Abstract
As we are heading for the third decade of the twenty-first century, Precision Agriculture is the most modern way to improve agricultural processes/actions that take place on arable land, like harvesting, irrigation or fertilizer use. Its main idea is based on the concept that every farm has different needs across its area. So, Precision Agriculture offers the capability to customize decisions regarding inputs like water and optimize agricultural equipment use like an agricultural vehicle so as to improve outputs. As its use spreads across the globe, it is interesting to study the range of innovation diffusion on agriculture in Greece and see the differentiation of Greek districts, their innovative actions and economic outcomes that come up. To achieve this goal a study took place across Greece collecting 1032 answers from farmers on mainland Greece and Crete. The Ascending Hierarchy Clustering was used to process the data and the results demonstrated that some crop categories are more profitable than others and there are districts consisted of common characteristics. In addition, an innovation map was created, in conjunction with its benefits.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Eise J, Foster K (2018) How to feed the world
Zarco-Tejada PJ, Bubbard N, Loudjani P (2014) Precision Agriculture: An Opportunity for EU Farmers - Potential Support with the CAP 2014-2020
Fountas S, Aggelopoulou K, Gemtos TA (2016) Precision agriculture: Crop management for improved productivity and reduced environmental impact or improved sustainability. In: Eleftherios I, Bochtis D, Vlachos D, Dimitrios A (eds) Supply Chain Management for Sustainable Food Networks. John Wiley & Sons, Ltd., pp 41–65
Hunt ER, Daughtry CST (2018) What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? Int J Remote Sens 39:5345–5376. https://doi.org/10.1080/01431161.2017.1410300
Zude-Sasse M, Fountas S, Gemtos TA, Abu-Khalaf N (2016) Applications of precision agriculture in horticultural crops. Eur J Hortic Sci 81:78–90. https://doi.org/10.17660/eJHS.2016/81.2.2
Guardo E, Di Stefano A, La Corte A, et al (2018) A fog computing-based IoT framework for precision agriculture. J Internet Technol 19:1401–1411. https://doi.org/10.3966/160792642018091905012
Basso B, Dumont B, Cammarano D, et al (2016) Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone. Sci Total Environ 545–546:227–235. https://doi.org/10.1016/j.scitotenv.2015.12.104
Moral FJ, Terron JM, Marques da Silva JR (2010) Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil Tillage Res 106:335–343
Yao R-J, Yang J-S, Zhang T-J, et al (2014) Determination of site-specific management zones using soil physico-chemical properties and crop yields in coastal reclaimed farmland. Geoderma 232:381–393
Córdoba MA, Bruno CI, Costa JL, et al (2016) Protocol for multivariate homogeneous zone delineation in precision agriculture. Biosyst Eng 143:95–107. https://doi.org/10.1016/j.biosystemseng.2015.12.008
Pedersen SM, Lind KM (2017) Precision agriculture: Technology and economic perspectives. Springer International Publishing
van Evert FK, Gaitán-Cremaschi D, Fountas S, Kempenaar C (2017) Can precision agriculture increase the profitability and sustainability of the production of potatoes and olives? Sustain 9. https://doi.org/10.3390/su9101863
Balafoutis A, Beck B, Fountas S, et al (2017) Precision agriculture technologies positively contributing to ghg emissions mitigation, farm productivity and economics. Sustain 9:1–28. https://doi.org/10.3390/su9081339
Liakos V, Tagarakis A, Aggelopoulou K, et al (2017) In-season prediction of yield variability in an apple orchard. Eur J Hortic Sci 82:251–259. https://doi.org/10.17660/eJHS.2017/82.5.5
Yost MA, Kitchen NR, Sudduth KA, et al (2019) A long-term precision agriculture system sustains grain profitability. Precis Agric 20:1177–1198. https://doi.org/10.1007/s11119-019-09649-7
Lambert D, Lowenberg-DeBoer J (2000) Precision Agriculture Profitability Review
Hedley CB, Yule IJ (2009) Soil water status mapping and two variable-rate irrigation scenarios. Precis Agric 10:342–355
Timmermann C, Gerhards R, Kuhbauch W (2003) The Economic Impact of Site-Specific Weed Control. Precis Agric 4:249–260
Vasileiadis VP, Sattin M, Otto S, et al (2011) Crop protection in European maize-based cropping systems: Current practices and recommendations for innovative Integrated Pest Management. Agric Syst 104:533–540
Joao RSM, Conrado DR, da Romeu RCC, et al (2016) Study of an electromechanical system for solid fertilizer variable rate planting. African J Agric Res 11:159–165. https://doi.org/10.5897/ajar2014.9349
He X, Ding Y, Zhang D, et al (2019) Development of a variable-rate seeding control system for corn planters Part II: Field performance. Comput Electron Agric 162:309–317. https://doi.org/10.1016/j.compag.2019.04.010
Guo J, Li X, Li Z, et al (2018) Multi-GNSS precise point positioning for precision agriculture. Precis Agric 19:895–911. https://doi.org/10.1007/s11119-018-9563-8
dos Santos AF, da Silva RP, Zerbato C, et al (2019) Use of real-time extend GNSS for planting and inverting peanuts. Precis Agric 20:840–856. https://doi.org/10.1007/s11119-018-9616-z
Shockley JM, Dillon CR, Stombaugh TS (2011) A Whole Farm Analysis of the Influence of Auto-Steer Navigation on Net Returns, Risk, and Production Practices. J Agric Appl Econ 43:57–75. https://doi.org/10.1017/s1074070800004053
Bora GC, Nowatzki JF, Roberts DC (2012) Energy savings by adopting precision agriculture in rural USA. Energy Sustain Soc 2:1–5. https://doi.org/10.1186/2192-0567-2-22
Ortiz B V., Balkcom KB, Duzy L, et al (2013) Evaluation of agronomic and economic benefits of using RTK-GPS-based auto-steer guidance systems for peanut digging operations. Precis Agric 14:357–375. https://doi.org/10.1007/s11119-012-9297-y
Jensen HG, Jacobsen L-B, Pedersen SM, Tavella E (2012) Socioeconomic impact of widespread adoption of precision farming and controlled traffic systems in Denmark. Precis Agric 13:661–677
Barnes A, De Soto I, Eory V, et al (2019) Influencing factors and incentives on the intention to adopt precision agricultural technologies within arable farming systems. Environ Sci Policy 93:66–74. https://doi.org/10.1016/j.envsci.2018.12.014
Radoglou-Grammatikis P, Sarigiannidis P, Lagkas T, Moscholios I (2020) A compilation of UAV applications for precision agriculture. Comput Networks 172:107148. https://doi.org/10.1016/j.comnet.2020.107148
Maes WH, Steppe K (2019) Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture. Trends Plant Sci 24:152–164. https://doi.org/10.1016/j.tplants.2018.11.007
Bac CW, Van Henten EJ, Hemming J, Edan Y (2014) Robots for High Value Crops: State of the Art Review and Challenges Ahead. J F Robot 31:888–911
Ball D, Ross P, English A, et al (2015) Robotics for Sustainable Broad-Acre Agriculture. In: Mejias L, Corke P, Roberts J (eds) Springer Tracts in Advanced Robotics. Springer, Cham
Nuske S, Wilshusen K, Achar S, et al (2014) Automated Visual Yield Estimation in Vineyards. J Fields Robot 31:837–860
Wachowiak MP, Walters DF, Kovacs JM, et al (2017) Visual analytics and remote sensing imagery to support community-based research for precision agriculture in emerging areas. Comput Electron Agric 143:149–164. https://doi.org/10.1016/j.compag.2017.09.035
Vuran MC, Salam A, Wong R, Irmak S (2018) Internet of underground things in precision agriculture: Architecture and technology aspects. Ad Hoc Networks 81:160–173. https://doi.org/10.1016/j.adhoc.2018.07.017
Schumpeter JA (1935) The Analysis of Economic Change Author (s): Joseph A. Schumpeter Source: The Review of Economics and Statistics, Vol. 17, No. 4 (May, 1935), pp. 2–10 Published by: The MIT Press Stable URL: http://www.jstor.org/stable/1927845. Anal Econ Chang 17:2–10
OECD (2005) Oslo Manual
Association of Greek Regions (2011) Regions of Greece. In: Assoc. Greek Reg.
Hellenic Statistical Authority (2020) Greece in Figures
Kountios G, Ragkos A, Bournaris T, et al (2018) Educational needs and perceptions of the sustainability of precision agriculture: survey evidence from Greece. Precis Agric 19:537–554. https://doi.org/10.1007/s11119-017-9537-2
ELSTAT (2019) Yearly Agricultural Research
Milone P, Ventura F (2019) New generation farmers: Rediscovering the peasantry. J Rural Stud 65:43–52. https://doi.org/10.1016/j.jrurstud.2018.12.009
Moschidis O (2015) Unified coding of qualitative and quantitative variables and their analysis with ascendant hierarchical classification. Int J Data Anal Tech Strateg 7:114–128. https://doi.org/10.1504/IJDATS.2015.068745
Moschidis O, Chadjipadelis T (2017) A method for transforming ordinal variables. Stud Classif Data Anal Knowl Organ 285–294. https://doi.org/10.1007/978-3-319-55723-6_22
Markos A, Moschidis O, Chadjipadelis T (2020) Sequential dimension reduction and clustering of mixed-type data. Int J Data Anal Tech Strateg 12:28–30
Benzecri J-P (1973) Analyse des Données (T.1: La Taxinomie, T.2: Correspondances). Dunod, Paris
Benzecri J-P (1981) Practique De l’Analyse des donnees. Vol 3: Linguistique et lexicologie. Dunod, Paris
Benzecri J-P (1982) Construction d’ une Classification Ascendante Hiérarchique par la recherché en chaîne des Voisins Réchiproques. Les Cah l’ Anal des Données VII:209–218
Benzecri J-P, Benzecri F, Maiti GD (1992) Pratique de l’ Analyse des Données en Médecine. Vol4: Medecine, Pharmacologie, Physiologie clinique. Statmatic, Paris
Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244
Aldenderfer MS, Blashfield RK (1984) Cluster Analysis. Sage Univ Pap 44:88
Moschidis O (2003) Contribution to comparative survey of multidimensional scales with the methods of multivariate analysis
Lebart L, Morineau A, Piron M (2000) Statistique Exploratoire Multidimensionnelle. Dunod, Paris
Morineau A (1984) Note sur la caracterisation statistique d’ une classe et les valeurs-tests. Bull Tech du Cent Stat Inform Appliquées 2:20–27
Bechrakis TE (1999) Multidimensional Data Analysis. Nea Sinora - Livani, Athens
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Falaras, A., Moschidis, S. (2021). Precision Agriculture’s Economic Benefits in Greece: An Exploratory Statistical Analysis. In: Bochtis, D.D., Pearson, S., Lampridi, M., Marinoudi, V., Pardalos, P.M. (eds) Information and Communication Technologies for Agriculture—Theme IV: Actions. Springer Optimization and Its Applications, vol 185. Springer, Cham. https://doi.org/10.1007/978-3-030-84156-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-84156-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-84155-3
Online ISBN: 978-3-030-84156-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)