Most Read Articles

Published in last 1 year |  In last 2 years |  In last 3 years |  All

Please wait a minute...


For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails

Select

China Swine Industry    2022, 17 (1): 38-49.   DOI: 10.16174/j.issn.1673-4645.2022.01.007 Abstract （6099）      PDF（pc） （1772KB）（1380）       Knowledge map    Save Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2022, 0 (3): 205-206.   Abstract （4308）      PDF（pc） （436KB）（193）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

Select

Analysis of the Contents and Correlations of Nutrient Components in Different Maize Cultivars Kai Li, Qinghua Huang, Ruqing Zhong, Liang Chen, Weifeng Yuan, Hongfu Zhang Journal of Agricultural Big Data    2020, 2 (4): 70-77.   DOI: 10.19788/j.issn.2096-6369.200409 Abstract （3839）   HTML （68）    PDF（pc） （879KB）（1478）       Knowledge map    Save This study analyzed the contents and composition of starch, non-starch polysaccharides (NSP), and nutritional components in grains of different maize cultivars, and evaluated correlations among the analyzed components. Based on 14 varieties maize samples collected by National Livestock and Poultry Breeding Data Center, the contents of starch, NSP, and nutritional components in grains of each maize cultivar were determined. The relationships among the components were examined using principal component analysis and simple correlation analysis. Starch was the most important component in the grain, accounting for 70.97%~76.98% of the dry matter, followed by crude protein, which comprised 7.86%~10.34% of the dry matter, and NSP, which comprised 6.98%~9.76% of the dry matter. The NSP were predominantly composed of arabinoxylans and cellulose. The content of insoluble non-starch polysaccharides (INSP) was significantly higher than that of soluble non-starch polysaccharides (SNSP). The crude fat and crude ash contents in the grain were low and accounted for 3.55%~4.98% and 1.08%~1.49% of the dry matter, respectively. Significant negative correlations were observed between starch content and crude protein, crude fat, crude ash, total non-starch polysaccharides (TNSP), INSP, and insoluble arabinoxylans (IAX) contents (P < 0.05). The TNSP and INSP contents showed strongly significant positive correlations with cellulose content (P < 0.01). The INSP content showed significant positive correlations with crude ash, cellulose, and IAX contents (P < 0.05). The SNSP content was significantly positively correlated with β-glucan and cellulose contents (P < 0.05), and significantly negatively correlated with total arabinoxylans (TAX) and soluble arabinoxylans (SAX) (P < 0.05). The TAX and IAX contents showed a strongly significant positive correlation (P < 0.01). Crude protein, crude ash, TAX, and IAX contents were significantly negatively correlated with arabinose: xylose ratio (A/X) of TAX and A/X of IAX (P < 0.05). A highly significant positive correlation was observed between A/X of TAX and A/X of IAX (r = 0.99, P < 0.01). Differences in the grain nutritional content were detected among the maize cultivars. Starch, crude protein, and NSP were the highest contributors to dry matter, and the predominant NSP components were arabinoxylans and cellulose. Correlations among certain nutritional components were observed, but further research on the relationship between them is needed. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Big Data of Plant Phenomics and Its Research Progress Chunjiang Zhao Journal of Agricultural Big Data    2019, 1 (2): 5-14.   DOI: 10.19788/j.issn.2096-6369.190201 Abstract （3813）   HTML （300）    PDF（pc） （1041KB）（5284）       Knowledge map    Save Plant phenomics is capable of acquiring gigantic multi-dimensional, multi-environment, and multi-source heterogeneous plant phenotyping datasets through integrated automation platforms and information retrieval technologies, based on which the big-data driven plant phenomics research is established. This emerging research domain aims to systematically and thoroughly explore the internal relationship between "gene-phenotype-environment" at the omics level, so that phenomics methods can be utilized to unravel the formation mechanism of specific biological traits in a comprehensive manner. As a result, it is greatly catalyzing the research progress of functional genomics, crop molecular breeding, and efficient cultivation. In this paper, we summarized the background, definition, initiation, and features of the big-data driven plant phenomics, followed by a systemic overview of the progress of this field, including the acquisition and analysis of plant phenotyping data, data management and relevant database construction techniques for administering big data generated, the prediction of phenotypic traits, and its connection with the plant omics research. Furthermore, this paper focuses on discussing present problems and challenges encountered by both plant research and related applications, including (1) the standardization of collecting plant phenotypes, (2) research and development (R&D) of diverse phenotyping devices, supporting facilities, and low-cost phenotyping equipment, (3) the establishment of big data platforms that can openly share phenotyping data and phenotypic traits information, (4) theoretical approaches for fusion algorithms and data mining techniques, and (5) collaborative, sharing and interactive mechanisms for the plant phenomics community to adopt. Finally, the paper puts forward suggestions in four aspects that need to be strengthened: (1) systematic design and standards of plant phenomics research, (2) revealing the mechanism of plant phenotype and environtype to facilitate intelligent equipment R&D, (3) the establishment of big data for plant phenomics, and (4) the formation of collaborations through academic networks and specialized research groups and laboratories. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2021, 0 (3): 10-11.   Abstract （3655）      PDF（pc） （364KB）（2018）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

Select

Financial Risk Analysis Based on Z-score Model -- Taking Xinjiang Tianrun Dairy Co.,Ltd as an Example Ayidana·BALEKATI, CHEN Changming China Dairy    2021, 0 (12): 11-17.   DOI: 10.12377/1671-4393.21.12.02 Abstract （3634）      PDF（pc） （1643KB）（2297）       Knowledge map    Save The Z-score model is a multivariate model proposed by Edward Altman to measure the financial risk of an enterprise. Taking Xinjiang Tianrun Dairy Co.Ltd.as an example,this paper combined the applicability of the Z-score model with the financial statement,using traditional financial accounting risk analysis with Z - score model financial risk analysis method,to analyze the cause of the financial risk. Furthermore,this work compared Xinjiang Tianrun Dairy Co.,Ltd with its leading corporation,Inner Mongolia Yili Industrial Group Co.,Ltd. in the same dairy industry and put forward corresponding countermeasures. The research results indicated that Xinjiang Tianrun Dairy Co.,Ltd. is in a healthy financial position,and the possibility of financial crisis in the short term is relatively low. But there is still a certain gap compared with Inner Mongolia Yili Industrial Group Co.,Ltd. which is the leading enterprise in the dairy industry. Reference | Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2018, 0 (5): 255-256.   DOI: 10.3969/j.issn.1000-6400.2018.05.255 Abstract （3511）   HTML （18）    PDF（pc） （521KB）（293）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

Select

Progress in the Application of Big Data in Agriculture in China Zhou Guomin Journal of Agricultural Big Data    2019, 1 (1): 16-23.   DOI: 10.19788/j.issn.2096-6369.190102 Abstract （3346）   HTML （333）    PDF（pc） （915KB）（3382）       Knowledge map    Save Big data have become a new resource in modern agriculture and an important focus for technological innovation in agricultural science. Big data not only promote the production, operation, management and service provisions of modern agriculture, but also advance the integration of primary, secondary and tertiary industries. In developed regions such as Europe and the United States, special attention has been paid to the role of big data in modern agriculture; in China, research and application of big data in agriculture have also developed rapidly. Agricultural data generally have large spatial and temporal coverages, and are difficult to collect and complicated to process. Therefore, it is of great significance for the research and application of agricultural big data in China to systematically review the progress regarding the application of such data and further clarify the direction of future development. In this study, a literature review is combined with related scientific research; definitions of big data in agriculture proposed by different researchers are compared and analyzed; concepts and matters relating to big data in agriculture are explained; and progress in the application of big data in agriculture in management and policy, engineering and application, and technology and infrastructure in recent years is systematically summarized. Finally, based on the current situation regarding the development of big data in agriculture in China, this review suggests three aspects where special attention should be paid to promote its development: the issues of platform and data, demand and application, and trade and sharing. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2022, 0 (11): 75-77.   Abstract （3266）      PDF（pc） （318KB）（118）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

Select

Research and Application of Big Data in Agriculture Jiang Hou, Yang Yaping, Sun Jiulin Journal of Agricultural Big Data    2019, 1 (1): 5-10.   DOI: 10.19788/j.issn.2096-6369.190101 Abstract （3062）   HTML （307）    PDF（pc） （2372KB）（3401）       Knowledge map    Save Against the background of China's strategy of promoting rural revitalization, big data has become a hot topic in agricultural research and application. Building on the basic characteristics of big data, this paper introduces the concept of agricultural big data and its typical characteristics. It summarizes current approaches for collecting agricultural big data, describes the application system and key technologies of an agricultural big data platform, and discusses the use of agricultural big data for agricultural decision-making, intelligent production, market match, agrometeorology and food safety. Finally, the paper examines the current difficulties in using agricultural big data. In sum, the paper contributes a foundation of understanding for the innovation and development of agricultural big data. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2021, 0 (3): 259-260.   Abstract （2939）      PDF（pc） （286KB）（653）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

Select

The Agricultural Pest and Disease Image Recognition Dataset in Nanjing, Jiangsu Province, in 2023 WANG BoYuan, GUAN ZhiHao, YANG Yang, HU Lin, WANG XiaoLi Journal of Agricultural Big Data    2023, 5 (2): 91-96.   DOI: 10.19788/j.issn.2096-6369.230214 Abstract （2899）   HTML （380）    PDF（pc） （4768KB）（5347）       Knowledge map    Save Agricultural pests and diseases pose a serious threat to crop yield and quality, making accurate and efficient detection and identification of pests and diseases crucial in agricultural production. In this paper, we propose a comprehensive agricultural pests and diseases dataset, which includes agricultural pest detection dataset, agricultural disease detection dataset, agricultural disease classification dataset, and rice phenotype segmentation dataset. By collecting and curating data from public sources and academic papers, we ensured the diversity and representativeness of the dataset. Rigorous quality control and validation measures were implemented during the data filtering, cleaning, and annotation processes to ensure the accuracy and reliability of the dataset. This dataset can be used for agricultural pest and disease recognition, as well as rice phenotype identification and other agricultural visual tasks. It provides valuable resources for agricultural pest and disease research and contributes to the sustainable development of agricultural production. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2021, 0 (10): 277-280.   Abstract （2876）      PDF（pc） （739KB）（299）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2022, 0 (9): 170-173.   Abstract （2835）      PDF（pc） （888KB）（138）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2022, 0 (1): 228-229.   Abstract （2727）      PDF（pc） （307KB）（157）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

Select

The Application, Problems and Development of China's Agricultural Smart Sensors Rui Yan, Zhen Wang, Yanhao Li, Zhemin Li, Xian Li Journal of Agricultural Big Data    2021, 3 (2): 3-15.   DOI: 10.19788/j.issn.2096-6369.210201 Abstract （2716）   HTML （145）    PDF（pc） （1468KB）（5517）       Knowledge map    Save Agricultural smart sensors are among the key technologies of intelligent agriculture. This paper describes the concept, characteristics, and implementation methods of smart sensors and introduces the composition, development, and application of agricultural smart sensors. The agricultural smart sensors were classified into three categories, based on the type of information they detect: life information, environmental information, and quality and safety sensors. The life information smart sensors detect plant and animal life information, and the environmental information smart sensors detect information about water, soil, livestock and poultry, and meteorological events. Currently, the application of agricultural smart sensors in China faces several problems. These include a low degree of integration (modular implementation), a heavy reliance on imports for the core components of agricultural smart sensors (sensor components and microcontroller), a low degree of intelligence, and limited application scope. The root causes of these problems mainly lie in the lack of core controllers dedicated to agriculture, the lack of self-developed high-end agricultural sensors, and the lack of dedicated wireless communication network protocols and high-precision smart sensor algorithms. The paper proposes some feasible countermeasures, such as manufacturing China’s “agricultural core” and high-performance MEMS sensors, constructing special agricultural wireless networks, and developing high-performance smart algorithms. If implemented, these countermeasures will help promote the intelligent manufacturing of agricultural smart sensors in China. With the rapid development of smart agriculture, China’s smart manufacturing of agricultural smart sensors is crucial. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

The Development Direction of Fresh Agricultural Products Supply Chain in China after COVID-19 Dongnan Li, Guoyang Pan, Qian Zhou, Bin Li Journal of Agricultural Big Data    2020, 2 (3): 42-51.   DOI: 10.19788/j.issn.2096-6369.200305 Abstract （2711）   HTML （85）    PDF（pc） （687KB）（2356）       Knowledge map    Save Fresh agricultural products mainly include vegetables, fruits, meat, eggs, milk, and aquatic products. The degree of freshness of perishable primary products determines their own value. In 2020, the COVID-19 outbreak has had a huge impact on people's lives, further affecting the development process of China's fresh agricultural products industry and posing new challenges to the existing supply chain system of fresh agricultural products. Our investigation and analysis show that during the epidemic period, there were negative phenomena such as the contradiction between supply and demand of fresh agricultural products, the slow information transmission of the supply chain, and the narrow purchase channels of suppliers. At the same time, we also identify positive outcomes, such as the recovery of cold chain logistics and new developments in the fresh agricultural supply chain. To further understand the outbreak period, we conducted a survey on the running status of fresh agricultural products. This paper investigates the rise in purchases of fruits and vegetables by studying data from 500 consumers in seven regions including central and southern region. We also investigate the problem of the quality of fruits and vegetables and the future development of the fruit and vegetable industry, and we identify three ways of understanding the changes in the fruit and vegetable supply system since the purchasing outbreak. The study concludes that during the imbalance in the supply and demand of fruits and vegetables, when demand for fruits and vegetables is high, problems arise because of limitations of the fresh product supply system in China. Four factors are important to the future development of the fresh agricultural products supply chain system: the integration of the online supply chain model; the wisdom of the logistics supply chain; the strategic alliance model of agricultural products supply chain; and the demand of the agricultural products supply chain pattern. Together these four factors can ensure the stability and orderly development of the fresh agricultural products industry in the context of normalized epidemic prevention. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2021, 0 (3): 281-282.   Abstract （2688）      PDF（pc） （297KB）（222）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

Select

The development of deep learning based Natural Language Processing (NLP) technology and applications in agriculture Cui Yunpeng, Wang Jian, Liu Juan Journal of Agricultural Big Data    2019, 1 (1): 38-44.   DOI: 10.19788/j.issn.2096-6369.190104 Abstract （2652）   HTML （137）    PDF（pc） （10801KB）（765）       Knowledge map    Save Deep learning is an emerging but rapidly advancing technology having a profound impact on modern natural language processing (NLP) technology. This paper discusses recent developments of NLP technology driven by deep neural networks (DNN), as well as new products and recent cases. In particular, the paper examines advances relevant to the agriculture domain, such as DNN-based word embedding vector construction, the computational ability to recognize and name domain-specific entities and agricultural literature terms. Additionally, it analyzes the implementation details of related technologies. Finally, the paper reviews the trends and outlook for NLP technology, highlighting the significance of NLP technology for intelligent applications in agriculture. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2022, 0 (6): 104-106.   Abstract （2526）      PDF（pc） （292KB）（138）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

Select

Construction of an Agricultural Big Data Platform for XPCC Cotton Production Xin Lv, Bin Liang, Lifu Zhang, Fuyu Ma, Haijiang Wang, Yangchun Liu, Pan Gao, Zhangze, HouTongyu Journal of Agricultural Big Data    2020, 2 (1): 70-78.   DOI: 10.19788/j.issn.2096-6369.200109 Abstract （2489）   HTML （90）    PDF（pc） （1192KB）（2109）       Knowledge map    Save The Xinjiang Production and Construction Corps (XPCC) have created a modern cotton planting system with regional characteristics in China. This new system advances Chinese production techniques in the aspects of agricultural intensification, scale, development of agricultural machinery, and application of modern agricultural technology. During the years of systematic development and performance, massive data were accumulated by XPCC in the cotton planting field. As big data technology has become an important driving force for the development of intelligent agriculture in China, how to apply this technology to further improve the intelligent level of the cotton planting system and realize the healthy, efficient, and sustainable development of the whole cotton industry chain is a key problem in strengthening and enhancing XPCC’s ability to reclaim and defend the Chinese border in the information age. Thus, we constructed a big data platform that covers the entire industrial chain for cotton production in China based on a mature commercial big data storage and analysis system framework to promote the cooperation of industry, colleges, and institutes for cotton production big data in the XPCC. This platform was comprised of data, model, system, and application layers from the bottom up. In each layer, the cotton production chain was analyzed using five dimensions of agricultural resources, agricultural monitoring, production management, agricultural machinery scheduling, and market prediction. After completion, the developed platform intends to provide big data for comprehensive management and sharing, remote-sensing monitoring, agricultural machinery operation monitoring and maintenance, intelligent decision-making, quality traceability, and market early warning and prediction services for cotton production to potential users involved in cotton production and management. Finally, this paper analyzes the problems encountered in data sharing, model upgrades, and service modes in the process of platform research and development, and puts forward some suggestions to provide references for agricultural big data resource sharing and platform construction in China. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Development and Visions of Blockchain Technology Hui Li, Yuming Yuan, Wenqi Zhao Journal of Agricultural Big Data    2020, 2 (2): 5-13.   DOI: 10.19788/j.issn.2096-6369.200201 Abstract （2483）   HTML （112）    PDF（pc） （1126KB）（1410）       Knowledge map    Save Blockchain (or distributed ledger) technology was introduced in 2008, when the famous Bitcoin cryptocurrency was initiated. Blockchain has been undergoing rapid growth in both academia and industry. Today, it is no exaggeration to say that blockchain has become a new, independent research topic—not a subtopic subsumed within cryptocurrencies. From a technical perspective, blockchain technology is based on various fundamental computing technologies, such as advanced cryptography, distributed data storage, peer-to-peer networking, and distributed consensus protocols. Generally, blockchain technology involves creating a shared, distributed ledger: that ledger can offer great flexibility and potential in resolving many important challenges in a complex computing context that involves multiple parties. Examples of such challenges include achieving mutual trust, privacy protection, and data consistency in large-scale business scenarios. Many business applications have already covered a broad range of industrial services, such as those related to finance, governance, medicine, and city construction. Blockchain technology is becoming increasingly adopted and applied; however, the current design of blockchain is practically far from sufficient—especially when dealing with critical domain challenges. Specifically, the key limitations of blockchain mainly derive from poor system scalability, weak resilience to external security attacks, and lack of computing interfaces for regulatory processes. Conversely, it is the very shortcomings of blockchain technology that motivate research efforts into many related technologies. Based on conventional blockchain design, new functional extensions and cryptography technical optimizations have been continuously proposed by researchers and practitioners: the aim is to make blockchain technology more practically applicable and meet various demands of different business users. In this overview paper, using the latest findings from both academic and industrial research, we systematically present the general architecture of blockchain technology with its five functional layers. The five-layered architecture comprises the following: a data layer; a network layer; a distributed consensus layer; a smart contract layer; and an application layer. We also provide a technical description of key theories and important techniques related to each functional layer. From the proposed general architecture of blockchain technology, we offer an in-depth explanation of its core technical extensions with respect to the following: blockchain integration with existing computer network techniques; the blockchain framework itself and important modules; and underlying critical cryptography techniques. Further, we discuss potential contributions that these promising technical extensions could provide with respect to reshaping and optimizing blockchain technology. Finally, following current developments with blockchain technology and its existing mainstream applications, the general views about future challenges and important directions for this technology is to facilitate future follow-up research. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2022, 0 (7): 203-205.   Abstract （2403）      PDF（pc） （749KB）（617）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

Select

China Swine Industry    2021, 16 (6): 22-26.   DOI: 10.16174/j.issn.1673-4645.2021.06.005 Abstract （2356）      PDF（pc） （1161KB）（336）       Knowledge map    Save Related Articles | Metrics | Comments（0）

Select

Analysis and Application of High-throughput Plant Phenotypic Big Data Collected from Unmanned Aerial Vehicles Peisen Yuan, Mingjia Xue, Yingjun Xiong, Zhaoyu Zhai, Huanliang Xu Journal of Agricultural Big Data    2021, 3 (3): 62-75.   DOI: 10.19788/j.issn.2096-6369.210307 Abstract （2343）   HTML （113）    PDF（pc） （1209KB）（1478）       Knowledge map    Save Plant phenotypes refer to the physical, physiological and biochemical characteristics and traits that are determined or influenced by genes and environmental factors. Accurate and rapid access to plant phenotypic information under different environmental conditions, and the analysis of the genetic and performance patterns of their genomes, can effectively promote research on the correlation between genomic and phenotypic information. The Unmanned Aerial Vehicle (UAV) high-throughput plant phenotyping platform is suitable for acquiring plant phenotypic data in field environments owing to the UAV’s mobility and flexibility, and it has the great advantages of a high data acquisition efficiency and low cost. With the help of advanced sensor technologies, such as hyperspectral imaging and LIDAR, the UAV provides a feasible way to efficiently acquire plant phenotypic data. Effective analyses and processing methods and techniques for plant phenotypic data acquired by UAVs must be employed. Thus, high-throughput plant phenotypic analyses based on UAV platforms provides an important tool for studying plant phenotypic information from the field. This paper summarizes and analyzes the latest research results of UAV-based high-throughput crop phenotyping using big data analysis technology and artificial intelligence, as well as its research principles, relevant algorithms, processes, key technologies and applications. The main focus is on big data processing and intelligent analysis techniques related to UAV-based high-throughput plant phenotype big data and to the analysis of typical phenotypes, such as plant height, leaf area index, and plant diseases. We analyzed the current research needs and provide both a summary and outlook on related applications. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

China Swine Industry    2023, 18 (3): 13-17.   DOI: 10.16174/j.issn.1673-4645.2023.03.002 Abstract （2246）      PDF（pc） （1412KB）（764）       Knowledge map    Save Related Articles | Metrics | Comments（0）

Select

Spectral and Imaging Datasets of Apple Leaf Disease and Insect Pests in China in 2015 Fei Gao, Xiaoli Wang, Tingting Liu, Zhuang Li, Rui Man Journal of Agricultural Big Data    2020, 2 (4): 120-124.   DOI: 10.19788/j.issn.2096-6369.200415 Abstract （2151）   HTML （129）    PDF（pc） （1305KB）（1848）       Knowledge map    Save China’s apple planting area and total output rank first in the world, but most of the current collection of spectral and imaging data from fruit trees focuses on apple leaf diseases and insect pests. In this study, the spectral reflectance and imaging data from apple leaves having three different diseases, spot leaf fall and powdery mildew, or the red spider insect pest, were collected from the National Apple Resource Nursery to provide data for the effective identification of apple leaves exposed to different diseases and insect pests. This will provide a foundation for the future use of aerospace remote sensing to monitor large-scale fruit tree diseases and associated insect pests. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2021, 0 (5): 179-181.   Abstract （2143）      PDF（pc） （341KB）（171）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2021, 0 (11): 244-245.   Abstract （2126）      PDF（pc） （315KB）（111）       Knowledge map    Save Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2021, 0 (4): 256-257.   Abstract （2095）      PDF（pc） （306KB）（140）       Knowledge map    Save Related Articles | Metrics | Comments（0）

Select

A Review of Agricultural Supply Chain Management and Its Prospects for the Future Ganqiong Li, Xin Li, Longhua Zhao, Shiwei Xu Journal of Agricultural Big Data    2020, 2 (3): 3-12.   DOI: 10.19788/j.issn.2096-6369.200301 Abstract （2069）   HTML （98）    PDF（pc） （1052KB）（2175）       Knowledge map    Save Agricultural supply chain is an indispensable part in the construction of modern agricultural market system in China. The establishment of a modern agricultural supply chain system is of great significance to promote the integrated development of primary, secondary and tertiary industries in rural areas, improve the modernization level of circulation, and enhance agricultural competitiveness and voice in the international market. Since entering WTO, China agriculture is highly open, and increasingly affected by the global agricultural supply chain in China. Strengthening agricultural supply chain management has become the focus of academic research, the difficulties of government concern and management control as well as the focus of agricultural producers and operators under the new situation of the global spread of COVID-19. Research of agricultural supply chain management started late in China, but it has developed rapidly in the past 10 years. Through literature research and related scientific research practice, this paper analyzes and compares the definitions of agricultural supply chain from different aspects, elaborates the concept and connotation of agricultural supply chain, and emphatically summarizes the research and application progress of agricultural supply chain management in theoretical innovation, mode innovation, risk management and technology innovation. Finally, combined with the new situation at home and abroad and the actual situation in China, the study puts forward the future research direction and hot areas that need to be further studied such as agricultural supply chain emergency management under emergencies, agricultural supply chain governance ability under the background of economic globalization, construction and management of green agricultural supply chain system, innovation of intelligent agricultural supply chain, and provide suggestions for promoting the development of agricultural supply chain management in China. Reference | Related Articles | Metrics | Comments（0）

Select

Influencing Factors and Control Measures of the Determination of Alkaline Phosphatase in Dairy Products ZHOU Ling, XU Guangxin China Dairy    2021, 0 (9): 102-105.   DOI: 10.12377/1671-4393.21.09.19 Abstract （2049）      PDF（pc） （1210KB）（717）       Knowledge map    Save Milk contains a large amount of alkaline phosphatase. Because its thermal stability is slightly higher than that of most pathogenic microorganisms in milk,such as Mycobacterium tuberculosis and Listeria,it is used as the activity index of pasteurization heat treatment evaluation. But it is easily affected by milk components, especially milk fat and additives. In addition,the endogenous alkaline phosphatase of microorganisms in milk and the reactivation of alkaline phosphatase during storage will affect the judgment of the final result. This paper expounds the inspection process through personnel training,equipment selection,inspection comparison,database establishment and so on. Reference | Related Articles | Metrics | Comments（0）

Select

Growth Curve Fitting Analysis of Main Beef Cattle Breeds in China Bingxing An, Lupei Zhang, Xinghai Duan, Jiachun Zhang, Yushan Zhao, Lanling Xiong, Bo Zhu, Yan Li, Lingyang Xu, Weifeng Yuan, Junmin Zhang, Junya Li, Huijiang Gao Journal of Agricultural Big Data    2020, 2 (4): 63-69.   DOI: 10.19788/j.issn.2096-6369.200408 Abstract （2040）   HTML （59）    PDF（pc） （961KB）（1147）       Knowledge map    Save This research was conducted to fit growth curves for, and optimize body weight prediction models of, the main beef cattle breeds in China. Based on more than 60,000 records of 22 cattle breeds collected by the National Center of Beef Cattle Genetic Evaluation, four non-linear models, namely Gompertz, logistic, von Bertalanffy and Brody, were used to fit growth curves, estimate the corresponding parameters and analyze the difference in back fat thickness and loin eye area among 11 beef cattle breeds at the age of 24 months. These models were used to determine breed characteristics and provide an accurate theoretical basis for future breeding programs. According to the R^2 and actual growth laws, the optimal weight prediction models were selected for male and female cattle and the corresponding parameters were estimated. Overall, the body weight of each breed gradually increased from birth to maturity, approaching the “S” curve. The growth rate was faster before 12 months of age, and then gradually decreased after 18 months of age. The growth trend of male and female cattle was essentially the same, but there were certain differences between breeds; for example, Wagyu bulls showed higher growth rates in early stages than Angus, and Wagyu cows showed lower growth rates in early stages than Angus. Meat-type breeds had advantages in mature weight and growth speed compared with dual purpose and local cattle breeds. When compared for back fat thickness and loin eye area at the age of 24 months, some local breeds such as Wenshan cattle, Bohai Black cattle and Nanyang cattle had greater fat deposition, and Jiaxian Red cattle, Jinnan cattle and Sanhe cattle were superior in meat production. This study provides more accurate weight prediction models than were previously available for bulls and cows of the major beef cattle breeds in China. Although some local cattle breeds are small in body size, they have the potential to produce high-quality beef products. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

China Swine Industry    2021, 16 (6): 13-16.   DOI: 10.16174/j.issn.1673-4645.2021.06.002 Abstract （1983）      PDF（pc） （1116KB）（493）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

Select

Mechanism and Regulation of Urea Decomposition by Bacterial Urease XIONG Zhanbo, ZHAO Shengguo, WANG Jiaqi China Dairy    2021, 0 (9): 3-7.   DOI: 10.12377/1671-4393.21.09.02 Abstract （1965）      PDF（pc） （3607KB）（1156）       Knowledge map    Save Urease can efficiently catalyze urea decomposition and produce carbon dioxide and ammonia. Limiting urease activity can effectively regulate urea decomposition process.In animal husbandry,ruminant urease can lead to excessive nitrogen emission.In this paper,the structural characteristics of urease activity center in bacteria,the mechanism of urease hydrolysis and the mechanism of urease activity regulation by inhibitors were reviewed,providing theoretical basis for effective regulation of urease activity and ideas for the development of new inhibitors. Reference | Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2021, 0 (7): 67-70.   Abstract （1918）      PDF（pc） （413KB）（108）       Knowledge map    Save Related Articles | Metrics | Comments（0）

Select

Determination of chilling requirements in Different Cultivation Areas of Sweet Cherry Bin Zhao, Xizhen Liu, Zhigang Lin, Xiang Yang, Jin Bai, Jiancai Li Journal of Agricultural Big Data    2022, 4 (3): 23-29.   DOI: 10.19788/j.issn.2096-6369.220303 Abstract （1896）   HTML （13）    PDF（pc） （773KB）（725）       Knowledge map    Save Objective The Chilling Requirement (CR) of different varieties of sweet cherries were studied, and the differences in CR of the same varieties of sweet cherries in different regions were compared. Methods From 2021 to 2022, the Utah model, the 7.2°C model and the 0-7.2°C model were applied to the six varieties of sweet cherries cultivated in Huludao City, Liaoning Province, namely Jiahong, Hongdeng, Mingzhu, American Red, Sand Honey Bean and Meizao, as well as Jiangxi The CR of four varieties of sweet cherries cultivated in the Fuzhou region, namely Sand Honey Bean, Mingzhu, Jiahong and Meizao, were estimated. Results For the sweet cherry varieties tested in the Huludao area, the estimated CR of the Utah model ranged from 469 to 564.5 C.U. The estimated CR of the 0-7.2 °C model ranged from 334 to 442 h. The estimated CR range is between 710 and 1218 h; for the sweet cherry varieties tested in Fuzhou, the estimated CR range of the Utah model is between 261.5 and 525 C.U. The estimated CR of the 0-7.2 °C model The estimated CR ranged from 98 to 301 h, and the estimated CR of the 7.2 °C model ranged from 105 to 328 h. For the sweet cherry varieties tested in the Huludao area, the estimated CR of the Utah model ranged from 469 to 564.5 C.U. The estimated CR of the 0-7.2 °C model ranged from 334 to 442 h. The estimated CR range is between 710 and 1218 h; for the sweet cherry varieties tested in Fuzhou, the estimated CR range of the Utah model is between 261.5 and 525 C.U. The estimated CR of the 0-7.2 °C model The estimated CR ranged from 98 to 301 h, and the estimated cooling capacity of the 7.2 °C model ranged from 105 to 328 h. Conclusion There is a big difference in the CR of the same variety of sweet cherries in the two regions. The Utah model estimated the difference is the smallest, and the preliminary judgment is more suitable for the comparative study of the CR of sweet cherries in different regions. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2022, 0 (8): 220-222.   Abstract （1893）      PDF（pc） （394KB）（96）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

Select

Progress of Research to Develop a Traceability System for Fresh Agricultural Products Using Blockchain Technology Zhe Zhang, Xinting Yang, Helong Yu, Shanshan Li, Chuanheng Sun Journal of Agricultural Big Data    2022, 4 (1): 25-34.   DOI: 10.19788/j.issn.2096-6369.220102 Abstract （1863）   HTML （88）    PDF（pc） （999KB）（1108）       Knowledge map    Save With continually improving living standards, the quality and safety of fresh agricultural products has come under public scrutiny. Fresh produce traceability is an indispensable and important link in the process of guaranteeing food quality and safety and is also an effective measure for improving consumers’ trust in food products. A blockchain database records and stores data within a chain structure. Using an encryption algorithm, a consensus mechanism, smart contracts, and other technologies, a blockchain provides an efficient solution to the challenge of ensuring data privacy and trust in multi-party cooperation. A traceability system for agricultural products that is based on blockchain technology entails the advantages of improving the transparency and efficiency of production, processing, transportation, and sales processes; enhancing data reliability; and reflecting data traceability. It can also eliminate unnecessary intermediaries in the agricultural product supply chain and enhance consumers’ confidence in traceable agricultural products. This paper introduces the blockchain and its technical features and presents an analysis of the entire supply chain for fresh agricultural products from planting and breeding, to production and processing, to logistics and transportation, through to distribution and sales. It proposes a traceability framework for fresh agricultural products that is based on blockchain and Internet of things devices and equipment and summarizes progress made in applying blockchain technology in the traceability of fruit and vegetables, livestock and poultry meat, and aquatic products. Moreover, it identifies key problems currently constraining the traceability of blockchain agricultural products, such as the low acceptance of blockchain links by consumers, concerns about user privacy and the confidentiality of transactions between market subjects, and an inability to ensure the authenticity of source data. Lastly, it discusses future developmental directions of fresh agricultural product traceability using blockchain technology, which can guide and inspire further in-depth research in this area. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Bulletin of Agricultural Science and Technology    2022, 0 (10): 214-216.   Abstract （1851）      PDF（pc） （388KB）（941）       Knowledge map    Save Related Articles | Metrics | Comments（0）