Spatial dependency and correlation of properties of soil cultivated with oil palm, Elaeis guineensis, in agroforestry systems in the eastern Brazilian Amazon

• Autor(a) principal: SILVA,Camila Santos da
• Data de Publicação: 2018
• Outros Autores: MENDONÇA,Bruno Araujo Furtado de, PEREIRA,Marcos Gervasio, ARAÚJO,Emanuel José Gomes de, CASTELLANI,Débora Christina
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Acta Amazonica
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0044-59672018000400280
• topic: cokriging; ordinary kriging; semivariogram; soil properties

ABSTRACT Geostatistics is a tool that can be used to produce maps with the distribution of nutrients essential for the development of plants. Therefore, the present study aimed to analyze the spatial variation in chemical attributes of soils under oil palm cultivation in agroforestry systems in the eastern Brazilian Amazon, and their spatial dependence pattern. Sixty spatially standardized and georeferenced soil samples were collected at each of three sampling sites (DU1, DU2, and DU3) at 0-20 cm depth. Evaluated soil chemical attributes were pH, Al3+, H+Al, K+, Ca2+, Mg2+, cation exchange capacity (CEC), P, and organic matter (OM). The spatial dependence of these variables was evaluated with a semivariogram analysis, adjusting three theoretical models (spherical, exponential, and Gaussian). Following analysis for spatial dependence structure, ordinary kriging was used to estimate the value of each attribute at non-sampled sites. Spatial correlation among the attributes was tested using cokriging of data spatial distribution. All variables showed spatial dependence, with the exception of pH, in one sampling site (DU3). Highest K+, Ca2+, Mg2+, and OM levels were found in the lower region of two sampling sites (DU1 and DU2). Highest levels of Al3+ and H+Al levels were observed in the lower region of sampling site DU3. Some variables were correlated, therefore cokriging proved to be efficient in estimating primary variables as a function of secondary variables. The evaluated attributes showed spatial dependence and correlation, indicating that geostatistics may contribute to the effective management of agroforestry systems with oil palm in the Amazon region.