Abstract
Abstract The objective of this work was to delineate irrigation management zones using geostatistics and multivariate analysis in different combinations of physical and hydraulic soil properties, as well as to determine the optimal number of management zones in order to avoid overlaping. A field experiment was carried out in a Quartzipsamment, for two years, in an irrigated orchard of table grape, in the Senador Nilo Coelho Irrigation Scheme, in the municipality of Petrolina, in the state of Pernanbuco, Brazil. Soil samples were collected for the determination of soil physico-hydraulic properties. A portable meter was used to measure soil apparent electrical conductivity. Spatial distribution maps were generated using ordinary kriging. Management zones for five different combinations of soil properties were defined using the fuzzy c-means clustering algorithm, and two indexes were applied to determine the optimal number of management zones. Two combinations of soil properties can be used in the management zone planning in order to monitor soil moisture.
Highlights
The knowledge of the spatial variability of soil physical and hydraulic properties which influence the soil water dynamics can improve irrigation management
Geostatistics can contribute to the understanding of this variability (Grego et al, 2014), but each soil property should be assessed separately
Kitchen et al (2005) and Valente et al (2012) suggest that management zones should be defined by evaluating more than one soil property
Summary
The knowledge of the spatial variability of soil physical and hydraulic properties which influence the soil water dynamics can improve irrigation management. Kitchen et al (2005) and Valente et al (2012) suggest that management zones should be defined by evaluating more than one soil property. Multivariate analysis can help to delineate management zones, besides facilitating the study of various soil properties. Li et al (2007) reported that multivariate analysis clustering effectively identifies management zones because it is simple, functional, and economically viable. Morari et al (2009) claim that the combination of geostatistical interpolation and multivariate clustering analysis aids the precision viticulture by enabling the efficient division of a cultivation area into management zones Li et al (2007) reported that multivariate analysis clustering effectively identifies management zones because it is simple, functional, and economically viable. Morari et al (2009) claim that the combination of geostatistical interpolation and multivariate clustering analysis aids the precision viticulture by enabling the efficient division of a cultivation area into management zones
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