In order to study the characteristics and causes of dry−wet climate change in Jilin province, the aridity index (AI) was calculated based on daily meteorological data from 46 meteorological stations in Jilin from 1961 to 2021. Linear tendency estimation and inverse distance weighted spatial interpolation based on ArcGIS 10.2 were used to analyze the spatiotemporal variation characteristics of AI, and contribution rate analysis was used to analyze the cause of AI. The results indicated that the AI values in Jilin province and its western, central and eastern regions exhibited negative trends from 1961 to 2021. There was a significant spatial difference in the average AI values in Jilin province, with a spatial distribution pattern of ‘high−low−high’ from west to east. It was divided into sub arid, sub humid and humid regions in Jilin province. The sub humid region had been expanding over the years, reaching its maximum area in the 2010s. ET₀ showed a downward trend, while the precipitation showed an upward trend in Jilin from 1961 to 2021, but the changes trend were not significant. The water vapor pressure and average temperature both showed a significant upward trend, with the climate tendency rate of 0.008kPa·10y⁻¹ (P<0.01) and 0.32℃·10y⁻¹ (P<0.01), respectively. The net solar radiation and wind speed both showed a significant downward trend, with the climate tendency rate of −0.077MJ·m⁻²·10y⁻¹(P<0.01) and −0.14m·s⁻¹·10y⁻¹(P<0.01), respectively. ET₀, net solar radiation, average temperature and wind speed gradually decreased from west to east, while the precipitation showed a gradual increase from west to east and the vapor pressure mainly exhibited a spatial distribution characteristic of ‘low−high−low’. The meteorological factors of vast majority of station had a negative contribution to AI. Precipitation was the dominant factor for the variations in AI values in Jilin province and its west and east, followed by wind speed and ET₀, but in the central of Jilin province, wind speed was the dominant factor for the AI change, followed by precipitation and ET₀. The research results can provide support for the formulation of strategies to cope with dry−wet climate change and the rational utilization of climate resources in Jilin province. 

Under the national strategy of "carbon peak and carbon neutrality", systematic assessment of the carbon sequestration potential of tea plants holds significant importance for realizing the value of ecological products in tea plantations. There is a huge difference in the growth rate of young tea plants under 2 years old and mature tea plants over 3 years old. By collecting the relevant research literature and fieldwork data of tea plant growth in domestic and international tea plantations from 1950 to 2023, this study constructed the model of tea plant biomass and carbon stock growth dynamics based on aboveground and belowground biomass data of young tea plants (0−2y) and mature tea plants (3−25y) to calculate and evaluate the carbon sequestration capacity of tea plants. The results showed that: (1) age−specific belowground biomass models of tea plants were established. Nonlinear model for mature tea plants (B_b=0.013B_a²−0.087B_a+3.269, R²=0.959, P<0.001) and linear model for young tea plants (B_b=0.665B_a−0.217, R²=0.933, P<0.001) were constructed based on the relationship between aboveground biomass (B_a) and belowground biomass(B_b). (2) The models for accounting tea plants carbon stock based on tea plants aboveground biomass were formed. Using the internationally recognized plant carbon conversion factor (0.5) provided by the Intergovernmental Panel on Climate Change(IPCC), carbon stock models for mature tea plants (C=0.006B_a²+0.492B_a+1.536, R²=0.995, P<0.001) and young tea plants(C=0.833B_a−0.108, R²=0.989, P<0.001) were developed based on total biomass data. (3) The tea plant carbon stock estimation model demonstrated simplicity and accuracy. Traditional methods relied on destructive whole−plant excavation to measure biomass, whereas the non−destructive model, based solely on aboveground biomass, enhanced both the efficiency and precision of carbon stock quantification. This approach offers distinct advantages for carbon stock quantification in tea plantations.

In order to study the simulation ability of BP neural network on the average temperature and average relative humidity in the facility greenhouse under three weather types of sunny, cloudy and overcast days, the meteorological data of the four-span plastic greenhouses in Songjiang district, Shanghai from March 1, 2021 to February 28, 2022 and the meteorological station of the same period were selected, and the average temperature, average relative humidity, average wind speed and solar altitude angle outside the greenhouse were used as input factors to construct temperature and humidity BP neural network prediction models for three weather types at the annual and seasonal scales, and the models were validated. The results showed that: (1) at the annual scale, the RMSE of the average temperature model was 0.8−1.1℃, and the R²≥0.9890 (P<0.001). The RMSE of the average relative humidity model was 2.9−4.0pp (percent point), and the R²≥0.9434 (P<0.001). Overcast days were the best simulations results, followed by cloudy days and sunny days at the lowest levels. (2) At the seasonal scale, the average RMSE value of the average temperature model was 0.7−0.9℃, and the average R²≥0.9765 (P<0.001). The average RMSE value of the average relative humidity model was 2.2−3.2pp (percent point), and the average R²≥0.9451 (P<0.001). Of these, the simulation accuracy was best in summer, followed by autumn, and the lowest in spring and winter. The simulation performance of the seasonal scale model was better than that of the annual scale model. (3) The average temperature and average relative humidity models at the annual and seasonal scales passed the independent sample test of the significant level of 0.001, indicating that the model has a good simulation effect and can be used to simulate the temperature and humidity in the greenhouse and practical application.

In order to study the impact of the underlying surface on convective precipitation in Beijing and improve the convective precipitation prediction ability, simulations from RMAPS−Urban model and observations from the Beijing station were used to study a specific case that occurred on August 16, 2021. The simulation results showed that the RMAPS−Urban model was able to well model the temporal and spatial distribution of precipitation, with the heavy precipitation area located in the western part of the city and the main precipitation period occurring around 22:00 on 16^th. In the early period of heavy precipitation, the urban heat island (UHI) was obvious in the urban area of Beijing, with UHI arriving 1.0−2.0℃. When the urban underlying surface was replaced by crop, the UHI was weakened below 1.0℃, the extreme precipitation decreased from 117.1mm to 56.6mm, and the rainstorm area in the western part of the urban area disappeared. Those indicated that the occurrence and development of convective precipitation was closely related to the urban underlying surface. Moreover, the sensible heat flux over the urban underlying surface was significantly higher than that over the surrounding regions, up to 100W·m⁻², which enhanced the convergence over the urban areas and provided the uplift for the formation of the convection. In addition, the perturbations in the potential temperature of the lower atmosphere (below 600m) were also enhanced by the urban underlying surfaces, offering unstable stratification conditions. Finally, larger roughness over the urban underlying surface caused more precipitation in the windward side of the city of the low level surface for the lower water vapor and energy can be transported up with the intensity of the convergence reaching −30×10⁻⁴·s⁻¹ and the rainwater mixing ratio exceeding 3g·kg⁻¹, which was more favorable for the occurrence of convective precipitation. 

Eddy covariance method and corresponding meteorological system were used to observe a Pinus sylvestris var. mongolica plantation in the western Liaoning province, to ascertain the process of energy flow, to reveal the relationship between the key parameters of energy partitioning and environmental factors in two years, and finally to provide support for a deep understanding of the complex relationship between terrestrial vegetation and climate change. In 2021 and 2022, downward longwave radiation offset 82% and 81% of upward longwave radiation, 12% and 11% of downward shortwave radiation (S_d) was reflected back to the atmosphere by the land surface, 20% and 21% of downward radiation was transformed into net radiation (R_n), respectively. In the non−growing season, the average ratio of soil heat flux (G) and latent heat flux (LE) to R_n were −0.20 and 0.20, respectively. In the growing season, the average ratio of sensible heat flux (H) and LE to R_n were 0.57 and 0.39, respectively. At half−hour and daily time scales, the general trend of evaporative fraction (EF) was contrary to that of Bowen ratio. Among the environmental factors that obviously influenced daily EF during the growing season of 2021, daily S_d, vapor pressure deficit and wind speed were negative effect, daily air relative humidity (H_a), top soil water content (SWC), and normalized difference vegetation index (NDVI) were positive effect, and the correlation coefficient between daily EF and SWC was highest. The important controlling factors of daily EF during the growing season of 2022 were daily S_d, air temperature, H_a, SWC and NDVI, all of those factors were positive effect, and the correlation coefficient of NDVI was highest. In the Pinus sylvestris var. mongolica plantation of the western Liaoning province, G was an important energy source during the non−growing season, H was the main energy consumption during the growing season, and water supply and vegetation growth dominated the energy partitioning in this plantation. 

The genus Rubus included medicinal and edible plants with great economic development potential. Study on climate, topography and soil variables effects for the suitable distribution of these species could provide a valuable reference for resource protection, introduction and utilisation. Using effective distribution data of Rubus and 28 climate, topography and soil environmental variables, the potential suitable distribution of Rubus in China was predicted using the optimized MaxEnt model based on the ENMeval package in R under the current (1970−2000) and future (2021−2040 and 2061−2080) climate conditions. The results showed that after optimization by the ENMeval package, the model’s Akaike information criterion (ΔAICc) was 0, indicating good predictive performance. Elevation, isothermality, the cation exchange capacity of the clay fraction in topsoil (0−30cm), the mean temperature of the wettest quarter and the minimum temperature of the coldest month were the important environmental variables that significantly affected the suitable distribution of Rubus. The highly suitable area for Rubus under the current (1970−2000) climate conditions was 192.72×10⁴km², mainly concentrated in Shaanxi, Guizhou and Fujian. Under the future (2021−2040 and 2061−2080) climate conditions and four greenhouse gas concentration pathways (SSP126, SSP245, SSP370 and SSP585), both the total and highly suitable areas for Rubus were significantly reduced to 52.11%−66.36%, 49.99%−71.61% compared with the current climate conditions, respectively. The reduction mainly occurring in the northwest, north, northeast, the Xizang autonomous region and Yunnan in southwest China. The prediction shows that the suitable area of Rubus will significantly decrease with future climate change. Ex situ conservation measures could be adopted in the predicted reduction areas of Rubus to protect these species.

Based on the quality data of ‘Xinmai 26’ (strong−gluten) and ‘Yangmai 15’ (weak−gluten) wheat varieties from six agro−meteorological stations in Henan province during 2021−2023, combined with concurrent meteorological data, correlation analysis was used to determine the relationships between key quality traits of different gluten−type wheat and meteorological factors. Stepwise regression was further employed to construct quantitative equations for the responses of main quality indices to meteorological variables. The results showed significant differences (P<0.05) in identical quality indices among stations for the same gluten−type wheat. Strong−gluten wheat generally exhibited superior quality compared to weak−gluten wheat, with grain protein content being 1.97 g·100g⁻¹ higher, wet gluten content 0.56pp higher, and sedimentation index 24.25 mL higher. Temperature and humidity factors during the jointing−booting stage exerted significant positive effects (r>0.28, P<0.05) on protein content and sedimentation index for both gluten types. Conversely, light intensity during the heading−flowering stage showed significant negative correlations (r<−0.45, P<0.05) with wet gluten content in both types. Specifically, protein content in strong−gluten wheat was primarily influenced by thermal and moisture factors during jointing−booting stage, along with light intensity during heading−flowering stage. For weak−gluten wheat, protein content additionally correlated closely with thermal factors during milk−ripening stage. Wet gluten content in both types responded significantly to light intensity during heading−flowering stage and thermal factors during milk−ripening to mature stage. Sedimentation index in strong−gluten wheat was sensitive to light intensity during heading−flowering stage, whereas weak−gluten wheat sedimentation index was more influenced by combined light and thermal factors during milk−ripening stage. Stepwise regression models indicated that quality indices of different gluten−type wheat were regulated by distinct meteorological factors, with significant temporal variations in factor effects across growth stages. This study reveals the regulatory mechanisms of meteorological factors on wheat quality formation, providing a scientific basis for the regionalized cultivation of high−quality special−purpose wheat and precision agricultural management. The findings have important implications for optimizing field practices, adapting to climate change, and enhancing wheat production quality.

In agricultural production, the combined application of organic fertilizer and chemical fertilizer is often used to reduce the negative effects of single application of chemical fertilizer, but the unreasonable proportion of organic fertilizer instead of chemical fertilizer can easily lead to crop yield reduction, and the effect of organic fertilizer instead of chemical fertilizer on farmland N₂O emission is not uniform. To explore the impact of organic fertilizers instead of chemical fertilizers on N₂O emissions and crop yields in farmland, the sunflower fields in the Hetao irrigation district of Inner Mongolia were used as the study site. Five fertilization treatments with chemical fertilizer (T0), organic fertilizer replacing 25% (T25), 50% (T50), 75% (T75) and 100% (T100) were set up in the experimental base of Ganzhao temple of Bayannur Academy of Agricultural and Animal Husbandry Sciences in 2023. The N₂O emission was determined by static chamber−gas chromatography. Combined with quantitative analysis of soil carbon and nitrogen indicators, enzyme activity and sunflower yield, the N₂O emission law and its relationship with soil indicators were clarified. Explore the appropriate proportion of organic fertilizer substitution in local sunflower production. The results showed that after additional fertilizer (July 13−August 10) was the peak stage of N₂O emissions. Compared with the T0 treatment, the T25, T50, T75 and T100 treatments significantly reduced the cumulative N₂O emissions from farmland during the growing season by 30%, 45%, 52% and 64% respectively. Compared with T0 treatment, the contents of soil soluble carbon and nitrogen (DOC and DON) were significantly increased by 11%−30% and 38%−53%, respectively. Nitrous oxide reductase (NOS) activity was significantly increased by 11%−32%; nitric oxide reductase (NOR) activity was significantly reduced by 14%−30%. The contents of DOC and DON were significantly correlated with NOS activity, and the content of DON was significantly correlated with NOR activity. The increase in NOS activity and the decrease in NOR activity resulted in a decrease in the cumulative N₂O emission from the farmland. Compared with T0 treatment, the sunflower yield of T25 treatment was significantly increased by 30%, and the sunflower yield of T50 treatment was not significantly reduced, and the N₂O emission intensity of T25 and T50 treatments was lower, which was suitable for the local stable production and emission reduction of organic fertilizer instead of chemical fertilizer fertilization scheme.

 Timely and accurate access to crop yield information is critical for decision−making in national food policy and safety assessments. Remote sensing technology, with low cost and high efficiency, provides an effective means for large−scale crop yield estimation. Strengthening the integration of agronomic knowledge into crop yield estimation models and addressing the scarcity of training samples are key challenges in current research. In this paper, authors synthesized existing literature, summarized data− and knowledge−driven methods for crop yield estimation, and systematically discussed the methods for constructing multi−scenario simulation datasets and modelling techniques for crop yield estimation based on hybrid modeling approaches. The paper also provided an overview of commonly used models and algorithms, and summarized the application of remote sensing technology to hybrid modeling methods. Finally, it comprehensively discussed the uncertainties in hybrid modeling and outlined the future trends and challenged in crop yield estimation studies. The results showed that hybrid modeling approaches, driven by both data and knowledge had made significant progress in crop yield estimation. By combining the advantages of data−driven and knowledge−driven models, these approaches reduced the reliance on ground−truth samples while enhancing the mechanistic support for the predictions. Limiting factors for improving the accuracy of crop yield estimation included uncertainties in remote sensing data sources, the uncertainties in knowledge−driven models when simulating crop physiological processes, and the uncertainties in the predictions of data−driven models. Future trends will focus on improving the quality and availability of input data, strengthening the theoretical foundation of knowledge−driven models, and advancing algorithm improvements in data−driven models.

Panax notoginseng has extremely high medicinal and economic value, likes temperature and humidity, temperature and humidity are important environment parameters affecting its growth. At present, the facility cultivation of Panax notoginseng is mainly based on artificial experience, and there is a serious lag in the regulation of the facility environment, which leads to Panax notoginseng being susceptible to diseases and insect pests resulting in yield reduction, and seriously hindering industry development. In this study, four deep machine learning algorithms, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short−Term Memory Neural Network (LSTM) and Informer model, were used to preliminarily optimize the temperature and humidity prediction model of Panax notoginseng, and an improved PCA−Informer model was constructed to enhance training efficiency and model performance. At the same time, environmental monitoring sensors were deployed for data collection was achieved through environmental monitoring sensors, and the PCA−Informer model was integrated into the platform software, and the main functional modules of the platform were realized by using the Django framework combined with Python technology to develop a platform for environmental monitoring, temperature and humidity prediction and early warning systems of Panax notoginseng facility cultivation. The results showed that: (1) the informer model had the highest prediction accuracy compared with the other three deep machine learning algorithms, the mean absolute error (MAE) of air temperature and humiditywas 0.860°C and 3.870pp, and the coefficients of determination (R²) was 0.959 and 0.964, respectively. (2) The PCA−Informer model constructed by adding Principal Component Analysis (PCA) algorithm to the Encoder layer of the Informer model could improved the training efficiency and performance of the temperature and humidity prediction model of facility cultivated Panax notoginseng. Compared with the Informer model, the MAE of the PCA−Informer model for predicting air temperature and humidity was reduced by 0.140°C and 0.621pp, respectively, and the R² was increased with 0.0100 and 0.0021, respectively. (3) The environmental monitoring, temperature and humidity prediction and early warning platform of Panax notoginseng facility cultivation realized the accurate prediction and early warning of temperature and humidity of Panax notoginseng in the next 3 days.

The price of jasmine flowers in Hengzhou, Guangxi has been significantly affected by heavy rains. It is of great importance to develop a model for assessing the risk of heavy rain disasters for jasmine, in order to reduce the economic losses caused by heavy rain disasters. Based on the meteorological observation and jasmine flower prices during 2022−2024, this study constructed a risk assessment model for heavy rain disasters. By combining GIS and remote sensing techniques, the model was built to predict daily heavy rain risk for jasmine flower using the information entropy weight method. The results showed that: (1) the key factor influencing the disaster risk of heavy rain on jasmine was the duration of the heavy rain, which accounted for 40.6% of impact. These were followed by total precipitation over 24 hours and the accumulated precipitation during heavy rain process. Typically, heavy rain events lead to a decrease in the daily price of jasmine flowers by 15.9% to 25.5%. (2) The risk of heavy rain disasters for jasmine in Hengzhou was classified into low (26.4%), medium (44.6%), and high (29.0%) levels. The high risk areas were mainly located in the southern and eastern parts of Hengzhou. (3) Based on the heavy rain disaster risk assessment model, a heavy rain disaster risk forecasting system for jasmine was constructed. The prediction accuracy reached 63.1% for the 38 heavy rain events in 2024. It precisely verified the heavy rain process from June 3 to 5 in 2024. The simulation slightly underestimated the risk on June 3 and 4, while its results on 5 were relatively consistent with the actual decline in flower prices. In conclusion, based on the refined prediction and warning information, the heavy rain disaster risk prediction system for jasmine can help flower farmers formulate response strategies, reduce production losses, and improve production efficiency. As a result, the meteorological disaster resistance of the jasmine industry in Hengzhou can be enhanced. 

During the general and full−bloom phases of potatoes, flowers were observed to be particularly small and densely distributed. In real−scene potato farmland images, the features of the flowers occupied minimal pixel regions, leading to frequently missed detections and low accuracy in mainstream recognition models. To address this issue and improve flower recognition accuracy, the YOLOv5CD5−256 model was proposed. Inspired by DenseNet, a CD5−256 dense structure was integrated into YOLOv5 by incorporating DenseBlocks, and attention mechanisms were incorporated to enhance feature extraction. For validation, images captured by eight real−scene monitoring systems in Ningxia potato fields from 2019 to 2023 were used as the dataset. The proposed model was compared with YOLOv5L, YOLOv8L, YOLOv8X, YOLOv9C and YOLOv9E. The results showed that the precision (P), recall (R) and mean average precision (mAP) of YOLOv5CD5−256 on the test set reached 0.83, 0.85 and 0.82, respectively. Each of these indicators was 0.20 higher than those of YOLOv5L and 0.15−0.17 higher than those of other models. It performed the best among the six models. In the early flowering stage of potatoes, all six models had good detection capabilities. When potatoes entered the full−bloom and peak−bloom stages, the average missed− detection rate of the YOLOv5CD5−256 model was 0.20−0.23 lower than that of other models, showing obvious advantages. This indicates that the proposed model can be applied to flower detection in different stages of potatoes, including the early, full−bloom and peak-bloom stages. Notably, its detection ability for small−feature and densely distributed flowers is significantly better than that of current mainstream models, and it can be used as a recognition model for potato flowers.

Based on daily meteorological observation data from approximately 1800 meteorological stations and agrometeorological observation data from 295 agrometeorological stations in the summer harvested grain and oil crops (winter wheat and rape) in 2025, statistical analysis, satellite remote sensing and field investigations were employed to analyze the climatic suitability, disaster indices and crop growth conditions during the growing seasons of winter wheat and rape. This study assessed the advantages and disadvantages of meteorological conditions on the growth, development and yield formation of summer harvested grain and oil crops. The results showed that most of production areas experienced abundant light and heat during the winter wheat and rape growing periods of 2024/2025(from Oct in 2024 to May in 2025). However, the overall precipitation was relatively low and exhibited an uneven temporal and spatial distribution. Severe spring droughts occurred in Henan, Shaanxi and Shanxi provinces negatively impacted the increase in the thousand−grain weight of winter wheat. In Hunan, Hubei and Jiangxi provinces, intermittent rainy weather accompanied by severe convective events in the spring, and periodic droughts during the autumn sowing period hindered the growth and development of rape. Still, the impact had been relatively modest. During the maturity and harvesting stages of the summer harvested of grain and rapeseed, predominantly sunny and favorable weather conditions prevailed. During the maturity and harvesting stage of winter wheat and rape, mostly sunny and favorable weather ensured smooth progress and high−quality harvesting. Overall, the meteorological conditions during the winter wheat growing season of 2024/2025 were characterized by favorable light and temperature conditions, but the water conditions deteriorated over time. In contrast, the meteorological conditions during the rapeseed growing season were more favorable than both the 30y average and the 2023/2024 season.