Skip to main content

ICT Tools by Farmers of Lucania Region in Italy

  • Conference paper
  • First Online:
  • 705 Accesses

Part of the book series: Springer Earth System Sciences ((SPRINGEREARTH))

Abstract

This study aimed to investigate how farms in the Lucania region of Italy cluster according to the level of innovation adopted. It used a questionnaire to ask if farms adopted information and communications technology (ICT) tools and, if so, what types were involved in management and/or production processes. A cluster analysis was done on the collected data. The results showed that using a k-means clustering method, two clusters appeared: innovators and the remaining group. Using boxplot representation, there were three groups: innovators, early adopters, and laggards. These results will be used to identify good practices in terms of smart devices adopted, within the H2020 project titled Short Supply Chain Knowledge and Innovation Network (SKIN).

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Ariza C, Rugeles L, Saavedra D, Guatiero B (2013) Measuring innovation in agricultural firms: a methodological approach. Electr J Kn Manage 11:185–198 www.ejkm.com

    Google Scholar 

  • Chen M, Mao S, Liu Y (2014) Big data: a survey. Mobile Netw Appl 19:171–209

    Article  Google Scholar 

  • Contò F, Faccilongo N, La Sala P (2015) The effects of cloud approach in short chain administration. Int J Agric Environ Inf Syst 6(1):19–31

    Article  Google Scholar 

  • Diederen P, van Meijel H, Wolters A, Bijak K (2003) Innovation adoption in agriculture: innovators, early adopters and laggards. Cah Econ Sociologie Rurales 67:30–50

    Google Scholar 

  • Di Falco S, Zoupanidou E (2017) Soil fertility, crop biodiversity, and farmers’ revenues: evidence from Italy. Ambio 46(2):162–172

    Article  Google Scholar 

  • Easterlin RA (2005) Diminishing marginal utility of income? Caveat emptor. Soc Indic Res 70(3):243–255

    Article  Google Scholar 

  • Esmeijer J, Bakker T, Ooms M, Kotterink B (2015) Data-driven innovation in agriculture: case study for the OECD KBC2-programme. TNO Report 2015:R10154

    Google Scholar 

  • European Commission (2015) Calls for proposals and related activities under the 2016–17 work programmes under Horizon 2020—the Framework Programme for Research and Innovation (2014–20) and under the Research and Training Programme of the European Atomic Energy Community (2014–18) complementing Horizon 2020. Off J Eur Union (Article number 2015/C399/2)

    Google Scholar 

  • Hastie TJ (2017) Generalized additive models. In: Statistical models in S. Routledge, Boca Raton, pp 249–307

    Chapter  Google Scholar 

  • Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recogn 36(2):451–461

    Article  Google Scholar 

  • Press SJ, Wilson S (1978) Choosing between logistic regression and discriminant analysis. J Am Stat Assoc 73(364):699–705

    Article  Google Scholar 

  • Ugwumba COA, Okoh RN, Ike PC, Nnabuife ELC, Orji EC (2010) Integrated farming system and its effect on farm cash income in Awka south agricultural zone of Anambra state, Nigeria. Am Eur J Agric Environ Sci 8(1):1–6

    Google Scholar 

  • Wagstaff K, Cardie C, Rogers S, Schrödl S (2001) Constrained k-means clustering with background knowledge. ICML 1:577–584

    Google Scholar 

  • Wasserman L (2004) Log-linear models. In: All of statistics. Springer, New York, pp 291–301

    Chapter  Google Scholar 

  • World Bank (2016) World development report 2016: digital dividends. World Bank, Washington, DC

    Google Scholar 

  • Zhang T, Ramakrishnan R, Livny M (1997) BIRCH: a new data clustering algorithm and its applications. Data Min Knowl Disc 1(2):141–182

    Article  Google Scholar 

Download references

Acknowledgements

The results presented in this chapter are part of the Short Supply Chain Knowledge and Innovation Network (SKIN) project (www.shortfoodchain.eu). This project has received funding from the European Union’s Horizon 2020 Framework Program for Research and Innovation under grant agreement no. 728055.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gianluigi De Pascale .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

De Pascale, G., La Sala, P., Faccilongo, N., Zaza, C. (2019). ICT Tools by Farmers of Lucania Region in Italy. In: Theodoridis, A., Ragkos, A., Salampasis, M. (eds) Innovative Approaches and Applications for Sustainable Rural Development. HAICTA 2017. Springer Earth System Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-02312-6_16

Download citation

Publish with us

Policies and ethics