Advances in Precision Coffee Growing Research: A Bibliometric Review
Abstract
:1. Introduction
2. Research Methodology
2.1. Research Procedure
2.2. Selection and Organization Procedures
2.3. Bibliometric Mapping and Clustering
3. Results and Discussion
3.1. Evolution of Publications
3.2. Relevant Publications and Characteristics of Papers
3.3. Most Influential Journals
3.4. Publications by Authors
3.5. Most Influential Countries
3.6. Organizations Related to Precision Coffee Growing’ Research
3.7. Keywords Related to Precision Coffee Growing
3.8. Trends in Precision Coffee Growing Research
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
- Chain-Guadarrama, A.; Martínez-Salinas, A.; Aristizábal, N.; Ricketts, T.H. Ecosystem services by birds and bees to coffee in a changing climate: A review of coffee berry borer control and pollination. Agric. Ecosyst. Environ. 2019, 280, 53–67. [Google Scholar] [CrossRef]
- Marin, D.B.; Alves, M.D.C.; Pozza, E.A.; Belan, L.L.; Freitas, M.L.D.O. Multispectral radiometric monitoring of bacterial blight of coffee. Precis. Agric. 2018, 20, 959–982. [Google Scholar] [CrossRef]
- Belan, L.L.; Junior, W.C.D.J.; De Souza, A.F.; Zambolim, L.; Filho, J.C.; Barbosa, D.H.S.G.; Moraes, W.B. Management of coffee leaf rust in Coffea canephora based on disease monitoring reduces fungicide use and management cost. Eur. J. Plant Pathol. 2020, 156, 683–694. [Google Scholar] [CrossRef]
- Júnior, P.P.; Moreira, B.C.; Silva, M.D.C.S.D.; Veloso, T.G.R.; Stürmer, S.L.; Fernandes, R.B.A.; Mendonca, E.; Kasuya, M.C.M. Agroecological coffee management increases arbuscular mycorrhizal fungi diversity. PLoS ONE 2019, 14, e0209093. [Google Scholar] [CrossRef] [Green Version]
- U.S. Department of Agriculture. Coffee: World Markets and Trade; U.S. Department of Agriculture, U.S. Government Printing Office: Washington, DC, USA, 2021.
- Santana, L.; Ferraz, G.; Cunha, J.; Santana, M.; Faria, R.; Marin, D.; Rossi, G.; Conti, L.; Vieri, M.; Sarri, D. Monitoring Errors of Semi-Mechanized Coffee Planting by Remotely Piloted Aircraft. Agronomy 2021, 11, 1224. [Google Scholar] [CrossRef]
- Cadenas, J.; Garrido, M.; Martínez-España, R.; Guillén-Navarro, M. Making decisions for frost prediction in agricultural crops in a soft computing framework. Comput. Electron. Agric. 2020, 175, 105587. [Google Scholar] [CrossRef]
- Murugan, D.; Garg, A.; Singh, D. Development of an Adaptive Approach for Precision Agriculture Monitoring with Drone and Satellite Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017, 10, 5322–5328. [Google Scholar] [CrossRef]
- Paccioretti, P.; Córdoba, M.; Balzarini, M. FastMapping: Software to create field maps and identify management zones in precision agriculture. Comput. Electron. Agric. 2020, 175, 105556. [Google Scholar] [CrossRef]
- Yost, M.A.; Kitchen, N.R.; Sudduth, K.A.; Massey, R.E.; Sadler, E.J.; Drummond, S.T.; Volkmann, M.R. A long-term precision agriculture system sustains grain profitability. Precis. Agric. 2019, 20, 1177–1198. [Google Scholar] [CrossRef]
- Alves, E.A.; Queiroz, D.M.; Pinto, F.A.C. Cafeicultura de precisão. In Boas Práticas Agrícolas na Produção de Café; Zambolim, L., Ed.; UFV: Viçosa, Brazil, 2007; p. 234. ISBN 8560027157. [Google Scholar]
- Kouadio, L.; Deo, R.C.; Byrareddy, V.; Adamowski, J.F.; Mushtaq, S.; Nguyen, V.P. Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties. Comput. Electron. Agric. 2018, 155, 324–338. [Google Scholar] [CrossRef]
- Andrade, A.D.; Ferraz, G.A.E.S.; De Barros, M.M.; Faria, R.D.O.; Da Silva, F.M.; Sarri, D.; Vieri, M. Characterization of the Transverse Distribution of Fertilizer in Coffee Plantations. Agronomy 2020, 10, 601. [Google Scholar] [CrossRef] [Green Version]
- Ferraz, G.A.E.S.; Da Silva, F.M.; De Oliveira, M.S.; Custódio, A.A.P.; Ferraz, P. Variabilidade espacial dos atributos da planta de uma lavoura cafeeira. Rev. Cienc. Agron. 2017, 48, 81–91. [Google Scholar] [CrossRef] [Green Version]
- Dos Santos, L.M.; Ferraz, G.A.E.S.; Barbosa, B.D.D.S.; Diotto, A.V.; Maciel, D.T.; Xavier, L.A.G. Biophysical parameters of coffee crop estimated by UAV RGB images. Precis. Agric. 2020, 21, 1227–1241. [Google Scholar] [CrossRef]
- Barros, M.M.; Volpato, C.E.S.; Silva, F.C.; Palma, M.A.Z.; Spagnolo, R.T. Avaliação de um sistema de aplicação de fertili-zantes a taxa variável adaptado à cultura cafeeira. Coffee Sci. 2015, 10, 223–232. [Google Scholar]
- Koutsos, T.M.; Menexes, G.C.; Dordas, C.A. An efficient framework for conducting systematic literature reviews in agricultural sciences. Sci. Total Environ. 2019, 682, 106–117. [Google Scholar] [CrossRef]
- Pollock, M.; Fernandes, R.M.; Becker, L.A.; Featherstone, R.; Hartling, L. What guidance is available for researchers conducting overviews of reviews of healthcare interventions? A scoping review and qualitative metasummary. Syst. Rev. 2016, 5, 1–15. [Google Scholar] [CrossRef] [Green Version]
- Sharma, G.; Bansal, P. Partnering Up: Including Managers as Research Partners in Systematic Reviews. Organ. Res. Methods 2020, 1–30. [Google Scholar] [CrossRef]
- Souza, V.H.S.; Dias, G.L.; Santos, A.A.R.; Costa, A.L.G.; Santos, F.L.; Magalhães, R.R. Evaluation of the interaction between a harvester rod and a coffee branch based on finite element analysis. Comput. Electron. Agric. 2018, 150, 476–483. [Google Scholar] [CrossRef]
- Centobelli, P.; Cerchione, R.; Chiaroni, D.; Del Vecchio, P.; Urbinati, A. Designing business models in circular economy: A systematic literature review and research agenda. Bus. Strat. Environ. 2020, 29, 1734–1749. [Google Scholar] [CrossRef]
- Coman, M.A.; Marcu, A.; Chereches, R.M.; Leppälä, J.; Broucke, S.V.D. Educational Interventions to Improve Safety and Health Literacy Among Agricultural Workers: A Systematic Review. Int. J. Environ. Res. Public Health 2020, 17, 1114. [Google Scholar] [CrossRef] [Green Version]
- Seuring, S.; Gold, S. Conducting content-analysis based literature reviews in supply chain management. Supply Chain Manag. Int. J. 2012, 17, 544–555. [Google Scholar] [CrossRef]
- Daim, T.U.; Rueda, G.; Martin, H.; Gerdsri, P. Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technol. Forecast. Soc. Chang. 2006, 73, 981–1012. [Google Scholar] [CrossRef]
- Liu, W.; Gu, M.; Hu, G.; Li, C.; Liao, H.; Tang, L.; Shapira, P. Profile of developments in biomass-based bioenergy research: A 20-year perspective. Scientometrics 2013, 99, 507–521. [Google Scholar] [CrossRef]
- Andrade-Valbuena, N.A.; Merigo-Lindahl, J.M.; Olavarrieta, S. Bibliometric analysis of entrepreneurial orientation. World J. Entrep. Manag. Sustain. Dev. 2019, 15, 45–69. [Google Scholar] [CrossRef]
- Sharifi, A.; Simangan, D.; Kaneko, S. Three decades of research on climate change and peace: A bibliometrics analysis. Sustain. Sci. 2020, 16, 1079–1095. [Google Scholar] [CrossRef]
- Mallett, R.; Hagen-Zanker, J.; Slater, R.; Duvendack, M. The benefits and challenges of using systematic reviews in international development research. J. Dev. Eff. 2012, 4, 445–455. [Google Scholar] [CrossRef]
- Chain, C.P.; Dos Santos, A.C.; De Castro, L.G.; Prado, J.W.D. Bibliometric analysis of the quantitative methods applied to the measurement of industrial clusters. J. Econ. Surv. 2018, 33, 60–84. [Google Scholar] [CrossRef] [Green Version]
- Pallottino, F.; Biocca, M.; Nardi, P.; Figorilli, S.; Menesatti, P.; Costa, C. Science mapping approach to analyze the research evolution on precision agriculture: World, EU and Italian situation. Precis. Agric. 2018, 19, 1011–1026. [Google Scholar] [CrossRef]
- Velasco-Muñoz, J.F.; Aznar-Sánchez, J.A.; Batlles-Delafuente, A.; Fidelibus, M.D. Rainwater Harvesting for Agricultural Irrigation: An Analysis of Global Research. Water 2019, 11, 1320. [Google Scholar] [CrossRef] [Green Version]
- Kane, D.A.; Rogé, P.; Snapp, S.S. A Systematic Review of Perennial Staple Crops Literature Using Topic Modeling and Bibliometric Analysis. PLoS ONE 2016, 11, e0155788. [Google Scholar] [CrossRef]
- Madani, F.; Weber, C. The evolution of patent mining: Applying bibliometrics analysis and keyword network analysis. World Pat. Inf. 2016, 46, 32–48. [Google Scholar] [CrossRef]
- Muhuri, P.K.; Shukla, A.K.; Abraham, A. Industry 4.0: A bibliometric analysis and detailed overview. Eng. Appl. Artif. Intell. 2019, 78, 218–235. [Google Scholar] [CrossRef]
- Noyons, E.C.M.; Moed, H.F.; Luwel, M. Combining mapping and citation analysis for evaluative bibliometric purposes: A bibliometric study. J. Am. Soc. Inf. Sci. 1999, 50, 115–131. [Google Scholar] [CrossRef]
- Börner, J.; Marinho, E.; Wunder, S. Mixing Carrots and Sticks to Conserve Forests in the Brazilian Amazon: A Spatial Probabilistic Modeling Approach. PLoS ONE 2015, 10, e0116846. [Google Scholar] [CrossRef] [Green Version]
- Garfield, P.E. Citation indexes for science. A new dimension in documentation through association of ideas†. Int. J. Epidemiol. 2006, 35, 1123–1127. [Google Scholar] [CrossRef] [Green Version]
- Moed, H.F.; Markusova, V.; Akoev, M. Trends in Russian research output indexed in Scopus and Web of Science. Scientometrics 2018, 116, 1153–1180. [Google Scholar] [CrossRef] [Green Version]
- Bakkalbasi, N.; Bauer, K.; Glover, J.; Wang, L. Three options for citation tracking: Google Scholar, Scopus and Web of Science. Biomed. Digit. Libr. 2006, 3, 1–8. [Google Scholar] [CrossRef] [Green Version]
- Pizzi, S.; Caputo, A.; Corvino, A.; Venturelli, A. Management research and the UN sustainable development goals (SDGs): A bibliometric investigation and systematic review. J. Clean. Prod. 2020, 276, 124033. [Google Scholar] [CrossRef]
- Barbara, K.; Charters, S.; Budgen, D.; Brereton, P.; Mark, T.; Linkman, S.; Jørgensen, M.; Mendes, E.; Visaggio, G. Guidelines for performing Systematic Literature Reviews in Software Engineering. Version 2.3. Durham UK. 2007. Available online: https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.117.471 (accessed on 22 March 2021).
- Nardi, P.; Di Matteo, G.; Palahi, M.; Mugnozza, G.S. Structure and Evolution of Mediterranean Forest Research: A Science Mapping Approach. PLoS ONE 2016, 11, e0155016. [Google Scholar] [CrossRef] [Green Version]
- Van Eck, N.J.; Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2009, 84, 523–538. [Google Scholar] [CrossRef] [Green Version]
- Van Eck, N.J.; Waltman, L. A Comparison of TwoTechniques for Bibliometric Mapping: Multidimensional Scaling and VOS Nees. J. Am. Soc. Inf. Sci. Technol. 2010, 61, 2405–2416. [Google Scholar] [CrossRef] [Green Version]
- Merton, R. The sociology of science: An episodic memoir. In The Sociology of Science in Europe; Southern Illinois University Press: Carbondale, IL, USA, 1977; pp. 3–141. [Google Scholar]
- Herwitz, S.; Johnson, L.; Dunagan, S.; Higgins, R.; Sullivan, D.; Zheng, J.; Lobitz, B.; Leung, J.; Gallmeyer, B.; Aoyagi, M.; et al. Imaging from an unmanned aerial vehicle: Agricultural surveillance and decision support. Comput. Electron. Agric. 2004, 44, 49–61. [Google Scholar] [CrossRef]
- Chemura, A.; Mutanga, O.; Dube, T. Separability of coffee leaf rust infection levels with machine learning methods at Sentinel-2 MSI spectral resolutions. Precis. Agric. 2016, 18, 859–881. [Google Scholar] [CrossRef]
- Sentelhas, P.C.; Gillespie, T.J.; Batzer, J.C.; Gleason, M.L.; Monteiro, J.E.B.A.; Pezzopane, J.; Pedro, M.J. Spatial variability of leaf wetness duration in different crop canopies. Int. J. Biometeorol. 2005, 49, 363–370. [Google Scholar] [CrossRef]
- Silva, F.M.; De Souza, Z.M.; De Figueiredo, C.A.P.; Júnior, J.M.; Machado, R.V. Spatial Variability Of Chemical Attributes And Productivity In The Coffee Cultivation. Ciência Rural. 2007, 37, 401–407. [Google Scholar] [CrossRef] [Green Version]
- Silva, F.M.; Souza, Z.M.; Figueiredo, C.A.P.; Vieira, L.H.D.S.; Oliveira, E. Spatial variability of chemical attributes and coffee productivity in two harvests. Cienc. Agrotecnol. 2008, 32, 231–241. [Google Scholar] [CrossRef] [Green Version]
- Cordero-Sancho, S.; Sader, S.A. Spectral analysis and classification accuracy of coffee crops using Landsat and a topographic-environmental model. Int. J. Remote Sens. 2007, 28, 1577–1593. [Google Scholar] [CrossRef]
- Silva, S.D.A.; Lima, J.S.D.S.; da Silva, J.M.; Teixeira, M.M. Spatial variability of chemical attributes of an Oxisol under coffee cultivation. Rev. Bras. Ciência Solo. 2010, 34, 16–23. [Google Scholar] [CrossRef]
- Ferraz, G.A.E.S.; da Silva, F.M.; Alves, M.D.C.; Bueno, R.D.L.; da Costa, P.A.N. Geostatistical analysis of fruit yield and detachment force in coffee. Precis. Agric. 2011, 13, 76–89. [Google Scholar] [CrossRef]
- Ferraz, G.A.E.S.; Da Silva, F.M.; Carvalho, L.C.C.; Alves, M.D.C.; Franco, B.C. Spatial And Temporal Variability Of Phosphorus, Potassium And Of The Yield Of A Coffee Field. Eng. Agric. 2012, 32, 140–150. [Google Scholar] [CrossRef] [Green Version]
- Armenta-Medina, D.; Ramirez-Delreal, T.A.; Villanueva-Vásquez, D.; Mejia-Aguirre, C. Trends on Advanced Information and Communication Technologies for Improving Agricultural Productivities: A Bibliometric Analysis. Agronomy 2020, 10, 1989. [Google Scholar] [CrossRef]
- Sott, M.K.; Furstenau, L.B.; Kipper, L.M.; Giraldo, F.D.; Lopez-Robles, J.R.; Cobo, M.J.; Zahid, A.; Abbasi, Q.H.; Imran, M.A. Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends. IEEE Access 2020, 8, 149854–149867. [Google Scholar] [CrossRef]
- Santana, L.S.; Ferraz, G.A.E.S.; Santos, L.M.; Maciel, D.A.; Barata, R.A.P.; Reynaldo, É.F.; Rossi, G. Vegetative vigor of maize crop obtained through vegetation indexes in orbital and aerial sensors images. Braz. J. Biosyst. Eng. 2019, 13, 195–206. [Google Scholar] [CrossRef] [Green Version]
- Pivoto, D.; Waquil, P.D.; Talamini, E.; Finocchio, C.P.S.; Corte, V.; Mores, G.D.V. Scientific development of smart farming technologies and their application in Brazil. Inf. Process. Agric. 2018, 5, 21–32. [Google Scholar] [CrossRef]
- Johnson, L.F.; Herwitz, S.R.; Lobitz, B.M.; Dunagan, S.E. Feasibility of monitoring coffee field ripeness with airborne multi-spectral imagery. Appl. Eng. Agric. 2004, 20, 845–849. [Google Scholar] [CrossRef]
- Cruz-O’Byrne, R.; Piraneque-Gambasica, N.; Aguirre-Forero, S.; Ramirez-Vergara, J. Microorganisms in coffee fermentation: A bibliometric and systematic literature network analysis related to agriculture and beverage quality (1965-2019). Coffee Sci. 2020, 15, 1–14. [Google Scholar] [CrossRef]
- Pabon, C.D.R.; Sánchez-Benitez, J.; Rosero, J.R.; Ramirez-Gonzalez, G.A. Coffee crop science metric: A review. Coffee Sci. 2020, 15, 1–11. [Google Scholar] [CrossRef]
- Sales, F.O.; Marante, Y.; Vieira, A.B.; Silva, E.F. Energy Consumption Evaluation of a Routing Protocol for Low-Power and Lossy Networks in Mesh Scenarios for Precision Agriculture. Sensors 2020, 20, 3814. [Google Scholar] [CrossRef] [PubMed]
- Marin, D.; Ferraz, G.; Guimarães, P.; Schwerz, F.; Santana, L.; Barbosa, B.; Barata, R.; Faria, R.; Dias, J.; Conti, L.; et al. Remotely Piloted Aircraft and Random Forest in the Evaluation of the Spatial Variability of Foliar Nitrogen in Coffee Crop. Remote Sens. 2021, 13, 1471. [Google Scholar] [CrossRef]
- Oré, G.; Alcântara, M.S.; Góes, J.A.; Oliveira, L.P.; Yepes, J.; Teruel, B.; de Castro, V.L.B.; Bins, L.S.; Castro, F.; Luebeck, D.; et al. Crop Growth Monitoring with Drone-Borne DInSAR. Remote Sens. 2020, 12, 615. [Google Scholar] [CrossRef] [Green Version]
R | Title | Authors | PY | Journal | NC |
---|---|---|---|---|---|
1° | Imaging From An Unmanned Aerial Vehicle: Agricultural Surveillance And Decision Support | Herwitz, et al. [46] | 2004 | Computers and Electronics in Agriculture | 277 |
2° | Separability Of Coffee Leaf Rust Infection Levels With Machine Learning Methods At Sentinel-2 Msi Spectral Resolutions | Chemura, et al. [47] | 2017 | Precision Agriculture | 45 |
3° | Spatial Variability Of Leaf Wetness Duration In Different Crop Canopies | Sentelhas, et al. [48] | 2005 | International Journal of Biometeorology | 45 |
4° | Spatial Variability Of Chemical Attributes And Productivity In The Coffee Cultivation | Silva 2, et al. [49] | 2007 | Ciencia Rural | 40 |
5° | Spatial Variability Of Chemical Attributes And Coffee Productivity In Two Harvests | Silva 2, et al. [50] | 2008 | Ciencia e Agrotecnologia | 41 |
6° | Spectral Analysis And Classification Accuracy Of Coffee Crops Using Landsat And A Topographic-Environmental Model | Cordero-Sancho and Sader [51] | 2007 | International Journal of Remote Sensing | 38 |
7° | Spatial Variability Of Chemical Attributes Of An Oxisol Under Coffee Cultivation | Silva 1, et al. [52] | 2010 | Revista Brasileira de Ciencia do Solo | 36 |
8° | Geostatistical Analysis Of Fruit Yield And Detachment Force In Coffee | Ferraz, et al. [53] | 2012a | Precision Agriculture | 33 |
9° | Feasibility Of Monitoring Coffee Field Ripeness With Airborne Multispectral Imagery | Johnson, et al. [46] | 2004 | Applied Engineering in Agriculture | 32 |
10° | Spatial And Temporal Variability Of Phosphorus, Potassium And Of The Yield Of A Coffee Field | Ferraz, et al. [54] | 2012b | Engenharia Agricola | 31 |
R | Journal | SJR 1 | CiteScore 2 | JCR 3 | H-i | ISSN | ND | NC |
---|---|---|---|---|---|---|---|---|
1° | Computers and Electronics in Agriculture [46] | 1.208 | 8.6 | 3.858 | 115 | 0168-1699 | 5 | 409 |
2° | Precision Agriculture [8,9,10] | 1.023 | 8.7 | 4.454 | 63 | 1385-2256 | 9 | 398 |
3° | Revista Brasileira de Ciência do Solo [52] | 0.505 | 2.5 | 1.2 | 51 | 0100-0683 | 8 | 291 |
4° | Engenharia Agrícola [54] | 0.289 | 1.4 | 0.603 | 27 | 0100-6916 | 11 | 256 |
5° | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing [8] | 1.246 | 7.2 | 3.827 | 88 | 1939-1404 | 4 | 190 |
6° | Ciência e Agrotecnologia [49,52] | 0.437 | 2.3 | 1.144 | 30 | 1413-70 | 4 | 152 |
R | Authors | Id. | H-i (Scopus) | H-i (WoS) | NC | ND |
---|---|---|---|---|---|---|
1° | Fábio Moreira da Silva [13,14,15,16,52] | Silva, F. M. | 12 | 12 | 303 | 20 |
2° | Gabriel Araújo e Silva Ferraz [6,13,14,15] | Ferraz, G. A. S. | 10 | 5 | 203 | 16 |
3° | Marcelo Silva de Oliveira [14,50] | Oliveira, M. S. | 10 | 9 | 192 | 11 |
6° | Ivoney Gontijo [55] | Gontijo, I. | 6 | 6 | 139 | 8 |
4° | Julião Soares de Souza Lima [52] | Lima, J. S. S. | 11 | 10 | 129 | 9 |
5° | Samuel de Assis Silva [52] | Silva, S. A. | 11 | 5 | 117 | 9 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Santana, L.S.; Ferraz, G.A.e.S.; Teodoro, A.J.d.S.; Santana, M.S.; Rossi, G.; Palchetti, E. Advances in Precision Coffee Growing Research: A Bibliometric Review. Agronomy 2021, 11, 1557. https://doi.org/10.3390/agronomy11081557
Santana LS, Ferraz GAeS, Teodoro AJdS, Santana MS, Rossi G, Palchetti E. Advances in Precision Coffee Growing Research: A Bibliometric Review. Agronomy. 2021; 11(8):1557. https://doi.org/10.3390/agronomy11081557
Chicago/Turabian StyleSantana, Lucas Santos, Gabriel Araújo e Silva Ferraz, Alberdan José da Silva Teodoro, Mozarte Santos Santana, Giuseppe Rossi, and Enrico Palchetti. 2021. "Advances in Precision Coffee Growing Research: A Bibliometric Review" Agronomy 11, no. 8: 1557. https://doi.org/10.3390/agronomy11081557