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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 （1624）   HTML （229）    PDF（pc） （4768KB）（2345）       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）

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Theory and Engineering Technology Implementation of Artificial Intelligence Retrieval Paradigm for Parameters of Remote Sensing Based on Big Data MAO KeBiao, YUAN ZiJin, SHI JianCheng, WU ShengLi, HU DeYong, CHE Jin, DONG LiXin Journal of Agricultural Big Data    2023, 5 (4): 1-12.   DOI: 10.19788/j.issn.2096-6369.230401 Abstract （267）   HTML （43）    PDF（pc） （1723KB）（226）       Knowledge map    Save In order to solve the "black box" problem of artificial intelligence application in geophysical parameter retrieval, and make artificial intelligence applications have physical significance, interpretability, and universality, the theory and technology of deep learning coupling physical and statistical methods are gradually being developed in various disciplinary fields. This study summarizes the author's more than 20 years of relevant research, and presents the artificial intelligence inversion paradigms and judgment conditions for remote sensing parameters based on the induction and deduction of the theory and judgment conditions of artificial intelligence geophysical parameter inversion paradigms. At present, a common problem encountered in many studies is that many artificial intelligence parameter retrieval uses theoretical simulation data to achieve high retrieval analysis accuracy, but the actual application retrieval accuracy is not ideal. Therefore, deep learning how to couple physical and statistical methods has become an urgent engineering and technical challenge that needs to be addressed. We will take passive microwave soil moisture and surface temperature retrieval as an example to illustrate that the accuracy of the physical model itself still needs to be greatly improved, or the simulated data only represents a small portion of the actual situation. We believe that there are significant limitations in using only physical models to simulate data for direct retrieval, and high-precision multi-source statistical data must be supplemented. At the same time, we can also improve the physical model by directly using deep learning to simulate data training and testing with actual data to verify the gap between the physical model and the actual situation, determine the errors of the physical model, and thus improve the physical model. Statistical methods are the most intuitive description of human beings, while physical methods summarize and generalize statistical methods. However, information or energy transmission in the real world is transmitted in quantum form, and many physical models have made many simplifications without depicting real physical phenomena well. Different neurons in deep learning are more suitable for describing and expressing the transmission methods of quantum information. Understanding the real world through calculus quantum information flow requires improving our cognitive thinking. How to collect data that meets the real situation (quantum information or energy transmission) is very important. We can fully utilize physical logic reasoning to construct physical formulas and statistical methods, and use big data thinking mode to improve the accuracy of geophysical parameter inversion under the guidance of paradigm theory and judgment condition framework. Proving through physical logic reasoning that the input variable can uniquely determine the output variable is a fundamental condition for forming a physically meaningful, interpretable, and universal retrieval or classification or prediction paradigm. Controlling the quality of collected data from the perspective of quantum information (energy) transmission is the key to achieving high-precision inversion engineering and technology for geophysical parameters. Improving the cognitive understanding of quantum information flow in calculus and identifying the limitations of physical models are of milestone significance for achieving high-precision inversion in artificial intelligence. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Dataset on the Compiled Materials of Costs and Profits of Rice, Wheat and Corn Products of China ZHAN ZiSen, ZHANG XiaoHeng, CHEN Bo Journal of Agricultural Big Data    2023, 5 (4): 110-117.   DOI: 10.19788/j.issn.2096-6369.230414 Abstract （264）   HTML （30）    PDF（pc） （362KB）（243）       Knowledge map    Save General Secretary Xi Jinping has pointed out that "the prosperity of industries is of paramount importance for rural revitalization." The Cost-Benefit Survey of Agricultural Products records information on inputs, outputs, and returns related to agricultural products, serving as the foundation for macroeconomic regulation and price management by government departments. In the new era and on the new journey, this dataset will play an even greater role in advancing the rural revitalization strategy. A large number of literature analyze the situation of China’s agricultural input factor use, productivity, cost and profit based on this dataset, but the introduction of details such as the sample selection of the dataset, the collection process, and the connotation of the relevant indicators needs to be enhanced. Therefore, this paper primarily compiles cost-benefit survey data for three types of grains, including early-season rice, mid-season rice, late-season rice, japonica rice, wheat, and maize, from 2005 to 2017 in 31 regions, forming a comprehensive dataset. This paper provides an introduction to the background, data collection methods, primary content of the data, and its implications and values. The data is collected by using a stratified random sampling procedure to improve the representativeness. The cost and profit data of rice farms contains the farm size, yield, output value, pesticide cost, fertilizer quantity and cost, seed quantity and cost, irrigation cost, labor quantity and cost, and land cost on per unit area. Relevant scholars can not only use the data to analyze the input-output situation of China's agricultural products, but also draw on the sampling method and quality control experience of the data. Data summary: Item Description Dataset name A Dataset on the Compiled Materials of Costs and Profits of Agricultural Products of China Specific subject area Agricultural economics Research topic Agricultural inputs and outputs, productivity Time range 2005—2017 Geographical scope National weighted average and 31 provinces, municipalities and autonomous regions (subject to change depending on the crop structure of each region, see text section for details). Data types and technical formats .xlsx Dataset structure This database contains 6 products for 3 grains, with three types of data for each product, including 28 survey items for the cost-benefit profile, 33 survey items for the cost and labor profile; and 29 survey items for the fertilizer input profile, as described in the main text. All in one excel file. Volume of data 0.7 MB Key index in dataset Yield, output, cost, revenue Data accessibility CSTR: 17058.11.sciencedb.agriculture.00083 DOI: 10.57760/sciencedb.agriculture.00083 Financial support National Natural Science Foundation of China（72003074） Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Image Dataset of Stored Grain Pests by Henan University of Technology YU JunWei, ZHAI FuPin Journal of Agricultural Big Data    2023, 5 (2): 85-90.   DOI: 10.19788/j.issn.2096-6369.230213 Abstract （232）   HTML （40）    PDF（pc） （903KB）（412）       Knowledge map    Save As grain pests cause a major post-harvest loss in stored grains, early detection and monitoring of grain pest activities become necessary for applying appropriate actions to reduce storage losses. With the development of artificial intelligence, image detection methods based on deep learning have been widely used in agriculture. However, current research in stored grain pest detection is relatively limited. The quality of the dataset will determine the level of knowledge that deep learning models can learn. Therefore, constructing a specialized dataset for grain pest detection and counting is of great significance. The proposed dataset GrainPest includes 500 original images of grain insects, 500 pixel-level saliency annotation images, 420 files with insect bounding boxes and 500 entries of pest counts. The data set covers various grain pests such as corn weevil, wheat moth, grain beetle, and corn borer, as well as different types of grain backgrounds such as wheat, corn, and rice. Due to the fact that many grains are not infected with pests, the GrainPest also includes 80 pure grain background images without any pest, which bring more challenge for saliency detection. The GrainPest provides a benchmark dataset to promote the research of saliency detection, object detection, and pests counting in stored grains, and the work will provide support for reducing grain storage losses and ensuring food security. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Analysis of China's Rural E-commerce Research Dataset JIA Cheng, YI HongMei Journal of Agricultural Big Data    2023, 5 (4): 95-102.   DOI: 10.19788/j.issn.2096-6369.230412 Abstract （227）   HTML （21）    PDF（pc） （359KB）（358）       Knowledge map    Save This study reviewed the data used in the studies on rural e-commerce in China. The rural e-commerce data are divided into two types of datasets based on the characteristics of targeted e-commerce interventions and agricultural product trading locations. The first dataset includes databases on comprehensive demonstration of e-commerce in rural counties, and Taobao Villages and e-commerce index. The second dataset involves databases on e-commerce of agricultural products, and cross-border e-commerce agrarian products. We presented the data sources, the definition of related indicators, and the time span of each dataset, and analyze the pros and cons of each data in answering the related research topics. This systematic review can be a benchmark for researchers interested in rural e-commerce to understand the data and assess the related studies using these datasets. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A 10 m Spatial Resolution Dataset for the Spatial Distribution of Cropland Resources in the Three Northeastern Provinces from 2020 to 2022 SHEN Ge, LIU Hang, LI DanDan, CHEN Shi, ZOU JinQiu Journal of Agricultural Big Data    2023, 5 (2): 2-8.   DOI: 10.19788/j.issn.2096-6369.230202 Abstract （211）   HTML （37）    PDF（pc） （1074KB）（531）       Knowledge map    Save Timely and accurate acquisition of cropland spatial distribution data is of great significance for agricultural production management, planting structure adjustment and food security. In this study, three northeastern provinces (Heilongjiang, Liaoning and Jilin) were selected as the research area. Based on massive Sentinel-2 data, a 12-month Normalized Difference Vegetation Index (NDVI) dataset of cropland and non-cropland samples in a specific year was established in each province. The Google Earth Engine remote sensing computing platform was used to conduct supervised classification according to the difference characteristics of NDVI between cropland and non-cropland samples, and the spatial distribution data set of cropland resources with 10 m spatial resolution in the three northeastern provinces during 2020-2022 was obtained. The data set is an update of the latest available cropland resource data set, which can provide data support and scientific services for the scientific protection of phaeozem in Northeast China, the implementation of the strategy of "storing grain in land, storing grain in technology" and the guarantee of food security. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Dataset of National Agricultural Machinery Purchase and Application Subsidy Information from 2021 to 2022 YANG WenJun, GU XiangQun, YANG ZhiHai Journal of Agricultural Big Data    2023, 5 (3): 49-55.   DOI: 10.19788/j.issn.2096-6369.230309 Abstract （190）   HTML （19）    PDF（pc） （326KB）（399）       Knowledge map    Save As an important policy to strengthen and benefit agriculture, the agricultural machinery purchase subsidy policy has a profound impact on many parties, such as new agricultural business entities, agricultural production, and the agricultural machinery industry. Through the collection of national agricultural machinery purchase and subsidy data, we can grasp the differences and trends in the purchase of agricultural machinery in various regions. We can then reasonably assess the impact of the policy, adjust the policy objectives and content, promote the healthy development of the agricultural machinery market, and better assist the construction of a strong agricultural country. At present, the Department of Agriculture and Rural Affairs of each province in China, through the information disclosure column of agricultural machinery purchase subsidies, releases data related to agricultural machinery purchase in real time. Through network crawling and processing, this dataset covers the subsidy data on the purchase and application of agricultural machinery in 23 provinces (autonomous regions and municipalities directly under the central government) such as Beijing, Tianjin, Shanxi, etc. in 2021-2022, totaling 222,6229 items. This dataset can be used to analyze the characteristics and differences of the purchase of agricultural machinery and the subsidy distribution in different regions, and provide a data basis for related scientific research and management decision-making. Data summary: Items Description Dataset name A Dataset of National Agricultural Machinery Purchase and Application Subsidy Information from 2021 to 2022 Specific subject area Agriculture economics Research topic Subsidies for the purchase of agricultural machinery Time range 2021-2022 Geographical scope Beijing, Tianjin, Shanxi and other 23 provinces (autonomous regions and municipalities directly under the central government) Data types and technical formats Preprocessed data (EXCEL format) Dataset structure It is divided into 3 files by province. The first one is Chongqing, Shanxi, Zhejiang, Shaanxi 2021-2022 dataset. The second is Henan, Hubei, Jiangxi, Xinjiang, Heilongjiang, Xizang 2021-2022 dataset. The third is Hebei, Gansu, Anhui, Fujian, Guangxi, Liaoning, Guizhou, Hainan, Ningxia, Qinghai, Tianjin, Beijing, Shanghai 2021-2022 dataset Volume of data 259.49 MB Key index in dataset 19 indicators, including province, county, township (town), corresponding character of the purchaser's name, purchaser's name, and item of the machin Data accessibility CSTR：31253.11.sciencedb.12793 DOI：10.57760/sciencedb.12793 Financial support Ministry of Education, Philosophy and Social Science Major Project (No. 20JZD015); National Natural Science Foundation of China (No. 72303076); National Innovation and Entrepreneurship Training Programme for College Students (No. 202310504074) Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Dataset on Spatial Distribution of Mangroves in Qi'ao Island, Zhuhai, Guangdong Province from 2000 to 2020 CAI HuiNa, WANG Ruei-Yuan Journal of Agricultural Big Data    2023, 5 (2): 9-15.   DOI: 10.19788/j.issn.2096-6369.230203 Abstract （188）   HTML （33）    PDF（pc） （3149KB）（321）       Knowledge map    Save Mangroves are distributed in the tropical and subtropical coastal intertidal zones, playing a critical role in the global carbon cycle and providing service value for ecological and ecological economic development. But with the development of human activities and the deterioration of the natural environment, mangrove resources have sharply decreased. Using Landsat images of 2000, 2005, and 2010, and Sentinel-2 images of 2015 and 2020, through decision tree classification and object-oriented classification methods, combined with field surveys, the study extracts the spatial distribution and area of mangroves each year, and produces the spatial distribution dataset of mangroves in Qi'ao Island from 2000 to 2020. The article dataset can analyze the spatial dynamic evolution of mangroves, providing important references for scientific research such as the dynamic changes of mangroves on Qi'ao Island and the evaluation of ecological environment quality; providing decision-making support for the protection, restoration, and management of mangrove wetlands; and providing basic data support for environmental monitoring in Zhuhai and Guangdong Province. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Statistical Dataset of Egg Consumption Preference Survey in China in 2020 LI LingYue, XIONG Hang, HE Juan Journal of Agricultural Big Data    2023, 5 (4): 130-137.   DOI: 10.19788/j.issn.2096-6369.230417 Abstract （179）   HTML （28）    PDF（pc） （475KB）（162）       Knowledge map    Save China, as the world's leading producer and consumer of eggs, relies significantly on eggs as a primary protein source in the dietary structure of its residents. Understanding consumer preferences for eggs in China holds substantial practical significance. This research aims to comprehend the preferences of Chinese consumers regarding egg production methods, certification standards, and other related attributes. The objective is to provide precise market positioning strategies, scientific foundations, and guidance for policymaking and dietary health education initiatives. The survey design primarily utilizes a discrete choice experiment (DCE) approach, supplemented by the collection of data on respondents' relevant food safety knowledge and demographic variables. Ultimately, the research team collected 1085 samples from consumers across 30 provincial-level administrative regions through an online questionnaire platform, forming the 2020 dataset on Chinese consumers' egg preferences. This dataset comprehensively encompasses 13 aspects, including the frequency and sources of egg purchases, as well as preferences for egg prices, rearing methods, hen breeds, and food safety certification attributes during the egg consumption process. The research findings reveal distinct consumer preferences concerning egg production methods and certification standards. This dataset serves as foundational information for understanding residents' consumption habits and preferences, ensuring national food safety, guiding market strategies for food enterprises, and providing robust support for evidence-based policymaking by the government. Data summary: Item Description Dataset name Statistical Dataset of Egg Consumption Preference Survey in China in 2020 Specific subject area Agricultural science Research topic Egg consumption preference Time range 2020 Geographical scope 30 Provincial-level administrative regions Data types and technical formats *.xlsx Dataset structure This dataset consists of two data files: the original data and the table data. It mainly includes the statistics of egg consumption habits and the statistics of consideration factors for purchasing eggs. Volume of dataset 741 KB Key index in dataset Purchase frequency, purchase source, purchase price, purchase weight, the concern about price, feeding method, freshness degree, brand, food safety certification, color, packaging, and size, the attribute level and results of choice experiment Data accessibility CSTR: 17058.11.sciencedb.agriculture.00067 DOI: 10.57760/sciencedb.agriculture.00067 https://www.scidb.cn/anonymous/SkIzaVly Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Training Dataset for Deep Neural Network Model Recognition of Common Cotton Diseases ZHAO HongXin, SHAO MingYue, PAN Pan, WANG ZhiAo, MU Qiang, HE ZiKang, ZHANG JianHua Journal of Agricultural Big Data    2023, 5 (4): 47-55.   DOI: 10.19788/j.issn.2096-6369.230405 Abstract （177）   HTML （22）    PDF（pc） （3734KB）（287）       Knowledge map    Save In the realm of cotton disease identification, the Deep Neural Network emerges as a pivotal paradigm. Progress in this sphere hinges on the availability of a comprehensive repository of scientific data, encapsulating a broader spectrum of diseases, variegated soil profiles, and multifaceted environmental attributes. Currently, this dearth of data serves as the principal bottleneck, impeding the advancement of state-of-the-art models and algorithms.Within this scholarly exposition, we present a meticulously curated cotton disease dataset, poised to bridge this knowledge chasm. This dataset comprehensively encompasses four prevalent cotton diseases: anthracnose, bacterial blight, brown spot, and wilt disease. These maladies' exemplars were meticulously gleaned from cotton fields situated in the Potianyang High-standard Farmland Demonstration Base, nestled serenely in Sanya, Hainan Province, China.The dataset unfolds as a magnum opus, comprising 3 453 high-resolution images. These vivid snapshots provide a panoramic view, capturing the pristine vitality of healthy leaves, juxtaposed with leaves beset by disease at various growth stages. The data acquisition, executed with precision, leveraged field random sampling methodologies, ensuring a faithful reflection of the natural complexity in real-world cotton plantations.Every image underwent meticulous scrutiny, with ten seasoned mavens in cotton pathology meticulously overseeing the annotation. An additional cohort of twenty annotators conducted a second round of annotations on randomly selected image subsets, fortifying the dataset's integrity and precision. The Vision Transformer model was employed to guarantee the dataset's resilience and accuracy.This hallowed dataset was meticulously gathered amidst the complexity of field environments, encapsulating the nuances of major cotton diseases in their native habitat. Its high image resolution, akin to an opulent tapestry of visual data, renders it an invaluable resource for pioneering research, astute training, and the relentless validation of astute, intelligent cotton disease recognition models and algorithms. This opulent repository caters to the discriminating tastes of researchers, practitioners, and sagacious decision-makers, furnishing them with a comprehensive and crystalline understanding of the multifaceted tapestry of cotton diseases and their intricate management. Data summary: Item Description Dataset name A Training Dataset for Deep Neural Network Model Recognition of Common Cotton Diseases Specific subject area Agricultural Science, Computer Science Time range December, 2021－August, 2023 Geographical scope This dataset covers the plain planting area of Potianyang Base in Sanya City, Hainan Province, with a central latitude and longitude of (109.165497,18.3931609999999) Data types and technical formats Cotton Image Format *. jpg, Cotton Disease Classification Standard Format *. txt Dataset structure The dataset consists of 3453 image files and one text file. The image files belong to a folder named Cotton Disease Data, all of which are *. JPG files. The folder where the text files belong is named the Cotton Disease Dataset, where all files are *. TXT Volume of data 2.74 GB Data accessibility CSTR：17058.11.sciencedb.agriculture.00029 DOI：10.57760/sciencedb.agriculture.00029 Financial support National Key R&D Plan (2022YFF0711805); Science and Technology Special Fund for Sanya Yazhou Bay Science and Technology City (SCKJ-JYRC-2023-45);Innovation Engineering of the Chinese Academy of Agricultural Sciences (CAAS - ASTIP - 2023 - AII, ZDXM23011); Special funds for basic research business of central level public welfare research institutes (Y2022XK24, Y2022QC17, JBYW - AII - 2022 - 14, JBYW - AII - 2023 - 06); Sanya Chinese Academy of Agricultural Sciences National South Breeding Research Institute South Breeding Special Project (YDLH01, YDLH07, YBXM10, ZDXM23011, YBXM2312) Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Economic and Social Data of Agriculture and Rural Areas: Resources and Methods XIONG Hang Journal of Agricultural Big Data    2023, 5 (3): 1-1.   DOI: 10.19788/j.issn.2096-6369.230301 Abstract （175）   HTML （48）    PDF（pc） （314KB）（183）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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Statistical Dataset of Land Economic Survey in Jiangsu Province, China WANG ZiYu, JI YueQing, ZHOU Li Journal of Agricultural Big Data    2023, 5 (4): 138-143.   DOI: 10.19788/j.issn.2096-6369.230418 Abstract （171）   HTML （36）    PDF（pc） （310KB）（175）       Knowledge map    Save Effective land scale management is crucial for achieving agricultural modernization in China. However, the current landscape is plagued by practical challenges, including the sluggish growth of land transfer and the hindrance of moderate scale management. Jiangsu Province, characterized by its high economic development and a well-established land transfer market, has successfully addressed these issues through innovative approaches such as land readjustment and the establishment of intermediary organizations. The dataset is based on the 2020-2022 China Land Economic Survey (CLES) database, and it has been organized into three datasets comprising 943 plots, 5923 households, and 114 villages, following standardized procedures. The data content includes information on land use and transfer, as well as scale. The empirical evidence provided by this data set offers valuable insights into land transfer and large-scale operation in Jiangsu Province, thereby serving as a reference for government departments in formulating effective policy interventions. Data summary: Item Description Dataset name Statistical Dataset of Land Economic Survey in Jiangsu Province, China Specific subject area Agricultural economics Research topic Land transfer and large-scale management Time range 2019-2021 Geographical scope Jiangsu province Data types and technical formats *.xlsx Dataset structure The dataset consists of three XLSX files: (1) 943 land parcel dataset: including information on land basic characteristics, ownership and input-output (2) 5923 household dataset: including information on household land use and transfer (3) 114 village dataset: including information on village land use, transfer, and large-scale management Volume of data 2.75 MB Key index in dataset The cultivated land area under management, The rate of arable land transfer Data accessibility CSTR：17058.11.sciencedb.agriculture.00078 DOI：10.57760/sciencedb.agriculture.00078 Financial support Major Bidding Program of National Social Science Foundation of China "Research on the Path and Policy System for Achieving High-quality Development of Grain Industry in China" (21&ZD101) General Program of National Natural Science Foundation of China " The Lone Wolf Dies but the Pack Survives: Local External Economies for Smallholders’ Farm-size Choice in Modern Agriculture" (72073066). Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Status and Analysis of Target Application Robot and Its Key Technologies SHI Hang, HU Jun, LI YuFei, LIU ChangXi, ZHANG Hui, ZHANG JianYe Journal of Agricultural Big Data    2023, 5 (2): 54-61.   DOI: 10.19788/j.issn.2096-6369.230208 Abstract （161）   HTML （11）    PDF（pc） （652KB）（96）       Knowledge map    Save The research and application of agricultural robots is an important trend in the development of smart agriculture, and the target application robot is an important branch in the field of agricultural robots. This study summarizes the current research status and progress of domestic and international targeted spraying robots, and introduces the working principle and main technical points of targeted spraying robots. The study analyzes the current research status of key technologies for targeted spraying robots, including plant diseases and insect pests detection technology, targeted spraying technology, autonomous walking and control technology, and elaborates on the research progress and challenges of these key technologies both at home and abroad. It is pointed out that currently, most targeted spraying robots domestically and internationally can achieve the effects of saving pesticides and improving pesticide utilization rate, but their development is mostly in the laboratory stage and cannot fully meet actual production tasks. This study can provide reference and ideas for the advancement of targeted spraying robot research and the development of intelligent agriculture. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Genome-wide Identification and Expression Analysis of WRKY Gene Family in Five Legumes CHEN NaiYu, ZHAO He, JIANG HuiXin, LING Lei, YIN YaJie, REN GuoLing Journal of Agricultural Big Data    2023, 5 (2): 16-26.   DOI: 10.19788/j.issn.2096-6369.230204 Abstract （142）   HTML （20）    PDF（pc） （4374KB）（389）       Knowledge map    Save In order to enhance understanding of the diversity and evolution of WRKY genes in leguminous plants, and to explore the functions of WRKY transcription factor family members and their applications in breeding, in this study, we analyzed the classification, basic physicochemical properties, evolutionary relationship, gene structure, chromosome location, conserved motifs, promoter elements, gene collinearity, expression in five legumes (Glycine max, Cicer arietinum, Phaseolus vulgaris, Medicago truncatula, Lotus japonicus) by using bioinformatics. A total of 185, 61, 90, 108 and 83 WRKY genes were identified, respectively. WRKY protein were identified, and the classification, basic physicochemical properties, evolutionary relationship, gene structure, chromosome location, conserved motifs, promoter elements, gene collinearity, expression analysis were systematically analyzed.The WRKY proteins in all five species were divided into three classes and five subclasses.The WRKY proteins derived from the same evolutionary clade were found to have similar genes and protein structures. There are gene replication events in members of WRKY gene family of the five leguminous plants, and there are significant differences in expression in each tissue.The expression patterns of WRKY genes in different tissues indicate that they may play an important role in the growth and development of leguminous plants. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification ZHANG MengMeng, WANG XiuJuan, KANG MengZhen, HUA Jing, WANG HaoYu, WANG FeiYue Journal of Agricultural Big Data    2023, 5 (4): 13-23.   DOI: 10.19788/j.issn.2096-6369.230402 Abstract （141）   HTML （21）    PDF（pc） （1259KB）（182）       Knowledge map    Save Accurate and efficient identification of crop diseases can enable farmers to take effective and targeted preventive measures in a timely manner, which is helpful to reduce the risk of yield reductions and economic losses caused by crop diseases. However, the recognition model that can achieve the effect of SOTA in other fields, especially in the application of rice disease identification, faces the challenge of insufficient available rice disease data, a limited range of disease varieties and low data quality. In this paper, a variety of classical convolutional neural networks are trained on two different datasets using transfer learning methods. We demonstrated that in addition to the optimization achieved through model structure, the training data set itself has an important impact on the training results. However, the scarcity of open-source agricultural data, coupled with the absence of a comprehensive open-source data sharing platform, remains a substantial obstacle. This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data, low level of education of most employees, underdeveloped distributed training systems and unsecured data security. To solve those challenges, this paper proposed a novel idea to construct an agricultural data sharing platform based on federated learning framework, aiming to address the deficiency of high-quality data in agricultural field training. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Identification of Stripe Rust Resistance and Molecular Detection of Resistance Genes in Yunnan Wheat Landrace Germplasm Resources CHEN Dan, DU Juan, ZHOU GuoYan, WU XiaoYang, BAI XiaoDong, WU ShaoYun, CAI Qing Journal of Agricultural Big Data    2023, 5 (2): 27-35.   DOI: 10.19788/j.issn.2096-6369.230205 Abstract （138）   HTML （17）    PDF（pc） （441KB）（121）       Knowledge map    Save In-depth identification of crop germplasm resources is the premise and basis for breeding and utilization. The Crop Genebank of Yunnan Province has preserved abundant wheat germplasm resources in Yunnan, but the resistance characteristics of stripe rust are still unclear. In this study, 260 Yunnan landrace wheat germplasm resources preserved in the Crop Genebank were determined by carrying out the identification of stripe rust resistance at adult plant stage in the field and the genotype analysis of three known resistance genes Yr5, Yr10 and Yr15, to clear the resistance level of stripe rust and the distribution of three known resistance genes Yr5, Yr10 and Yr15, which provided a theoretical reference for the screening of excellent resistance resources and the exploration and utilization of resistance genes. The results showed that 38 of 260 Yunnan landrace wheat germplasm resources showed immune and near immune resistance to stripe rust at adult stage in Songming experimental site in 2022, accounting for 14.62 %. There were 93 high resistance materials, accounting for 35.77 %, 46 medium resistance materials, accounting for 17.69 %, and 83 medium and high susceptible materials, accounting for 31.92 %. At the same time, using capillary electrophoresis detection technology, there were 11 materials carrying Yr10 resistance gene in 260 Yunnan landrace wheat germplasm resources, accounting for 4.23 %, and 0 materials carrying Yr5 and Yr15 resistance genes. In summary, 260 Yunnan landrace wheat resources are rich in stripe rust resistance materials. The proportion of immune, near immune and high resistance materials are 50.39 %, and resistance genes Yr5 and Yr15 are undetected. It is speculated that the above materials are likely to carry unknown new resistance genes. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Statistical Dataset of Fixed Observation Point Survey of Farmers' Cooperatives in Sichuan Province from 2020 to 2021 ZHANG SheMei, DENG JieHao, MO JingMei Journal of Agricultural Big Data    2023, 5 (3): 32-37.   DOI: 10.19788/j.issn.2096-6369.230306 Abstract （134）   HTML （23）    PDF（pc） （323KB）（141）       Knowledge map    Save Farmers' cooperatives have become an important part of China's modern agricultural industrial organization, and their development quality is directly related to the process of China's agricultural and rural modernization. By establishing fixed observation points with 456 (2020) and 537 (2021) farmers’ cooperatives in 10 counties (cities and districts) in Sichuan Province, the research group carried out field research for two consecutive years, collected detailed data on the production and operation of farmers’ cooperatives from 2020 to 2021, and processed and sorted out the data according to scientific methods to obtain 993 pieces of internal governance structure, industrial integration, green technology adoption, and demonstration drive that reflected the development of farmers' cooperatives. The operation quality dataset of farmers' cooperatives in Sichuan Province from 2020 to 2021 was formed. This data provide the possibility for improving the internal governance structure of cooperatives, lay a foundation for the research on improving the operation quality of farmers' cooperatives, and provide important support for the decision-making of the government and relevant ministries. Data summary: Item Description Dataset name Statistical Data Set of Fixed Observation Point Survey of Farmers' Cooperatives in Sichuan Province from 2020 to 2021 Specific subject area Agricultural economics and management Time range 2020-2021 Geographical scope Sichuan Province Data types and technical formats *.xlsx Dataset structure The dataset includes data on the internal governance structure, industrial integration, the adoption of green technology, demonstration and other data of farmers’ cooperatives in 10 counties (cities and districts) in Sichuan Province from 2020 to 2021, corresponding to one worksheet for each year, with a total of 100 records. Volume of data 2119.68 KB Key index in dataset Established type, contribution ratio of members, leading industry, scale of operation, governance structure, product sales, industrial convergence, the adoption of green technology, agricultural income, profit distribution and the role of demonstration in cooperatives. Data accessibility CSTR：17058.11.sciencedb.agriculture.00060 DOI：10.57760/sciencedb.agriculture.00060 Financial support National Natural Science Foundation of China (No.71673195) Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Construction and Application of Monitoring Platform for Saline-alkali Land in Cold Regions ZHANG HaiFeng, ZHANG Yu, LAI YongCai, ZHENG YanYan, LIU Kai, BI HongWen Journal of Agricultural Big Data    2023, 5 (2): 122-131.   DOI: 10.19788/j.issn.2096-6369.230217 Abstract （132）   HTML （8）    PDF（pc） （4593KB）（236）       Knowledge map    Save Saline-alkali land is an important reserve arable land resource in the country. Selecting and breeding salt-tolerant rice varieties in cold regions, and developing and utilizing saline-alkali land are important ways to promote food production and ensure national food security. In accordance with the national guiding spirit of "transforming from controlling saline-alkali land adaptation crops to selecting saline-alkali-tolerant plants to adapt to saline-alkali land, tapping the potential for saline-alkali land development and utilization, and striving to achieve breakthroughs in key core technologies and important innovation fields", a series of issues such as the imperfect technical system for saline-alkali land improvement have been addressed through collaborative research using new generation information technologies such as the Internet of Things and big data. A monitoring platform for saline-alkali land in cold regions has been built that integrates dynamic monitoring of the meteorological and soil environment of saline-alkali land, collection of phenotypic data of rice in cold regions, and visual display of data, achieving the integration of information technology in multiple disciplines such as germplasm resources, biotechnology, and soil fertilizers. This article introduces the digital monitoring methods and key technologies of data collection content for the improvement and utilization of saline-alkali land in cold regions from the perspective of platform architecture and functional implementation. The platform has been applied in different accumulated temperature zones such as Daqing, Jiamusi, and Heihe in Heilongjiang Province. The results show that the platform can assist scientific researchers to timely and comprehensively grasp the meteorological and soil information of saline-alkali plots, effectively reduce the impact of adverse environmental factors, and form a complete data chain; The platform aggregates and processes phenotypic traits, yield and quality data of rice germplasm resources at different phenological stages, excavates the environmental stress response rules of germplasm resources, and guides the scientific research and production of rice in saline-alkali lands, providing information technology support for the development and utilization of saline-alkali land resources. And realizing digital monitoring of saline-alkali-tolerant rice screening and soil improvement in cold regions. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Rule of Law Safeguards for Agricultural and Rural Data Security in the Context of Digital Village Construction FEI YanYing, ZHANG XuFan Journal of Agricultural Big Data    2023, 5 (3): 11-18.   DOI: 10.19788/j.issn.2096-6369.230303 Abstract （130）   HTML （21）    PDF（pc） （330KB）（192）       Knowledge map    Save The construction of digital villages is facing data security risks. The study explored the rule of law needs of agricultural and rural data security in the context of the construction of digital villages, sorted out and analyzed the current situation of the rule of law in agricultural and rural data security. To address the rule of law dilemma in agricultural and rural data security, the study proposed rules of law countermeasures to safeguard agricultural and rural data security by perfecting the legal system of agricultural and rural governmental data security and open sharing, perfecting the agricultural and rural enterprises' data security protection system, and raising the data security awareness of the rural population which will help safeguard the security of agricultural and rural data at the level of the rule of law and assist in the construction of digital villages. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Design and Implementation of An Equine Intelligent Breeding Big Data Platform LIU YanHong, CAO KeTao, CHEN XinWen, LI JinXing, XIONG Tao, DU XueMei, BAI Tao, ZHENG WenXin, GUO LeiFeng Journal of Agricultural Big Data    2023, 5 (3): 93-103.   DOI: 10.19788/j.issn.2096-6369.230313 Abstract （124）   HTML （12）    PDF（pc） （3494KB）（219）       Knowledge map    Save With the continuous development of information technology, intelligent farming is being increasingly applied in modern livestock industry. Modern advanced information technology is gradually being applied throughout the entire process of equine farming. Utilizing technologies such as big data and artificial intelligence to promote the intelligent development of the equine industry and improve equine farming efficiency is one of the important pathways towards modernization and technological advancement of the equine industry. In this study, an equine intelligent farming big data platform based on the four-layer system architecture, including the device layer, data layer, data processing layer, and application layer, was developed using the latest information technologies such as big data, artificial intelligence, and the Internet of Things. The platform integrates five functional modules such as record management, epidemic prevention management, breeding management, behavior management, and environmental management, enabling data collection, analysis, model building, and application throughout the equine farming process. This research can provide insights for the construction of intelligent farming in equine breeding bases and enterprises, achieve fine monitoring and management of equine farming, improve equine production efficiency and breeding benefits, and provide more reference significance for the future development of the equine industry. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Dataset for Constructing Agricultural Knowledge Graph CHEN Lei, ZHOU Na, ZHU PengXuan, YUAN Yuan Journal of Agricultural Big Data    2024, 6 (1): 1-8.   DOI: 10.19788/j.issn.2096-6369.100002 Abstract （123）   HTML （12）    PDF（pc） （9350KB）（50）       Knowledge map    Save Improving the efficiency of agricultural production and optimizing the problems in agricultural production through information technology is crucial for the development of agriculture in China. At present, the development of information technology has generated massive amounts of data, which are mostly distributed on the Internet in fragmented and unstructured forms. Especially in the domain of agriculture, using traditional search engines for information retrieval is difficult to efficiently and accurately obtain valuable agricultural information, often requiring a lot of time and effort to collect and organize secondary data from massive unorganized data. To address the above issues, this paper utilizes web crawler technology to mine data from publicly available agricultural websites. Through automatic or semi-automatic data cleaning, denoising, and other processes, unstructured data are recombined into structured data, which is ultimately stored in the form of a knowledge graph. The dataset for constructing agricultural knowledge graph includes item data for 11 agricultural categories, such as grain crops, cash crops, fruits, vegetables, etc. Specifically, it includes 461 types of grain crops, 2 208 types of cash crops, 1 294 types of fruits, 257 types of vegetables, 118 types of edible fungi, 1 161 types of flowers and trees, 142 types of aquatic products, 113 types of pesticides, 1 605 types of crop diseases and pests, 519 types of veterinary drugs, and 603 types of Chinese herbal medicines, totaling 8 481 subcategories. The agricultural knowledge graph constructed based on this dataset has 90 508 triplets, which can provide basic data support for the development of human-machine interactive intelligent applications such as agricultural knowledge Q&A and recommendation systems. Meanwhile, integrating agricultural knowledge graph into generative large language models can help achieve more efficient and accurate information retrieval and intelligent decision-making in vertical domains. Data summary: Items Description Dataset name A Dataset for Constructing Agricultural Knowledge Graph Specific subject area Computer Science and Technology; Other disciplines in Agronomy Research topic Agricultural knowledge graph; Data mining; Artificial intelligence Time range 2020 - 2023 Geographical scope China Data types and technical formats *.JSON Dataset structure The constructed agricultural knowledge graph includes item data for 11 agricultural categories, such as grain crops, cash crops, fruits, vegetables, etc. Specifically, it includes 461 types of grain crops, 2208 types of cash crops, 1294 types of fruits, 257 types of vegetables, 118 types of edible fungi, 1161 types of flowers and trees, 142 types of aquatic products, 113 types of pesticides, 1605 types of crop diseases and pests, 519 types of veterinary drugs, and 603 types of Chinese herbal medicines, totaling 8481 subcategories. The data of each major category are saved separately in JSON format files. Volume of data 14.6 MB Key index in dataset Category of crops; Number of triples Data accessibility DOI:10.57760/sciencedb.agriculture.00016 CSTR：17058.11.sciencedb.agriculture.00016 https://doi.org/10.57760/sciencedb.agriculture.00016 Financial support National Natural Science Foundation of China (Grants No. 32071901, 32271981) and the Database in National Basic Science Data Center (NO. NBSDC-DB-20) Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Dataset of the Statistics on Tomato Transaction Price in the Wholesale Market in Beijing-Tianjin-Hebei Region from 2012 to 2019 KONG FanTao, AN Min, SUN Wei, LIU JiFang, HU Lin, CAO ShanShan Journal of Agricultural Big Data    2023, 5 (2): 62-67.   DOI: 10.19788/j.issn.2096-6369.230209 Abstract （119）   HTML （19）    PDF（pc） （1011KB）（202）       Knowledge map    Save Tomato，one of the vegetables popular among Chinese residents，is widely grown throughout the country. In recent years, the price of tomato has shown the characteristics of large fluctuation range and instability. Hebei is the main production area of tomato, while Beijing and Tianjin are important sales areas of tomato. Taking the Beijing-Tianjin-Hebei region as a whole, the historical price data of tomato within the region are analyzed to explore the fluctuations of tomato market price. It is of great significance to perfect circulation pattern of regional agricultural products, balance tomato production structure and stabilize tomato market price. This data set fully combines the vegetable price data provided by the National Agricultural Product Business Information Public Service Platform, the National Wholesale Market Price Information System and other platforms, and the field survey data of the fund project team. Through comparison and integration with the field survey data, the authenticity and consistency of the basic data set are ensured. Through processing the daily average price information of tomato in 29 wholesale markets of agricultural products in the Beijing-Tianjin-Hebei region from 2012 to 2019, the text data and image data reflecting the price information of tomato wholesale markets in the Beijing-Tianjin-Hebei region from 2012 to 2019 were obtained, including weekly and monthly average price of tomato market, a total of 1536 articles; There are 78 historical fluctuation charts of weekly average price and monthly average price, and line charts of month-on-month and year-on-year price change rates. The obtained results have been tested by manual quality, and the data processing has been completed in accordance with standardized steps. The obtained data sets can provide basic data support for the research and monitoring and early warning of tomato market price fluctuations in the Beijing-Tianjin-Hebei region. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Aphid Image Dataset Based on Natural Background DONG Wei, ZHU JingBo, GUAN BoLun, KONG JuanJuan, LI RunMei, ZHANG Meng, ZHANG LiPing Journal of Agricultural Big Data    2023, 5 (3): 112-117.   DOI: 10.19788/j.issn.2096-6369.230315 Abstract （115）   HTML （25）    PDF（pc） （2433KB）（232）       Knowledge map    Save Agricultural pests are important reasons affecting crop yield and quality. Aphid is an important group of agricultural pest. Detecting and counting aphids is an important link for early detection and management of this pest. With the development of information technology, many experts and scholars have conducted extensive research on the identification of agricultural pests using computer vision, and have made certain progress. High-quality and large-scale basic data often play a decisive role in the development of computer vision, but the lack of this kind of image data is one of the challenges faced by pest identification. Aphids have features such as small size, dense distribution, inter insect shelter, and multiple forms of same species. These features also pose a serious challenge for the detection and counting of aphids. This article provides a total of 6287 high-definition original images, including a dataset of 13 agricultural pests (aphids) including peach aphid, cotton aphid, and grain constrictor aphid, etc. These aphid images were collected using DSLR cameras in a natural field environment. In order to ensure the high quality and reliability of the data, these images are cleaned and organized by professional personnel, and identified and classified by experts in the field of plant protection. This dataset can provide a data foundation for recognition, detection, counting and classification of aphids. Data summary: Items Description Dataset name Aphid Image Dataset Based on Natural Background Specific subject area Plant protection Research topic Aphid Time range 2013-2023 Geographical scope China Data types and technical formats Data type: image; Technical formats:*.jpg Dataset structure The dataset contains a total of 6287 images of 13 types of aphids, including Hyalopterus amygdali, Myzus persicae, Aphis gossypii, Rhopalosiphum padi, Aphis spiraecola, Aphis craccivora, Uroleucon formosanum, Sitobion miscanthi, Brevicoryne brassicae, Lipaphis erysimi, Rhopalosiphum maidis, Panaphis juglandis, and Nippolachnus piri. Volume of data 16.8 GB Data accessibility CSTR: https://cstr.cn/17058.11.sciencedb.agriculture.00030 DOI: https://doi.org/10.57760/sciencedb.agriculture.00030 Financial support General Program of National Natural Science Foundation of China “Research on Few-shot Pest Recognition Inspired by Knowledge Transfer and Causal Reasoning”（32171888） Anhui Academy of Agricultural Sciences Research Platform Project “Agricultural Intelligent Technology Research and Development Center”（2023YL1014） Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Analysis on the Development Trend of Breeding Intelligent Equipment Under the Background of New-Generation Information Technology HU TianCi, WANG WenSheng, QI JingWei, JIANG ChengXiang, CHEN XinWen, ZHENG WenXin, GUO LeiFeng Journal of Agricultural Big Data    2023, 5 (3): 56-68.   DOI: 10.19788/j.issn.2096-6369.230310 Abstract （112）   HTML （11）    PDF（pc） （3802KB）（212）       Knowledge map    Save In the current background of national promotion of the digital agriculture and smart farming, the breeding intelligent equipment began to develop vigorously. Using intelligent equipment in large-scale farms can improve breeding efficiency and reduce labor costs. At present, the domestic intelligent equipment has not been popularized, the domestic research on intelligent equipment is not sufficient, some of the intelligent equipments are still in the research and development stage, which can not reday for mass-scale production. Taking cattle and sheep breeding as example, the paper 1) summarizes breeding intelligent equipment and equipment function introduction demanded in the various stages of farming, 2) lists some of the current domestic research in the intelligent equipment, 3) introduces the impact of the new-generation technology, such as big data, artificial intelligence, etc. on the breeding intelligent equipment, 4) discusses the significance and existing problems of the domestic current development of breeding intelligent equipment, and 5) analyses the future development of the breeding intelligent equipment under the current background. The breeding intelligent equipment can accelerate the development of China's digital animal husbandry and improve the economic benefits of animal husbandry, which embraces the expansive prospects. Moreover, the breeding intelligent equipment can be used as hardware support for intelligent breeding and precision feeding, laying the foundation for the construction of a new generation of intelligent farm construction. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Food Consumption Behavior Analysis Dataset Based on Eye Tracking: A Case Study on the Functional Foods ZHEN ShiHang, LIU Qi, LUO Rui, TIAN SiYu, SHEN Shuang, REN YanJun Journal of Agricultural Big Data    2023, 5 (4): 103-109.   DOI: 10.19788/j.issn.2096-6369.230413 Abstract （107）   HTML （11）    PDF（pc） （527KB）（110）       Knowledge map    Save With the rapid advancement of eye-tracking technology and its integration within the domain of behavioral economics, the utilization of eye-tracking technology in a field of consumer behavior research has gradually become ubiquitous. In recent years, there has been a rapid surge in research that combines eye-tracking analysis in the field of food consumption behavior. Of particular interest are functional foods, which have garnered considerable consumer interest. This dataset leverages eye-tracking technology to conduct selection experiments, capturing visual attention data from 151 consumers during the process of purchasing functional pure milk. It encompasses five dimensions of indicators: total fixation duration, average fixation duration, fixation frequency, first fixation duration, pupil diameter, and the final purchase decision. In total, the dataset comprises 755 data points. The results obtained have undergone meticulous manual quality-checks, and data processing has been accomplished according to standardized procedures. This dataset serves as valuable support for researchers aiming to analyze the factors influencing the consumption of functional food from the perspective of behavioral economics. The dataset also provides basic data and experimental paradigms for the application of eye-tracking technology in the realm of visual marketing of functional food. Data summary: Item Description Dataset name Functional food consumption behavior analysis data set based on eye tracking Specific subject area Food nutrition and health, agricultural economic management Research topic Consumer behavior analysis Time range 2023 Data types and technical formats .xlsx Dataset structure The dataset includes a total of 755 records, consisting of the purchasing results of consumers in the process of selecting functional foods, as well as the corresponding eye-tracking data. Total fixation duration, average fixation duration, fixation frequency, first fixation duration, pupil diameter. Volume of data 182 KB Key index in dataset Total fixation duration, average fixation duration, first fixation duration, fixation frequency, pupil diameter Data accessibility CSTR：17058.11.A46AE3.20231028.16.cs.3748 DOI：10.12205/A46AE3.20231028.16.cs.3748 Financial support Qin Chuangyuan of Shaanxi Provincial Science and Technology Department cited high-level innovation and entrepreneurship talent projects (QCYRCXM -2022-145) Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Journal of Agricultural Big Data    2023, 5 (2): 1-1.   DOI: 10.19788/j.issn.2096-6369.230201 Abstract （103）   HTML （26）    PDF（pc） （237KB）（315）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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A Dataset on the Effects of Temperature and Salt Stress on Seed Germination and Recovery of Three Species of Salsola L. in Xinjiang LI QuanSheng, SUN Wei, LIU JiFang, HU Lin, CAO ShanShan Journal of Agricultural Big Data    2023, 5 (2): 68-74.   DOI: 10.19788/j.issn.2096-6369.230210 Abstract （102）   HTML （6）    PDF（pc） （1319KB）（96）       Knowledge map    Save Seed germination is a key stage of population settlement, and the seed germination strategy of saline plants is an important factor affecting the settlement and distribution of plant species in saline environments. The Chenopodiaceae Salsola L. is a major population-building species of saline vegetation that can tolerate poor and arid habitats, and is one of the most diverse and widely distributed plant types in Xinjiang. This dataset is based on the principle of physiology and ecology in the laboratory, aiming at the characteristics of seed germination. The germination data of wingless seeds of S. rigida Pall., S. arbuscula Pall. and S.nitraria Pall. collected in mid-October 2017 in the Junggar Basin, Xinjiang were studied under the different temperatures (2-5℃, 5-15℃, 5-25℃ and 15-25℃, 12-hour light / dark alternation), salt stress(0, 100, 200, 300 and 400 mmol/L NaCl) and rehydration. A number of winged and non-winged Salsola L. seeds are randomly selected, and their diameters and 1000-grain weights are measured respectively. This dataset has important practical and guiding significance for the restoration, introduction and domestication of Salsola L. vegetation and development and utilization in arid areas. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Dataset of Soil Nutrient Characteristics of Apple Orchard in 2019 in Xingcheng City, Liaoning Province LI YanQing, LI Zhuang, ZHANG Yi, CHENG CunGang, ZHANG Ning Journal of Agricultural Big Data    2023, 5 (2): 80-84.   DOI: 10.19788/j.issn.2096-6369.230212 Abstract （102）   HTML （9）    PDF（pc） （294KB）（156）       Knowledge map    Save Xingcheng City, in Liaoning Province, is one of the advantageous apple production regions in the Bohai Bay area, where apple industry has greatly promoted rural revitalization. Studying the spatial distribution characteristics of soil fertility plays the basis role for realizing scientific fertilization in orchards and promoting high-quality development. Through the research on the spatial distribution and profile distribution of soil nutrients in apple orchards in Xingcheng, the data set of soil nutrient in 2019 was obtained, including soil pH, EC, SOM, TSN, AHN, AP, AK, ACa, AMg in soil layers 0-30 cm. Users can obtain the database by phone or email, or contact the Research Institute of Pomology, CAAS. The establishment and sharing of the dataset of soil nutrient characteristics of apple orchard in 2019 in Xingcheng can support the research for the distribution of soil nutrient, providing the theoretical basis for scientific fertilization, orchard soil fertility and fruit quality improvement. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Data Set of Pollen Scanning Electron Microscope Observation of Some Farm Varieties of Deciduous Fruit Trees in Advantageous Production Areas from 2012 to 2017 CHEN XiaoJing, LIU TingTing, LI HaoXian, HU Lin, FAN JingChao, CAO ShangYin Journal of Agricultural Big Data    2023, 5 (2): 75-79.   DOI: 10.19788/j.issn.2096-6369.230211 Abstract （100）   HTML （4）    PDF（pc） （541KB）（103）       Knowledge map    Save China is one of the most important fruit tree origin centers in the world, and in the evolution of deciduous fruit tree farmers, the kinship background of fruit trees has a certain probability of heterozygosity, resulting in some difficulties in classification and utilization. In addition, many farm varieties have the same name but foreign bodies, and it is not possible to judge their classification system by name. Pollen characteristics are controlled by genes and rarely change under the influence of the external environment, which can be used for identification and classification between plant species and even between varieties. In genetics, pollen shows strong conservation and stability, which indicates that plant pollen morphology is helpful for the classification and identification of plant species and varieties, and is also of great significance in exploring the origin and evolution of plants. This dataset is a pollen scanning electron microscope observation of some excellent varieties of 11 tree species such as pomegranate, apple, peach, chestnut, walnut and grape in Liaoning, Shandong, Shaanxi and other key distribution areas and advantageous production areas in China from 2012 to 2017, and the data include: collection number, variety name, observation method, observation instrument and observation result information. The dataset has a total of 571 symbol records, and the article dataset is all raw data without any processing. In this study, scanning electron microscope was used to scan the pollen of some agricultural varieties of deciduous fruit trees in China's dominant production areas from 2012 to 2017, and the results of electron microscope observation and analysis were basically consistent with traditional taxonomy. Therefore,the results of electron pollen microscope analysis of farm varieties can be used for the identification and classification of farm varieties, and the differences in pollen morphology, size and outer wall ornament can provide a basis for the kinship and classification identification of farm varieties from the perspective of spores. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Production and Management Survey Statistical Dataset of Banana Farmers in Guangdong and Hainan Province in 2018 YANG Qian, HUANG EnLin, SUN HuiRong, ZHU YueJi Journal of Agricultural Big Data    2023, 5 (3): 38-43.   DOI: 10.19788/j.issn.2096-6369.230307 Abstract （98）   HTML （9）    PDF（pc） （311KB）（79）       Knowledge map    Save Banana is the fruit with the largest production in tropical and subtropical regions of China, and has been the typical cash crop in agricultural economy of tropical regions in China. Our project team visited 8 banana producing cities or counties in Guangdong and Hainan provinces of China and collected a micro-level dataset on production management of banana farmers in 2018.Our project team uses scientific methods to design questionnaires, conduct field survey, input data, and organize data and finally obtain 446 pieces of data reflecting the production and management status of banana farmers. In addition, 8 statistical descriptive tables were drawn. These data formed the production and management survey statistical dataset of banana farmers in Guangdong and Hainan provinces in 2018, including detailed data on the basic characteristics of banana farmers, farmer households’ characteristics, livelihoods and production, banana production and sales situation, and other aspects. This dataset provides the possibility for research on the production and operation of the banana industry, supplies the micro-level data to support the research on innovative technology and pesticide application of the banana industry, and presents important support for the government and relevant stakeholders to make scientific decisions. Data summary: Items Description Dataset name Production and Management Survey Statistical Dataset of Banana Farmers in Guangdong and Hainan Province in 2018 Specific subject area Agricultural economics and management Research topic Production and management of banana farmers Time range 2018 year Geographical scope Guangdong Province, Hainan Province Data types and technical formats *.xlsl Dataset structure This dataset consists 446 pieces of text data, 8 data tables. It mainly includes the micro-data of production and management of banana farmers in Guangdong Province and Hainan Province in 2018 Volume of data 631.35 KB Key index in dataset Basic information of respondents, basic information of respondents' spouses, family information, living and production information, banana production information (innovative technologies adoption, pesticide application, drip fertigation system), banana sales information, internet use, social network and social capital. Data accessibility CSTR：17058.11.sciencedb.agriculture.00036 DOI：10.57760/sciencedb.agriculture.00036 Financial support The National Natural Science Foundation of China (No. 72363010) The National Natural Science Foundation of China (No. 71863006) Hainan Provincial Natural Science Foundation of China (No.720RC581) Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Cost Benefit Survey Statistical Dataset of China's Grain Processing Enterprises from 2013 to 2016 WANG LuAn, ZHANG LiXiang, ZHANG Jing Journal of Agricultural Big Data    2023, 5 (3): 26-31.   DOI: 10.19788/j.issn.2096-6369.230305 Abstract （97）   HTML （17）    PDF（pc） （305KB）（88）       Knowledge map    Save Food security is a top priority for a country. Grain processing enterprises are an important part of ensuring food security, the micro-foundation of structural reform on the agricultural supply side, and an important component of achieving agricultural modernization. The research group visited 176 grain processing enterprises of different scales in 17 provinces of China, collected detailed data on the production and operation of enterprises from 2013 to 2016, and processed the data using scientific methods to obtain 704 pieces of data reflecting the production and operation status of grain processing enterprises, including financial costs, labor costs, daily processing capacity, and daily processing capacity. All the data formed a cost-benefit dataset for grain processing enterprises in China from 2013 to 2016. The dataset provides the possibility for research on grain processing capacity, assists in conducting research on the cost-benefit of grain processing enterprises, and supports decision-making by the government and relevant departments. Data summary: Item Description Dataset name Cost Benefit Survey Statistical Dataset of China's Grain Processing Enterprises from 2013 to 2016 Specific subject area Agricultural economics Research topic Costs and benefits of grain processing enterprises Time range 2013-2016 Geographical scope Jiangsu, Jiangxi, Zhejiang, Guangdong, Guangxi, Hebei, Shandong, Hubei, Hunan, Henan, Inner Mongolia, Shaanxi, Shanxi, Heilongjiang, Jilin, Liaoning, Sichuan Data types and technical formats *.xlsx Dataset structure This data consists of 6 data files, including: Summary of Cost and Benefit Data for Large Rice Processing Enterprises, Summary of Cost and Benefit Data for Medium Rice Processing Enterprises, Summary of Cost and Benefit Data for Small Rice Processing Enterprises, Summary of Cost and Benefit Data for Large Flour Processing Enterprises, Summary of Cost and Benefit Data for Medium Flour Processing Enterprises, and Summary of Cost and Benefit Data for Small Flour Processing Enterprises Volume of data 444 KB Key index in dataset Enterprise nature, enterprise scale, operating rate, daily processing capacity, raw material sources, raw material costs, labor costs, electricity expenses, financial expenses, main product income, by-product income, net profit Data accessibility CSTR:17058.11.A0007.20231023.00.ds.3746 DOI:10.12205/A0007.20231023.00.ds.3746 Financial support The National Social Science Foundation Project "Innovative Research on the Mechanism and Agricultural Support Policy of 'Corn to Rice' in Northeast China" (No. 17CJY033); Project of the Ministry of Agriculture and Rural Affairs on "Improving the Resilience of the International Food Supply Chain" (No. B020101); Research on the Impact of Big Data Analysis on Social Networks and Social Mobility on the Income Level of Migrant Workers (No. K672001); Research on the Mechanism of Reducing Gender Wage Gap under the Background of Unbalanced Regional Economic Development (No. Z27021) Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Sensitivity Analysis and Adaptability Evaluation of RiceSM Model WANG XueYing, CHEN XianGuan, TANG ShunJie, FENG LiPing Journal of Agricultural Big Data    2023, 5 (2): 97-108.   DOI: 10.19788/j.issn.2096-6369.230215 Abstract （96）   HTML （14）    PDF（pc） （580KB）（117）       Knowledge map    Save Crop model can quantitatively describe crop growth and development processes and their relationships with environmental factors, and have important applications in agricultural production management decisions and other areas. Model parameter debugging is an important step before crop growth simulation models are applied, and often requires a lot of time and effort for debugging. Sensitivity analysis can screen out sensitive parameters with high efficiency, and is an important part of model localization, which is of great significance for model application. Sensitivity analysis was conducted on the crop parameters of the RiceSM model based on the Morris method and EFAST method to screen out the sensitive parameters of maturity, leaf area index, total biomass and yield among the output variables, and to compare and analyze the similarities and differences between the results of the two methods. The results showed that basic development factor from transplanting to jointing stage K3, basic development factor from seeding to transplanting stage K2 and dry matter distribution coefficients of leaf from transplanting to jointing stage CLV1 were the most sensitive parameters affecting the main output results of RiceSM model, and the results of the sensitive parameters obtained by the two methods were generally consistent, but the importance of each sensitive parameter differed slightly. The validation results showed that the normalized root mean square error (NRMSE) of simulated and measured values of early and late rice leaf area index ranged from 21.63% to 47%, and the NRMSEs of simulated and measured values of stem, leaf, spike, aboveground biomass and yield of early and late rice ranged from 4.77% to 39.51%, 5.46% to 6.64%, 3.78% to 4. 15% and 2.78% to 3.52% and between 9.29% and 12.12% respectively. The model was able to better simulate the dynamics of bio-mass, leaf area index and yield formation in early and late rice. The results of the study provide a reference for the localization of the model. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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The Enlightenment of Factors Influencing Data Reuse on the Development Direction of Warehouse-Based Data Management Platforms SUN YuXiao, LI YanLi, LI Feng, LI Bin Journal of Agricultural Big Data    2023, 5 (3): 2-10.   DOI: 10.19788/j.issn.2096-6369.230302 Abstract （94）   HTML （11）    PDF（pc） （1264KB）（90）       Knowledge map    Save As a powerful tool and knowledge foundation for scientific research, scientific data have attracted the attention of researchers throughout its entire process of data behavior. Data reuse, as a key component, is of great significance for the development of scientific data management and sharing. Taking the Agricultural Environment Data Sharing Service Platform, developed by the Institute of Agricultural Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, as the starting point, the article introduces relevant research on data reuse and the evolution of the definition of scientific data reuse in terms of time dimension. From the perspective of the impact framework of data reuse, it analyzes the impact of researchers, scientific data, and data platforms on data reuse. Moreover, the article explores the popular and widely-used warehouse-based data management platform models, and proposes the next development inspiration, regarding to the current situation and problems of agricultural data sharing service platforms. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Design and Application of IoT Intelligent Management Platform for Nanfan Breeding Bases LI JiaLe, ZHAO HongXin, LIN Jia, HE ZiKang, XU RuYi, YU GuoPing, ZHOU GuoMin, ZHANG JianHua Journal of Agricultural Big Data    2023, 5 (4): 37-46.   DOI: 10.19788/j.issn.2096-6369.230404 Abstract （93）   HTML （11）    PDF（pc） （3011KB）（124）       Knowledge map    Save The Nanfan breeding base is the "gas pedal" of China's agricultural breeding, and its strategic position is very important. In order to improve the efficiency of agricultural data collection at the Nanfan breeding base, strengthen the capacity of farmland management and meet the needs of large-scale crop breeding, there is an urgent need to build an intelligent management platform for bases based on the IoT. Taking the Potianyang base in Sanya City, Hainan Province as an object, an IoT intelligent management platform for the Nanfan breeding bases has been developed. The platform deploys sensors, video monitoring, intelligent irrigation and other equipment, and develops five functional modules, such as crop weather and soil environment sensing, video monitoring, pests monitoring. Real-time monitoring of the field's weather, soil, pests, spores and other environmental information, as well as crop growth conditions can achieve intelligent and precise irrigation. The real-time sensing and intelligent warning function of the platform can realize remote monitoring and regulation of the breeding environment, providing breeders with intelligent decision-making. By intelligently obtaining crop phenotypic information, the platform also provides breeders with a large amount of experimental materials, greatly improving the efficiency and accuracy of breeding work and providing technical support for large-scale field crop breeding. In the future, relying on modern technologies such as artificial intelligence, robots and drones, the intelligent acquisition of crop phenotypic data will replace manual phenotypic measurements, the intelligent management of crop plus generation breeding will replace manual management, and the intelligent evaluation and screening of excellent varieties will replace experienced breeders, which will realize the integration of the sky and land of the Nanfan breeding bases and the intelligent digitalization of large-scale field breeding. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Study on the Change of Soybean Planting Structure in Heilongjiang Province Under the Background of Soybean Revitalization Plan at County Level XIN Rui, LU ZhongJun, FU Bin, WANG Ting, HUANG Nan, LIU KeBao, LIU YanXia Journal of Agricultural Big Data    2023, 5 (2): 44-53.   DOI: 10.19788/j.issn.2096-6369.230207 Abstract （89）   HTML （9）    PDF（pc） （1973KB）（130）       Knowledge map    Save Heilongjiang Province is an important base of soybean production in China. The study on the soybean planting structure and dynamic changes in the Heilongjiang Province can provides a reference for optimizing the production mode, adjusting the production layout and accelerating the reform of agricultural supply structure. Based on Sentinel-2 satellite and RS and GIS technology, this paper extracted soybean spatial distribution information in Hailun City, Heilongjiang Province in 2018, 2019 and 2020, then analyzed the dynamic change of soybean planting structure and recropping. The results show: 1) In the first year of the implementation of the Soybean Revitalization Plan in 2019, the soybean planting area in Hailun City increased significantly, with a change rate of 53.07%; In 2020, soybean planting area decreased slightly, but the total amount increased by 30.46% compared with that in 2018. 2) The soybean continuous cropping rate in 2019 and 2020 was high, with the rate higher in 2020 than that in 2019, and higher in the west of Hailun than in the east. From 2018 to 2020, the rate of soybean crop for three consecutive years was higher in Hailun City as a whole. The rate of soybean continuous cropping every other year in Hailun City was on the low side. Since the implementation of the Soybean Revitalization Plan, the soybean planting area increased significantly, yet resulting in high rate of continuous cropping and low rate of continuous cropping every other year. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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The Whole Process Organization and Operation of Household Follow-up Survey: A Case Study of Agricultural and Rural Modernization Surveys LU XuRan, LI GuCheng, XIONG Hang Journal of Agricultural Big Data    2023, 5 (4): 66-70.   DOI: 10.19788/j.issn.2096-6369.23040 Abstract （89）   HTML （10）    PDF（pc） （336KB）（88）       Knowledge map    Save To accelerate the building of a strong agriculture and promote the modernization of agriculture and rural areas, it is necessary to observe and investigate the current situation of the modernization of agriculture and rural areas in China. Through the follow-up survey, we can deeply understand the development process and current situation of the modernization of agriculture and rural areas in our country, and support the scientific decision-making of the national and local implementation of the rural revitalization strategy and support the frontier academic research on the issues relating to agriculture, rural areas and farmers. Based on the agricultural and rural modernization surveys organized by the College of Economics and Management and Macro Agriculture Research Institute of Huazhong Agricultural University for 5 consecutive years, this paper summarizes the whole process of survey organization from the preparation and personnel organization before the survey, institutional guarantee and standardization work during the survey, to the summary and maintenance after the survey, which mainly includes teacher team formation and responsibilities, questionnaire design and electronization, investigator recruitment and team formation, survey training, survey sampling, work guarantee, quality inspection and control, data management, evaluation and summary, survey report writing, fixed observation point maintenance, etc.. Reference | Related Articles | Metrics | Comments（0）

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Analysis of the Research Status of Arbuscular Mycorrhizal Fungi on Plant Adverse Resistance Based on Scientometric Tools MA Jun, LI Shan, CAO Kai, BAO EnCai, HE ChaoXing Journal of Agricultural Big Data    2023, 5 (2): 109-121.   DOI: 10.19788/j.issn.2096-6369.230216 Abstract （87）   HTML （5）    PDF（pc） （3388KB）（62）       Knowledge map    Save This paper adopts the scientometric analysis method to the research state and explore the trends of arbuscular mycorrhizal fungi in improving plant to stress based on the, using the scientific measurement method of big data to conduct quantitative analysis and visualization of Chinese and English literature in the past 30 years (1991-2021) in Web of Science and CNKI databases. The changes in the number of literatures, national and institutional cooperation networks, research hotspots and research trends in the field of arbuscular mycorrhizal fungal resistance are visually displayed. The results showed as follows: 1) The research on arbuscular mycorrhizal fungal resistance has been paid more attention by researchers, and the most frequent keywords are “salt stress” and “drought stress”, which are the hot research directions of arbuscular mycorrhizal fungal resistance; 2) The major domestic and foreign research institutes are focused on the Spanish National Research Council (CSIC), the Chinese Academy of Sciences, Northwest A&F University and Yangtze University) and other universities and research institutes; 3) The study of arbuscular mycorrhizal fungi on heavy metal stress and their synergistic effects with other microorganisms have gradually become a research hotspot. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Dataset for Analyzing the Horizontal Transmission Mechanism of Domestic Financial Markets to Agricultural Commodity Prices, 2017-2021 WEI TongYang, XU Ke, XU Lei Journal of Agricultural Big Data    2023, 5 (3): 19-25.   DOI: 10.19788/j.issn.2096-6369.230304 Abstract （86）   HTML （11）    PDF（pc） （1035KB）（95）       Knowledge map    Save The transmission mechanism of agricultural commodity price volatility is a research topic that has attracted much attention. The factors influencing agricultural commodity prices are gradually diversified and complicated, and non-traditional factors including the influence of financial markets are gradually highlighted. Traditionally, it is believed that the price of agricultural products is mainly affected by supply and demand factors, but with the increasingly close connection between the price of agricultural products and financial markets such as currencies, stocks, futures and so on, the financial attributes of agricultural products have gradually come to the fore, and the influence of related non-traditional factors has become more and more obvious, and the factors affecting the price of agricultural products have gradually become diversified and complex. Comprehensively analyzing the various studies on the transmission of financial market fluctuations on agricultural commodity prices can reveal the collection of data from different perspectives, different periods and different variables, and reveal its transmission mechanism, which is of great value. However, the existing studies collect data and analyze the relationship and transmission mechanism of financial factors on the price volatility of agricultural products more from the perspective of derivatives and currency, and the financial factors considered are not comprehensive enough and the relevant data are not complete enough. Based on this, this dataset selects more diversified financial factors and collects the four most representative types of domestic financial market data. The dataset for the analysis of the horizontal transmission mechanism of agricultural commodity prices in this study contains the original data dataset and the preprocessed data dataset, which are obtained through public access, and both include agricultural commodity prices and the four types of domestic financial market data, namely, aggregate domestic demand, the money market, the stock market, and the real estate market, and the data are monthly data, with a time range of January 2017 to February 2021, for a total of 50 months. The dataset construction includes three steps of dataset variable determination, data authority source determination and collection, and data pre-processing. To ensure the dataset quality, the measures are taken as follows: first, in the data collection process, the financial factor variables that mainly affect the price fluctuation of agricultural products are selected, and no important variables are omitted. Second, in the data collection source link, the data are collected through authoritative data source channels. Third, in the data pre-processing process, professional methods are used to fill the empty data, and the logarithmic form is adopted to avoid heteroscedasticity and volatility caused by the data. This dataset is shared to provide data support for the study of the horizontal transmission mechanism of agricultural commodity prices in the domestic financial market, and at the same time, it can provide data support for the decision-making of the relevant enterprises and the macro-adjustment of the government. Data summary: Items Description Dataset name Dataset for Analyzing the Horizontal Transmission Mechanism of Domestic Financial Markets to Agricultural Commodity Prices, 2017-2021 Specific subject area Agricultural economics Research topic Transmission of agricultural commodity price Time range January 2017 - February 2021 Geographical scope China Data types and technical formats *.XLSX Dataset structure Including the original data set and preprocessing data set two excel documents. The original dataset document contains time (month and year), wholesale price of agricultural products 200 index, industrial value-added growth rate, broad money supply, 7-day interbank lending rate, the Shanghai Composite Index, the area of sales of commercial properties, sales of commercial properties, housing sales prices and other 9 variables, totaling 459 records. The preprocessed data set document is the data document after preprocessing such as fixed-base conversion and reduction of serial fluctuation on the basis of the original data set, which contains 7 variables such as time (month and year), logarithm of wholesale price of agricultural products 200 index, logarithm of the year-on-year growth rate of value added of industry, logarithm of the supply of broad money, logarithm of 7-day interbank lending rate, logarithm of the Shanghai Composite Index, logarithm of the price of housing sales, etc., totaling 357 records. Volume of data 29.94 KB Key index in dataset Main monthly indicators of the domestic financial market and the agricultural price index for the same period Data accessibility CSTR:17058.11.sciencedb.agriculture.00031 DOI: 10.57760/sciencedb.agriculture.00031 https://agri.scidb.cn/preview?dataSetId=877bab09989f4e319afdb0fe2d0702ff&version=V1 Financial support This work was supported by The Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Science (CAAS-ASTIP-2023-AII) Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Correlation Between Biomass of Hulunbuir Enclosed Grassland and Natural Grassland and Its Impact on Grassland Ecological Compensation Mechanism YUAN Bo, YAN YiDan, CUO MeJi, NIE YingYing, XU LiJun Journal of Agricultural Big Data    2023, 5 (2): 36-43.   DOI: 10.19788/j.issn.2096-6369.230206 Abstract （81）   HTML （3）    PDF（pc） （889KB）（116）       Knowledge map    Save The Hulunbuir Leymus chinensis Meadow Grassland is a grassland formation at the eastern end of the Central Asian Grassland Subregion in the Eurasian Grassland Region. It is one of the representative types of meadow grasslands, and its grassland ecosystem is undergoing dual pressures from human interference such as grazing and climate change, resulting in varying degrees of degradation. Based on the correlation between grazing disturbance intensity and grassland biomass, this paper selected the natural Leymus chinensis meadow inside and outside the fence in Hulunbeier area to carry out long-term observation on the composition of plant community in the field (enclosed in 2005), obtained the composition data of plant community in the meadow grassland (2009-2015), and studied the response of community coverage, community abundance, and community height to grazing intensity on this basis. Research has shown that enclosure significantly increases the aboveground biomass of grasslands, and the degree of improvement is positively correlated with the duration of enclosure; Meanwhile, moderate grazing interference will not cause a decrease in the abundance of grassland communities. Among them, the latter conclusion has enlightening significance for the grassland ecological compensation mechanism currently aimed at balancing grassland ecological protection and productivity improvement. Proper enclosure and grazing prohibition in severely degraded areas can help protect and build grasslands, partially reflecting the necessity of ecological compensation. At the same time, further analysis also indicates that there is still room for exploration in optimizing relevant policies. This study helps to deepen the understanding of grazing response and feedback mechanisms in grassland ecosystems, and can provide scientific basis for the protection and sustainable utilization of grassland ecosystems. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on Trusted Management of Blockchain-based Food Safety Knowledge Graph LIU YuJie, LIAN XiaoQin, ZHAO ZhiYao, LI Yue, ZHANG Xin Journal of Agricultural Big Data    2023, 5 (3): 69-82.   DOI: 10.19788/j.issn.2096-6369.230311 Abstract （75）   HTML （7）    PDF（pc） （2530KB）（150）       Knowledge map    Save In recent years, knowledge graphs have played an important role in the field of food safety. However, there are several issues with food safety knowledge graph information in the whole-life cycle circulation, such as severe data centralization, poor information circulation, easy data tampering, low traceability efficiency, and sharing and co-management difficulty. As an innovative architecture of the new generation of information technology, blockchain offers a new solution for the circulation and management of food safety knowledge graph with its features of decentralization, distributed processing, high privacy, open and transparent data, and difficult data tampering. This paper first analyzes the current situation and critical problems of food safety knowledge graph. Then, it reviews the application status of blockchain in the field of food safety. Based on this, a trustworthy management framework for food safety knowledge graph based on blockchain is constructed. Meanwhile, it designs a multimodal storage mode suitable for food safety knowledge graph, R-TBFT consensus optimization mechanism combined with Raft and TBFT consensus mechanism, and customized smart contracts for food safety knowledge graph. Finally, through the analysis of the feasibility and operation process of the proposed model, it is shown that this model can realize the sharing and interoperability of food safety knowledge graph along the entire chain. It can also ensure the security and reliability of data storage and transmission, providing a reliable source of food information for enterprises, regulatory authorities, and consumers. In summary, this paper provides a feasible solution and valuable reference for the innovative application of new generation information science in the field of food safety. Table and Figures | Reference | Related Articles | Metrics | Comments（0）