Influence of spatial variability of soil chemical attributes on the nutritional status and growth of the rubber tree

The productive potential of the rubber tree (Hevea brasiliensis) is dependent on its genetic composition, in addition to edaphoclimatic factors and management practices. However, as soil properties are not homogenous, knowing the spatial variability of soil attributes would be important to increase productivity and reduce production costs. In this context, the objective of this study was to determine the spatial variability of chemical attributes of the soil and its influence on the nutritional status and growth of rubber tree clones. Clones FX 3864, FDR 5788, CDC 312, and RRIM 600 were planted at Jaturnaíba Farm, in the municipality of Silva Jardim, Rio de Janeiro State, Brazil. The sampling sites were distributed at a spacing of 20 × 20 m on the northern and southern sides of the relief. The chemical attributes of the soil (pH, Ca2+, Mg2+, K+, P, Al3+, H+Al, sum of bases, cation exchange capacity, and base saturation) were evaluated at a depth of 0­20 cm in the different clone plantations. Additionally, the N, P, K, Ca, and Mg content as well as trunk circumference and total plant height, were also evaluated. Geostatistics was used to determine the spatial variability of the soil and clone attributes, while Ordinary Kriging was used to draw variability maps of the variables. A difference in the distribution of the variables, which was dependent on the slope of the relief, was detected through the maps. The southern side presented better conditions as some degradation was observed on the northern side. Certain soil characteristics influenced the distribution of the attributes of the planted clones; for example, the low concentration of Ca2+ in the soil caused Ca deficiency in the FX clone on the southern slope, indicating that liming did not supply enough nutrients for this clone. Our results showed that the variability in soil attributes influenced the nutritional status and growth of the rubber tree clones, indicating that variability maps can guide the planting and management of the rubber tree, providing more efficient management.

Spatial dependency and correlation of properties of soil cultivated with oil palm, Elaeis guineensis, in agroforestry systems in the eastern Brazilian Amazon / Dependência espacial e correlação das propriedades do solo cultivado com dendezeiro, Elaeis guineensis, em sistemas agroflorestais na Amazônia Oriental

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.

A geoestatística é uma ferramenta utilizada para produzir mapas de distribuição de nutrientes essenciais para o desenvolvimento das plantas. O presente estudo teve como objetivo analisar a variação espacial dos atributos químicos do solo sob cultivo de dendê em sistemas agroflorestais na Amazônia Oriental brasileira, e seu padrão de dependência espacial. Sessenta amostras de solo espacialmente padronizadas e georreferenciadas foram coletadas em cada um de três locais de amostragem (UD1, UD2 e UD3), na profundidade de 0-20 cm. Os atributos químicos do solo avaliados foram: pH, Al3+, H+Al, K+, Ca2+, Mg2+, capacidade de troca catiônica do solo (CTC), P e matéria orgânica (MO). A dependência espacial dos atributos foi avaliada com análise semivariográfica, ajustando-se três modelos teóricos (esférico, exponencial e gaussiano). Após a análise de dependência espacial, a krigagem ordinária foi empregada para estimar os valores de cada atributo em locais não amostrados. A correlação espacial entre os atributos foi testada utilizando a cokrigagem para espacialização dos dados. Todas as variáveis mostraram dependência espacial, exceto pH em UD3. Os maiores teores de K+, Ca2+, Mg2+ e MO foram encontrados na região mais baixa da paisagem, em UD1 e UD2. Os maiores teores de Al3+ e H+Al foram observados na região mais baixa da paisagem, em UD3. Algumas variáveis foram correlacionadas, portanto a cokrigagem mostrou-se eficiente na estimativa das variáveis primárias em função das secundárias. Os atributos avaliados mostraram dependência e correlação espacial, indicando que a geoestatística pode contribuir para o manejo efetivo de sistemas agroflorestais com dendê na região amazônica.