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Tomato Ripeness Detection Method Based On Improved YOLOv5 LIU Yang, GONG Zhi-hong, LI Zhen-fa, LIU Tao, ZHAO Zhuo, WANG Teng-ge Chinese Journal of Agrometeorology    2024, 45 (12): 1521-1532.   DOI: 10.3969/j.issn.1000-6362.2024.12.012 Abstract （812）      PDF（pc） （10145KB）（108）       Knowledge map    Save In order to improve the recognition accuracy of tomato fruit ripeness and to realize online nondestructive automatic detection in tomato planting chain, this study proposes a tomato ripeness detection method based on improved YOLOv5. In the field of agriculture, accurate identification of tomato ripeness is very important, which can help agricultural production to rationalize labor arrangements and timely harvesting, thus improving the yield and quality of agricultural products. Traditional target detection algorithms face some challenges in tomato ripeness recognition, such as misidentification and missed detection, due to factors such as vines and leaf shading between tomato fruits and light interference. Therefore, this study had carried out a series of optimizations of YOLOv5 to address these problems in order to improve the accuracy and robustness of the algorithm. In the first place, an ECA efficient channel attention module was added to its backbone network Backbone, which generated channel weights by one-dimensional convolution and captured small targets that could be easily ignored in tomatoes of different ripening stages by interacting with the k neighboring channels of each channel, thus enhancing the expressiveness and accuracy of tomato features and effectively mitigating the effects of occlusion and light interference on the recognition results. Moreover, the PAFPN in the Neck structure was replaced by BiFPN with bidirectional weighted fusion capability. BiFPN was able to bi-directionally fuse features of different scales, which better handled the occlusion problem between tomato fruits and improves the accuracy of the recognition, and this optimization also mitigated the effect of multi-targets on the recognition accuracy, which enabled the algorithm to perform better in complex scenarios. Finally, a P2 module for small-target detection was added to the Head structure. The P2 module was able to better combine the advantages of shallow and deep tomato features to improve the detection performance of small-target tomatoes, so that it can accurately detect the target even when there are small-target tomatoes and other complex situations in the image. Through a series of ablation experiments, authors obtained the optimal improved algorithm YOLOv5-tomatoA. Compared to traditional target detection networks such as YOLOv3-Tiny, SSD300 and Faster R-CNN, the algorithm performs well in complex scenes such as occlusion and uneven illumination, with an average accuracy mean and F1 score of 97.4% and 95.4%, respectively, and the recognition of an image takes only 14.7ms, which can simultaneously satisfy the high-precision and fast-response tomato fruit recognition. The improved YOLOv5 network structure also optimizes the memory footprint and resource consumption, occupying only 15.9M, making the model more lightweight. This mean that the algorithm had low equipment requirements for realizing online non-destructive testing of tomato ripening, which can provide a more convenient real-time monitoring tool for agricultural activities. This technique can also be applied to the design of automatic tomato picking robots, which provides a strong support to realize the automation and intelligence of the tomato planting process. Therefore, this improved YOLOv5-tomatoA algorithm has important practical value in the field of tomato ripeness detection and is expected to provide more accurate and intelligent management decision support for agricultural production. Related Articles | Metrics | Comments（0）

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Heavy Rains and Floods Impact on Agricultural Product Supply Chain and Countermeasures CHEN Ning-yuan, ZHANG Xi-cai, NI fang-fang Chinese Journal of Agrometeorology    2024, 45 (10): 1236-1246.   DOI: 10.3969/j.issn.1000-6362.2024.10.12 Abstract （672）      PDF（pc） （371KB）（561）       Knowledge map    Save In the context of global climate change, Chinese mainland often faces the threat of heavy rainfall and flooding, and the impact of different regions is different, posing serious challenges to agricultural production and supply chain management. This paper analyzed the spatiotemporal heterogeneity of China's agricultural product supply chain, including complex origin distribution, seasonal production, diversified types of agricultural products, and the close connection of each link of the supply chain; analyzed the impact of heavy rain and flood disasters on the supply chain from planting to final sales, from farmland waterlogging and soil erosion in the production link, to road damage in the logistics and transportation link, the decline in the quality of agricultural products, and then to the price fluctuation and market uncertainty in the sales link. Post-disaster short-term relief operations and post-disaster long-term recovery and reconstruction measures. It aims to improve the resilience of agricultural supply chains, reduce agricultural losses, ensure the sustainability of agricultural product supply, and provide experience for future disaster risk management. Related Articles | Metrics | Comments（0）

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Reports on Weather Impacts to Agricultural Production in Summer 2024 WU Men-xin, LI Yi-jun, ZHAO Xiao-feng, HE Liang, LIU Wei Chinese Journal of Agrometeorology    2024, 45 (12): 1533-1535.   DOI: 10.3969/j.issn.1000-6362.2024.12.013 Abstract （635）      PDF（pc） （348KB）（181）       Knowledge map    Save The relationship between meteorological factors and agricultural production in China was analyzed using statistical methods, based on daily national meteorological data for the summer of 2024. The results showed that the national average temperature in the summer (June−August) was 22.4°C, 1.2°C higher than the average of the same period from 1961 to 2020 and the maximum value since 1961. The national average precipitation was 342.6mm, which was 20.7mm more than the average of the same period from 1961 to 2020. The national average sunshine duration was 630.0h, 35.1h less than the average of the same period from 1961 to 2020. In most of the agricultural region, the sunshine, heat conditions, and soil moisture content were suitable, and general meteorological conditions were favorable for the growth and production of grain crops. At the same time, the frequent meteorological disasters in the summer had a certain effect on the growth and yield formation of the autumn harvest crops. The North China and Huang-Huai region experienced drought and waterlogging, which affected the growth of summer sowing crops. The heavy rainfall occurred in Jiangnan and south China region, and some early rice suffered from "flowering-stage heavy rain" disaster. There was more precipitation in the central and southern parts of northeast China region, which affected the crop growth and yield formation. The Sichuan basin and the middle and lower reaches of the Yangtze river sustained high temperature weather, which affected the growth of single-season rice and local featured crops in some areas. Related Articles | Metrics | Comments（0）

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Research on the Construction of Knowledge Graphs for Agricultural Meteorological Disasters: A Review QIU Ming-hui, XIE Neng-fu, JIANG Li-hua, WU Huan-ping, CHEN Ying, LI Yong-lei Chinese Journal of Agrometeorology    2024, 45 (10): 1216-1235.   DOI: 10.3969/j.issn.1000-6362.2024.10.11 Abstract （513）      PDF（pc） （1642KB）（2125）       Knowledge map    Save Efficient utilization of massive heterogeneous data is the key factor to enhance the intelligence of agricultural disaster management. Therefore, it is important to explore techniques for constructing multi-source heterogeneous agricultural meteorological disaster knowledge graphs for dynamic monitoring of agricultural meteorological disasters and intelligent management decision making. This paper analyzed the data sources, types, and characteristics required for knowledge graph construction in the agricultural meteorological disaster domain through literature studies and proposed a framework for knowledge graph construction that combined top-down and bottom-up approaches. The paper also examined key techniques and the current application status of knowledge graph construction from the perspective of schema layer construction, entity extraction, relation extraction, and knowledge fusion. In addition, it explored the applications of agricultural meteorological disaster knowledge graphs in the fields of monitoring and early warning, risk assessment, intelligent service, and decision support. It summarized the challenges of constructing agricultural meteorological disaster knowledge graphs and discussed the future development directions. Integrating information from the different modalities could make knowledge graph more comprehensive and accurate in describing and expressing the knowledge and information in the field of agricultural meteorological disasters, which could help to mitigate the losses caused by agricultural meteorological disasters and improve the accuracy and efficiency of decision-making. In the future, agricultural meteorological disaster knowledge graph will be constructed by incorporating large language models, advanced knowledge extraction methods to achieve complex entity and relationship extraction, and multi modal data. Further research is needed to advance the technical study of agricultural meteorological disaster knowledge graph. Related Articles | Metrics | Comments（0）

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Bibliometric-based Analysis on International Hotspots for Cimate-smart Agriculture DENG Ming-jun, JIANG Bing-huan, CAO Xiao-tian, LUO Wen-bing Chinese Journal of Agrometeorology    2024, 45 (9): 1079-1093.   DOI: 10.3969/j.issn.1000-6362.2024.09.011 Abstract （474）      PDF（pc） （1569KB）（415）       Knowledge map    Save Climate-smart agriculture (CSA) has become a central element in driving the greening agenda in agriculture. An in-depth exploration of global progress of CSA research globally can help to improve the level of understanding and assessment of the field by both academics and practitioners alike. Based on the Web of Science database resources, this paper analyzed the international CSA research hotspots using a bibliometric approach, based on 814 papers published on CSA topics between 2014-01-01 and 2023-08-11. The main findings were as follows: (1) since 2018, there had been a notable surge in the publication of CSA-related papers and subsequent citations. The core of CSA research predominantly revolved around environmental science, sustainability studies, agronomy, and interdisciplinary fields; (2) Keyword hotspots mainly included climate-smart agriculture, climate change, adoption, conservation agriculture, management, food security, mitigation, and farmers, etc.; (3) The research hotspots mainly included conservation agriculture, the impact and quantification of CSA on soil organic carbon, the effects of CSA application in different regions, the key factors affecting the adoption of CSA by farmers, and CSA-related decision support, which revealed that the effects of CSA implementation vary in different countries and regions, and that the key factors for the adoption of CSA by farmers were diversified, which suggested that policymakers must consider diversified factors comprehensively when designing and implementing CSA strategies to ensure the effective promotion and localized application of CSA measures across the globe. This suggested that policymakers should consider the diversity of factors when designing and implementing CSA strategies to ensure the effective promotion and localized application of CSA measures in all parts of the world; (4) The 25 references with a surge in citations from 2014−2023 were mainly focused on the core topics of conservation agriculture, the application and challenges of CSA, as well as the impacts of climate change on agriculture and coping strategies. Related Articles | Metrics | Comments（0）

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Impact Assessment of Extreme Climate Events on Maize Meteorological Yield in Northeast China by Machine Learning TANG Jie, DONG Mei-qi, ZHAO Jin, LI Hao-tian, YANG Xiao-guang Chinese Journal of Agrometeorology    2025, 46 (2): 258-269.   DOI: 10.3969/j.issn.1000-6362.2025.02.012 Abstract （430）      PDF（pc） （3065KB）（538）       Knowledge map    Save In the context of global climate change, the frequency, intensity, and duration of extreme climate events are increasing and strengthening, which greatly affects agricultural production. The three provinces in Northeast China are the main maize-producing areas in the country, and the region most significantly affected by climate change. It is crucial to explore the effects of extreme climate events on maize meteorological yield in the three provinces and safeguard China's food security and economic development. In the current study, a machine learning model was constructed based on the historical meteorological data and statistical maize yield data to clarify the impact extreme climate events on maize meteorological yield in northeast China during the historical (1981−2014) and future (2031−2060) periods. The results showed that high temperature and high- temperature-drought compound events had the greatest impact on maize meteorological yield during the historical period, with meteorological yield decreasing by 13.2% and 15.9%, respectively. Meanwhile, the extreme temperature events had a greater impact on maize meteorological yield compared to extreme precipitation events. In the future, the climate show a warming trend, Compared with the SSP1−2.6 (low−emission) scenario, the magnitude of maize meteorological yield reduction in Northeast China under the SSP5-8.5 (high-emission) scenario is more pronounced, and more attention needs to be paid to the impact of extreme precipitation events on maize meteorological yield in the future. Related Articles | Metrics | Comments（0）

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System Theory, Resource Theory and Solution Ideas of the Current Problems Related to Agrometeorology PAN Zhi-hua, HOU Ying-yu Chinese Journal of Agrometeorology    2025, 46 (2): 270-274.   DOI: 10.3969/j.issn.1000-6362.2025.02.013 Abstract （389）      PDF（pc） （304KB）（293）       Knowledge map    Save In recent years, there has been a "wandering phenomenon" in the development of agrometeorology, and some key questions need to be answered effectively. Based on system theory and resource theory, this paper deeply analyzed the connotation of agrometeorology, and expanded the relationship between meteorological conditions and agricultural (crop) production, and provided ideas and solutions to related problems. The results showed that the elements such as atmosphere, soil, crops and technical conditions make up the agrometeorological system. Climatic factors were not only natural, but also productive, and were involved in the whole process of agricultural production. The functional relationship between crop production and meteorological conditions could be established. Agrometeorological indices were the dynamic combinations of meteorological elements. Technological conditions could change the availability of climatic conditions and the sensitivity of agricultural production to climate. There were three properties of climate resources: quantity, space-time potential and quality. Different combinations of elements of the hydrometeorological system determine different objectives. This research could play important role in promoting agro meteorology. Related Articles | Metrics | Comments（0）

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Analysis on Change Characteristics of Carbon Footprint of Wheat Life Cycle in Different Provinces from 2015 to 2020 TAN Xin, LI Hao-ru, WANG Zi-jian, GONG Juan-di, HAO Wei-ping Chinese Journal of Agrometeorology    2024, 45 (8): 809-821.   DOI: 10.3969/j.issn.1000-6362.2024.08.001 Abstract （378）      PDF（pc） （1835KB）（309）       Knowledge map    Save In recent years, the carbon footprint of agricultural production has been a hot issue in the research on greenhouse gas (GHG) emissions reduction in China. Based on the statistical data of yield, sown area and agricultural production inputs in 14 wheat-growing provinces from 2015 to 2020, authors calculated the carbon footprint of inputs for wheat life cycle, studied the dynamic changes of carbon footprint of wheat life cycle and analyzed the proportion of carbon footprint for different agricultural inputs, and revealed the change rule of carbon footprint for wheat life cycle and the composition of emission sources. These results can provide a theoretical basis for realizing energy conservation and emission reduction in agricultural production and green low-carbon development. The results showed that the average carbon footprint per unit area and the average carbon footprint per unit yield of wheat life cycle in 14 provinces from 2015 to 2020 were 4315.4 kgCO2eq×ha−1 and 999.4 kgCO2eq×t−1, respectively, which showed a downward trend. The average carbon footprint per unit area and the average carbon footprint per unit yield of wheat life cycle in Shanxi province and Shaanxi province in northern winter (autumn sowing) wheat production region, the Inner Mongolia autonomous region and the Ningxia Hui autonomous region in spring wheat production region, and winter-spring wheat production area (Xinjiang) were higher than the average annual level of the total study regions in this paper. While the average carbon footprint per unit area and the average carbon footprint per unit yield of wheat life cycle in southern winter (autumn sowing) wheat production region (Sichuan, Jiangsu, Anhui and Hubei), Heilongjiang province and Gansu province in spring wheat production region and Shandong province and Henan province in northern winter (autumn sowing) wheat production region were lower than the average annual level of the total study regions of this paper. In the carbon footprint structure of wheat life cycle, the average carbon footprint of irrigation electricity, chemical fertilizer, diesel fuel, wheat seed and pesticides accounted for 34.2%, 51.6%, 7.3%, 3.7% and 3.2%, indicating that chemical fertilizer and irrigation electricity were the main sources of agricultural carbon footprint. Chemical fertilizer was the main source of agricultural carbon footprint in Southern winter (autumn sowing) wheat production region (Jiangsu, Anhui, Hubei and Sichuan), with an average proportion of 76.6%, 71.3%, 69.6% and 70.0%, respectively. Therefore, reducing chemical fertilizer input and increasing utilization efficiency were important emission reduction measures in these regions. Meanwhile, irrigation electricity was the main source of agricultural carbon footprint in Hebei province and Shanxi province in northern winter (autumn sowing) wheat production region and winter- spring wheat production region (Xinjiang), with an average proportion of 45.6%, 54.8% and 65.2%. Therefore, promoting the application of water-saving irrigation and developing low-energy-consuming machinery and equipment were important emission reduction measures in these regions. In short, reducing chemical fertilizer input and promoting water-saving irrigation technology to reduce the carbon footprint of wheat life cycle in study regions were effective management measures, and considering regional socio-economic and agricultural conditions of each province, formulating targeted measures to reduce agricultural greenhouse gas emissions is the key to promote the green and low-carbon development of agriculture.  Related Articles | Metrics | Comments（0）

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Spatial-temporal Characteristics and Risks of High-temperature Heat Damage of Rice in Southwest China CHEN Dong-dong, LI Xiao-wei, ZHANG Lu-yang, LUO Zi-zi, ZHANG Jian-ping, CHEN Xiao Chinese Journal of Agrometeorology    2024, 45 (8): 860-871.   DOI: 10.3969/j.issn.1000-6362.2024.08.005 Abstract （291）      PDF（pc） （6206KB）（224）       Knowledge map    Save High-temperature and heat damage is the major agricultural meteorological disasters in southwest China, and it is important to study them to ensure rice production. Based on daily meteorological data from 351 meteorological stations in the southwestern region from 1980 to 2022, this study focused on the critical growth stages of rice (heading-milk grain stage and milk grain-mature stage) to analyze the spatial and temporal variations and risk of high-temperature and heat damage using the heat damage cumulative index. The results provide a theoretical basis for the rational placement of rice cultivation. The results showed a significant increase in the number of stations experiencing high temperatures and heat damage between 1980 and 2022 during two critical growth stages of rice, with the increasing rates of 6.4 and 4.1 percentage points per decade. Taking a decade-by-decade perspective, heat stress and damage were relatively light in the 1980s, with the total number of stations experiencing heat stress during the two growth phases being 16.3% and 7.8% of the total number of stations, respectively. However, the high-temperature and heat damage were more severe in the last 13 years, with a total number of stations experiencing heat damage accounting for 38.1% and 22.2% of the total stations. The year with the highest occurrence of high-temperature and heat damage during both growth stages was in 2022, with the total number of stations experiencing heat stress accounting for 51.6% and 37.6% of the total stations, respectively. The annual tendency rate during the heading and milking stage had inceased significantly in the southwestern region, mainly concentrated in the central and western parts of Chongqing, the eastern part of Guizhou, and the northeastern and southern parts of the Sichuan basin. Only a few stations showed a decreasing trend. The tendency rate during the ripening stage of rice increased significantly in most areas of the Sichuan basin and Chongqing. The spatial distribution of the number of days, frequency, and risk of high-temperature and heat damage during the two critical growth stages of rice were generally consistent, showing a high concentration in the northeast and a low concentration in the southwest. The high-risk areas of high-temperature and heat damage were mainly concentrated in the central and western parts of Chongqing, the northern part of Chongqing, the northeastern part of the Sichuan basin, and the central and southern parts of the basin. This study achieved a quantitative assessment of high temperature and heat damage, improved the fineness of spatial distribution, and provided more guidance for actual rice production. Related Articles | Metrics | Comments（0）

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Temporal and Spatial Variations of Extreme Temperature and Precipitation Events in the Cropping Region across Northeast China LI Hao-tian, DONG Mei-qi, ZHAO Jin, TANG Jie, YANG Xiao-guang Chinese Journal of Agrometeorology    2025, 46 (2): 145-156.   DOI: 10.3969/j.issn.1000-6362.2025.02.002 Abstract （281）      PDF（pc） （9154KB）（307）       Knowledge map    Save  Global warming has led to a significant increase in the frequency of extreme weather and climate events, especially in northeast China, which is bound to affect the grain output of the three northeastern provinces. Based on historical ground meteorological observation data (1981−2014) and future climate change prediction data (2031−2060), this paper systematically analyzed the spatial−temporal distribution characteristics and future occurrence trends of extreme temperature and precipitation events by defining four extreme temperature indices and three extreme precipitation indices related to crop production. The results showed that the number of low temperature days (CD) decreased, the number of high temperature days (HD), low temperature intensity (CSI) and high temperature intensity (HSI) increased, the number of continuous wet days (CWD) decreased, the number of continuous dry days (CDD) increased, the number of heavy precipitation days (R20) decreased, the number of extreme high temperature days increased significantly and the extreme maximum temperature was the same as the historical stage. The extreme minimum temperature is in a state of warming. In the future, the overall temperature in the crop growing areas of the three provinces in northeast China would continue to rise, the number of continuous wet days would increase, the number of continuous dry and heavy precipitation days would decrease, and the precipitation variability and spatial difference would be large and the fluctuation range would be greater than the historical stage, and the uncertainty of abnormal precipitation would be strengthened, showing a trend of warming and drying, especially in the south and southeast. The results can provide reference for agricultural production in Northeast China to cope with climate change. Related Articles | Metrics | Comments（0）

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Climate and Land Use Change Effect on Natural Runoff in Shiyang River Basin CAO Jin-jun, MA Hai-hua Chinese Journal of Agrometeorology    2024, 45 (11): 1290-1301.   DOI: 10.3969/j.issn.1000-6362.2024.11.004 Abstract （278）      PDF（pc） （5279KB）（313）       Knowledge map    Save  Climate warming and human activities have a major impact on the spatial and temporal characteristics of surface runoff in the Shiyang river basin, and the adaptive management of water resources in the basin is facing serious challenges. With three independent drainage Dajinghe river system, Liuhe river system and Xidahe river system as the research object, the statistical method analyzed the average flow evolution in 1960−2020, using Mann-Kendall test and Pettitt test to determine the runoff sequence mutation point, set the combination of climate and land use change, and used SWAT model to identify the runoff variation in Shiyang river basin, addressing the unequal allocation of water resources. The results showed that: (1) from 1960 to 2020, the annual average runoff decline rate of Xidahe river, Liuhe river and Dajing river system was 0.01m3·s−1, 0.07m3·s−1, 0.01m3·s−1 respectively. The runoff years for the Liuhe river and Xidahe river were in 1973 and 2002, respectively, while the Dajing river system was in its natural state. (2) The SWAT model had a good adaptability to the Shiyang river basin, and the determination coefficient and the Nash efficiency coefficient were both higher than 0.50. (3) From 1960 to 2020, the contribution of precipitation, average temperature, relative humidity, solar radiation and average wind speed to the annual average runoff were 75%, 53%, 55%, 55%, and 52%, respectively. The contribution of land use change to the annual average runoff was 17%. The coupled contribution of six influence factors to annual average runoff reduction was 67%, indicating that climate change had great influence on runoff in Shiyang river basin. The research results could provide a reference for the adaptive management of watershed water resources. Related Articles | Metrics | Comments（0）

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Trend and Influencing Factors of Green Development of Sichuan Planting Industry LIN Xu, QI Yan-bin, XIE Wei Chinese Journal of Agrometeorology    2024, 45 (11): 1265-1275.   DOI: 10.3969/j.issn.1000-6362.2024.11.002 Abstract （275）      PDF（pc） （1566KB）（297）       Knowledge map    Save Ensuring food security and while achieving low-carbon development is a practical issue for the agricultural economy and the priority for carbon neutrality in the planting industry. Based on the time series data of Sichuan planting industry, the change of carbon emission and emission intensity was reviewed, and the input-output super-efficiency model was applied to evaluate the green development efficiency of Sichuan planting industry, and the influencing factors were analyzed by Tobit regression model. The results showed that carbon intensity of Sichuan planting industry decreased from 0.717 t to 0.208 t per 10000 yuan of GDP over the 2010−2022 period, with the continued expansion of the planting area being the core driver of the increase. At the same time, the green development trend of Sichuan planting industry showed the evolution characteristics of "continuous low efficiency− rapid stretching−stable development". In terms of influencing factors, the unique agricultural industrial structure and climate environment, as well as technological investment, have driven the green development of planting industry in Sichuan, while the high proportion of grain and oil crop cultivation was an objective challenge. The fiscal policy had improved the utilization level of planting industry to climate resources, but there was also a significant inhibitory effect. Therefore, while actively adapting to global warming, achieving a long-term balance between food security and green development of the planting industry, and guiding the green development willingness of planting entities with policy orientation, should be a direction that the government should continue to focus on. Related Articles | Metrics | Comments（0）

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Monetization Evaluation of Greenhouse Gas Emission Reduction Benefits Based on Corn Stalk Pyrolysis Polygeneration Technology GU Ding-yuan, HUO Li-li, YAO Zong-lu, ZHAO Li-xin, YU Jia-dong, ZHAO Ya-nan Chinese Journal of Agrometeorology    2024, 45 (11): 1253-1264.   DOI: 10.3969/j.issn.1000-6362.2024.11.001 Abstract （272）      PDF（pc） （2086KB）（475）       Knowledge map    Save Biomass energy is a globally recognized renewable energy with zero carbon properties, but its carbon reduction benefits have not been effectively reflected. Based on the whole life cycle evaluation (LCA) method, combined with the economic benefit evaluation method and the environmental impact monetization method, this study built a EGE model for the greenhouse gas emission reduction benefit evaluation (EGE) of straw pyrolysis polygeneration technology. The EGE model took the whole chain of straw collection, off-field storage and transportation, pyrolysis transformation and product utilization as the boundary. The comprehensive economic and environmental benefits of different pyrolysis polygeneration technologies under different production scales was explored. The results showed that the external heating pyrolysis carbon gas co-production technology has the best economic benefits in large-scale application, and at the annual production scale of 0.5×104 ~ 10×104t of straw, each 1t of straw consumption can reduce 1.01−1.07tCO2eq. Greenhouse gas emission reduction could significantly improve the economic benefits of straw pyrolysis polygeneration technology projects, and each 1t of straw consumption could increase the benefit of 57.5−103.1 yuan, which increased the yield rate of straw pyrolysis polygeneration technology projects by 1.6−14.0PP. It is estimated that the utilization potential of straw in 2030 and 2060 would reach 1.24×108t and 1.67×108t respectively, and the emission reduction income of straw pyrolysis polygeneration technology can reach 1.0×1010−3.7×1010 yuan. The results of this study provide technical support for the development of biomass energy industry under the background of realizing the dual-carbon goal. Related Articles | Metrics | Comments（0）

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Report on Weather Impacts to Agricultural Production in Spring 2024 WU Men-xin, ZHAO Yun-cheng, LI Yi-jun, HE Yan-bo, ZHANG Lei Chinese Journal of Agrometeorology    2024, 45 (8): 938-941.   DOI: 10.3969/j.issn.1000-6362.2024.08.012 Abstract （267）      PDF（pc） （344KB）（162）       Knowledge map    Save The relationship between meteorological factors and agricultural production in China was analyzed using statistical methods based on daily national meteorological data in the spring of 2024. The results showed that the national average temperature in the spring (March-May) was 12.1°C, 1.4°C higher than the same period from 1961 to 2020 and the maximum value since 1961. The national average precipitation was 156.2mm, which was 18.9mm more than the same period from 1961 to 2020. The national average sunshine hours was 635.7h, 12.6h less than the same period from 1961 to 2020. In the northern winter wheat region, the sunshine hours, heat condition, and soil moisture content were suitable, and the meteorological conditions were generally favorable for the growth and production of winter wheat. In the most areas of the spring sowing areas in northern China, the water and heat conditions were favorable, which was suitable for crop sowing and seedling emergence. Spring sowing progress was faster than that in 2023 and the seedlings were growing well. The frequent rainy weather in the south led to waterlogging in some low-lying cropland, affected the growth and development of rapeseed and the early rice fields in some areas affected by heavy rainfall were flooded for a short time and the corn appeared to lodging. In southern Sichuan and central and eastern Yunnan, the precipitation had been less since last winter, and drought was heavier in some areas, which was unfavorable to the production of grain and oil crops in summer harvest and the growth of spring sown crops at the seedling stage. Related Articles | Metrics | Comments（0）

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Impact of Extreme Climate Change on Maize Yield in Beijing-Tianjin-Hebei Region from 1980 to 2020 LIU Bing, YANG Yang, HAO Zhuo Chinese Journal of Agrometeorology    2024, 45 (7): 766-776.   DOI: 10.3969/j.issn.1000-6362.2024.07.007 Abstract （262）      PDF（pc） （10082KB）（138）       Knowledge map    Save  Extreme climate change, which can cause agricultural problems, has become a global hot topic. In recent decades, the Beijing-Tianjin-Hebei region has experienced several extreme climate events that have had a significant impact on grain yields. This study evaluted the impact of climate change on grain yields in the Beijing-Tianjin-Hebei region from 1980 to 2020 using meteorological data and maize yield per unit area data at eight sites and selected 25 national meteorological stations. Four types of statistical methods were selected, including linear regression, inverse distance weighting interpolation, M-K test, and Pearson correlation, to analyze the characteristics of climate change (maximum, minimum and average temperature, and growing degree days) and its impacts on maize yields. The results revealed: (1) the growing degree days (GDD) and temperature indices such as extreme maximum temperature (TXx) and high temperature days (Htd) exhibited an upward trend over time, with increase rates of 58.31℃·d·10y−1, 0.39℃·10y−1 and 0.96d·10y−1, respectively. However, the low temperature indices (extreme minimum temperature, low temperature days) tended to decrease with decrease rates of 0.28℃·10y−1 and 2.8d·10y−1, respectively. Mutation analysis indicated a higher mutation rate for high temperature indices compared to low temperature indices, indicating a clear warming trend in the Beijing-Tianjin-Hebei region from 1980 to 2020. (2) The spatial distribution of extreme temperature index terms was different. High value of high temperature indices were primarily concentrated in economically developed cities such as Beijing and Tianjin, while low temperature indices were mainly concentrated in the northern (Zhangbei) and southwestern (Xingtai) areas. (3) The grain yield presented a fluctuate increase, with climate yield of maize fluctuating greatly (−1179 to 831kg·ha−1). There were three climatic bumper years (2004, 2005 and 2006) and two lean years (1999, 2000) from 1990 to 2020 in the study area. Correlation analysis indicated that GDD、TXx、Htd were the primary response indices for grain yields in the Beijing-Tianjin-Hebei region, it can be seen that when TXx≥36℃, Htd≥4d, the climatic yield of maize decreases gradually.  Related Articles | Metrics | Comments（0）

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Current Status and Development Trend of Food Life-cycle Carbon Emissions Research LIU Chang, SHANG Jie Chinese Journal of Agrometeorology    2024, 45 (10): 1131-1145.   DOI: 10.3969/j.issn.1000-6362.2024.10.004 Abstract （255）      PDF（pc） （5706KB）（376）       Knowledge map    Save The impact of food production and consumption processes on the environment has become increasingly apparent in the context of ongoing economic development and the environment. Especifically, the issue of greenhouse gas emissions in the life cycle of food has emerged as a major challenge in the realm of climate change and ecological balance. A comprehensive analysis of international and domestic research progress on food life cycle carbon emissions can reveal the research trends and frontier hotspots in this area. By conducting searches in English and Chinese literature databases, including the Web of Science (WoS) and China National Knowledge Infrastructure (CNKI), utilizing the scientific knowledge graph and the CiteSpace literature visualization tool, an in-depth analysis and visual representation were carried out. The results showed that research on carbon emissions from food lifecycle began to emerge internationally in 2003, while domestic attention to the issue began in 2009, and both had exhibited steady growth in their respective research activities. While Chinese research had largely emphasized the food production aspect, international research had taken a more holistic approach, encompassing research on both food production and consumption, the entire food system, and the supply chains. In addition, there was a greater emphasis internationally on the management of carbon emissions within food systems and supply chains, whereas Chinese research was more skewed towards the study of regional disparities. In the future, research on carbon emissions in the food lifecycle was likely to place increased emphasis on the equilibrium between food production and consumption, with emerging industries like food delivery services gaining attention. Regional disparities in food lifecycles’ carbon emissions were crucial for the sustainable development of regional food production and consumption. Hybrid life cycle assessment was poised to become a mainstream research method, with future research comprehensively evaluating multiple aspects of food life cycle carbon emissions. The effectiveness of policies is also set to be a key focus of future research aimed at driving carbon reduction in the food life cycle. Related Articles | Metrics | Comments（0）

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Immobiliaztion Effect of Arsenic in Contaminated Red Soils and Its Enzyme Activities after Application of Lanthanum-modified Biochar XIE Jin-ni, LI Lian-fang, LV Peng, WANG Zi-han, YAN Ao, KANG Meng-qi, ZHOU Xue, YE Jing Chinese Journal of Agrometeorology    2024, 45 (12): 1391-1404.   DOI: 10.3969/j.issn.1000-6362.2024.12.001 Abstract （255）      PDF（pc） （4305KB）（323）       Knowledge map    Save Biochar pyrolysis at high temperature with limited oxygen plays an important role in the resource utilization of agricultural waste, carbon sequestration and emission reduction, indicating the great potential of biochar for remediating contaminated environment. As a functional material for soil remediation, its ability of adsorption and fixation for heavy metals is still insufficient, which limits the large-scale promotion and application of biochar. Nowadays, it has been an increasingly important research area to enhance the adsorption and fixation capacity of biochar through modification design of engineered materials. In this study, wood chips were used as raw materials to prepare biochar (BC), and then lanthanum modified biochar (LBC) was manufactured. Aiming to remediate arsenic contaminated red soil and compare the immobilization difference, these two kinds of amendments (LBC, BC) were applied into the experimental soils separately, and the blank soil without material addition was used as control treatment. All these above treatments was cultivated for 30days under the soil moisture content with 30%, 70%, and 100% field water capacity respectively, and the corresponding remediation effects of arsenic contaminated red soil by using LBC and BC were investigated. The results were as following: (1) LBC addition was beneficial for alleviating the acidification of southern red soils. When the cultivation experiment was finished, soil pH treated by LBC was enhanced obviously and the increased pH ranged from 0.86 to 1.20 units under three kinds of soil moisture content with 30%, 70%, and 100% field water capacity. In comparison with BC treatment, the soil pH also increased by 0.09−0.44 units after LBC addtion. (2) LBC application led to the obvious immobilization effect of arsenic in red soils, and the related fixation efficiency under three kinds of soil water content was up to 54.7%−90.0% during the whole soil cultivation period, and the immobilization efficiency reached 81.0%−85.8% after 30days of cultivation. On the contrary, soil treated by BC resulted in the arsenic activation of soils with the increased percent 135.4%−895.9% compared to the control. (3) The immobilization effect of LBC on soil arsenic is mainly related to the transformation of arsenic in various speciation, especially from non-specialized adsorption forms to more stable ones such as residue forms. In the meanwhile, BC resulted in the enhancement of non-specialized adsorption arsenic and promoted the activation of soil arsenic. (4) LBC was capable of immobilizing arsenic in red soils with high efficiency, and did not obviously excert negative influence on soil enzyme activity. The application of LBC was able to improve the activity of soil urease and catalase although it led to a slight decrease in soil phosphatase and sucrase activities. It is worth mention that LBC treatment remained higher soil sucrase activities than those for BC treatments. Overall, it manifest that LBC has great potential for remediating arsenic contaminated red soil. Related Articles | Metrics | Comments（0）

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Different Varieties and Planting Densities Influence on the Transpiration Rate of Xinjiang Cotton ZHAO Ming-ze, ZHANG Ze-shan, WANG Xue-jiao, SONG Yan-hong, SUN Shuai, HU Yan-ping, PARHAT Maimaiti, ZHANG Li-zhen, BAKE Batur, LI Jie Chinese Journal of Agrometeorology    2024, 45 (11): 1357-1368.   DOI: 10.3969/j.issn.1000-6362.2024.11.010 Abstract （251）      PDF（pc） （6725KB）（251）       Knowledge map    Save To study the impact of different varieties and planting densities on the transpiration rate of cotton in Xinjiang, a field experiment was conducted in 2022 in Wulanwusu, Xinjiang. Three cotton varieties (' Zhongmian 979'  ' Zhongmian 703'  and 'Guoxin cotton' ) and two planting densities (D1:22 plants·m−2; D2:11 plants·m−2) were establish for treatment. The transpiration rate was measured using a heat ratio stem flow meter, and the differences in daily average transpiration, daily transpiration change, and cumulative transpiration of cotton under different weather conditions (sunny, rainy) and time scales were compared to clarify the water consumption rules of cotton under different varieties and densities in northern Xinjiang. The results showed that: (1) planting density had a significant impact on the cumulative and daily transpiration rates of cotton population. The cumulative and daily transpiration rates of cotton increased significantly under D1 planting density. Under D1 planting density treatment, the cumulative and daily transpiration rates of three varieties were significantly higher than those under D2 planting density treatment (an average increase of 51.2%). (2) Cotton varieties had a significant impact on individual and group transpiration, with ' Zhongmian 703'  higher stem flow rate, daily transpiration rate, and cumulative transpiration than other varieties. (3) The transpiration of a single cotton plant showed a "几" shaped variation pattern on a daily scale. During the day (9：00−21：00), the transpiration was relatively stable, but there was still a slight stem flow at night due to root pressure. (4) The transpiration rate and amount of cotton decreased month by month from July to September. The daily transpiration curve of cotton in September gradually transitioned to a unimodal pattern, and the daytime transpiration starts later (10：00) and ends earlier (20：00). (5) Accumulated transpiration had a positive correlation with both seed cotton yield and average leaf area, but it was not significant. Related Articles | Metrics | Comments（0）

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Potential Impacts of Climate Change on Rice Yield in Sichuan-Chongqing Area YANG Man-shan, GUAN Kai-xin, ZHANG Wen-meng, JIANG Chao-yue, LIU Zhi-juan, YANG Xiao-guang Chinese Journal of Agrometeorology    2024, 45 (10): 1146-1159.   DOI: 10.3969/j.issn.1000-6362.2024.10.005 Abstract （247）      PDF（pc） （24714KB）（116）       Knowledge map    Save  The cultivation of the double-cropping systems involving early and late rice was investigated within the Sichuan-Chongqing area, and a panel model was constructed based on historical meteorological data (1981−2020) and rice yield data (1997−2020) to analyze the impacts of future climate (2021−2060, SSP1−2.6 and SSP5−8.5) on rice yield in study area. The results showed that: (1) from 2021 to 2060, the rice yield in most areas of Sichuan and Chongqing would increase by 10.0% to 30.0% compared with the historical yield, and the southern and southeastern areas of Sichuan would decrease by 0 to 10%. (2) Under future climate change, the change of mean climate (precipitation and radiation) and extreme climate (high temperature and drought) would reduce rice yield by 0 to 2.0% in most areas of Sichuan and Chongqing, and the change of extreme state of precipitation would reduce rice yield by 0 to 2.0% in Chongqing and Dazhou. The impact of temperature on yield of early and late rice was very different. The impact of precipitation and radiation on yield of early and late rice was basically the same. During 2021−2060, the temperature, precipitation and radiation in most areas showed an upward trend, while the precipitation in a few areas (Chongqing and Panzhihua) showed a downward trend. While the change of precipitation and radiation, drought and high temperature led to the decrease of rice production in study area. The mean climate of radiation and the extreme climate of drought contributed most to rice yield under future SSP1−2.6 and SSP5−8.5, respectively. Under future climate change, the high and stable yield of rice should be ensured under extreme drought and high temperature events in Sichuan-Chongqing area. Related Articles | Metrics | Comments（0）

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Using Temperature Models to Estimate ET0 in Data-scarce Regions with Limited Solar Radiation Data ZHOU Jun-wei, DONG Qin-ge Chinese Journal of Agrometeorology    2024, 45 (7): 701-714.   DOI: 10.3969/j.issn.1000-6362.2024.07.002 Abstract （247）      PDF（pc） （6454KB）（290）       Knowledge map    Save Accurate estimation of reference crop evapotranspiration (ET0) is essential for water resources planning and irrigation scheduling. However, the absence of solar radiation (Rs) data is a common problem affecting the estimation of ET0. This study investigates the feasibility of employing temperature-based models to estimate Rs and proposes effective methodologies for obtaining more convenient and accurate ET0 estimates. To evaluate the effectiveness of different approaches, authors compared nine empirical models (M1−M9) and three machine learning algorithms (RF, GRNN and ANN) for daily Rs estimation. This analysis utilized data from 339 national basic meteorological stations in China, spanning the period from 2001 to 2018. Subsequently, authors proposed two strategies for estimating daily ET0 in regions where solar radiation data is limited or unavailable. The results showed that (1) temperature-based models exhibited satisfactory accuracy (R2> 0.6) for daily Rs estimation, with machine learning algorithms outperforming their empirical counterparts. The machine learning accuracies are ranked as follows: Artificial Neural Network (ANN) > Generalized Regression Neural Network (GRNN) > Random Forest (RF). And empirical models are ranked in descending order of accuracy: M9 > M8 > M6 > M7 > M5 > M2 > M3 > M1 > M4. The accuracies of twelve models in the four climatic zones are indicated as follows: the temperate continental zone (TCZ) > the temperate monsoon zone (TMZ) > the subtropical monsoon zone (SMZ) > the mountain plateau zone (MPZ). (2) The comprehensive assessment for nine empirical models indicates that the Hargreaves-Samani model (M1) is the most reliable for solar radiation estimation. Its estimated results are close to those of the other models, and the coefficient of variation of the parameters (0.10) is much lower than that of the other empirical models. Thus, combining the model with the nationally calibrated parameters computed by the Kriging interpolation method allows for reliable values of the daily solar radiation. (3) Machine learning techniques show variations in estimating daily ET0 across different climate zones. The machine learning accuracies are ranked as ANN>GRNN>RF, and TCZ>TMZ>MPZ>SMZ in the four climate zones. (4) The accuracies of the two daily ET0 estimation strategies, with or without actual Rs calibration, are very close. Both strategies provide accurate daily ET0 estimates (R2>0.95) with an average R2 improvement of only 0.39% for strategy I compared to strategy II. In conclusion, this study provides new ideas to address the scarcity of solar radiation data and highlight the potential of machine learning in ET0 estimation. This approach can be effectively applied to reference crop evapotranspiration estimates in regions where solar radiation data is scarce. Related Articles | Metrics | Comments（0）