农作物合理规划种植是平衡农业产业经济效益与资源可持续发展的关键，是实现农民增收致富的重要途径。以山西省灵丘县农作物种植规划为研究对象，结合地块类型、作物轮作及市场需求对种植的影响，利用随机参数量化单产、成本及价格的年际风险对种植规划的影响，同时融合作物互补替代机制，以种植利润和协调度为双目标，设计并构建3种农作物种植规划模型并对其进行评价。结果表明：2023年山西省灵丘县实际种植41种农作物的生产利润为5926348.25元，种植协调度66.67；双目标规划（M1）种植方案实现利润增加71.01%，种植协调度增加60.75%。考虑市场风险的双目标规划（M2）种植方案利润增加75.42%，种植协调度提升60.94%。互补替代条件下双目标规划（M3）种植方案的利润增幅达164.42%，种植协调度提升60.75%。3种种植方案均可实现灵丘县农作物种植协调度超过60%的提升，利润增幅超70%。双目标规划种植模型具有较好的利润和种植协调度提升能力，可为该县在农作物种植规划和农作物产业布局方面提供支撑。本研究设计构建的农作物种植双目标规划模型及其规划结果对优化地区农作物种植结构和布局，对推进农业产业供给侧结构性改革，提升农业产业经济效益和协调农业产业可持续发展具有一定指导价值。

Yunnan province was selected as the study area. A carbon accounting system was established for major agricultural inputs, including chemical fertilizers, pesticides, agricultural plastic films, irrigation and mechanized operations. This framework was used to systematically assess the carbon emission properties of crop production from 2005 to 2019. The Tapio decoupling model was applied to examine the evolving relationship between carbon emissions from the use of agricultural production materials and economic growth in the province’s planting industry. The logarithmic mean divisia index (LMDI) decomposition method was employed to quantitatively identify the driving factors. The GM(1,1) gray prediction model was then used to forecast carbon emission trends for the period 2020–2029. The results showed that from 2005 to 2019, carbon emissions from agricultural production material inputs in Yunnan’s crop production followed an inverted U-shaped trend. Emissions reached a peak of 398.67×10⁴t in 2017, before falling to 336.14×10⁴t in 2019. Over the same period, the intensity of carbon emissions per unit of agricultural output value declined each year, with an average annual reduction of 8.47%. Fertilizer use had been identified as the biggest contributor to carbon emissions. The relationship between carbon emissions from agricultural input use and economic growth alternated among four regimes: weak decoupling, expansive coupling, expansive negative decoupling and strong decoupling. After 2018, this relationship shifted from weak decoupling to strong decoupling. The decomposition analysis indicated that economic size, energy structure and agricultural industrial structure were positive driving forces for the growth of carbon emissions, with economic scale having the most significant effect. In contrast, energy intensity and population size were negative driving forces. After confirming that the data were suitable for the GM(1,1) gray prediction model, the forecast results showed that carbon emissions from agricultural production material inputs in Yunnan’s crop production would continue to decline from 2020 to 2029. By 2029, the total is expected to decrease to 177.81×10⁴t. Based on the findings, it is recommended to enhance the technological level of agricultural production in Yunnan, strengthen technical training and the dissemination of low-carbon concepts, optimize cropping structure, and establish sound agricultural management systems. These measures will help improve the carbon reduction efficiency of crop production and support the long-term, green and sustainable development of agriculture in the province.

This study investigated the response of soil moisture content to precipitation events in Chongqing from April to October 2023 using hourly monitoring data on precipitation and soil relative humidity. Focusing on typical farmland clay loam, variations in soil relative humidity (0−50cm) were analyzed by Random forest regression and simple linear regression methods under diverse precipitation types, intensities and initial soil moisture conditions. The research aimed to elucidate soil moisture dynamics under natural precipitation and provide technical support for soil moisture forecasting and agricultural drought early warning. The results showed that: (1) increasing precipitation amounts significantly enhanced soil moisture content variation and recharged at 40−50cm depths. Light rain events only affected the 0−20cm layer, whereas moderate to heavy precipitation events activate soil moisture content response down to 20−50cm. During the torrential events, the 40−50cm soil moisture increment reached 5.92 percentage points (pp) compared to the pre−event baseline. (2) Higher precipitation intensity improved infiltration efficiency. Under grade IV intensity events, the 40−50cm soil moisture lag time (0.76h) and recharge rate (2.29pp·h⁻¹) showed shorter and higher versus Grade I events (lag time: 2.9h; recharge rate: 0.1pp·h⁻¹). (3) Under drought conditions (initial relative humidity<60%), the 0−10cm response intensity per unit precipitation (0.38pp·mm⁻¹) was 2.4 times higher than the 40−50cm layer (0.16pp·mm⁻¹). Moderate to heavy precipitation events (10−50mm) with Grades II−III intensity (5−15mm·h⁻¹) increased the plow layer (0−20cm) relative humidity by 10−30pp, significantly alleviating drought. Under optimal moisture (relative humidity≥60%), soil moisture increments decreased by approximately 50pp across all layers versus drought conditions.

With the intensification of global climate change, extreme precipitation events are becoming more frequent and intense, which alters regional soil water dynamics and subsequently affects soil nitrous oxide (N₂O) emissions and microbial mechanisms. However, the underlying mechanism of N₂O emission in response to extreme precipitation events remains unclear. This review summarized the effects of extreme precipitation on soil N₂O emissions across different land-use types (e.g., cropland, grassland, forest and wetland), based on differences in N₂O fluxes among these systems, and explored the associated microbial mechanisms, aiming to address the challenges of achieving carbon neutrality in agricultural systems. The results showed that extreme precipitation generally increased soil N₂O emissions by modifying soil moisture dynamics, carbon and nitrogen availability and microbial functional genes expression. Nevertheless, the specific effects were jointly influenced by multiple factors, including soil type, land use practices and precipitation characteristics. At the microbial level, extreme precipitation rapidly stimulated microbial activity, alters the abundance and activity of functional genes related to nitrification and denitrification (such as amoA, nirS, nosZ), and consequently regulated the pathways of N₂O emissions. Future research should expand the range of land-use types, enhance the understanding of microbial community structure and functional gene expression, and comprehensively consider the interactive effects of multiple environmental factors on soil N₂O emissions. This would provide a theoretical basis for mitigating soil N₂O emissions and support the achievement of carbon peaking and carbon neutrality goals.

In order to conduct an in−depth investigation of the nitrogen nutrition diagnosis method and nitrogen fertilizer management recommendation scheme for oilseed flax based on the SPAD−502 chlorophyll meter, the oilseed flax variety Longya 11 was used as the experimental material. The study examined the relationships between SPAD values in oilseed flax leaves and nitrogen nutrition diagnostic indices, as well as nitrogen application rates, under different nitrogen treatments (N0: 0kg·ha⁻¹, N1: 60kg·ha⁻¹, N2: 120kg·ha⁻¹, N3: 180kg·ha⁻¹) and different nitrogen splitting ratios (T1: all as basal fertilizer, T2: basal fertilizer: budding fertilizer=2:1, T3: basal fertilizer: branching fertilizer: budding fertilizer=1:1:1). Additionally, the impact of nitrogen application rates on oilseed flax grain yield was analyzed. The results showed that under different nitrogen application levels, at the same growth stage of oilseed flax, the leaf SPAD values consistently showed a gradual increasing trend with higher nitrogen application rates across all treatments, in the order of N3>N2>N1>N0. Under different split nitrogen application ratios, the leaf SPAD values at all growth stages followed the order T2>T3>T1. The SPAD values of oilseed flax leaves at each key growth stage in the N3T2 treatment were significantly higher than those in the other treatments under each integrated nitrogen management regime, indicating a higher chlorophyll content in the leaves under this treatment. The SPAD values in the branching, budding, anthesis and kernel stages showed a significant linear correlation with the nitrogen nutrient diagnostic index and nitrogen application rate. The relationship between grain yield and nitrogen application rate followed a quadratic function: y=−0.0288x²+8.1724x+1090.2083. The maximum seed yield of 1670kg·ha⁻¹ was achieved at a nitrogen application rate of 142kg·ha⁻¹. The optimal SPAD values across growth stages ranged from 53.4 to 73.7, while the critical SPAD thresholds fell within 41.0 to 57.6. Additionally, recommended fertilization equations were established for key growth stages of oilseed flax. 

To address the limitations of traditional retrieval methods under cloudy conditions and the inability of the GIIRS (Geostationary interferometric infrared sounder) operational products to provide full−layer atmospheric temperature and humidity profiles, this study proposed a deep learning based approach using convolutional neural networks (CNN). The method was applied to the GIIRS observations from December 2019 and July 2020 with  focusing on optimizing both the network architecture and its hyperparameters. Radiosonde measurements during the same periods were used as the truth to evaluate the performance of the proposed CNN retrieval algorithm alongside the GIIRS Level 2 (L2) operational products. The results showed that the CNN model was able to retrieve the full layer temperature and humidity profiles. When evaluated against 453 independent test samples, the CNN achieved a mean temperature RMSE of 3.152K across all vertical levels and a maximum correlation coefficient of 0.995, demonstrating strong generalization and predictive accuracy. With GIIRS observations, the CNN exhibited the highest temperature retrieval accuracy in the mid−to−lower troposphere under clear sky conditions, with RMSE values ranging from approximately 3K to 5K. Under cloudy conditions, while the GIIRS L2 products were limited to retrieving temperatures above the cloud tops, the CNN was capable of retrieving the full layer temperature profile. Moreover, the CNN significantly outperformed the L2 products in retrieval accuracy under cloudy conditions. Furthermore, through transfer learning, the CNN was adapted to retrieve humidity profiles for December 2019 and July 2020. In both clear−sky and cloudy conditions, the CNN achieved a full−layer humidity RMSE of less than 2g·kg⁻¹. 

To explore the regulatory effect of LED far-red side light (730nm) supplementation (based on red and blue light) before harvest on the growth and quality of hydroponic lettuce, an experiment was conducted using Italian lettuce as the test material in a fully intelligent and environmentally controlled plant factory. In this experiment, the top lighting of the hydroponic tank on the growing rack was used as the main light source, and side LED supplementary lighting was added 6d before harvesting. The control treatment (CK) received red and blue light (4:1, light intensity 150.0μmol·m⁻²·s⁻¹, duration of 3h), while the experimental groups were supplemented with far-red light at 5 different intensities (12.5, 25.0, 50.0, 75.0 and 150.0μmol·m⁻²·s⁻¹) on the basis of CK treatment. By analyzing the effects of different light treatments on lettuce yield and quality, this study aimed to alleviate the problem of insufficient side light for lettuce on planting racks and provide a theoretical basis and technical parameters for improving the quality and efficiency of hydroponic lettuce in plant factory with artificial light. The results showed that compared with CK, supplementary far-red light significantly increased the shoot fresh weight, dry weight and leaf area of lettuce in the 1^st row (closest to the side lamps) and the 2^nd row. The magnitude of the increase rised as the intensity of the far-red light increases. At a far-red light intensity of 150.0μmol·m⁻²·s⁻¹, the shoot dry weight was 93% higher than that of CK. The complementary far-red side light also increased the ascorbic acid content in the 1^st to 3^rd leaf layers of lettuce in the 1^st and 2^nd rows. Specifically, at a far-red light intensity of 50.0μmol·m⁻²·s⁻¹, the ascorbic acid content in the 2^nd leaf layer of the 1^st row reached the highest level (213μg·g⁻¹). However, as far-red light intensity increased, the contents of photosynthetic pigments, soluble proteins, soluble sugars, total phenols and flavonoids in the 1^st to 3^rd leaf layers of lettuce in the 1^st and 2^nd rows gradually decreased, reaching the lowest values at 150.0μmol·m⁻²·s⁻¹. This trend was negatively correlated with the indicators such as the shoot fresh weight, shoot dry weight and leaf area of lettuce growth. Comprehensively considering lettuce yield, quality indicators and energy consumption for supplementary lighting, the far-red side light supplementation treatment at 50.0μmol·m⁻²·s⁻¹ achieved the best cultivation effect.

The red−edge band and red−edge vegetation index have been proven to be related to fine−scale identification of crop types, and evaluating the impact of red−edge information from satellite remote sensing images on the extraction accuracy of specific crop cultivation can provide an effective reference for the deeper application of related satellite data in agriculture, and at present there are fewer assessment studies on the impact of red−edge information of the new generation of environmental satellites, HJ−2. In order to explore the impact of red−edge information from HJ−2 satellite remote sensing images on the extraction accuracy of winter wheat planting areas, Xiao county of Anhui province was taken as the study area, and four planting extraction schemes with different bands and derived vegetation indices were constructed from single image of winter wheat in the jointing period, on the basis of which, the Jeffries−Matusita（J−M）distance was used to calculate the separability between sample categories, and Random forest algorithm was applied for the classification of the land covers, realized the comparative analysis for category separability between winter wheat and other vegetation, recognition accuracy and the distribution map of winter wheat planting area under different schemes. The results showed that under the condition of all red−edge information participation, compared with the condition of no red−edge information and no derived vegetation index participation, the category separability between winter wheat and other vegetation improved from 1.9337 to 1.9988, and the classification accuracies of the recognition accuracy, UA（user accuracy）, PA（producer accuracy）, OA（overall accuracy） and Kappa, improved from 86.92%, 90.37%, 85.11% and 0.77 to 98.45%, 95.67%, 94.95% and 0.92, respectively, the AA（area accuracy） improved from 80.38% to 98.94%, the RE（relative error）decreased from 19.62% to 1.06%, the misclassification phenomenon and the “phenomenon of salt and pepper” in the distribution map of winter wheat planting areas were significantly reduced, and the identified winter wheat planting plots were complete, smooth and had good boundary continuity. This study demonstrates that the introduction of HJ−2 satellite remote sensing imagery red−edge information to participate in the classification could effectively improve the accuracy of winter wheat planting extraction and mapping effect, which was of important reference significance for the popularization and application of domestic red−edge satellite and the accurate monitoring of winter wheat.

Nitrogen is the most demanded nutrient element for rice, but excessive application of nitrogen can easily lead to a mismatch between rice growth and the availability of light−temperature resources, which is becoming one of the factors limiting the high yield and quality of rice. In this study, used three high−quality rice as variety materials, 5 nitrogen application amount (N₀: 0, N₁₂₀: 120kg·ha⁻¹, N₁₅₀: 150kg·ha⁻¹, N₁₈₀: 180kg·ha⁻¹ and N₂₁₀: 210kg·ha⁻¹) were set to analyze the effect of different nitrogen levels on light−temperature utilization, yield and grain quality of rice. The aim was to provide a reference for enhancing yield potential and optimizing the utilization of climate resource for high−quality rice cultivation in northern Jiangxi. The results showed that with the increase of nitrogen amount, the whole growth period of the tested high−quality rice in 2023–2024 was prolonged by 4−11d and 4−13d respectively, and the prolongation mainly occurring in the reproductive growth stage. The ≥10°C accumulated temperature and solar radiation during the whole growth period generally showed an increasing trend, the increase range of ≥10°C accumulated temperature was 2.5%−8.7% in 2023 and 2.6%−11.4% in 2024, increase range of solar radiation was 1.1%−8.3% in 2023 and 2.0%−9.1% in 2024. The ≥10°C accumulated temperature production efficiency, light production efficiency and light utilization efficiency first increased and then decreased, maximized in N₁₅₀ treatment. The grain number per panicle and yield maximized under N₁₅₀ treatment, while the seed setting rate showed a decreasing trend, and the effective panicle increased with the increase of nitrogen rate. Rice processing quality was best in N₁₅₀ treatment; appearance quality decreased with the increase of nitrogen rate; amylose content first decreased and then increased, both ‘YXYLS’ and ‘TFY308’ lowest in N₁₅₀ treatment. gel consistency first increased and then decreased, except for ‘YXYLS’ maximized in N₁₈₀ treatment in 2023, all other treatments maximized in the N₁₅₀ treatment. Overall, both high yield, good quality and high light−temperature utilization rate can be obtained by applying 150kg·ha⁻¹ in whole growth period. This nitrogen rate is recommended for high−quality indica rice in northern Jiangxi.

Based on the biological characteristics of Litchi chinensis, meteorological observation data and GIS data from 16 stations in Qu county and its surrounding areas, as well as Hejiang county, from 1991 to 2024 were utilized. Through climate simulation analysis and GIS zoning technology, the climatic feasibility of introducing Litchi chinensis to Qu county was systematically evaluated. The annual accumulated temperature, average January temperature, and average annual extreme minimum temperature were selected as indicators, and a comprehensive weighting method was applied to develop a climatic zoning plan for Litchi chinensis introduction. The results indicated that: (1) most areas of Qu county had an average annual temperature of 17.0–17.9°C. In regions below 300 m in elevation, the annual accumulated temperature≥10.0°C exceeded 5500°C·d. It was projected that the flowering and fruiting period of Litchi chinensis in Qu county would be 7–10 days later than that in Hejiang county, indicating climatic potential for introducing late−maturing Litchi chinensis cultivars. However, winter frost was identified as the major limiting factor. (2) The extreme minimum temperature in January in Qu county was recorded as −3.3°C (−2.1°C at an 80% probability level), making it difficult for open−field cultivation to safely overwinter. Meteorological disasters such as midsummer high temperatures (extreme reaching 44.0°C), summer droughts, and torrential rain floods further increased the risks associated with Litchi chinensis introduction. (3) Over the past 34 years, a significant warming trend was observed in Qu county, with the annual accumulated temperature ≥10.0°C increasing significantly (172.46°C·d per decade). However, winter warming was not significant, and the risk of frost remained perennial. Thus, cold protection measures were deemed necessary for Litchi chinensis introduction. (4) Climatic zoning revealed that river valley areas south of 31°N in Qu county with elevations below 300m were suitable for Litchi chinensis introduction, while regions at 300–400m elevation were marginally suitable. Hilly areas above 400m elevation were unsuitable. It was recommended that late−maturing Litchi chinensis introduction be prioritized in areas with elevations below 300m in Qu county. In conjunction with local climatic characteristics, cost−effective winter cold protection technologies should be simultaneously developed. Furthermore, cold protection techniques, cultivar optimization, and industrial resilience should be integrated. Through measures such as facility agriculture, selection of cold−resistant cultivars, and establishment of disaster prevention and control systems, the success rate of Litchi chinensis introduction could be enhanced. Sustainable industrial models were suggested to be gradually explored to avoid blind introduction and expansion.

 Based on hail observation data from eastern Qinghai province between 1981 and 2024, this study investigated spatiotemporal variations in the frequency, diameter and duration of hail events using methods such as Mann−Kendall test and wavelet analysis. The aim was to understand the formation, development and anomaly patterns of hail in this region, thereby providing a basis for hail suppression, disaster mitigation and the safeguarding of agricultural production. The results showed that: (1) the mean annual number of hail events in eastern Qinghai province from 1981 to 2024 was 35.5. The average diameter of hail was 6.2mm, and the average duration per event was 6.9min. (2) During the study period, the annual hail frequency decreased significantly at a rate of 1.08 events per year, while the hail diameter and duration exhibited no significant trends. Hail occurrence displayed strong seasonality, with frequency, diameter and duration all peaking in July. On a diurnal scale, hail events primarily occurred in the afternoon (14:00 to 17:00), whereas the maximum hail diameter and duration were observed at 21:00. The frequency of hail events, hail diameter and duration exhibited abrupt changes in 2001, 1994 and 1984 respectively, showing periodic oscillations of 2−3 years, 5 years and 5−6 years. (3) The spatial pattern of hail frequency from 1981 to 2024 was consistent with the interdecadal distribution, both exhibiting more occurrences in the northwest and less occurrences in the southeast. Additionally, hail frequency demonstrated a significant positive correlation with altitude. In conclusion, hail activity in eastern Qinghai has shown a decreasing trend over the past 44 years, which is relatively favorable for local agricultural production. However, due to the complexity of the regional climate system, attention should still be paid to localized severe hail events.

Weather index insurance, as an innovative insurance product, serves as a key risk management tool in mitigating agricultural meteorological disasters. It features low operating costs and reduced susceptibility to moral hazard and adverse selection. Nevertheless, it faces the dual challenges of base risk and systemic risk. This paper systematically provided a systematic review of current research on the control of base risk and systemic risk in weather index insurance, with a particular focus on the risk modeling perspective. It examined existing methodologies in key areas such as weather index design, modeling of the relationship between weather indices and crop yields, and the modeling of agricultural meteorological systemic risks. The strengths and limitations of these approaches were analyzed, along with emerging trends. Finally, the study identified areas for further research, aiming to provide valuable insights for the optimizing design of weather index insurance in China, advancing of agricultural meteorological risk management and the safeguarding of food security. The results indicated that multi-source data integration, multiple disaster causes, multiple indicators and customized weather indicators were the main approaches to control base risk at the level of weather index design. At the modeling level of the weather index-yield relationship, high dimensionality, asymmetry, nonlinearity and interactions were key to managing base risk. Spatiotemporal dependence modeling of multiple variables was the primary focus for measuring systemic risk, while reinsurance, purchasing financial derivatives and expanding risk pools were the primary strategies for mitigating systemic risk. The coupling mechanisms of multiple disaster factors remained unclear, and the impact mechanism of crop damage was not well understood. Existing research had not effectively addressed the quantitative assessment of crop yield loss due to the interaction of multiple disaster types. Future research might explore the complex dependencies among various meteorological risks and their compound effects on crop yield reduction through data and model-driven approaches. In terms of product design, most researches adopted a step-by-step approach, which could lead to cumulative errors and result in a mismatch between the final product and the intended objectives. Future research should further explore an integrated framework for the optimal design of weather index insurance. Moreover, base risk and systemic risk exhibit a trade-off and may transform into each other. Therefore, it is essential to examine the boundary of insurance liability from a holistic perspective that considers both types of risk.

Temperature derivatives is mainly utilized to reduce or avoid the potential risks to agriculture and related industries from severe weather. The study calculated the temperature index, as the underlying of temperature derivatives, and soybean meteorological yield income respectively based on 65 county−level daily temperatures and soybean yield data from 1980 to 2020 in Heilongjiang province. Then the optimal hedging strategies for exchange−traded temperature derivatives were constructed for the 65 samples, which were evaluated using two financial risk indicators of value at risk (VaR) and expected shortfall (ES) to assess the potential effects of temperature derivatives in managing weather risk in Heilongjiang's soybean industry. The results showed that after the use of temperature derivatives, VaR improved by 17.63% to 63.78% and ES improved by 12.66% to 43.64% for the 65 samples, for an average improvement of 38.91% and 29.24%, respectively. The contract months for optimal hedging strategy were concentrated in July and September, especially concentrated in September for the optimal of expected shortfall. The results indicated that the use of temperature derivatives could significantly transfer the weather risk of soybean cultivation in Heilongjiang, with the risk months being mainly July and September, and the severe low−temperature risk in September, which was in line with the fact that soybean in Heilongjiang is vulnerable to cold damage in July and frost damage in September, the latter of which usually leads to severe loss of yield.

During the autumn (September–November) of 2025, the national average temperature was 10.8℃, marking the seventh consecutive year above 10.0℃. This ranked as the sixth highest for the corresponding period since 1961. The national average precipitation reached 155.6mm, 32.4% more than the climatological average of the same period from 1991 to 2020, while the average sunshine hour was 546.9h, 4.1% less than the climatological average of the same period from 1991 to 2020. Most agricultural regions in northeast and south China experienced favorable thermal conditions and adequate sunlight, while meteorological conditions generally were conducive to the grain filling, maturation, harvesting and drying of autumn crops. However, during mid−September to mid−October 2025, prolonged and widespread overcast and rainy weather occurred in the autumn-sowing areas in the north of China such as the Huanghuai region, the east of the northwest region, hindering the progress of autumn harvesting and sowing and adversely affecting the emergence and early growth of winter wheat. The strong winds and heavy rains brought by the typhoons caused waterlogging in low-lying farmlands in areas such as eastern Jiangnan, the southeastern coastal regions and western south China, adversely affecting the grain filling, maturation, harvesting and drying of autumn crops.