The technology of irrigation is vital for agricultural production. Thus, description of spatial patterns of both water application and available water capacity in the soil, as well as their interactions, is essential to maximize efficiency of water use in irrigated areas. The objective of this study was to analyze spatial variability of available water capacity in the soil and water application via irrigation using geostatistics. The experiment was conducted in a commercial mango orchard in Cambisol irrigated by micro sprinkler system, in the municipality of Alto do Rodrigues, RN. Analyses of descriptive statistics and geostatistics were performed using the programs GeoR and GS+. Geostatistics was found suitable for describing the structure of spatial dependence of available water capacity in the soil and the flow rate distributed in the area by sprinklers. Moreover, even with good results for Christiansen Uniformity Coefficient (CU) and Distribution Uniformity Coefficient (DU), the area showed spatial variability of flow rate.

Water scarcity is one of the main problems in the Semiarid region of Brazil, which can be mitigated by water resource management strategies. The objective of this work was to classify waters of a watershed in the Semiarid region of Brazil and select the water attributes that most affect the quality of waters used for irrigation (QWI), using multivariate statistics. The study area was the Riacho da Bica watershed, which is between the municipalities of Portalegre and Viçosa, Rio Grande do Norte, Brazil. The QWI was determined using water samples from 15 collections carried out from 2016 to 2018, in five specific points of the watershed, starting in the spring and following the water course. The water attributes evaluated were: electrical conductivity (EC), potential hydrogen (pH), and sodium (Na+), potassium (K+), magnesium (Mg2+), calcium (Ca2+), carbonate (CO32-), chloride (Cl-), and bicarbonate (HCO3-) contents. The water quality data were subjected to multivariate statistics through factorial analysis (FA) and principal component analysis (PCA). The application of multivariate statistics through FA-PCA generated four principal components. The attributes that most explained the QWI variation were potassium, calcium, and pH for Factor 01, and sodium and RAS for Factor 02. The watershed waters were classified as low risk of salinity and medium risk of sodicity (C1S2) for irrigation purposes.