Implications of converting native forest areas to agricultural systems on the dynamics of CO2 emission and carbon stock in a Cerrado soil, Brazil

The conversion of native vegetation to agricultural areas leads to a natural process of carbon loss but these systems can stabilize in terms of carbon dynamics depending on the management and conversion time, presenting potential to both store and stabilize this carbon in the soil, resulting in lower soil respiration rates. In this context, this study aimed to investigate the effect of converting native Cerrado forest areas to agricultural systems with a forest planted with Eucalyptus camaldulensis and silvopastoral systems on the dynamics of CO2 emission and carbon stock at different soil depths. The experimental sites are located in the Midwest of Brazil, in the coordinates 20°22'31″ S and 51°24'12″ W. Were evaluated soil CO2 emission (FCO2), soil organic carbon, the degree of humification of soil organic matter (HLIFS), soil temperature, soil moisture, and soil chemical and physical attributes. The soil of the area is classified as an Oxisol (Haplic Acrustox). Soil samples were collected at depths of 0.00-0.10, 0.10-0.20, 0.20-0.30, and 0.30-0.40 m. The lowest FCO2 values were found in the silvopastoral system (1.05 μmol m-2 s-1), followed by the native forest (1.65 μmol m-2 s-1) and the eucalyptus system (1.96 μmol m-2 s-1), indicating a 36% reduction in FCO2 compared to the conversion of the native forest to the silvopastoral system and an increase of 19% when converting the native forest to the eucalyptus system. The soil chemical attributes (N, K+, Ca2+, H++Al3+, CEC, and organic carbon) showed a decrease along the profile. The shallowest depths (0.00-0.10 and 0.10-0.20 m) presented no differences between systems but the subsequent depths (0.20-0.30 and 0.30-0.40 m) had a difference (95% confidence interval), relative to N, Ca2+, H++Al3, CEC, and organic carbon stock (OCS), and the soil under silvopastoral system showed a higher concentration of these attributes than the native forest. The multivariate analysis showed that the eucalyptus and silvopastoral systems did not differ from the forest in the shallowest soil layer but differed from each other. This behavior changed from the second assessed depth (0.10-0.20 m), in which the silvopastoral system stands out, differing both from the eucalyptus system and from the native forest, and this behavior is maintained at the following depths (0.20-0.30 and 0.30-0.40 m). OCS, H++Al3, CEC, and nitrogen are strongly related to land use change for silvopastoral system. Regarding the behavior/relationship of attributes as a function of depth, the silvopastoral system contributed to soil carbon accumulation and stability over consecutive years.

Estimate of lysine nutritional requirements for Japanese quail breeders

Background

Japanese quail breeders are the basis for genetic improvement and multiplication for commercial layers, however, there have been no known studies on the optimal lysine level for these birds. Thus, study the egg output response to the lysine (Lys) supply using different e-functions and evaluate the that best fit, have allowed the partition the lysine requirements for maintenance, both weight and egg output maximum.

Methods

The objectives of this study were to identify the responses to various Lys levels, identify the functions related to these responses and determine the ideal Lys intake amount for Japanese quail breeders. A completely randomized design of seven treatments with seven replicated was used. Treatments consisted of diet supplementation by Lys in concentrations of 16.8, 11.8, 8.4, 6.7, 5.0, 3.4, and 1.7 g/kg. Six exponential models were adjusted.

Results

The level of Lys was found to affect bird responses (P < 0.001). The birds responded to the levels provided, allowing for the creation of a lysine response curve. A monomolecular function with four parameters was balanced against the statistics of adjustment and selection of models. It was possible to estimate the level of lysine required for maintenance as 133 ± 2 mg/kg BW0.67, and based an average of 41% efficiency, 22 mg Lys produced 1 g of egg output (EO). The daily intake calculated by the monomolecular factorial model was 284 mg Lys for a bird with 0.170 kg body weight and production of 10 g EO/day. The four-parameter monomolecular function proposed in this study is adequate for interpreting the animal response and calculating lysine intake for breeders.

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.

High spatial resolution solar-induced chlorophyll fluorescence and its relation to rainfall precipitation across Brazilian ecosystems

The detection of Solar-Induced chlorophyll Fluorescence (SIF) by remote sensing has opened new perspectives on ecosystem studies and other related aspects such as photosynthesis. In general, fluorescence high-resolution studies were limited to proximal sensors, but new approaches were developed to improve SIF resolution by combining OCO-2 with MODIS orbital observations, improving its resolution from 0.5° to 0.05 on a global scale. Using a high-resolution dataset and rainfall data some SIF characteristics of the satellite were studied based across 06 contrasting ecosystems in Brazil: Amazonia, Caatinga, Cerrado, Atlantic Forest, Pampa, and Pantanal, from years 2015-2018. SIF spatial variability in each biome presented significant spatial variability structures with high R² values (>0.6, Gaussian models) in all studied years. The rainfall maps were positively and similar related to SIF spatial distribution and were able to explain more than 40% of SIF's spatial variability. The Amazon biome presented the higher SIF values (>0.4 W m^-2 sr^-1 μm^-1) and also the higher annual rainfall precipitation (around 2000 mm), while Caatinga had the lowest SIF values and precipitations (<0.1 W m^-2 sr^-1 μm^-1, precipitation around 500 mm). The linear relationship of SIF to rainfall across biomes was mostly significant (except in Pantanal) and presented contrasting sensitivities as in Caatinga SIF was mostly affected while in the Amazon, SIF was lesser affected by precipitation events. We believe that the features presented here indicate that SIF could be highly affected by rainfall precipitation changes in some Brazilian biomes. Combining rainfall with SIF allowed us to detect the differences and similarities across Brazil's biomes improving our understanding on how these ecosystems could be affected by climate change and severe weather conditions.

An empirical model for estimating daily atmospheric column-averaged CO2 concentration above São Paulo state, Brazil

BACKGROUND

The recent studies of the variations in the atmospheric column-averaged CO₂ concentration ([Formula: see text]) above croplands and forests show a negative correlation between [Formula: see text]and Sun Induced Chlorophyll Fluorescence (SIF) and confirmed that photosynthesis is the main regulator of the terrestrial uptake for atmospheric CO₂. The remote sensing techniques in this context are very important to observe this relation, however, there is still a time gap in orbital data, since the observation is not daily. Here we analyzed the effects of several variables related to the photosynthetic capacity of vegetation on [Formula: see text] above São Paulo state during the period from 2015 to 2019 and propose a daily model to estimate the natural changes in atmospheric CO₂.

RESULTS

The data retrieved from the Orbiting Carbon Observatory-2 (OCO-2), NASA-POWER and Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) show that Global Radiation (Qg), Sun Induced Chlorophyll Fluorescence (SIF) and, Relative Humidity (RH) are the most significant factors for predicting the annual [Formula: see text] cycle. The daily model of [Formula: see text] estimated from Qg and RH predicts daily [Formula: see text] with root mean squared error of 0.47 ppm (the coefficient of determination is equal to 0.44, p < 0.01).

CONCLUSION

The obtained results imply that a significant part of daily [Formula: see text] variations could be explained by meteorological factors and that further research should be done to quantify the effects of the atmospheric transport and anthropogenic emissions.

Avoiding burning practice and its consequences on the greenhouse gas emission in sugarcane areas southern Brazil

There is a growing need of sustainable solutions for balancing agricultural production with the reduction of its environmental impacts. The rapid increase in sugarcane cultivation and the progressive conversion of pre-harvest burning (BH) to green harvest (GH) have brought into debate the contribution of agricultural sector to the greenhouse gas (GHG) mitigation. This study focused on the estimated GHG emission from sugarcane cultivation during years in which sugarcane areas in southern Brazil expanded and passed throughout an important transition, from 2006 to 2012, when harvest adopted was changed from burned to not-burned based. Sugarcane management and harvest were mapped through visual interpretation of Landsat-type satellite images, and the areas under sugarcane cultivation were distinguished according to each agricultural phase and harvest regime (i.e., manual harvest with burning vs. green mechanized harvest). Based on a broad data review and applying the IPCC (2006) methodologies, the results were expressed in terms of kilograms of carbon dioxide equivalent (kg CO₂eq ha^-1). Avoiding burn prior to harvest, even during expansion of sugarcane areas, promoted a mean reduction of GHG emission from 901 to 686 kg CO₂eq ha^-1 relative to harvest phase (24% lower) and an increase from 1418.3 to 1507.9 kg CO₂eq ha^-1 related to the ratoon maintenance phase (6% higher). Analyzing the total GHG emission per unit of cultivated sugarcane area (hectare), it was observed a decrease from 2275 to 2034 kg CO₂eq ha^-1 (11% reduction). The gradual transition of pre-harvest burning on that period has contributed to the reduction of GHG emission associated with sugarcane production being an important step towards GHG mitigation while still providing more sustainable sugar and ethanol production in southern Brazil.

Machine Learning to predict tuberculosis in cattle from the state of Sao Paulo, Brazil

Tuberculosis is a well-known and worldwide spread zoonosis. In Brazil 1.594.787 cases were confirmed cases since 2001, where, in Sao Paulo state, 8.226 deaths were reported. This study aims to present steps related to the use of machine learning algorithms for predictive analysis for bovine tuberculosis. For this, an application was made based on data from farms in state of São Paulo, Brazil, of an epidemiological survey, using a specific questionnaire, carried out on farms (n = 1,743). Response variable was presented by apparent prevalence of positive properties for disease, and predictors by (k = 77) predictors related to type of farm, type of lactation, number of animals on property. Application was organized according to following steps: division of data in training (75%) and testing (25%), pre-processing of predictors, learning and model evaluation. In the learning step, algorithm for adjusting gradient boosted trees models was used. The hyperparameters of algorithms were optimized by 10-fold cross-validation, to select those corresponding to best models. Models showed an accuracy of 88.07%, with an error in learning process equal to 3%. In the test / model validation procedure (n = 436), an error in 12% estimate was observed. Five important predictors were daily milk production, number of cows, type of farm, bovine breed and slaughter of adult animals. Proportion of false positives among all individuals whose response of interest was observed was 2.06%, and proportion of false negatives among those with a response of absent interest was 9.86%. It is hoped that, with increase in trained surveillance to detect the disease and availability of data, it will be possible to develop predictive models of machine learning with potential to efficiently assist professionals in disease control and assist in education program in animal health

Key messages

Predictive analyzes in health: application for tuberculosis in cattle from the state of Sao Paulo, Brazil. An infectious disease and zoonosis important to the world that needs support to develop means to control and consequently eradicate it.

Correlation study and histopathological description of intestinal alterations in dogs infected with Leishmania infantum

The aim of this work was a correlation study and histopathological description of alterations associated with the presence of Leishmania infantumamastigote in the intestinal wall of dogs infected with canine visceral leishmaniasis (CVL). Three groups were used: G1 (n = 8), comprising naturally infected dogs with CVL with amastigotes of L. infantum in the small and large intestines; G2 (n = 9), infected dogs with CVL, without intestinal amastigotes; and G3 (n = 3), uninfected dogs. Histochemistry and immunohistochemistry methods were used for histopathology and amastigotes identification. 47.1% (8/17) of dogs from G1 group had amastigotes in the mucosa, submucosa and muscle layers of the small and large intestines and it was observed a prominent inflammatory reaction characterized by chronic infiltration of mononuclear cells: macrophages, lymphocytes and plasma cells. Comparison between the groups showed only a significant difference in relation to mucosal microscopic structural alterations in dogs from G1 in relation to G2 and G3. Parasite burden showed significant correlations with the microscopic alterations and clinical status of dogs in G1. By the conclusion, the inflammatory reactions caused by the parasites in the intestines might have contributed towards alterations in digestive processes, worsening the dogs' clinical status of CVL.

A review on soil carbon accumulation due to the management change of major Brazilian agricultural activities

Agricultural areas deal with enormous CO2 intake fluxes offering an opportunity for greenhouse effect mitigation. In this work we studied the potential of soil carbon sequestration due to the management conversion in major agricultural activities in Brazil. Data from several studies indicate that in soybean/maize, and related rotation systems, a significant soil carbon sequestration was observed over the year of conversion from conventional to no-till practices, with a mean rate of 0.41 Mg C ha-1 year-1. The same effect was observed in sugarcane fields, but with a much higher accumulation of carbon in soil stocks, when sugarcane fields are converted from burned to mechanised based harvest, where large amounts of sugarcane residues remain on the soil surface (1.8 Mg C ha-1 year-1). The higher sequestration potential of sugarcane crops, when compared to the others, has a direct relation to the primary production of this crop. Nevertheless, much of this mitigation potential of soil carbon accumulation in sugarcane fields is lost once areas are reformed, or intensive tillage is applied. Pasture lands have shown soil carbon depletion once natural areas are converted to livestock use, while integration of those areas with agriculture use has shown an improvement in soil carbon stocks. Those works have shown that the main crop systems of Brazil have a huge mitigation potential, especially in soil carbon form, being an opportunity for future mitigation strategies.

Greenhouse gas emission associated with sugar production in southern Brazil

BACKGROUND

Since sugarcane areas have increased rapidly in Brazil, the contribution of the sugarcane production, and, especially, of the sugarcane harvest system to the greenhouse gas emissions of the country is an issue of national concern. Here we analyze some data characterizing various activities of two sugarcane mills during the harvest period of 2006-2007 and quantify the carbon footprint of sugar production.

RESULTS

According to our calculations, 241 kg of carbon dioxide equivalent were released to the atmosphere per a ton of sugar produced (2406 kg of carbon dioxide equivalent per a hectare of the cropped area, and 26.5 kg of carbon dioxide equivalent per a ton of sugarcane processed). The major part of the total emission (44%) resulted from residues burning; about 20% resulted from the use of synthetic fertilizers, and about 18% from fossil fuel combustion.

CONCLUSIONS

The results of this study suggest that the most important reduction in greenhouse gas emissions from sugarcane areas could be achieved by switching to a green harvest system, that is, to harvesting without burning.