Silicon attenuates nutritional disorder of phosphorus in seedlings of Eucalyptus grandis × Eucalyptus urophylla.

BACKGROUND: Nutritional disorders of phosphorus (P), due to deficiency or toxicity, reduce the development of Eucalyptus spp. seedlings. Phosphorus deficiency often results in stunted growth and reduced vigor, while phosphorus toxicity can lead to nutrient imbalances and decreased physiological function. These sensitivities highlight the need for precise management of P levels in cultivation practices. The use of the beneficial element silicon (Si) has shown promising results under nutritional stress; nevertheless, comprehensive studies on its effects on Eucalyptus spp. seedlings are still emerging. To further elucidate the role of Si under varying P conditions, an experiment was conducted with clonal seedlings of a hybrid Eucalyptus spp. (Eucalyptus grandis × Eucalyptus urophylla, A207) in a soilless cultivation system. Seedlings were propagated using the minicutting method in vermiculite-filled tubes, followed by treatment with a nutrient solution at three P concentrations: a deficient dose (0.1 mM), an adequate dose (1.0 mM) and an excessive dose (10 mM), with and without the addition of Si (2mM). This study assessed P and Si concentration, nutritional efficiency, oxidative metabolism, photosynthetic parameters, and dry matter production. RESULTS: Si supply increased phenolic compounds production and reduced electrolyte leakage in seedlings provided with 0.1 mM of P. On the other hand, Si favored quantum efficiency of photosystem II as well as chlorophyll a content in seedlings supplemented with 10 mM of P. In general, Si attenuates P nutritional disorder by reducing the oxidative stress, favoring the non-enzymatic antioxidant system and photosynthetic parameters in seedlings of Eucalyptus grandis × Eucalyptus urophylla. CONCLUSION: The results of this study indicate that Eucalyptus grandis × Eucalyptus urophylla seedlings are sensitive to P deficiency and toxicity and Si has shown a beneficial effect, attenuating P nutritional disorder by reducing the oxidative stress, favoring the non-enzymatic antioxidant system and photosynthetic parameters.

Artificial neural networks and adaptive neuro-fuzzy inference systems for prediction of soil respiration in forested areas southern Brazil.

The purpose of this study was to estimate the temporal variability of CO2 emission (FCO2) from O2 influx into the soil (FO2) in a reforested area with native vegetation in the Brazilian Cerrado, as well as to understand the dynamics of soil respiration in this ecosystem. The database is composed of soil respiration data, agroclimatic variables, improved vegetation index (EVI), and soil attributes used to train machine learning algorithms: artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The predictive performance was evaluated based on the mean absolute error (MEA), root mean square error (RMSE), mean absolute percentage error (MAPE), agreement index (d), confidence coefficient (c), and coefficient of determination (R2). The best estimation results for validation were FCO2 with multilayer perceptron neural network (MLP) (R2 = 0.53, RMSE = 0.967 µmol m-2 s-1) and radial basis function neural network (RBF) (R2 = 0.54, RMSE = 0.884 µmol m-2 s-1) and FO2 with MLP (R2 = 0.45, RMSE = 0.093 mg m-2 s-1) and RBF (R2 = 0.74, 0.079 mg m-2 s-1). Soil temperature and macroporosity are important predictors of FCO2 and FO2. The best combination of variables for training the ANFIS was selected based on trial and error. The results were as follows: FCO2 (R2 = 16) and FO2 (R2 = 29). In all models, FCO2 outperformed FO2. A primary factor analysis was performed, and FCO2 and FO2 correlated best with the weather and soil attributes, respectively.

Machine learning for prediction of soil CO2 emission in tropical forests in the Brazilian Cerrado.

Soil CO2 emission (FCO2) is a critical component of the global carbon cycle, but it is a source of great uncertainty due to the great spatial and temporal variability. Modeling of soil respiration can strongly contribute to reducing the uncertainties associated with the sources and sinks of carbon in the soil. In this study, we compared five machine learning (ML) models to predict the spatiotemporal variability of FCO2 in three reforested areas: eucalyptus (RE), pine (RP) and native species (RNS). The study also included a generalized scenario (GS) where all the data from RE, RP and RNS were included in one dataset. The ML models include generalized regression neural network (GRNN), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF). Initially, we had 32 attributes and after pre-processing, including Pearson's correlation, canonical correlation analysis (CCA), and biophysical justification, only 21 variables remained. We used as input variables 19 soil properties and climate variables in reforested areas of eucalyptus, pine and native species. RF was the best model to predict soil respiration to RE [adjusted coefficient of determination (R2 adj): 0.70 and root mean square error (RMSE): 1.02 µmol m-2 s-1], RP (R2 adj: 0.48 and RMSE: 1.07 µmol m-2 s-1) and GS (R2 adj: 0.70 and RMSE: 1.05 µmol m-2 s-1). Our findings support that RF and GRNN are promising for predicting soil respiration of reforested areas which could help to identify and monitor potential sources and sinks of the main additional greenhouse gas over ecosystems.