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Combined use of remote sensing and soil sensors to detect variability in orchards with previous changes in land use and landforms: consequences for management

Published online by Cambridge University Press:  01 June 2017

J. A. Martínez-Casasnovas ,
E. Daniele ,
A. Uribeetxebarría ,
A. Escolà ,
J. R. Rosell-Polo ,
L. Sartori  and
J. Arnó
J. A. Martínez-Casasnovas*
Affiliation:
Research Group in AgroICT & Precision Agriculture (GRAP), University of Lleida – Agrotecnio Center, Lleida, Catalonia, Spain
E. Daniele
Affiliation:
Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro (PD), Italy
A. Uribeetxebarría
Affiliation:
Research Group in AgroICT & Precision Agriculture (GRAP), University of Lleida – Agrotecnio Center, Lleida, Catalonia, Spain
A. Escolà
Affiliation:
Research Group in AgroICT & Precision Agriculture (GRAP), University of Lleida – Agrotecnio Center, Lleida, Catalonia, Spain
J. R. Rosell-Polo
Affiliation:
Research Group in AgroICT & Precision Agriculture (GRAP), University of Lleida – Agrotecnio Center, Lleida, Catalonia, Spain
L. Sartori
Affiliation:
Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro (PD), Italy
J. Arnó
Affiliation:
Research Group in AgroICT & Precision Agriculture (GRAP), University of Lleida – Agrotecnio Center, Lleida, Catalonia, Spain
*
†E-mail: j.martinez@macs.udl.cat
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Abstract

The present work investigated the application of detailed airborne images and a resistivity soil sensor (Veris 3100) to detect soil and crop spatial variability to assist in orchard management. The research was carried out in a peach orchard (Prunus persica). Soil apparent electrical conductivity (ECa), NDVI from a multispectral image (0.25 m/pixel) and soil properties at 40 sampling points (0–30 cm) were acquired. The ECa was standardized at 25°C. It showed a strong relationship with former landforms, altered by land levelling. A positive correlation of EC25 with EC1:5, water holding capacity at −1500 kPa and soil depth was found. NDVI was correlated only in the textural fractions coarser than clay. Two types of management zones were proposed: a) to improve the water holding capacity of soils and b) to regulate tree vigour and yield.

Keywords

apparent electrical conductivitymultispectral imagesvegetation indexmanagement zones.
Type
Precision Horticulture and Viticulture
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 492 - 497
Copyright
© The Animal Consortium 2017 

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References

Canci, M, Wallace, J, Mattiske, E and Malcolm, A 2004. Evaluation of airborne multi-spectral imagery for monitoring the effect of falling groundwater levels on native vegetation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 34, 1–6.Google Scholar
Corwin, DL and Lesch, SM 2003. Application of Soil Electrical Conductivity to Precision Agriculture: Theory, Principles, and Guidelines. Agronomy Journal 95 (3), 455–471.Google Scholar
Cots-Folch, R, Martínez-Casasnovas, JA and Ramos, MC 2009. Agricultural trajectories in a mediterranean mountain region (Priorat, Ne Spain) as a consequence of vineyard conversion plans. Land Degradation and Development 20 (1), 1–13.CrossRefGoogle Scholar
De Benedetto, D, Castrignano, A, Diacono, M, Rinaldi, M, Ruggieri, S and Tamborrino, R 2013. Field partition by proximal and remote sensing data fusion. Biosystems Engineering 114 (4), 372–383.Google Scholar
Fulton, A, Schwankl, L, Lynn, K, Lampinen, B, Edstrom, J and Prichard, T 2011. Using EM and VERIS technology to assess land suitability for orchard and vineyard development. Irrigation Science 29, 497–512.Google Scholar
Jensen, JR 1996. Introductory digital image processing: remote sensing perspective, 2nd ed. Prentice-Hall, Englewood Cliffs, NJ, USA.Google Scholar
Käthner, J and Zude-Sasse, M 2015. Interaction of 3D soil electrical conductivity and generative growth in Prunus domestica L. European Journal of Horticultural Science 80 (5), 231–239.Google Scholar
Khan, FS, Zaman, QU, Chang, YK, Farooque, AA, Schumann, AW and Madani, A 2016. Estimation of the rootzone depth above a gravel layer (in wild blueberry fields) using electromagnetic induction method. Precision Agriculture 17, 155–167.Google Scholar
Ma, R, McBratney, A, Whelan, B, Minasny, B and Short, M 2010. Comparing temperature correction models for soil electrical conductivity measurement. Precision Agriculture 12, 55–66.Google Scholar
Martínez-Casasnovas, JA and Ramos, MC 2009. Soil alteration due to erosion, ploughing and levelling of vineyards in north east Spain. Soil Use and Management 25 (2), 183–192.Google Scholar
Moral, FJ, Terrón, JM and Marques da Silva, JR 2010. Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil & Tillage Research 106, 335–343.Google Scholar
Nainggolana, D, de Vente, J, Boix-Fayos, C, Termansen, M, Hubacek, K and Reed, MS 2012. Afforestation, agricultural abandonment and intensification: Competing trajectories in semi-arid Mediterranean agro-ecosystems. Agriculture, Ecosystems & Environment 159 (15), 90–104.Google Scholar
Ortega-Blu, R and Molina-Roco, M 2016. Evaluation of vegetation indices and apparent soil electrical conductivity for site-specific vineyard management in Chile. Precision Agriculture 17 (4), 434–450.Google Scholar
Pascual, M, Urbina, V, Dalmases, J and Nolla, JM 2006. Historia y situación actual de la fruticultura en Lleida (History and present situation of fruticulture in Lleida). Fruticultura profesional 158, 5–15.Google Scholar
Pedrera-Parrilla, A, Van De Vijver, E, Van Meirvenne, M, Espejo-Pérez, AJ, Giráldez, JV and Vanderlinden, K 2016. Apparent electrical conductivity measurements in an olive orchard under wet and dry soil conditions: significance for clay and soil water content mapping. Precision Agriculture 17, 531–545.Google Scholar
Priori, S, Martini, E, Andrenelli, MC, Magini, S, Agnelli, AE, Bucelli, P, Biagi, M, Pellegrini, S and Costantini, EAC 2013. Improving Wine Quality through Harvest Zoning and Combined Use of Remote and Soil Proximal Sensing. Soil Science Society of America Journal 77 (4), 1338–1348.Google Scholar
Rhoades, JD 1999. Soil salinity assessment: methods and interpretation of electrical conductivity measurements. FAO, Irrigation and Drainage 57, Rome, Italy 150 pp.Google Scholar
Soil Survey Staff 2014. Keys to Soil Taxonomy, 12th ed. United States Department of Agriculture, Washington DC, USA.Google Scholar
Sudduth, KA, Kitchen, NR, Wiebold, WJ, Batchelor, WD, Bollero, GA, Bullock, DG, et al. 2005. Relating apparent electrical conductivity to soil properties across the north-central USA. Computers and Electronics in Agriculture 46, 263–283.Google Scholar
Taylor, JA, McBratney, AB and Whelan, BM 2007. Establishing management classes for broadacre grain production. Agronomy Journal 99, 1366–1376.Google Scholar