0

PT AU BA BE GP AF BF CA TI SO SE BS LA DT CT CY CL SP HO DE ID AB C1 C3 RP EM RI OI FU FP FX CR NR TC Z9 U1 U2 PU PI PA SN EI BN J9 JI PD PY VL IS PN SU SI MA BP EP AR DI DL D2 EA PG WC WE SC GA PM OA HC HP DA UT J Armenta-Medina, D; Ramirez-delReal, TA; Villanueva-Vasquez, D; Mejia-Aguirre, C Armenta-Medina, Dagoberto; Ramirez-delReal, Tania A.; Villanueva-Vasquez, Daniel; Mejia-Aguirre, Cristian Trends on Advanced Information and Communication Technologies for Improving Agricultural Productivities: A Bibliometric Analysis AGRONOMY-BASEL English Article bibliometrics; precision agriculture; science mapping; smart farming; IoT PRECISION AGRICULTURE; SCIENTIFIC-RESEARCH; WORLD; INDICATORS; KNOWLEDGE; SUPPORT; SYSTEMS; MODEL; TOOL; EU In this work, an exhaustive revision is given of the literature associated with advanced information and communication technologies in agriculture within a window of 25 years using bibliometric tools enabled to detect of the main actors, structure, and dynamics in the scientific papers. The main findings are a trend of growth in the dynamics of publications associated with advanced information and communication technologies in agriculture productivity. Another assertion is that countries, like the USA, China, and Brazil, stand out in many publications due to allocating more resources to research, development, and agricultural productivity. In addition, the collaboration networks between countries are frequently in regions with closer cultural and idiomatic ties; additionally, terms' occurrence are obtained with Louvain algorithm predominating four clusters: precision agriculture, smart agriculture, remote sensing, and climate smart agriculture. Finally, the thematic-map characterization with Callon's density and centrality is applied in three periods. The first period of thematic analysis shows a transition in detecting the variability of a nutrient, such as nitrogen, through the help of immature georeferenced techniques, towards greater remote sensing involvement. In the transition from the second to the third stage, the maturation of technologies, such as unmanned aerial vehicles, wireless sensor networks, and the machine learning area, is observed. [Armenta-Medina, Dagoberto; Ramirez-delReal, Tania A.; Villanueva-Vasquez, Daniel] CONACyT Consejo Nacl Ciencia & Tecnol, Direcc Catedras, Insurgentes Sur 1582, Ciudad de Mexico 03940, Mexico; [Armenta-Medina, Dagoberto; Villanueva-Vasquez, Daniel; Mejia-Aguirre, Cristian] INFOTEC Ctr Invest & Innovac Tecnol Informac & Co, Circuito Tecnopolo Sur 112,Fracc Tecnopolo Pocito, Aguascalientes 20313, Aguascalientes, Mexico; [Ramirez-delReal, Tania A.] CentroGEO Ctr Invest Ciencias Informac Geoespacia, Circuito Tecnopolo Norte 117,Col Tecnopolo Pocito, Aguascalientes 20313, Aguascalientes, Mexico INFOTEC - Centro de Investigacion e Innovacion en Tecnologias de la Informacion y Comunicacion Armenta-Medina, D (corresponding author), CONACyT Consejo Nacl Ciencia & Tecnol, Direcc Catedras, Insurgentes Sur 1582, Ciudad de Mexico 03940, Mexico.; Armenta-Medina, D (corresponding author), INFOTEC Ctr Invest & Innovac Tecnol Informac & Co, Circuito Tecnopolo Sur 112,Fracc Tecnopolo Pocito, Aguascalientes 20313, Aguascalientes, Mexico. dagoberto.armenta@infotec.mx; tramirez@centrogeo.edu.mx; daniel.villanueva@infotec.mx; criss_ja299@hotmail.com Villanueva Vasquez, Daniel/0000-0001-8512-7630; Ramirez-delReal, Tania Aglae/0000-0002-1638-5086 National Council of Science and Technology [737, 735]; 576 of the Catedras Conacyt Program National Council of Science and Technology; 576 of the Catedras Conacyt Program The authors are very grateful to the National Council of Science and Technology. Additionally, this paper was sponsored by project numbers 737, 735, 576 of the Catedras Conacyt Program. Rodriguez MA, 2019, IFIP ADV INF COMM TE, P467, DOI 10.1007/978-3-030-28464-0_40; Aria M, 2017, J INFORMETR, V11, P959, DOI 10.1016/j.joi.2017.08.007; Barcelo-Ordinas JM, 2013, PRECISION AGRICULTURE '13, P801; Borghi E., 2016, EMBRAPA MILHO SORGO; Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106; Bouchet-Valat M., SNOWBALLC SNOWBALL S; Buzheng W., 1995, BEIJING NONGYE DAXUE; Cahlik T, 2000, SCIENTOMETRICS, V49, P373, DOI 10.1023/A:1010581421990; CALLON M, 1991, SCIENTOMETRICS, V22, P155, DOI 10.1007/BF02019280; Chen WL, 2019, IEEE INTERNET THINGS, V6, P5209, DOI 10.1109/JIOT.2019.2899128; Chernyi AI, 2009, SCI TECH INF PROCESS, V36, P351, DOI 10.3103/S0147688209060069; Court C.D., 2018, EDIS, V2018, DOI [10.32473/edis-fe1021-2017, DOI 10.32473/EDIS-FE1021-2017]; COURTIAL JP, 1990, SCIENTOMETRICS, V19, P127, DOI 10.1007/BF02130469; De Meo P., 2011, Proceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications (ISDA), P88, DOI 10.1109/ISDA.2011.6121636; DEWI C, 2019, AS C INT INF DAT SYS, P31; Dimitri C., 2005, 20 CENTURY TRANSFORM, P1; Dincer H., 2019, ORG TRANSFORMATION M, P245, DOI [10.4018/978-1-5225-7074-5.ch013, DOI 10.4018/978-1-5225-7074-5.CH013]; Eastwood CR, 2012, AGR SYST, V108, P10, DOI 10.1016/j.agsy.2011.12.005; Egea FJ, 2018, NEW BIOTECHNOL, V40, P103, DOI 10.1016/j.nbt.2017.06.009; Farooq MS, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9020319; Fuglie K, 2016, GLOB FOOD SECUR-AGR, V10, P29, DOI 10.1016/j.gfs.2016.07.005; Gebbers R, 2010, SCIENCE, V327, P828, DOI 10.1126/science.1183899; Guanziroli C.E., 2006, EC TEXTO DISCUSSAO, V186, P1; Haboudane D, 2004, REMOTE SENS ENVIRON, V90, P337, DOI 10.1016/j.rse.2003.12.013; Han Shuqing, 2018, J PHYS C SER, V1087; Hank TB, 2019, SURV GEOPHYS, V40, P515, DOI 10.1007/s10712-018-9492-0; Herian M., 2016, 2016 NEBRASKA MANUFA; Hopkins M., 2018, PRECISIONAG; Hopkins M., PRECISIONAG; Hoy R. M., 2020, ASABE Distinguished Lecture Series; Huang JK, 2017, J INTEGR AGR, V16, P2933, DOI 10.1016/S2095-3119(17)61756-8; Hunt W, 2012, J AGRIC EDUC EXT, V18, P9, DOI 10.1080/1389224X.2012.638780; Igal Aisenberg, EMCOMPASS NO 46; Kamilaris A, 2018, COMPUT ELECTRON AGR, V147, P70, DOI 10.1016/j.compag.2018.02.016; Khush GS, 2001, NAT REV GENET, V2, P815, DOI 10.1038/35093585; KUSHWAHA M, 2017, INT J RECENT INNOVAT, V5, P1300; Lakitan B, 2019, J SCI TECHNOL POLICY, V10, P251, DOI 10.1108/JSTPM-11-2017-0061; Lopez-Herrera A., 2010, INT J HYBRID INTELLI, V7, P17, DOI DOI 10.3233/HIS-2010-0102; Mahlein AK, 2019, CURR OPIN PLANT BIOL, V50, P156, DOI 10.1016/j.pbi.2019.06.007; Meo SA, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0066449; Mesas-Carrascosa FJ, 2015, BIOSYST ENG, V137, P73, DOI 10.1016/j.biosystemseng.2015.07.005; Michelet B., 1988, THESIS; Missouri Economic Indicator Brief: Manufacturing Manufacturig Industries, MISS EC IND BRIEF MA; Morgan N., CALIFORNIA; Muangprathub J, 2019, COMPUT ELECTRON AGR, V156, P467, DOI 10.1016/j.compag.2018.12.011; Mulla DJ, 2013, BIOSYST ENG, V114, P358, DOI 10.1016/j.biosystemseng.2012.08.009; Oliver MA, 2010, GEOSTATISTICAL APPLICATIONS FOR PRECISION AGRICULTURE, P1, DOI 10.1007/978-90-481-9133-8_1; Olmstead A.L., 2017, GIANNINI FDN AGR EC, V17, P1; Ort DR, 2014, SCIENCE, V344, P483, DOI 10.1126/science.1253884; Pallottino F, 2018, PRECIS AGRIC, V19, P1011, DOI 10.1007/s11119-018-9569-2; Pardey P.G., 2018, AGRISCIENCE AGRIBUSI, P13, DOI DOI 10.1007/978-3-319-67958-7_2; Pardey PG, 2013, AGR ECON-BLACKWELL, V44, P103, DOI 10.1111/agec.12055; Peng Y, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0142295; Martinho VJPD, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10062080; Pimentel D, 1996, SCI TOTAL ENVIRON, V188, pS86, DOI 10.1016/0048-9697(96)05280-1; Pivoto D., 2018, INFORM PROCESSING AG, V5, DOI [DOI 10.1016/J.INPA.2017.12.002, 10.1016/j.inpa.2017.12.002]; Ponce-Rojas J., 2011, INT J COMMUN, V4, P180, DOI 10.4236/ijcns.2011.43022; PRETTY JN, 1995, WORLD DEV, V23, P1247, DOI 10.1016/0305-750X(95)00046-F; Qiu ZJ, 2007, OUTLOOK AGR, V36, P255, DOI 10.5367/000000007783418499; Raitzer DA, 2008, AGR SYST, V96, P108, DOI 10.1016/j.agsy.2007.06.004; Ribaudo M., 2011, ERR127 USDA EC RES S; Robinson D., SPANISH BANK IBERCAJ; Rossel RAV, 2006, GEODERMA, V131, P59, DOI 10.1016/j.geoderma.2005.03.007; Schreyer P., 2000, TECHNICAL REPORT; Serrano J, 2020, AGRONOMY-BASEL, V10, DOI 10.3390/agronomy10020218; Setti G, 2013, IEEE ACCESS, V1, P232, DOI 10.1109/ACCESS.2013.2261115; Shuai CM, 2010, OUTLOOK AGR, V39, P169, DOI 10.5367/oa.2010.0008; Stackpole A., 2018, FARM TRACTOR EFFICIE; Stagakis S, 2012, ISPRS J PHOTOGRAMM, V71, P47, DOI 10.1016/j.isprsjprs.2012.05.003; Suebsombut P, 2017, 2017 INTERNATIONAL CONFERENCE ON DIGITAL ARTS, MEDIA AND TECHNOLOGY (ICDAMT): DIGITAL ECONOMY FOR SUSTAINABLE GROWTH, P136, DOI 10.1109/ICDAMT.2017.7904950; Taylor M, 2018, J PEASANT STUD, V45, P89, DOI 10.1080/03066150.2017.1312355; Teodoro A, 2017, EUR J TOUR RES, V17, P136; Tilman D, 1998, NATURE, V396, P211, DOI 10.1038/24254; Tippmann S, 2015, NATURE, V517, P109, DOI 10.1038/517109a; Tzounis A, 2017, BIOSYST ENG, V164, P31, DOI 10.1016/j.biosystemseng.2017.09.007; United States Department of Agriculture (USDA), US AGR TRAD DAT UPD; United States Department of Agriculture (USDA), STAT AGR OV NEBR; United States Department of Agriculture (USDA), STAT AGR OV MISS; United States Department of Agriculture (USDA), STAT AGR OV FLOR; United States Department of Agriculture (USDA) National Agricultural Statistics Service Information (NASS), 2017, 2017 CENS AGR STAT L; Vinkler P, 2008, SCIENTOMETRICS, V74, P237, DOI 10.1007/s11192-008-0215-z; Werner D., 2005, NITROGEN FIXATION AG, V4; Zhang CH, 2012, PRECIS AGRIC, V13, P693, DOI 10.1007/s11119-012-9274-5; Zhuang YH, 2013, SCIENTOMETRICS, V96, P203, DOI 10.1007/s11192-012-0918-z 84 12 12 7 42 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4395 AGRONOMY-BASEL Agronomy-Basel DEC 2020 10 12 1989 10.3390/agronomy10121989 http://dx.doi.org/10.3390/agronomy10121989 24 Agronomy; Plant Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Agriculture; Plant Sciences PK2CF gold 2023-01-20 WOS:000602258100001