Based on multi−source data using the improved CASA model and soil heterotrophic respiration model, the net ecosystem productivity (NEP) of Shanghai in 2000−2016 was calculated to characterize the carbon sink status of vegetation, and the spatial and temporal dynamic evolution characteristics of vegetation carbon source/sink and their influencing factors were investigated using the trend analysis, stability analysis, and partial correlation analysis. The results showed that (1) the annual mean value of NEP for vegetation in the Shanghai region from 2000 to 2016 was 96.73gC·m⁻²·y⁻¹, with a decreasing trend in temporal variation of 1.87gC·m⁻²·y⁻¹ (P<0.05). (2) The area of the vegetation carbon source area in Shanghai accounted for 24.20%, and the area of the carbon sink area accounted for 75.80%, and the vegetation space as a whole showed a carbon sink situation. (3) The average slope of the vegetation NEP trend was −3.63, and the coefficient of variation was 76.41% of the area of medium and high stability level, with a high overall stability. (4) Vegetation NEP showed a weak negative correlation with temperature, a significant negative correlation with soil heterotrophic respiration with a partial correlation coefficient of −0.48, and a significant positive correlation with precipitation and solar radiation with partial correlation coefficients of 0.57 and 0.43, respectively.

Based on daily data from 117 meteorological stations in Henan province (1960-2014) and data from 10 climate models in the Sixth Coupled Model Intercomparison Project (CMIP6), this study evaluated the simulation ability of multi-model ensemble (MME) models for annual precipitation in Henan during the historical period (1960-2014) and analyzed the spatiotemporal changes in precipitation under different SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) for the period 2015-2100, in order to provide a basis for agricultural production in Henan province. The results indicated: (1) from 1960 to 2014, the spatial correlation coefficient (R) between the MME models and observations exceeded 0.95, with a standard deviation (RSD) of 1.05mm and root mean square error (RMSE) of 0.31 mm, indicating that the MME models performed better than individual climate model. (2) Compared to the historical reference period (1960-2014), annual precipitation in Henan under all four scenarios for 2015-2100 showed an increasing trend, with July cumulative precipitation ranging between 150-230mm, higher than in other months. (3) In terms of cyclical changes, all four scenarios exhibited multi-timescale features, with different precipitation cycles at various time scales. The primary periods for scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 were 15y, 25y, 49y, and 26y, respectively. (4) Under all four scenarios, compared to the baseline historical period (1960-2014), the annual precipitation in Henan showed an increasing trend in the near term (2021-2040), mid-term (2041-2060), and late term (2080-2100). Moreover, the higher the emission scenario, the greater the increasing trend. All four scenarios exhibited a spatial distribution feature that increased from northwest to southeast. The findings provide theoretical reference for forecasting future regional precipitation and scientific basis for agricultural production in Henan province.

Soil wind erosion poses a significant threat to environmental degradation and hinders the development of agriculture and animal husbandry in China. This study investigated the spatiotemporal distribution and variation characteristics of wind erosion climatic erosivity across different timescales, using daily meteorological data from 186 stations in the agro-pastoral ecotone of northern China spanning 1961−2020. It quantitatively analyzed the impacts of meteorological factors, including wind speed, precipitation, and reference evapotranspiration (ET₀), on wind erosion. The results revealed a spatial pattern of soil wind erosion with higher values in the east and lower in the west, peaking in regions such as Tongliao and Chifeng in central Inner Mongolia and the border between Zhangjiakou in Hebei province, attributed primarily to high wind speeds, low precipitation, and intense evapotranspiration. Significant seasonal variations in wind erosion were observed, with the highest climatic erosivity in spring and the lowest in summer, particularly pronounced in the eastern region. Over the past 60 years, wind erosion in the study area had shown a notable decreasing trend, influenced jointly by changes in wind speed, precipitation, and ET₀, with wind speed playing a dominant role. However, the drivers of wind erosion variation differed across regions; decreases in wind speed and increases in precipitation led to reduce wind erosion climatic erosivity in the central and eastern parts, while increased in wind speed and ET₀, albeit accompanied by rising precipitation, counteracted each other's effects in the western region, resulting in insignificant changes in wind erosion climatic erosivity. This study, which explores the spatiotemporal variations of wind erosion in the agro-pastoral ecotone of northern China across multiple meteorological factors and timescales, provides scientific theoretical guidance for regional agricultural production, wind prevention, and erosion mitigation.

Based on daily meteorological data in the hilly areas of central Sichuan from 1970 to 2023, the modified FAO climate production potential model was used to analyze the temporal and spatial dynamic characteristics of climate change and rice climate production potential. Then, based on the rice production data over the years, the utilization of climate resources was evaluated, and the sensitivity model was used to explore the impact of climate change on climate production potential. The results showed that: (1) during the study period, the heat resources in the hilly area of central Sichuan increased significantly, the interannual fluctuation of precipitation was large, the sunshine resources were scarce, the heat and light resources were latitudinal distribution, and the water resources were meridional distribution. (2) The climatic production potential of rice in the hilly region of central Sichuan province showed a "increase decrease increase" change, and the annual average value was 15198kg·ha⁻¹, which was high in the southeast and low in the northwest. (3) The annual average utilization rate of climate resources was 50.94%, with great potential for yield increase in the future. (4) Sensitivity analysis shows that an increase in water resources had a positive impact on climate production potential, while an increase in heat resources had a negative impact. In the background of future climate change, the significant warming trend in the region will limit the increase in climate production potential, and the interannual fluctuations in precipitation will seriously affect the stability of climate production potential. In the future, the structure of rice cultivation should be moderately adjusted, climate resources should be explored reasonably, and water resource utilization should be optimized to improve the utilization rate of climate resources in the hilly areas of central Sichuan.

In order to improve the prediction accuracy of air temperature in the solar greenhouse, Genetic algorithm (GA) was introduced to optimize the initial weights and thresholds of BP neural network. Using hourly meteorological forecast data (air temperature, relative humidity, and wind speed) outside the greenhouse and air temperature data of 1−3 hours before the forecast inside the greenhouse, the hourly prediction models of the temperature in the solar greenhouse with earth and brick walls were conducted based on GA−BP neural network. Simulation results by GA−BP neural network were compared with that simulated by the stepwise regression (SR) and BP neural network models. The results showed that in the greenhouse with earth wall, the root mean square error (RMSE) and the normalized root mean square error (NRMSE) between observed and simulated hourly air temperature with the SR model were 0.82−2.01℃ and 5.13%−9.97%, respectively. The RMSE and NRMSE between observed and simulated hourly air temperature with the BP neural network model were 0.82−1.79℃ and 5.13%−7.74%, respectively. The RMSE and NRMSE between hourly air temperature observed and simulated hourly air temperature with the GA−BP neural network model were 0.62−1.47℃ and 3.88%−6.40%, respectively. In the greenhouse with brick wall, the RMSE and NRMSE between observed and simulated hourly air temperature with the SR model were 1.07−2.60℃ and 5.11%−16.98%, respectively. The RMSE and NRMSE between observed and simulated hourly air temperature with the BP neural network model were 1.11−2.29℃ and 6.12%−14.95%, respectively. The RMSE and NRMSE between observed and simulated hourly air temperature with the GA−BP neural network model were 0.89−1.73℃ and 4.76%−11.30%, respectively. The error index values representing the difference in observed and simulated hourly air temperatures with the GA−BP neural network model were lower than that with the SR and BP neural network models. The introduction of genetic algorithm to optimize BP neural network can improve the prediction accuracy of hourly temperature. The temperature fluctuation in the greenhouse with the brick wall was greater than that in the greenhouse with the earth wall because the thermal insulation of brick wall was slightly worse than that of earth wall. Therefore, the temperature forecast accuracy for the greenhouse with earth wall was higher than that for the greenhouse with brick wall. The forecast error increased with the advance of the leading time of forecast. The GA−BP model established by selecting hourly air temperature, relative humidity, and wind speed outside the greenhouse and air temperature data of 1−3 hours before the forecast inside the greenhouse as modeling factors had the best accuracy. GA−BP neural network model could be used to predict hourly temperature inside the solar greenhouse with high accuracy and stability.

As an indispensable component of freshwater resources, snow accumulation and melting processes are profoundly affected by temperature fluctuations. Accurate estimation and analysis of temperature data is paramount for ensuring the security of ecosystems in watershed and promoting the sustainable utilization of water resources use. This paper focus on the Manas river basin, which was located in the middle section of Tianshan mountain in Xinjiang. Based on hourly meteorological observation data from 139 national observation stations in the basin from 2012 to 2017, the optimal set of temperature factors was selected using the embedded method. Two kinds of precise temperature estimation models were constructed: the Long Short−Term Memory Network (LSTM) and the Long Short−Term Memory Network−Generalized Regression Neural Network (LSTM-GRNN). These models were used to simulate the distribution of land surface air temperatures in the study region. The results indicated that: (1) the temperature data simulated by the two models exhibited a similar trend to that observed in the measured data, with correlation coefficients R² of 0.89 for the LSTM model and 0.94 for the LSTM-GRNN model, indicating that both models were capable of achieving comparable results. (2) Hourly temperature estimates of both the LSTM model and the LSTM-GRNN model were in close proximity to the actual observations, the root mean square error (RMSE) for each season (spring, summer, autumn and winter) of the LSTM model was 1.93℃, 2.67℃, 2.16℃and 1.71℃, respectively. In comparison, the RMSE values of the LSTM-GRNN model were 1.79℃, 2.42℃, 1.91℃ and 1.46℃, with an overall improvement of 10.4% in accuracy over the former. Both models showed differences in estimation accuracy across seasons, with the highest accuracy in winter and spring, the second in autumn, and the lowest in summer. (3) In contrast to the LSTM model, which was constrained to single−site estimation, the LSTM-GRNN model, which integrated the spatial characteristics of meteorological data, could provide a higher accuracy of the spatial distribution of air temperature, and a more accurate representation of the spatial and temporal distribution of hourly temperatures in the Manas river basin. The research results will facilitate the generation of data for the simulation of snowmelt in the region and the protection against disasters in subsequent studies.

Taking Yuqing county of Guizhou province as the study area, data from the rice leaffolder (RLF) field surveys and meteorological stations during the main local RLF occurrence period (June-August) from 2011 to 2020 and methods such as correlation analysis, mathematical statistics, and machine learning were used to analyze the effects of meteorological conditions on the population abundance of larvae and adults of RLF in Guizhou province, screen for key meteorological influence factors, and explored the predictive effects of different modeling methods. The results showed that: (1) meteorological factors that favored the increase in RLF larvae were mean minimum temperature, precipitation, mean relative humidity, and minimum relative humidity. Mean temperature, minimum temperature, mean precipitation, mean relative humidity, mean minimum relative humidity, sunshine hours, and mean 0cm ground temperature were found to favor an increase in RLF adults. (2) The lagged effect of meteorological conditions on RLF’s field populations in Guizhou province was greater. The RLF field population abundance was mainly influenced by mean temperature, mean minimum temperature, precipitation, minimum relative humidity, mean wind speed, and mean 0cm ground temperature, which were per-five-day daily averaged between June to August each year. The longest lag effect could be up to about 30d, with significant effect period of 3, 4, and 5 pentads in advance. (3) The results of the model simulation varied considerably between the different modeling schemes. Non-linear models (R²=1.00, MAE=2.62 individual, RMSE=3.89 individual) were more effective than linear models (R²=0.46, MAE=164.98 individual, RMSE=240.66 individual), and the simulation effect for the adult population (R²=0.68, MAE=81.29 individual, RMSE=117.98 individual) was better than that for the larva population (R²=0.67, MAE=118.78 individual, RMSE=173.92 individual).

Edible fungi has certain photo−sensitivity during the mushroom emergence stage, but the optimal light environment parameters for the formation of the morphology and color of Pleurotus citrinopileatus Singer are not clear. Pleurotus citrinopileatus Singer were planted in in an environmentally controllable growth chambers with different light qualities that were respectively pure white light (CK), pure green light (G), pure red light (R), pure blue light (B), and far red light (Fr). The optimal light formula suitable for the growth and development of Pleurotus citrinopileatus Singer was investigated by analyzing the physical and spectral characteristics of mushroom exposed to different light treatments. The results showed that the number of mushroom buds in Pleurotus citrinopileatus Singer exposed to R and Fr treatments increased by 0.87% and 1.73% respectively, compared to the control. However, R and Fr treatments caused deformation of Pleurotus citrinopileatus Singer fruiting bodies, showed as soft stipe, thin pileus, and lighter colors in the later stage of growth. B treatment decreased the number of mushroom buds and stipe length of Pleurotus citrinopileatus Singer, but significantly promoted the increase in stipe diameter, pileus diameter, and fruiting body weight of Pleurotus citrinopileatus Singer, with a increase of 35.52%, 18.30%, and 23.66% respectively (P<0.05), compared to the control. The spectral parameters color saturation (C value) and hue angle (Hue value) of Pleurotus citrinopileatus Singer exposed to B treatment increased by 2.72% and 1.64% respectively, compared to the control, while the color index (CCI value) and color ratio (a^*/b^* value) decreased by 44.62% and 80.00% respectively. Meanwhile, smaller chromatism (∆E value) and deepened simulated color were detected in Pleurotus citrinopileatus Singer treated with B in relative to the control. In addition, the reflectance in the 400-700 nm wavelength range of the Pleurotus citrinopileatus Singer pileus exposed to B treatment was lower than that of the control, indicating that the pileus of Pleurotus citrinopileatus Singer under B treatment absorbed more blue light. Therefore, based on the comprehensive characteristics and spectral characteristics of Pleurotus citrinopileatus Singer, blue light was more suitable for industrial cultivation of Pleurotus citrinopileatus Singer, which provided a reference for the regulation of light environment in the industrial production of Pleurotus citrinopileatus Singer in future.

A hydroponic planting experiment in the southern plastic greenhouse with lettuce was conducted to investigate the effects of two nutrient solution concentrations supply on leaf number, plant height, fresh weight, dry weight and leaf nutrient element content (Ca, Mg, Fe, Mn, Cu, Zn) at different growth stages under high temperature environment. The two levels of temperature environment for lettuce growth were set as high temperature environment (H) and normal temperature environment (N). The nutrient solution concentration was expressed by EC (μS·cm⁻¹), which was set as normal concentration (CK, 1500μS·cm⁻¹) and elevated concentration (C1, 1900μS·cm⁻¹). The results showed that, (1) high temperature environment increased daily average temperature in the growth of lettuce leaves and nutrient solution by 13.9℃ and 12.0℃, daily minimum temperature by 13.9℃and 11.8℃, daily maximum temperature by 14.4℃and 12.7℃, respectively, compared with normal temperature environment. (2) Under normal temperature environment, elevated concentration increased leaf number, plant height, shoot fresh weight, root fresh weight, shoot dry weight and root dry weight at the harvesting stage by 15.2%, 16.9%, 63.2%, 24.4%, 28.5% and 6.6%, respectively, compared with normal concentration. However, under high temperature environment, normal concentration increased above growth indicators by 54.2%, 41.0%, 249.5%, 496.0%, 169.6% and 353.4% at the harvesting stage, respectively, compared with elevated concentration. (3) High temperature environment promoted the contents of Ca, Mg and Fe in lettuce leaves. Under normal temperature environment, elevated concentration significantly promoted the contents of Mn and Cu in lettuce leaves at the harvesting stage by 22.7% and 61.5%, while unsignificant difference occurred in the contents of Ca, Mg, Fe and Zn, compared with normal concentration. Furthermore, under high temperature environment, normal concentration significantly promoted the contents of Ca and Mn in lettuce leaves at the harvesting stage by 34.5% and 44.9%, while didn’t affect the contents of Mg, Cu and Zn significantly, compared with elevated concentration. It is suggested that temperature environment and nutrient solution concentration have significant effects on hydroponic lettuce growth and nutrient absorption, and increasing lettuce yield, quality and fertilizer efficiency can be achieved by adjusting nutrient solution concentration during the actual production of hydroponic lettuce in Summer and Autumn/Winter.

Tomato was planted in the artificial light plant factory and exposed to different light treatments. Pure red light was as the control, and set up two blue light intervention methods under red background light. One was blue light supplementation intervention with intensity of 32，40，64 and 80 μmol·m⁻²·s⁻¹ respectively, namely R/RB₃₂, R/RB₄₀, R/RB₆₄ and R/RB₈₀，and the other was blue light substitution intervention with the intervention time intervals of 0, 1min, 1h, and 4h respectively, namely RB, R/RB_(1min) R/RB_(1h) and R/RB_(4h). The growth, antioxidant system and fluorescence characteristics were analyzed to investigate the effects of different light modes of red and blue light on tomato seedling. The result showed that: (1)compared with pure red light, blue light supplementation intervention reduced tomato plant height by 9.94% to 19.62%, but blue light substitution intervention increased tomato plant height by 12.29% to 36.31%. (2)Biomass of tomato seedlings were both increased under two blue light intervention modes, and the shoot biomass was increased with the increase of supplementary blue light intensity. Among which, the highest dry weight and fresh weight were both detected under R/RB₈₀ blue light supplementation intervention treatments, which were enhanced by 115.45% and 198.35% respectively, while the highest dry weight and fresh weight were both observed under R/RB_(1min) in blue light substitution intervention treatments, which were enhanced by 119.35% and 152.31% respectively, compared to the control (P＜0.05). (3)The activity of superoxide dismutase (SOD), catalase (CAT) were both increased under R/RB₈₀ treatment, while the content of active oxygen (ROS) was decreased in tomato leaves. R/RB_(1min) treatment increased the activity of CAT in stems and leaves by 2.84% and 18.35% respectively, while ROS content was decreased by 4.64% and 20.8% respectively, compared to the control. (4)Blue light intervention modes both improved fluorescence parameters such as Ψ_o (the probability of excitons captured by the reaction center transferring electrons to the primary quinone acceptor (QA) and then to other electron acceptors), F_v/F_m (maximal photochemical efficiency), F_v/F_o (potential photochemical efficiency), ET_o/RC (the energy captured by the unit reaction center for electron transfer), while reduced DI_o/CS (thermal dissipation of photosynthetic system). Moreover, the fluorescence parameter values under blue light supplementation mode were mostly higher than those under blue light substitution mode. The values of TR_o/RC (the energy captured by the unit reaction center for reducing QA), ET_o/CS (quantum yield of electron transfer per unit area), and ET_o/RC were the highest under R/RB₈₀ treatment, which were increased by 0.5%, 0.6%, and 2.1% respectively compared to the control. PI_abs (leaf photosynthetic performance index), ET_o/RC, and Ψ_o were increased by 17.02%, 9.53%, and 5.44% under R/RB_(1min) treatment, respectively, compared to the control. Overall, the electron transfer efficiency of tomato leaf photosynthetic system was improved under blue light intervention with red light background, which was beneficial for seedling morphology and biomass increase. Among which, R/RB₈₀ and R/RB_(1min) treatments were the better treatments in this experiment.

Based on the temperature and precipitation data from the late rice region of Hubei, spanning the period from 1970 to 2022, this paper delves into the characteristics of chilling dew wind in Hubei, encompassing its climate characteristics, atmospheric circulation pattern and sea surface temperature background. A simulation model for the beginning date of chilling dew wind was constructed using the Hyperparameters Tuning of Random Forest method and previous circulation indices, in order to provide a reference for preventing and mitigating the impact of the chilling dew wind. The results showed that: (1) over the past 53 years, the chilling dew wind had shown a trend of delayed beginning date and reduced total number of days, with a delay rate of nearly 0.4d·10y⁻¹ and a reduction rate of nearly 44d·10y⁻¹. The decade with highest cumulative number of stations was 1970−1980, the lowest in 2001−2010. Since 1990s, the proportion of severe station occurrence had been increasing, indicating a shift towards fewer but more intense occurrences of the chilling dew wind. (2) The typical circulation pattern associated with chilling dew wind in Hubei was characterized by a "− + −" anomalous distribution of geopotential height from west to east over the mid-to-high latitudes of Eurasia, accompanied by the northward transport of warm and humid air from the southern regions. (3) The beginning date of chilling dew wind was significantly correlated with the large−scale meridional circulation anomaly in Eurasia and the anomalous sea surface temperature (SST) in the western Pacific warm pool and the north of the South Atlantic. The beginning date of chilling dew wind was earlier (later) when the geopothetic height near the Ural mountains increases (decreases). When SST over western Pacific warm pool was abnormally cold (warm), and the north of the South Atlantic SST showed cold to warm (warm to cold) distribution from north to south, the beginning date of chilling dew wind tend to be earlier (later). (4)The simulation error using hyperparameters tuning of random forest method was minimal. The historical fitting rate from 1970 to 2007 was 91%, and average absolute error of sample test from 2008 to 2020 was 2.9d, indicating that the model had a good ability to simulate the beginning date of chilling dew wind in Hubei. 

This study established a method for evaluating the yield loss due to chilling injury during the flowering stage of rice to improve the ability to assess the chilling injury. Key parameters for rice growth by the ORYZA.v3 model were determined using the experimental observation data from Tonghua agricultural meteorological experimental station in Jilin from 1993 to 2000. The sensitivity of the model to simulate the chilling injury of rice was analyzed based on the calibrated model and meteorological data from 1961 to 2021. The method for evaluating chilling injury during the flowering stage of rice was established by combining the 0.125°×0.125° European Centre for Medium-range Weather Forecasts (ECMWF) grid data with real-time weather data. The method was used to quantitatively assess the loss of rice yield due to chilling injury from 2022 to 2023. The results showed that: (1) the ORYZA.v3 model could effectively reproduce the rice development and growth, for the reason that the MAE values were 1.2d, 2.6d and 345.7kg·ha⁻¹ with the rice flowering stage, maturing stage and yield. (2) There was a significant correlation between the intensity of chilling injury (chilling daily mean temperature, duration) and the yield reduction of rice. When the chilling injury occurred during the flowering stage of rice, the yield reduction increased by 2.13% along with the daily mean temperature decreasing 1℃. In addition, the reduction in yield increased by 2.66% with the duration of 1d chilling injury. The simulation error of the model was less than 10.0% when compared to the historical disaster data, indicating high simulation accuracy. Thus, the ORYZA.v3 model was able to quantitatively simulate the effect of sterile−type chilling injury on rice yield loss. (3) The yield reduction of rice under the light, moderate, and serious chilling injury were estimated to be 3.0%, 3.7% and 5.1%, respectively, the TS scores were 0.76, 0.50, and 0.63 from 2022 to 2023. The distribution of chilling injury was in good agreement with the measured values, while the comprehensive TS score was 0.63, which indicated a good estimation.

As an important factor for evaluating wheat quality, grain protein content (GPC) is crucial for guiding agricultural production and enhancing the market value of wheat. To advance the development of GPC remote sensing monitoring techniques, this paper systematically summarized the latest research, with a particular focus on analyzing the strengths, weaknesses, and challenges of diverse GPC remote sensing monitoring models. Results showed that remote sensing data from various platforms−including ground, unmanned aerial vehicles (UAVs), and satellites−each exhibit distinct advantages in monitoring GPC in wheat. However, as data scalability increased, the accuracy of GPC monitoring tends to decrease slightly. In terms of model construction, the development of wheat GPC monitoring models from empirical models to semi−empirical models or coupled remote sensing and crop growth models had increased agronomic parameters and ecological factors, which effectively improved both accuracy and spatio-temporal scalability. It was shown that the semi-empirical models were the preferred option for monitoring GPC. After adding meteorological factors into the Beijing wheat GPC model that integrated spectral information and agronomic parameters, the model's R² increased by 0.242. Currently, there were still many challenges in terms of model accuracy and regionally applications such as the reliability of GPC data, the complexity of the vertical distribution of nitrogen in wheat, and the limitations of regional expansion of the models. To address these issues, this paper proposed to evaluate ground-based GPC, fusing effective data, mine spectral information and explore multi-scale transformation methods in the future. In addition, a multi-scale GPC monitoring model based on collaborative observations from ground stations, UAVs and satellites can be constructed to achieve efficient, accurate and comprehensive monitoring of wheat quality.