Soil sampling is a fundamental procedure in the decision making regarding the management of the soil, thus, a sampling plan should represent as accurately as possible the evaluated crop field. Therefore, the objectives of this study were to suggest a soil sampling approach and soil sampling point allocation using spatial analyses and compare to the classic statistic method in irrigated mango orchards in the Brazilian semi-arid region. The experiment was carried out in three commercial mango orchards located in the region of the São Francisco Valley, Brazil. Soil samples were collected in 0-0.2 m and 0.2-0.4 m depths following regular grids where the number of samples varied from 50 to 56. Soil texture, soil bulk density, soil total porosity, microporosity, macroporosity, pH, Ca, Mg, Na, K, Al, P, potential acidity, and the sum of basis were evaluated. Classical and geostatistical statistics were used to determine the ideal number of soil samples. Fuzzy c-means clustering technique was used to separate the areas into homogeneous zones and to allocate the sampling points. The wide method of 20 individual soil samples proved to be inefficient. On the other hand, the use of geostatistics proved to be efficient and is required for each crop field. The c-means clustering was adequate to separate the areas into homogeneous zones and, thus, to assist the sampling point allocation.

Optimizing the recommendation for the management of Liming Demand (LD) is essential for cost reduction and increased yield mango. Therefore, the objective of this study was to delimit management zones for the limestone recommendation in areas of irrigated mango in the Brazilian semiarid using precision agriculture techniques. The experiment was carried out in three commercial mango orchards located in the region of the San Francisco Valley, Brazil. Soil samples were collected in 0.0-0.2 m and 0.2-0.4 m depths following regular grids where the number of samples varied from 50 to 56. Soil analyses were performed. The kriging and inverse distance weighting method were used to interpolated the maps.  The LD map was performed from the potential cation exchange capacity (T) and bases saturation (BS) maps. It was verified that the Mandacaru area obtained 63.42% of the field requiring liming, being subdivided into four management zones. The Sempre Verde area obtained 1.20% of the area requiring liming and the Barreiro de Santa Fé area did not present a need for the LD map. The use of precision farming techniques to delineate management zones was adequate to separate the areas into smaller and more homogeneous zones, for a more precise recommendation of liming demand.

Understanding the relationship between the levels of nutrients in the soil and those found in the plant is of fundamental importance for site-specific fertility management in mango (Mangifera indica L.) crop fields. This study aimed to evaluate the spatial distribution of macronutrient contents both in the soil and in the leaf and their correlations in commercial mango orchards under semiarid region conditions and to delimit the management zones using soil and leaf data. The experiment was carried out in three commercial areas in San Francisco Valley, Brazil, cultivated with irrigated mango. Soil samples were collected in 0-0.2 and 0.2-0.4 m depths as well as leaf samples following sample grids. Ca, Mg, K, P, and N contents from soil and leaf samples were determined. Descriptive and geostatistics analyses were performed. Co-kriging was used for the delimitation of management zones. Positive spatial correlations were obtained between soil Ca2+ and leaf Ca contents (R2 = 0.80-0.93), soil K+ and leaf K contents (R2 = 0.35-0.61), and soil Mg2+ and leaf P contents (R2 = 0.51). Negative correlations were observed for soil Mg2+ and leaf Ca contents(R2 = 0.79-0.93) and soil Mg2+ and leaf K contents (R2 = 0.98). The soil 0-0.2 m depth had the greatest influence on mango Ca and K uptake. The negative correlation between soil Mg2+ and leaf Ca shows the competition existing in the plant uptake process. It was possible to delimit specific management zones using co-kriging for the three areas using soil and leaf data.