大数据驱动的天敌资源调查与监测体系构建研究

doi:10.19788/j.issn.2096-6369.200410

Abstract

Abstract: Objectives

Biological control using natural enemies offers an effective alternative to chemical control of pests and diseases. Biological control is also an important component of sustainable agriculture. China hosts an abundance of natural enemy resources. However, for resource exploitation, the comprehensiveness and systematic organization of existing tools are inadequate. Monitoring methods are not standardized and have high technical thresholds. Therefore, timely and effective utilization of natural enemies is limited. The proliferation of big data in recent years has provided novel tools suitable for investigation, monitoring, and informatization of natural enemy resources. It is thus necessary to develop a natural enemy resources survey and monitoring system based on big data concepts.

Methods

To realize the transformation from sample data to global data for the survey of natural enemy resources, the following four aspects of transformation are required: 1) transformation of data collection procedures from traditional methods to automated methods; 2) transformation of investigators from professionals to a majority of agricultural personnel; 3) transformation of data reporting from regular and fixed-point monitoring to object-based open reporting; and 4) transformation of system operation from centralized management to decentralization.

Results

With these objectives in mind, the National Data Center for Insect Natural Enemies, Basic Long-term Scientific and Technological Research in Agriculture was established in 2017. At present, the center has established a working concept of realizing "big data" in stages and conducted initial attempts to process and analyze the data. Over the course of three years, the center gradually established a standardized workflow from sampling to data management and analysis, collected a large amount of data, and performed preliminary analyzes.

Conclusions

The National Data Center for Insect Natural Enemies closely adheres to the big data concept and has established a natural enemy resources survey and monitoring system, which is an important attempt to establish a living species resource information system for agricultural ecosystems.

Key words: natural enemy, resources, big data, survey and monitoring system, agricultural big data

CLC Number:

S431.9

Jiale Lv, Xuenong Xu. Conceptual Establishment of a Natural Enemy Resources Survey and Monitoring System Based on Big Data[J].Journal of Agricultural Big Data, 2020, 2(4): 78-85.

Figures/Tables 6

Fig.1

Table 1

Fig.2

Table 2

Table 3

Table 4

References 23

1	Bellows TS, Fisher TW. Handbook of Biological Control [M]. New York: Academic Press, 1999.
2	中国科学院动物研究所, 浙江农业大学. 天敌昆虫图册[M]. 北京: 科学出版社, 1978.
	Institute of Zoology, CAS, Zhejiang Agricultural University. Atlas of Insect Natural Enemies [M]. Beijing: Science Press, 1978.
3	中国农业百科全书编辑部. 中国农业百科全书（昆虫卷）[M]. 北京: 中国农业出版社, 1996.
	Editorial Department of China Agricultural Encyclopedia. China Agricultural Encyclopedia (Insects) [M]. Beijing: Science Press, 1996.
4	张柱亭, 秦绍钊, 李静等. 贵州省黔东南地区草地贪夜蛾寄生性天敌资源调查初报[J]. 植物保护, 2019, 45(06): 65-69.
	Zhang Z T, Qin S Z, Li J, et al. Preliminary report on the investigation of natural enemy resources of Spodoptera frugiperda in southeast Guizhou. [J]. Plant Protection, 2019, 45(06): 65-69.
5	何国玮. 赣州柑橘木虱天敌昆虫资源调查及天敌捕食功能研究[D]. 呼和浩特: 内蒙古农业大学, 2019.
	He G W. Investigation and functional response research of natural enemy resources of citrus psyllid in Ganzhou [D]. Huhehaote: Inner Mongolia Agricultural University, 2019.
6	黄智华, 赵进龙, 崔永和等. 云南玉溪烟田害虫及天敌资源调查分析研究[J]. 西南农业学报, 2019, 32(05): 1067-1073.
	Huang Z H, Zhao J L, Cui Y H, et al. Investigation and analyses of insect pests and their natural enemies in tobacco fields in Yuxi, Yunnan[J]. Southwest China Journal of Agricultural Sciences, 2019, 32(05): 1067-1073.
7	朱丹, 鲁佳雄, 钟问等. 新疆马铃薯叶甲寄生蜂天敌资源调查研究[J]. 新疆农业科学, 2017, 54(12): 2248-2254.
	Zhu D, Lu J X, Zhong W, et al. Investigation of parasitizing wasps resources controlling potato leaf beetles in Xinjiang[J]. Xinjiang Agricultural Sciences, 2017, 54(12): 2248-2254.
8	蒲德强, 王勇, 刘虹伶等. 四川烟区烟蚜天敌资源与分布调查[J]. 烟草科技, 2016, 49(9): 27-32.
	Pu D Q, Wang Y, Liu H L, et al. Investigation on resources and distribution of natural enemies of Myzus persicae in Sichuan tobacco producing area[J]. Tabacco Science and Technology, 2016, 49(9): 27-32.
9	Harrington R, Taylor M S, Alderson L J, et al. The Rothamsted insect survey: gold standard aphid monitoring [J]. NJF Report, 2013, 9(7): 7-10.
10	Harrington R. The Rothamsted insect survey strikes gold [J]. Antenna, 2014, 38(3): 158-166.
11	Lohr S. The origins of ‘Big Data’: an etymological detective story[N]. The New York Times, 2013, Feb. 1.
12	Snijders C, Matzat U, Reips U. “Big data”: Big gaps of knowledge in the field of internet science [J]. International Journal of Internet Science, 2012, 7(1): 1-5.
13	Delgado J A, Short N M, Roberts D., et al. Big data analysis for sustainable agriculture on a geospatial cloud framework [J]. Frontiers in Sustainable Food Systems, 2019, 3: 54.
14	Wolfert S, Ge L, Verdouw C, et al. Big data in smart farming-a review [J]. Agricultural Systems, 2017, 153: 69-80.
15	Malaise R. A new insect-trap [J]. Entomologisk Tidskrift, 1937, 58: 148–160.
16	Gressitt J L. An improved malaise trap[J]. Pacific Insects, 1962, 1(4): 87–90.
17	Kress W J, Erickson D L. DNA barcodes: Genes, genomics, and bioinformatics [J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(8): 2761-2762.
18	Garrett S. G. E., Skevington P. J.. An introduction to electronic commerce [J]. BT Technology Journal, 1999, 17: 11-16.
19	Farooq Q, Fu P, Hao Y, et al. A review of management and importance of E-commerce implementation in service delivery of private express enterprises of China [J]. Original Research, 2019, 1: 1-9.
20	Dixon A F G. Insect predator-prey dynamics: ladybird beetles and biological control[M]. New York: Cambridge University Press, 2000.
21	任顺祥, 王兴民, 庞虹等. 中国瓢虫原色图鉴[M]. 北京: 科学出版社, 2009.
	Ren S X, Wang X M, Pang H, et al. Colored Pictorial Handbook of Ladybird Beetles in China. [M]. Beijing: Science Press, 2009.
22	van Lenteren J C. The state of commercial augmentative biological control: plenty of natural enemies, but a frustrating lack of uptake [J]. BioControl, 2012, 57: 1-20.
23	徐学农, 吕佳乐, 王恩东. 国际捕食螨研发与应用的热点问题及启示[J]. 中国生物防治学报, 2013, 29(02): 163-174.
	Xu X N, Lv J L, Wang E D. Hot spots in international predatory mite studies and lessons to us[J]. Chinese Journal of Biological Control, 2013, 29(02): 163-174.

	商品信息	天敌资源信息
“商家”提供的直接信息	品名	物种名
	商品类别	分类阶元（纲、目、科、属）
	库存量	密度
	产地	采集地点
	图片与影像（商品、商品应用、使用等）	图片（标本、生态、环境）
	产品描述	生物学性状
“用户”提供的信息	销量	需求量
“用户”提供的信息	评价	评价
关联信息	产区	分布情况
	生产时间	发生时间
	相关产品（同时、同地、相似产品）	关联物种
	外界因素对销售影响（促销活动导致销量上升、特殊原因导致需求上升等）	环境因素对密度、优势种等造成影响

纲	目	科
昆虫纲	膜翅目	姬蜂总科、茧蜂科、蚜茧蜂科、赤眼蜂科、姬小蜂科、胡蜂总科、小蜂总科、金小蜂科、蚜小蜂科、细蜂科、蚁科、鳌蜂科、锤角细蜂科、方头泥蜂科、广肩小蜂科、环腹瘿蜂科、旗腹蜂科、青蜂总科、跳小蜂科、土蜂科、肿腿蜂科、蛛蜂科
	双翅目	食蚜蝇科、虻科、瘿蚊科、寄蝇科、蜂虻科、沼蝇科、盗虻科
	半翅目	花蝽科、长蝽科、盲蝽科、姬蝽科、猎蝽科
	鞘翅目	瓢虫科、隐翅虫科、步甲科、虎甲科
	脉翅目	草蛉科、粉蛉科
	蜻蜓目	蜻蛉总科、蜓总科、细蟌总科
	螳螂目	螳螂科
	缨翅目	蓟马科
	革翅目	蠼螋科
蛛形纲	蜘蛛目	狼蛛科、肖蛸科、蟹蛛科、圆蛛科、球腹蛛科、跳蛛科、微蛛科、皿蛛科、管巢蛛科、猫蛛科、盗蛛科、漏斗蛛科、圆颚蛛科
蛛形纲	蜱螨目	植绥螨科、绒螨科、大赤螨科、囊螨科、长须螨科、镰螯螨科、吸螨科、小黑螨科、赤螨科、巨须螨科、镰螨科、蒲螨科

监测到该瓢虫的实验站数量	瓢虫种类
10+	异色瓢虫（52）、龟纹瓢虫（50）、七星瓢虫（34）、多异瓢虫（22）、六斑月瓢虫（16）、二星瓢虫（10）
6	黄宝盘瓢虫
5	稻红瓢虫、十一星瓢虫
4	红点唇瓢虫
3	赤星瓢虫、横斑瓢虫、红肩瓢虫、十三星瓢虫、中国双七瓢虫
2	八斑和瓢虫、粗网巧瓢虫、刀角瓢虫、方斑瓢虫、黑襟毛瓢虫、黄缘巧瓢虫、柯氏素菌瓢虫、十斑盘瓢虫、十二斑褐菌瓢虫、双带盘瓢虫、四斑广盾瓢虫、四斑裸瓢虫、四星瓢虫、细纹裸瓢虫、小毛瓢虫、隐斑瓢虫
1	波裂臀方瓢虫、大红瓢虫、二斑盘瓢虫、广东隐势瓢虫、海南隐势瓢虫、海南纵条瓢虫、黑条长瓢虫、红星盘瓢虫、后斑小瓢虫、黄褐巧瓢虫、刻点小艳瓢虫、菱斑巧瓢虫、六条瓢虫、六星瓢虫、拟小食螨瓢虫、庞氏毛瓢虫、十二斑广盾瓢虫、十二斑巧瓢虫、十二星瓢虫、十五星裸瓢虫、食螨瓢虫、束小瓢虫、素菌瓢虫、梯斑巧瓢虫、西南龟瓢虫、狭臀瓢虫、棕色毛瓢虫

作物类型	作物	省份	月份
作物类型	作物	省份	5月	6月	7月	8月	9月	10月	11月
粮油作物	小麦	江苏	+*
		河南	+
		山东	+	+
		山西	+	+
		辽宁	+
		黑龙江	+	+
	大豆	江苏				+
		河北		+
		内蒙古			+
		山西				+
		辽宁				+
		黑龙江		+	+	+	+
	水稻	贵州				+
		江西		+	+
		浙江
		江苏			+	+	+
	谷子	河北			+	+
	马铃薯	湖北	+
	马铃薯	河北		+
	向日葵	甘肃	+	+		+
	向日葵	江苏			+		+
果树	桃	湖南			+	+		+
		河北		+
		甘肃					+
	柑橘	江西	+	+	+
	柑橘	湖南	+	+	+	+	+	+
	苹果	甘肃	+		+	+
		河北		+	+
		陕西							+
		辽宁	+
	荔枝	广东	+			+	+	+	+
	荔枝	广西	+
	火龙果	贵州			+	+		+
	梨	江西				+
	梨	甘肃	+
	桑葚	广西			+		+	+
	山楂	河北		+
蔬菜	叶菜	海南		+
		广西	+						+
		河北		+	+	+
	茄子	海南	+	+				+
	茄子	河北	+
	菜豆	海南			+
	菜豆	浙江		+
经济作物	玉米	广西	+	+			+	+
		山西			+	+	+
		河南			+	+
		山东			+	+
		河北		+	+	+	+
		内蒙古				+
		吉林		+	+	+
		甘肃		+	+	+	+
		黑龙江		+	+	+	+
	棉花	安徽	+	+	+	+	+	+
		江苏		+				+
		山东		+		+	+

[1]	XU Jia, KANG Guiling, YU Linsong, ZHAO Yuyan, LIU Jingbing. Construction of Provincial Agricultural Geological Survey Information System Platform for Land Quality Evaluation [J]. Journal of Agricultural Big Data, 2023, 5(1): 116-125.
[2]	DAI Guowei, LIU Tingting, LI Haoxian, HU Lin, FAN Jingchao, CAO Shangyin. 2012—2017 Data Set for the Survey of Deciduous Fruit Tree Farm Variety Resources in Dominant Production Areas [J]. Journal of Agricultural Big Data, 2023, 5(1): 24-28.
[3]	GUAN Bolun, DONG Wei, ZHANG Liping, YANG Qianjin, WANG Yan. Development and Application of Traceability Tracking Platform for Ratooning Rice [J]. Journal of Agricultural Big Data, 2023, 5(1): 55-67.
[4]	Xiaoqing He, Meijuan Fang, Dong Liu, Zhijie Wang, Yubo Wang. A Dataset of Coccinellidae and Other Natural Enemies in Hengshui, Hebei Province from 2018 to 2020 [J]. Journal of Agricultural Big Data, 2022, 4(4): 16-23.
[5]	Yingjie Jiang, Xingxing Sun, Huiling Gu, Fan Wang, Kai Wang, Hongyang Li, Bo Gao, Jixiang Hu. A Dataset of Predacious Natural Enemies in Yancheng, Jiangsu from 2018 to 2021 [J]. Journal of Agricultural Big Data, 2022, 4(4): 45-51.
[6]	Jiale Lv, Zhenhui Wang, Xuenong Xu. Survey and Preservation Methods of Natural Enemy Resources in Agroecosystems [J]. Journal of Agricultural Big Data, 2022, 4(4): 5-15.
[7]	Shengbo Cong, Tiantian Yang, Dong Xu, Nina Yang, Huaiheng Wu, Ling Wang, Haichen Yin, Wenjing Li, Peng Wan. A Dataset of Ladybird Beetles and Spiders in Tea Orchards East Hubei Province from 2018 to 2020 [J]. Journal of Agricultural Big Data, 2022, 4(4): 60-66.
[8]	Wenming Zhang, Chuantao Huang, Yonghai Sun, Kaixiong Hou, Min Lu, Wenjing Wang, Congzhi Fei. A Dataset of Coccinellids in Yunnan Red Pear, etc Crops, Chuxiong Prefecture, Yunnan Province from 2018 to 2021 [J]. Journal of Agricultural Big Data, 2022, 4(4): 87-93.
[9]	Xin Wang, Leifeng Guo. Application and Construction of Big Data Fusion Framework for Anti-poverty Monitoring: A Systematic View of Data, Models, and Applications [J]. Journal of Agricultural Big Data, 2022, 4(2): 108-118.
[10]	Simiao Sun, Yuan Gao, Kun Wang, Dajiang Wang, Lianwen Li. Fruit Quality Analysis of 23 Apple Germplasm Resources [J]. Journal of Agricultural Big Data, 2022, 4(2): 13-19.
[11]	Bo Li, Wenjun Ma, Zhongming Wang, Jiaojiao Wang. Establishment and Application of Scientific Big Data Platform for Forest and Grass [J]. Journal of Agricultural Big Data, 2022, 4(2): 69-77.
[12]	Yiguang Zhao, Qingyao Luo, Shanshan Zheng, Yabin Pu, Benhai Xiong. Development and Application of China Domestic Animal Germplasm Resources Data Platform System [J]. Journal of Agricultural Big Data, 2022, 4(2): 78-87.
[13]	Yuxiao Sun, Yanli Li, Feng Li, Qian Chen. Research and Development Suggestions on Scientific Data Sharing at Home and Abroad [J]. Journal of Agricultural Big Data, 2022, 4(2): 88-98.
[14]	Yun Tao, Xiefeng Cheng. Comparative Study on Regional Big Data Development and Regional Agricultural Big Data Construction Level [J]. Journal of Agricultural Big Data, 2022, 4(1): 125-135.
[15]	Yuxin Wu, Juanle Wang, Baomin Han. Preparation of a Balance Sheet on Forest Resources in Motuo County, Tibet [J]. Journal of Agricultural Big Data, 2022, 4(1): 136-144.

Conceptual Establishment of a Natural Enemy Resources Survey and Monitoring System Based on Big Data

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 23

Related Articles 15

Metrics

Comments

Recommended 0