Monitoring and early warning is an important means to effectively prevent risks in agricultural production,consumption and price.In particular,with the change of modes of national administration against the background...Monitoring and early warning is an important means to effectively prevent risks in agricultural production,consumption and price.In particular,with the change of modes of national administration against the background of big data,improving the capacity to monitor agricultural products is of great significance for macroeconomic decision-making.Agricultural product information early warning thresholds are the core of agricultural product monitoring and early warning.How to appropriately determine the early warning thresholds of multi-temporal agricultural product information is a key question to realize real-time and dynamic monitoring and early warning.Based on the theory of abnormal fluctuation of agricultural product information and the research of substantive impact on the society,this paper comprehensively discussed the methods to determine the thresholds of agricultural product information fluctuation in different time dimensions.Based on the data of the National Bureau of Statistics of China(NBSC)and survey data,this paper used a variety of statistical methods to determine the early warning thresholds of the production,consumption and prices of agricultural products.Combined with Delphi expert judgment correction method,it finally determined the early warning thresholds of agricultural product information in multiple time,and carried out early warning analysis on the fluctuation of agricultural product monitoring information in 2018.The results show that:(1)the daily,weekly and monthly monitoring and early warning thresholds of agricultural products play an important early warning role in monitoring abnormal fluctuations with agricultural products;(2)the multitemporal monitoring and early warning thresholds of agricultural product information identified by the research institute can provide effective early warning on current abnormal fluctuation of agricultural product information,provide a benchmarking standard for China's agricultural production,consumption and price monitoring and early warning at the national macro level,and further improve the application of China's agricultural product monitoring and early warning.展开更多
Spodoptera frugiperda(Lepidoptera:Noctuidae)is an important migratory agricultural pest worldwide,which has invaded many countries in the Old World since 2016 and now poses a serious threat to world food security.The ...Spodoptera frugiperda(Lepidoptera:Noctuidae)is an important migratory agricultural pest worldwide,which has invaded many countries in the Old World since 2016 and now poses a serious threat to world food security.The present monitoring and early warning strategies for the fall army worm(FAW)mainly focus on adult population density,but lack an information technology platform for precisely forecasting the reproductive dynamics of the adults.In this study,to identify the developmental status of the adults,we first utilized female ovarian images to extract and screen five features combined with the support vector machine(SVM)classifier and employed male testes images to obtain the testis circular features.Then,we established models for the relationship between oviposition dynamics and the developmental time of adult reproductive organs using laboratory tests.The results show that the accuracy of female ovary development stage determination reached 91%.The mean standard error(MSE)between the actual and predicted values of the ovarian developmental time was 0.2431,and the mean error rate between the actual and predicted values of the daily oviposition quantity was 12.38%.The error rate for the recognition of testis diameter was 3.25%,and the predicted and actual values of the testis developmental time in males had an MSE of 0.7734.A WeChat applet for identifying the reproductive developmental state and predicting reproduction of S.frugiperda was developed by integrating the above research results,and it is now available for use by anyone involved in plant protection.This study developed an automated method for accurately forecasting the reproductive dynamics of S.frugiperda populations,which can be helpful for the construction of a population monitoring and early warning system for use by both professional experts and local people at the county level.展开更多
The primary goal of Chinese agricultural development is to guarantee national food security and supply of major agricultural products. Hence, the scientiifc work on agricultural monitoring and early warning as wel as ...The primary goal of Chinese agricultural development is to guarantee national food security and supply of major agricultural products. Hence, the scientiifc work on agricultural monitoring and early warning as wel as agricultural outlook must be strengthened. In this study, we develop the China Agricultural Monitoring and Early-warning System (CAMES) on the basis of a comparative study of domestic and international agricultural outlook models. The system is a dynamic and multi-market partial equilibrium model that integrates biological mechanisms with economic mechanisms. This system, which includes 11 categories of 953 kinds of agricultural products, could dynamical y project agricultural market supply and demand, assess food security, and conduct scenario analysis at different spatial levels, time scale levels, and macro-micro levels. Based on the CAMES, the production, consumption, and trade of the major agricultural products in China over the next decade are projected. The fol owing conclusions are drawn:i) The production of major agricultural products wil continue to grow steadily, mainly because of the increase in yield. i ) The growth of agricultural consumption wil be slightly higher than that of agricultural production. Meanwhile, a high self-sufifciency rate is expected for cereals such as rice, wheat, and maize, with the rate being stable at around 97%. i i) Agricultural trade wil continue to thrive. The growth of soybean and milk im-ports wil slow down, but the growth of traditional agricultural exports such as vegetables and fruits is expected to continue.展开更多
The efficient and accurate collection of agricultural product market information serves as the basis for the effective regulation of agricultural product markets.To achieve a comprehensive,accurate,and timely collecti...The efficient and accurate collection of agricultural product market information serves as the basis for the effective regulation of agricultural product markets.To achieve a comprehensive,accurate,and timely collection of agricultural product market information,this study puts forth a technique for collecting holographic information of agricultural product markets.A portable human-machine interaction device with an Advanced RISC(reduced instruction set computer)Machines(ARM)-based processor as the core is developed.Holographic information such as agricultural product market trading time,trading place,product name,price,and trading volume can be collected.Via embedded technology and component technology,innovative agricultural product market positioning,and matching,standardized collection,and data processing,and in combination with intelligent algorithms for analysis and early warning,a mobile application terminal for information collection is developed,namely,a holographic information collector for agricultural product markets(named Nongxincai).By using a layered structure,the hardware integrates microprocessor,storage,power,application and communication interface,and human-machine interaction modules.The device has the advantages of miniaturization,the whole-machine power consumption of less than 0.5 W,and continuous operating time of at least 10 h.A supporting software system for Nongxincai has also been developed,in which Microsoft Windows Mobile 6.5 is employed as the operating system,and the CPU frequency is up to 600 MHz.This configuration fully meets the computing requirements of map processing and large-volume data processing and has high compatibility.Nongxincai has been popularized and applied in 12 provinces/municipalities in China.It has played an important role in the monitoring and early warning of different varieties of agricultural products and target prices of soybean and cotton.展开更多
Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction m...Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction method,the photographic method has the advantages of simple operation and high extraction accuracy.However,when soil moisture and acquisition times vary,the extraction results are less accurate.To accommodate various conditions of FVC extraction,this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index(NDVI)greyscale image of wheat by using a density peak k-means(DPK-means)algorithm.In this study,Yangfumai 4(YF4)planted in pots and Yangmai 16(Y16)planted in the field were used as the research materials.With a hyperspectral imaging camera mounted on a tripod,ground hyperspectral images of winter wheat under different soil conditions(dry and wet)were collected at 1 m above the potted wheat canopy.Unmanned aerial vehicle(UAV)hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera.The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat,and the extraction effects of the two methods were compared and analysed.The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered,while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated.The absolute values of error were 0.042 and 0.044,the root mean square errors(RMSE)were 0.028 and 0.030,and the fitting accuracy R2 of the FVC was 0.87 and 0.93,under dry and wet soil conditions and under various time conditions,respectively.This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction.展开更多
Lately,in some regions and seasons in China,urban consumers have paid high in buying fresh agricultural products while farmers get unreasonable income from producing them.To seek the reason for the phenomenon and expl...Lately,in some regions and seasons in China,urban consumers have paid high in buying fresh agricultural products while farmers get unreasonable income from producing them.To seek the reason for the phenomenon and explore ways to simulate it,this study constructed and implemented a complex network model named the Bi-Level Multi-Local-World(BI-MLW model)with characteristics of an interdependent coupling relationship between its participants.To verify the validity of the model,this study implemented an experimental simulation under Small Decentralized Operation Mode(SDOM)and Large Centralized Operation Mode(LCOM)scenarios using Cucurbita pepo and Cucumber in the Tianjin area of China as sample empirical products.Results indicate that nodes do not increase edges rapidly which reflects that even large firms in agricultural business cannot occupy markets fleetly.Furthermore,under the SDOM scenario the BI-MLW model exposes scale-free features with a small average degree value and low average clustering coefficient,while under the LCOM scenario,the model displays a rising average clustering coefficient and a lowered average path length.Both of which are consistent with the common view in literature and features of reality.Thus,the BI-MLW model specially designed for fresh agricultural products supply chain can improve the descriptive ability than conventional Erdös-Rényi(ER),Barabási-Albert(BA),Bianconi-Barabási(BB)network models.展开更多
Ethylene(C2 H4),as a plant hormone,its emission can be served as an indicator to measure fruit quality.Due to the limited physiochemical reactivity of C2 H4,it is a challenge to develop high performance C2 H4 sensors ...Ethylene(C2 H4),as a plant hormone,its emission can be served as an indicator to measure fruit quality.Due to the limited physiochemical reactivity of C2 H4,it is a challenge to develop high performance C2 H4 sensors for fruit detection.Herein,this paper presents a resistive-type C2 H4 sensor based on Pd-loaded tin oxide(SnO2).The C2 H4 sensing performance of proposed sensor are tested at optimum operating temperature(250℃)with ambient relative humidity(51.9%RH).The results show that the response of Pd-loaded SnO2 sensor(11.1,Ra/Rg)is about 3 times higher than that of pristine SnO2(3.5)for 100 ppm C2 H4.The response time is also significantly shortened from 7 s to 1 s compared with pristine SnO2.Especially,the Pd-loaded SnO2 sensor possesses good sensitivity(0.58 ppm 1)at low concentration(0.05-1 ppm)with excellent linearity(R2=0.9963)and low detection limit(50 ppb).The high sensing performance of Pd-loaded SnO2 are attributed to the excellent adsorption and catalysis effects of Pd nanoparticle.Meaningfully,the potential applications of C2 H4 sensor are performed for monitoring the maturity and freshness of fruits,which presents a promising prospect in fruit quality evaluation.展开更多
基金The Science and Technoloav Innovation Program of the Chinese Academy of Agricultural Sciences(CAAS-ASTIP-2020-A11-02)is appreciated for supporting this study.
文摘Monitoring and early warning is an important means to effectively prevent risks in agricultural production,consumption and price.In particular,with the change of modes of national administration against the background of big data,improving the capacity to monitor agricultural products is of great significance for macroeconomic decision-making.Agricultural product information early warning thresholds are the core of agricultural product monitoring and early warning.How to appropriately determine the early warning thresholds of multi-temporal agricultural product information is a key question to realize real-time and dynamic monitoring and early warning.Based on the theory of abnormal fluctuation of agricultural product information and the research of substantive impact on the society,this paper comprehensively discussed the methods to determine the thresholds of agricultural product information fluctuation in different time dimensions.Based on the data of the National Bureau of Statistics of China(NBSC)and survey data,this paper used a variety of statistical methods to determine the early warning thresholds of the production,consumption and prices of agricultural products.Combined with Delphi expert judgment correction method,it finally determined the early warning thresholds of agricultural product information in multiple time,and carried out early warning analysis on the fluctuation of agricultural product monitoring information in 2018.The results show that:(1)the daily,weekly and monthly monitoring and early warning thresholds of agricultural products play an important early warning role in monitoring abnormal fluctuations with agricultural products;(2)the multitemporal monitoring and early warning thresholds of agricultural product information identified by the research institute can provide effective early warning on current abnormal fluctuation of agricultural product information,provide a benchmarking standard for China's agricultural production,consumption and price monitoring and early warning at the national macro level,and further improve the application of China's agricultural product monitoring and early warning.
基金supported by the National Natural Science Foundation of China(31727901)the National Key R&D Program of China(2021YFD1400702)the Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences.
文摘Spodoptera frugiperda(Lepidoptera:Noctuidae)is an important migratory agricultural pest worldwide,which has invaded many countries in the Old World since 2016 and now poses a serious threat to world food security.The present monitoring and early warning strategies for the fall army worm(FAW)mainly focus on adult population density,but lack an information technology platform for precisely forecasting the reproductive dynamics of the adults.In this study,to identify the developmental status of the adults,we first utilized female ovarian images to extract and screen five features combined with the support vector machine(SVM)classifier and employed male testes images to obtain the testis circular features.Then,we established models for the relationship between oviposition dynamics and the developmental time of adult reproductive organs using laboratory tests.The results show that the accuracy of female ovary development stage determination reached 91%.The mean standard error(MSE)between the actual and predicted values of the ovarian developmental time was 0.2431,and the mean error rate between the actual and predicted values of the daily oviposition quantity was 12.38%.The error rate for the recognition of testis diameter was 3.25%,and the predicted and actual values of the testis developmental time in males had an MSE of 0.7734.A WeChat applet for identifying the reproductive developmental state and predicting reproduction of S.frugiperda was developed by integrating the above research results,and it is now available for use by anyone involved in plant protection.This study developed an automated method for accurately forecasting the reproductive dynamics of S.frugiperda populations,which can be helpful for the construction of a population monitoring and early warning system for use by both professional experts and local people at the county level.
基金supported by the National Natural Science Foundation of China (71303238)the National Science and Technology Support Plan Projects (2012BAH20B04)the compilation group of the China Agricultural Outlook Report (2015–2024)
文摘The primary goal of Chinese agricultural development is to guarantee national food security and supply of major agricultural products. Hence, the scientiifc work on agricultural monitoring and early warning as wel as agricultural outlook must be strengthened. In this study, we develop the China Agricultural Monitoring and Early-warning System (CAMES) on the basis of a comparative study of domestic and international agricultural outlook models. The system is a dynamic and multi-market partial equilibrium model that integrates biological mechanisms with economic mechanisms. This system, which includes 11 categories of 953 kinds of agricultural products, could dynamical y project agricultural market supply and demand, assess food security, and conduct scenario analysis at different spatial levels, time scale levels, and macro-micro levels. Based on the CAMES, the production, consumption, and trade of the major agricultural products in China over the next decade are projected. The fol owing conclusions are drawn:i) The production of major agricultural products wil continue to grow steadily, mainly because of the increase in yield. i ) The growth of agricultural consumption wil be slightly higher than that of agricultural production. Meanwhile, a high self-sufifciency rate is expected for cereals such as rice, wheat, and maize, with the rate being stable at around 97%. i i) Agricultural trade wil continue to thrive. The growth of soybean and milk im-ports wil slow down, but the growth of traditional agricultural exports such as vegetables and fruits is expected to continue.
基金Project funded by Special fund for Agricultural Information Monitoring and Early Warning of the Ministry of Agriculture and Rural Affairs,ChinaScientific and technological innovation project of the Chinese Academy of Agricultural Sciences,China(CAAS-ASTIP-2019-AII-01)+1 种基金National Key Research and Development Project(2016YFD0300602)Young Elite Scientists Sponsorship Program by CAST(2019-2021QNRC001).
文摘The efficient and accurate collection of agricultural product market information serves as the basis for the effective regulation of agricultural product markets.To achieve a comprehensive,accurate,and timely collection of agricultural product market information,this study puts forth a technique for collecting holographic information of agricultural product markets.A portable human-machine interaction device with an Advanced RISC(reduced instruction set computer)Machines(ARM)-based processor as the core is developed.Holographic information such as agricultural product market trading time,trading place,product name,price,and trading volume can be collected.Via embedded technology and component technology,innovative agricultural product market positioning,and matching,standardized collection,and data processing,and in combination with intelligent algorithms for analysis and early warning,a mobile application terminal for information collection is developed,namely,a holographic information collector for agricultural product markets(named Nongxincai).By using a layered structure,the hardware integrates microprocessor,storage,power,application and communication interface,and human-machine interaction modules.The device has the advantages of miniaturization,the whole-machine power consumption of less than 0.5 W,and continuous operating time of at least 10 h.A supporting software system for Nongxincai has also been developed,in which Microsoft Windows Mobile 6.5 is employed as the operating system,and the CPU frequency is up to 600 MHz.This configuration fully meets the computing requirements of map processing and large-volume data processing and has high compatibility.Nongxincai has been popularized and applied in 12 provinces/municipalities in China.It has played an important role in the monitoring and early warning of different varieties of agricultural products and target prices of soybean and cotton.
基金supported by the Beijing Natural Science Foundation,China(4202066)the Central Public-interest Scientific Institution Basal Research Fund,China(JBYWAII-2020-29 and JBYW-AII-2020-31)+1 种基金the Key Research and Development Program of Hebei Province,China(19227407D)the Technology Innovation Project Fund of Chinese Academy of Agricultural Sciences(CAAS-ASTIP2020-All)。
文摘Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction method,the photographic method has the advantages of simple operation and high extraction accuracy.However,when soil moisture and acquisition times vary,the extraction results are less accurate.To accommodate various conditions of FVC extraction,this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index(NDVI)greyscale image of wheat by using a density peak k-means(DPK-means)algorithm.In this study,Yangfumai 4(YF4)planted in pots and Yangmai 16(Y16)planted in the field were used as the research materials.With a hyperspectral imaging camera mounted on a tripod,ground hyperspectral images of winter wheat under different soil conditions(dry and wet)were collected at 1 m above the potted wheat canopy.Unmanned aerial vehicle(UAV)hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera.The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat,and the extraction effects of the two methods were compared and analysed.The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered,while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated.The absolute values of error were 0.042 and 0.044,the root mean square errors(RMSE)were 0.028 and 0.030,and the fitting accuracy R2 of the FVC was 0.87 and 0.93,under dry and wet soil conditions and under various time conditions,respectively.This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction.
基金the support of Technology Innovation Project Fund of the Chinese Academy of Agricultural Sciences(CAAS-ASTIP-2020-AII-01)the Agricultural Monitoring and Early Warning Research Team of Agricultural Information Institute of Chinese Academy of Agricultural Sciences.
文摘Lately,in some regions and seasons in China,urban consumers have paid high in buying fresh agricultural products while farmers get unreasonable income from producing them.To seek the reason for the phenomenon and explore ways to simulate it,this study constructed and implemented a complex network model named the Bi-Level Multi-Local-World(BI-MLW model)with characteristics of an interdependent coupling relationship between its participants.To verify the validity of the model,this study implemented an experimental simulation under Small Decentralized Operation Mode(SDOM)and Large Centralized Operation Mode(LCOM)scenarios using Cucurbita pepo and Cucumber in the Tianjin area of China as sample empirical products.Results indicate that nodes do not increase edges rapidly which reflects that even large firms in agricultural business cannot occupy markets fleetly.Furthermore,under the SDOM scenario the BI-MLW model exposes scale-free features with a small average degree value and low average clustering coefficient,while under the LCOM scenario,the model displays a rising average clustering coefficient and a lowered average path length.Both of which are consistent with the common view in literature and features of reality.Thus,the BI-MLW model specially designed for fresh agricultural products supply chain can improve the descriptive ability than conventional Erdös-Rényi(ER),Barabási-Albert(BA),Bianconi-Barabási(BB)network models.
基金supported by the National Science Funds for Excellent Young Scholars of China(No.61822106)National Science Funds for Creative Research Groups of China(No.61421002)+1 种基金Natural Science Foundation of China(No.61671115)Central Public-interest Scientific Institution Basal Research Fund(No.Y2019XK18)。
文摘Ethylene(C2 H4),as a plant hormone,its emission can be served as an indicator to measure fruit quality.Due to the limited physiochemical reactivity of C2 H4,it is a challenge to develop high performance C2 H4 sensors for fruit detection.Herein,this paper presents a resistive-type C2 H4 sensor based on Pd-loaded tin oxide(SnO2).The C2 H4 sensing performance of proposed sensor are tested at optimum operating temperature(250℃)with ambient relative humidity(51.9%RH).The results show that the response of Pd-loaded SnO2 sensor(11.1,Ra/Rg)is about 3 times higher than that of pristine SnO2(3.5)for 100 ppm C2 H4.The response time is also significantly shortened from 7 s to 1 s compared with pristine SnO2.Especially,the Pd-loaded SnO2 sensor possesses good sensitivity(0.58 ppm 1)at low concentration(0.05-1 ppm)with excellent linearity(R2=0.9963)and low detection limit(50 ppb).The high sensing performance of Pd-loaded SnO2 are attributed to the excellent adsorption and catalysis effects of Pd nanoparticle.Meaningfully,the potential applications of C2 H4 sensor are performed for monitoring the maturity and freshness of fruits,which presents a promising prospect in fruit quality evaluation.
