Spatialization of Soil Chemical and Physical Attributes in an Agroforestry System, Seropédica, Brazil

Abstract

ABSTRACT:The purpose of this study was to spatialize the chemical and physical attributes of the soil in an agroforestry system in Seropédica, Rio de Janeiro, Brazil. Thirtyone soil samples were collected from 0-10 cm, 10-20 cm, and 20-40 cm depths, and each sampling point was georeferenced. The pH (in H 2 O), potential acidity (H+Al), calcium (Ca ), sodium (Na + ), potassium (K + ), phosphorus (P), organic carbon (C), cation exchange capacity of the soil (T value), base saturation (V value), total clay, total sand, silt, and density of fine roots were measured. The software ArcGIS 10.2 was used to perform the semivariogram analysis and the fitting of the models, and spatial interpolation was performed using a first-order trend ordinary kriging process with spherical, exponential, and Gaussian spatial models. Based on the results, only the exponential and Gaussian models were fitted to the variables, except for the Mg 2+ and V value variables, which presented no spatial dependence, thus showing a pure nugget effect (PNE). Distribution maps were generated for the variables (except for those exhibiting PNE), which showed correlation between the variables pH and Al 3+, organic carbon and cations, phosphorus and total clay, and silt and sand. Overall, geostatistics could be applied to spatialize the chemical and physical attributes of the soil in the agroforestry system, except in the case of Mg 2+ and the V value. ), sódio (Na + ), potássio (K + ), fósforo (P), carbono orgânico (C), capacidade de troca catiônica do solo (Valor T), saturação por bases (Valor V), argila total, areia total, silte e densidade de raízes finas. O software ArcGIS 10.2 foi utilizado para fazer a análise semivariográfica e o ajuste dos modelos, e posteriormente, foi empregado a interpolação espacial através da Krigagem Ordinária de primeira ordem de três modelos espaciais, esférico, exponencial e gaussiano. De acordo com os resultados, apenas os modelos exponencial e gaussiano foram ajustados para as variáveis, exceto para as variáveis Mg 2+ e Valor V, pois não apresentaram dependência espacial, assim expressando efeito pepita puro (EPP). Foram gerados os mapas de distribuição para as variáveis (exceto para aquelas que exibiram EPP), onde ocorreu uma correlação entre as variáveis pH e Al 3+ , carbono orgânico e cátions, fósforo e argila total, e silte e areia. A geoestatística pode ser aplicada para espacializar os atributos químicos e físicos do solo no sistema agroflorestal, exceto no caso do Mg 2+ e Valor V. ESPACIALIZAÇÃO DOS ATRIBUTOS QUÍMICOS E FÍSICOS DO SOLO EM UM

Spatial Analysis of Granulometry and Humic Substances of an Ecological Restoration Area in the Brazilian Atlantic Forest

“…While weak and moderate spatial dependence were observed in the clay and C-HUM fractions, respectively, strong spatial dependence was observed in the other variables. Silva et al [21] found different results from the present study.…”

Spatialization of Fractions of Organic Matter in Soil in an Agroforestry System in the Atlantic Forest, Brazil

“…Verification of spatial dependence by the semivariogram calculation was performed, in which the exponential, spherical, and Gaussian theoretical models were adjusted and tested in order to verify which model best represented the distribution of the variables studied, based on the equations and the three physical models presented by Silva et al (2016). To validate and verify the performance of the models in the estimation of the variables, the following parameters were used: lowest degree of spatial dependence (DSD %), highest correlation coefficient (R), highest coefficient of determination (R²), highest Willmott's concordance index (d), lowest standard error of the estimate (SEE), and lowest root-mean-squareerror (RMSE) (WILLMOTT, 1982;CAMBARDELLA et al, 1994;SILVA et al, 2016). DSD can be classified as follows, with values ≤ 25% suggesting data with strong spatial dependence, values between 25% and 75%, suggesting moderate spatial dependence, and values ≥ 75% suggesting weak spatial dependence.…”

Space Pattern of Forest Species and Its Relationship With Agricultural Factors Agroflorestal System

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“…According to Silva et al (2016), before SAF was implanted the area had Musa sapientum L. and Carcom papaya L. monocultures fertilized only with cattle manure. The implementation of the system started in 2000, following a multistratified arrangement.…”