Mapping and managing vineyard homogeneous zones through proximal geoelectrical sensing

References

• André F, van Leeuwen C, Saussez S, Van Durmen R, Bogaert P, Moghadas D, de Rességuier L, Delvaux B, Vereecken H, Lambot S. 2012. High-resolution imaging of a vineyard in south of France using ground-penetrating radar, electromagnetic induction and electrical resistivity tomography. J Appl Geophys. 78:113–122.
   Web of Science ®Google Scholar
• Andrenelli MC, Magini S, Pellegrini S, Perria R, Vignozzi N, Costantini EAC. 2013. The use of the ARP© system to reduce the costs of soil survey for precision viticulture. J Appl Geophys. 99:24–34.
   Web of Science ®Google Scholar
• Anselin L. 1995. Local indicators of spatial association-LISA. Geogr Anal. 27:93–115.
   Web of Science ®Google Scholar
• Arno J, Martinez-Casasnovas JA, Ribes-Dasi M, Rosell JR. 2009. Review. Precision viticulture. Research topics, challenges and opportunities in site-specific vineyard management. Span J Agric Res. 7:779–790.
   Web of Science ®Google Scholar
• Baluja J, Tardaguila J, Ayestaran B, Diago MP. 2013. Spatial variability of grape composition in a Tempranillo (Vitis vinífera L.) vineyard over a 3-year survey. Precis Agric. 14:40–58.
   Web of Science ®Google Scholar
• Bonfante A, Agrillo A, Albrizio R, Basile A, Buonomo R, De Mascellis R, Gambuti A, Giorio P, Guida G, Langella G, et al. 2015. Functional homogeneous zones (fHZs) in viticultural zoning procedure: an Italian case study on Aglianico vine. Soil. 1:427–441.
   Web of Science ®Google Scholar
• Bramley RGV, Ouzman J, Boss PK. 2011. Variation in vine vigour, grape yield and vineyard soils and topography as indicators of variation in the chemical composition of grapes, wine and wine sensory attributes. Aust J Grape Wine R. 17:217–229.
   Web of Science ®Google Scholar
• Brillante L, Bois B, Mathieu O, Lévêque J. 2016. Electrical imaging of soil water availability to grapevine: a benchmark experiment of several machine-learning techniques. Precis Agric. 17:637–658.
   Web of Science ®Google Scholar
• Brillante L, Mathieu O, Bois B, van Leeuwen C, Lévêque J. 2015. The use of soil electrical resistivity to monitor plant and soil water relationships in vineyards. Soil. 1:273–286.
   Google Scholar
• Christensen BN, Sorensen K. 1998. Surface and borehole electric and electomagnetic methods for hydrological investigation. Eur J Environ Eng Geophys. 3:75–90.
   Google Scholar
• Cousin I, Besson A, Bourennane H, Pasquier C, Nicoullaud B, King D, Richard G. 2009. From spatial-continuous electrical resistivity measurements to the soil hydraulic functioning at the field scale. C R Geosci. 341:859–867.
   Web of Science ®Google Scholar
• Diacono M, De Benedetto D, Castrignano A, Rubino P, Vitti C, Abdelrahman HM, Sollitto D, Cocozza C, Ventrella D. 2013. A combined approach of geostatistics and geographical clustering for delineating homogeneous zones in a durum wheat field in organic farming. NJAS-Wagen J Life Sc. 64-65:47–57.
   Web of Science ®Google Scholar
• Gebbers R, Luck E, Dabas M, Domsch H. 2009. Comparison of instruments for geoelectrical soil mapping at the field scale. Near Surf Geophys. 7:179–190.
   Web of Science ®Google Scholar
• Getis A, Ord K. 1992. The analysis of spatial association by use of distance statistics. Geogr Anal. 24:189–206.
   Web of Science ®Google Scholar
• Hartigan JA, Wong MA. 1979. Algorithm AS 136: A K-means clustering algorithm. J Roy Stat Soc C-App. 28:100–108.
   Google Scholar
• Heimsath AM, Dietrich WE, Nishiizumi K, Finkel RC. 1999. Cosmogenic nuclides, topography, and the spatial variation of soil depth. Geomorphology. 27:151–172.
   Web of Science ®Google Scholar
• IUSS Working Group WRB. 2015. World reference base for soil resources 2014, update 2015 international soil classification system for naming soils and creating legends for soil maps. Rome: FAO. World Soil Resources Reports No. 106.
   Google Scholar
• Jobbágy EG, Jackson RB. 2001. The distribution of soil nutrients with depth: global patterns and the imprint of plants. Biogeochemistry. 53:51–77.
   Web of Science ®Google Scholar
• Klute A. 1986. Methods of soil analysis: part 1—physical and mineralogical methods. Madison (WI): SSSA Book Series 5.1. SSSA, ASA.
   Google Scholar
• Lovisolo C, Lavoie-Lamoureux A, Tramontini S, Ferrandino A. 2016. Grapevine adaptations to water stress: new perspectives about soil/plant interactions. Theor Exp Plant Physiol. 28:53–66.
   Web of Science ®Google Scholar
• Martinez de Toda F, Tardaguila J, Sancha JC. 2007. Estimation of grape quality in vineyards using a new viticultural index. Vitis. 46:168–173.
   Web of Science ®Google Scholar
• Martini E, Comina C, Priori S, Costantini EAC. 2013. A combined geophysical-pedological approach for precision viticulture in the Chianti hills. B Geofis Teor Appl. 54:165–181.
   Web of Science ®Google Scholar
• McBratney A, Minasny B, Whelan B. 2011. Defining proximal soil sensing. In: IUSS, editor. Second global workshop on proximal soil sensing. Montreal: IUSS Working Group on Proximal Soil Sensing; p. 144–146.
   Google Scholar
• Moral FJ, Terrón JM, Marques da Silva JR. 2010. Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil Till Res. 106:335–343.
   Web of Science ®Google Scholar
• Morari F, Castrignanò A, Pagliarin C. 2009. Application of multivariate geostatistics in delineating management zones within a gravelly vineyard using geo-electrical sensors. Comput Electron Agr. 68:97–107.
   Web of Science ®Google Scholar
• Neely HL, Morgan CLS, Hallmark CT, McInnes KJ, Molling CC. 2016. Apparent electrical conductivity response to spatially variable vertisol properties. Geoderma. 263:168–175.
   Web of Science ®Google Scholar
• Panissod C, Dabas M, Jolivet A, Tabbagh A. 1997. A novel mobile multipole system (MUCEP) for shallow (0–3 m) geoelectrical investigation: the ’Vol-de-Canards’ array. Geophys Prospec. 45:983–1002.
   Web of Science ®Google Scholar
• Peeters A, Zude M, Käthner J, Ünlü M, Kanber R, Hetzroni A, Gebbers R, Ben-Gal A. 2015. Getis–Ord’s hot- and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree data. Comput Electron Agr. 111:140–150.
   Web of Science ®Google Scholar
• Priori S, Fantappiè M, Magini S, Costantini EAC. 2013b. Using the ARP-03 for high-resolution mapping of calcic horizons. Int Agrophy. 27:313–321.
   Web of Science ®Google Scholar
• Priori S, Martini E, Andrenelli MC, Magini S, Agnelli AE, Bucelli P, Biagi M, Pellegrini S, Costantini EAC. 2013a. Improving wine quality through harvest zoning and combined use of remote and soil proximal sensing. Soil Sci Soc Am J. 77:1338–1348.
   Web of Science ®Google Scholar
• Ramos MC, Jones GV, Yuste J. 2015. Phenology and grape ripening characteristics of cv. Tempranillo within the Ribera del Duero designation of origin (Spain): influence of soil and plot characteristics. Eur J Agron. 70:57–70.
   Web of Science ®Google Scholar
• Rossi R, Pollice A, Diago MP, Oliveira M, Millán B, Bitella G, Amato M, Tardaguila J. 2013. Using an automatic resistivity profiler soil sensor on-the-go in precision viticulture. Sensors. 13:1121–1136.
   PubMed Web of Science ®Google Scholar
• Saxton KE, Rawls WJ. 2006. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci Soc Am J. 70:1569–1578.
   Web of Science ®Google Scholar
• Schepers AR, Shanahan JF, Liebig A, Schepers JS, Johnson SH, Luchiari A Jr. 2004. Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agron J. 96:195–203.
   Web of Science ®Google Scholar
• Sebastian B, Baeza P, Santesteban LG, de Miguel PS, de La Fuente M, Lissarrague JR. 2015. Response of grapevine cv. Syrah to irrigation frequency and water distribution pattern in a clay soil. Agric Water Manag. 148:269–279.
   Web of Science ®Google Scholar
• Son Y, Oh M, Lee S. 2010. Estimation of soil weathering degree using electrical resistivity. Environ Earth Sci. 59:1319–1326.
   Web of Science ®Google Scholar
• Sparks DL, Page AL, Helmke PA, Loeppert RH. 1996. Methods of soil analysis part 3—chemical methods. Madison (WI): SSSA Book Ser. 5.3. SSSA, ASA.
   Google Scholar
• Stadlera A, Rudolph S, Kupisch M, Langensiepen M, van der Kruk J, Ewert F. 2015. Quantifying the effects of soil variability on crop growth using apparent soil electrical conductivity measurements. Eur J Agron. 64:8–20.
   Web of Science ®Google Scholar
• Tardaguila J, Baluja J, Arpon L, Balda P, Oliveira MT. 2011. Variations of soil properties affect the vegetative growth and yield components of Tempranillo grapevines. Precis Agric. 12:762–773.
   Web of Science ®Google Scholar
• Triantafilis J, Laslett GM, McBratney AB. 2000. Calibrating an electromagnetic induction instrument to measure salinity in soil under irrigated cotton. Soil Sci Soc Am J. 64:1009–1017.
   Web of Science ®Google Scholar
• Unamunzaga O, Besga G, Castellon A, Uson MA, Chery P, Gallejones P, Aizpurua A. 2014. Spatial and vertical analysis of soil properties in a Mediterranean vineyard soil. Soil Use Manage. 30:285–296.
   Web of Science ®Google Scholar
• Van Leeuwen C. 2010. Terroir: the effect of the physical environment on the vine growth, grape ripening and wine sensory attributes. In: Reynolds AG, editor. Managing wine quality. Vol. 1. Cambridge: Woodhead Publishing; p. 273–315.
   Google Scholar
• Vanuytrecht E, Raes D, Steduto P, Hsiao TC, Fereres E, Heng LK, Vila MG, Moreno PM. 2014. AquaCrop: FAO’s crop water productivity and yield response model. Environ Model Softw. 62:351–360.
   Web of Science ®Google Scholar
• Zhou M, Wang J, Cai L, Fan Y, Zheng Z. 2015. Laboratory investigations on factors affecting soil electrical resistivity and the measurement. IEEE T Ind Appl. 51:5358–5365.
   Web of Science ®Google Scholar
• Zuo D, Xu Z, Peng D, Song J, Cheng L, Wei S, Abbaspour KC, Yang H. 2015. Simulating spatiotemporal variability of blue and green water resources availability with uncertainty analysis. Hydrol Process. 29:1942–1955.
   Web of Science ®Google Scholar