Automatic Recovery Estimation of Degraded Soils by Artificial Neural Networks in Function of Chemical and Physical Attributes in Brazilian Savannah Soil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)CNPq: 309380/2017-0The Oxisols is predominant in 54% of Brazilian territories and characterized by high weathering, relatively low chemical properties, and adequate structure. This study aimed to analyze the Oxisols through an Artificial Neural Network (ANN) with the purpose of estimating its recovery in function to soil chemical and physical attributes. The chemical attributes considered were: pH, cation exchange capacity (CEC), base saturation (V%), phosphorus (P), magnesium (Mg2+), and potassium (K+) and for the physical attributes, bulk density, soil porosity and soil resistance to penetration. The ANN used in this study is the Multilayer Perceptron (MLP), composed of three layers, input, intermediate and the output and with backpropagation training algorithm (supervised training). The intermediate layer is composed by 10 neurons and the layer of exit by 1 neuron, which has a function of informing the levels of chemical recovery (high, medium and low chemical attributes of the soil) and soil physics (recovered, partially recovered or not recovered). From the results obtained by ANN showed that the network reached an adequate training, with low mean square error (MSE). Therefore, ANN is a powerful and automatic alternative for the recovery estimation of degraded soils

Effect of K2SO4 concentration on extractability and isotope signature (δ13C and δ15N) of soil C and N fractions

Determination of the labile soil carbon (C) and nitrogen (N) fractions and measurement of their isotopic signatures (δ13C and δ15N) has been used widely for characterizing soil C and N transformations. However, methodological questions and comparison of results of different authors have not been fully solved. We studied concentrations and δ13C and δ15N of salt-extractable organic carbon (SEOC), inorganic (N-NH4+ and N-NO3-) and organic nitrogen (SEON) and salt-extractable microbial C (SEMC) and N (SEMN) in 0.05 and 0.5mK2SO4 extracts from a range of soils in Russia. Despite differences in acidity, organic matter and N content and C and N availability in the studied soils, we found consistent patterns of effects of K2SO4 concentration on C and N extractability. Organic C and N were extracted 1.6-5.5 times more effectively with 0.5mK2SO4 than with 0.05mK2SO4. Extra SEOC extractability with greater K2SO4 concentrations did not depend on soil properties within a wide range of pH and organic matter concentrations, but the effect was more pronounced in the most acidic and organic-rich mountain Umbrisols. Extractable microbial C was not affected by K2SO4 concentrations, while SEMN was greater when extracted with 0.5mK2SO4. We demonstrate that the δ13C and δ15N values of extractable non-microbial and microbial C and N are not affected by K2SO4 concentrations, but use of a small concentration of extract (0.05mK2SO4) gives more consistent isotopic results than a larger concentration (0.5m)