⁻¹, respectively. The annual average solar radiation and annual average relative humidity showed a downward trend, with rates of 0.46MJm⁻²y⁻¹ and 0.17 percent pointsy⁻¹, respectively. (2) The NPP calculated by 5 models showed an upward trend, but the NPP values were different, ranging from 739.35gCm⁻²y⁻¹ to 958.48gCm⁻²y⁻¹, with an average of 862.19gCm⁻²y⁻¹. Among them, the Miami model had the maximum estimated value (958.48gCm⁻²y⁻¹), and the Zhou Guangsheng model had the minimum estimated value (739.35gCm⁻²y⁻¹). (3) The random forest algorithm showed that precipitation was the predominant factor affecting NPP in the region. The applicability analysis indicated that the estimated value of the Zhou Guangsheng model was the closest to MODIS NPP, with the relative error, RMSE, and MAE of 1.45%, 451.05gCm⁻²y⁻¹, and 446.03gCm⁻²y⁻¹, respectively, and the correlation coefficient was the highest (0.49). This study showed that the Zhou Guangsheng model was more suitable for NPP estimation in this area, and priority should be given to the Zhou Guangsheng model when using the climate productivity model to estimate NPP in the rocky mountainous areas of North China.

In order to grasp the evolutionary trend of drought and vegetation in various regions of North China, and to analyze the degree of response of vegetation to different drought types, it is of guiding significance for drought mitigation work. In the paper, data from 90 meteorological stations in North China were used to construct the standardized precipitation evapotranspiration index (SPEI), and the normalized vegetation index (NDVI) was selected to quantify the vegetation coverage. Then, the Sen's slope estimator, Mann-Kandall test, correlation analysis, and Copula-Bayesian conditional probability formula were used to analyze the drought sensitivity of vegetation in North China. The results show that: (1) the SPEI value in most parts of North China showed a extremely significant increase, the SPEI value in some parts of North China showed a extremely significant decrease, the NDVI value in the western and northern regions of North China showed an extremely significant increase, and the rest of the regions showed an extremely significant decline. (2) Drought and vegetation in North China were dominated by insignificant spatial clustering, with high and low values of drought and vegetation clustering in small areas. (3) In North China, the correlation between annual scale SPEI and NDVI was good, the response rate of vegetation to drought was slower and the sensitivity was low, the correlation between month-scale SPEI and NDVI was better in some parts of Henan, and the vegetation was more sensitive to drought, and the joint distribution function of NDVI and SPEI was the most consistent with the Clayton Copula function. (4) The likelihood of lowest vegetation coverage occurring in North China was decreasing with decreasing drought, the likelihood of lower vegetation coverage occurring was increasing from extreme to moderate drought and decreasing from moderate to light drought, and the likelihood of medium, higher and highest vegetation coverage all increase with decreasing drought.

In order to compare the regulatory effects of different oligosaccharides on the growth and yield of greenhouse cucumber, the chitosan oligosaccharide(GT) and mixed-oligosaccharide(KP) were selected as the preparation treatments, while the conventional control(CG) and clear water control(CK) were compared as controls. The relative leaf chlorophyll content(SPAD), chlorophyll fluorescence parameters, leaf cell structure, root morphologies and root vigor(RV), antioxidant enzyme activity and endogenous hormone contents were all tested in this study. The results showed that KP treatment could effectively promote the growth and development of cucumber leaves and roots. Compared with the other treatments, the SPAD, maximum photochemical quantum yield(Fv/Fm), photochemical quenching coefficient(qP), non-photochemical quenching coefficient(NPQ) and RV of the KP treatment were all significantly increased by 4.15%-15.63%, 6.49%-20.03%, 33.33%-152.63%, 10.37%-71.62% and 8.51%-171.43%. Meanwhile, the superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) activities were significantly increased by 6.75%-91.49%, 3.85%-97.62% and 3.00%-28.15%; the IAA content of cucumber leaves were enhanced larger than 5.43% in the late growth stage, and the abscisic acid (ABA) content decreased by 4.35%-18.63%. The yield and soluble sugar content of the KP treatment were increased by 13.72% and 11.52%, respectively. For the GT treatment, unstable effects were obtained, including the root and crown growth, endogenous hormones and antioxidant enzyme activity. The photosynthetic physiological characteristics of leaves, such as SPAD, chlorophyll fluorescence parameters and leaf ultrastructure, and RV in the fruit set stage were improved slightly. Compared with GT, KP showed a clear advantage in promoting coordinated root and crown growth, regulating antioxidant enzyme activity, and balancing the cucumber's endogenous hormones. The correlation analysis results showed that the yield of cucumber was significantly positively correlated with NPQ, RV, SOD and POD activity(P<0.01), and positively correlated with Fv/Fm(P<0.05). While SOD and POD were negatively correlated with ABA content(P<0.05). In conclusion, exogenous mixed-oligosaccharides mainly promoted the coordinated growth of root and shoot, especially maintained the high photosynthesis efficiency, root vitality and the stability of leaf functional structure in the late growth period, regulated the activity of antioxidant enzymes and the balance between growth promoting hormone and growth inhibiting hormone, which lead to the stability of cell membrane function in the late growth period, delay natural aging of leaves, and the improvement of yield and quality of cucumber.

To assess the climate risks of spring frost on the Loess Plateau, the daily minimum temperature observation data during spring frost period (April 1-May 31) from 1981 to 2020 for 13 meteorological stations on the Loess Plateau grapevine area were collected, and the station ratio, frequency, number of days, intensity and risk were studied by mathematical statistics and spatial analysis methods. The results showed that during the spring frost period of grapevine on the Loess Plateau from 1981 to 2020, the average daily minimum temperature showed a highly significant upward trend with 0.42℃·10y⁻¹ of climate tendency, the lowest daily minimum temperature showed a highly significant downward trend with −0.75℃·10y⁻¹ of climate tendency. The negative climate tendency rates of lowest daily minimum temperature were distributed sporadically and the higher absolute value was in Zuoquan with −0.76℃·10y⁻¹. The station ratio of slight spring frost on the Loess Plateau decreased, while the moderate, severe and comprehensive spring frost increased. The frequency and number of days of slight and moderate spring frost decreased, while the severe spring frost showed increased, and the intensity of severe spring frost showed a significant upward trend; the comprehensive frequency and number of days of spring frost decreased, while the intensity showed a significant upward trend. The frequency, number of days, and intensity of spring frosts increased from southwest to northeast for all spring frost levels. The climatic risk of grapevines on the Loess Plateau decreased in the risk of slight and moderate spring frosts, and increased in the risk of severe and comprehensive spring frosts. The high-risk areas were mainly distributed in Zuoquan, Taigu and other places in the north, and the low-risk areas were mainly distributed in Huyi and Weinan in the south. The results could provide a scientific basis for the prevention and mitigation of grapevine spring frost on the Loess Plateau.

Millet is a characteristic crop of semi-arid regions, with high drought tolerance and stable yield. The semi-arid region of Inner Mongolia and Hebei is the dominant millet production region, with the highest planting area and total yield of millet in China. Drought is threatening millet production in this region. In order to reveal the impact of drought on the millet growth processes and increase the capacity of millet production to cope with drought, the crop water deficit index (CWDI) was used to investigate millet drought in this study. Based on the daily meteorological data from 27 meteorological stations in the study area from 1961 to 2019, the temporal and spatial variation characteristics of the millet drought were analyzed by combining drought intensity, drought frequency, and drought station ratio. The results showed that:（1）in the past 60 years, the drought intensity of millet in the semi-arid area of Inner Mongolia and Hebei showed a significant downward trend. The frequency and station ratio of millet drought decreased with increased drought level.（2）The drought intensity and frequency in each growth stage of millet was in order of: jointing period>seedling period>maturity period>grain filling period>heading period. （3）The drought intensity and frequency of millet decreased gradually from northwest to southeast while the occurrence range of moderate drought, severe drought, and extreme drought decreased with time. The reduced intensity and frequency of drought is in favor of millet production in semi-arid region of Inner Mongolia and Hebei.

In this paper, the reliability and accuracy of the FY-3 and MODIS NDVI data in Xinjiang from 2014 to 2020 was analyzed by using numerical comparison, correlation analysis and trend consistency analysis, and the reliability and accuracy of FY-3 and MODIS were studied. Based on FY-3 NDVI data, the vegetation change in Xinjiang from 2014 to 2020 was studied by using coefficient of variation analysis method. The results show that the MODIS NDVI monthly mean from 2014 to 2020 were generally lower than those of FY-3, however, the change rules of the two sets of data were consistent, but there were certain differences in NDVI between the two data in sandland, grassland, woodland, cultivated land, spring 2017, summer 2016 and 2018, autumn 2015 and 2019, and winter 2019, the difference areas were mainly located in Altai mountain district, Ili river valley and the eastern section of Tianshan mountains; in 2020, the average NDVI data for each land use type and season in FY-3 was greater than the average MODIS NDVI data, and the FY-3 NDVI data were closer to the true value. At the same time, the NDVI data R2, RMSE, MAD, RE, θ for each land use type and season in FY-3 were lower than the MODIS NDVI data, indicating that the FY-3 data tends to be concentrated, with higher stability than the MODIS data, the NDVI data of FY-3 superior to MODIS NDVI. In Xinjiang, the internal stability of vegetation varies greatly seasonally and spatially, with small fluctuations in summer and large fluctuations in spring and autumn on a seasonal scale. Spatially, regions with better vegetation coverage, such as grasslands, forests, and oasis farming areas, have stronger internal stability of vegetation, and vice versa.

In the winter of 2023/2024, the national average temperature was −2.8℃, which was 0.3℃higher than the same period of the perennial value (1991−2020). The average precipitation in winter was 50.8mm, 19.8% higher than the same period, showing a distribution pattern of more in the north and less in the south. Many places in the central and eastern regions have more snowfall days, more accumulated snowfall and deeper snow. The national average sunshine was close to perennial value, and the eastern part of southwest China is more than the same period. Most regions of the winter wheat areas in the north have abundant light and heat conditions, within many large-scale rain and snow weather were conducive to soil moisture increase. The soil moisture is the best in the past decade, and meteorological conditions are conducive to the safe overwintering of winter wheat. Most regions of southern was enough warm and sunshine hours for the growth and development of rape, wheat, winter crops and economic fruits. In winter, the central and eastern regions experienced three large-scale cold wave weather processes, which affected the production of protected agriculture, and some rape, open vegetables and economic forest fruits in southern provinces were frozen. There is much snowfall in Northeast China and northern Xinjiang, which is not conducive to agriculture and animal husbandry production.