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User requirements for a satellite-based advisory platform

Published online by Cambridge University Press:  01 June 2017

E. Anastasiou*
Affiliation:
Department of Farm Machinery, Agricultural University of Athens, Iera Odos 75, Athens, Greece
Z. Tsiropoulos
Affiliation:
Department of Farm Machinery, Agricultural University of Athens, Iera Odos 75, Athens, Greece
S. Fountas
Affiliation:
Department of Farm Machinery, Agricultural University of Athens, Iera Odos 75, Athens, Greece
A. Osann
Affiliation:
Agrisat Iberia S.L., Avenida Primera 18, Albacete, Spain
D. Protic
Affiliation:
Faculty of Civil Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, Beograd, Serbia
M. Simeonidou
Affiliation:
Draxis Environmental S.A., Mitropoleos 63 Street, Thessaloniki, Greece
L. Xenidis
Affiliation:
Draxis Environmental S.A., Mitropoleos 63 Street, Thessaloniki, Greece
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Abstract

APOLLO, a newly funded H2020 EU project will develop an agricultural advisory platform for small farmers based on Copernicus Sentinel satellites. It will provide services for tillage scheduling, irrigation scheduling, crop growth monitoring and yield estimation. The aim of this study was to identify the farmers’ requirements of the APOLLO platform. In total 121 farmers were interviewed in Spain, Serbia and Greece. More than 90% of the farmers pointed out that smart agriculture and use of satellite data in agriculture are important. Additionally, more than 80% want to have access to historical data and a flexible subscription policy to the platform according to their needs and use. However, significant differences exist among farmers of these countries in terms of technology awareness and penetration, which should be taken into consideration for developing a successful platform.

Type
Satellite Applications
Copyright
© The Animal Consortium 2017 

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References

Baptista, A and Sousa, V 2006. Transferability of DEMETER. A case study of The Irrigation Scheme of Veiga De Chaves, in AIP Conference Proceedings (Naples), 10-11 November 2005, 852, 84–92.Google Scholar
Bastiaanssen, WGM and Ali, S 2003. A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agriculture, Ecosystems and Environment 94, 321340.Google Scholar
D’Urso, G, Richter, K, Calera, A, Osann, MA, Escadafal, R, Garatuza-Pajan, J, Hanich, L, Perdigao, A, Tapia, JB and Vuolo, F 2010. Earth Observation products for operational irrigation management in the context of the PLEIADeS project. Agricultural Water Management 98 (2), 271282.Google Scholar
Fountas, S, Sorensen, CG, Tsiropoulos, Z, Cavalaris, C, Liakos, V and Gemtos, T 2015. Farm Machinery Management Information System. Computers and Electronics in Agriculture 110, 131138.Google Scholar
Guo, HD, Zhang, L and Zhu, LW 2015. Earth observation big data for climate change research. Advances in Climate Change Research 6 (2), 108117.Google Scholar
Kostyuchenko, YV, Bilous, Y, Movchan, D, Marton, L and Kopachevsky, I 2013. Toward Methodology of Satellite Observation Utilization for Agricultural Production Risk Assessment. IERI Procedia 5, 2127.CrossRefGoogle Scholar
Lawson, LG, Pedersen, SM, Sorensen, CG, Pesonen, L, Fountas, S, Werner, A, Oudshoorn, FW, Herold, L, Chatzinikos, T, Kirketerp, IM and Blackmore, S 2011. A four nation survey of farm information management and advanced farming systems: A descriptive analysis of survey responses. Computers and Electronics in Agriculture 77 (1), 720.Google Scholar
Marques da Silva, JR, Damasio, CV, Sousa, AMO, Bugalho, L, Pessanha, L and Quaresma, P 2015. Agriculture pest and disease risk maps considering MSG satellite data and land surface temperature. International Journal of Applied Earth Observation and Geoinformation 38, 4050.Google Scholar
Moran, MS, Inoue, Y and Barnes, EM 1997. Opportunities and Limitations for Image-Based Remote Sensing in Precision Crop Management. Remote Sensing of the Environment 61, 319346.Google Scholar
Rajendran, S, Al-Sayigh, AR and Al-Awadhi, T 2016. Vegetation analysis study in and around Sultan Qaboos University, Oman, using Geoeye-1 satellite data. The Egyptian Journal of Remote Sensing and Space Science 19 (2), 297311.Google Scholar
Ruef, A and Markard, J 2010. What happens after a hype? How changing expectations affected innovation activities in the case of stationary fuel cells. Technology Analysis and Strategic Management 22 (3), 317338.Google Scholar
Sawyer, P and Kotonya, G 2001. Software Requirements. In Abran, A., Moore, J.W., Bourque, P. and Dupuis, R. (Eds.), SWEBOK – Guide to the Software Engineering Body of Knowledge. IEEE Computer Society, California (2001), pp. 3055.Google Scholar
Vuolo, F, Essl, L and Atzberger, C 2015. Costs and benefits of satellite-based tools for irrigation management. Frontiers in Environmental Science 3, 52.Google Scholar
Wiegers, K and Beatty, J 2013. Software Requirements, Third Edition Redmont, WA: Microsoft Press.Google Scholar
Yousuf, M and Asger, M 2015. Comparison of Various Requirements Elicitation Techniques. International Journal of Computer Applications 116, 815.Google Scholar