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.

Soil CO2 emission and soil attributes associated with the microbiota of a sugarcane area in southern Brazil.

The spatial structure of soil CO2 emission (FCO2) and soil attributes are affected by different factors in a highly complex way. In this context, this study aimed to characterize the spatial variability patterns of FCO2 and soil physical, chemical, and microbiological attributes in a sugarcane field area after reform activities. The study was conducted in an Oxisol with the measurement of FCO2, soil temperature (Ts), and soil moisture (Ms) in a regular 90 × 90-m grid with 100 sampling points. Soil samples were collected at each sampling point at a depth of 0-0.20 m to determine soil physical (density, macroporosity, and microporosity), particle size (sand, silt, and clay), and chemical attributes (soil organic matter, pH, P, K, Ca, Mg, Al, H + Al, cation exchange capacity, and base saturation). Geostatistical analyses were performed to assess the spatial variability and map soil attributes. Two regions (R1 and R2) with contrasting emission values were identified after mapping FCO2. The abundance of bacterial 16S rRNA, pmoA, and nifH genes, determined by real-time quantitative PCR (qPCR), enzymatic activity (dehydrogenase, urease, cellulase, and amylase), and microbial biomass carbon were determined in R1 and R2. The mean values of FCO2 (2.91 µmol m-2 s-1), Ts (22.6 °C), and Ms (16.9%) over the 28-day period were similar to those observed in studies also conducted under Oxisols in sugarcane areas and conventional soil tillage. The spatial pattern of FCO2 was similar to that of macropores, air-filled pore space, silt content, soil organic matter, and soil carbon decay constant. No significant difference was observed between R1 and R2 for the copy number of bacterial 16S rRNA and nifH genes, but the results of qPCR for the pmoA gene presented differences (p < 0.01) between regions. The region R1, with the highest FCO2 (2.9 to 4.2 µmol m-2 s-1), showed higher enzymatic activity of dehydrogenase (33.02 µg TPF g-1 dry soil 24 h-1), urease (41.15 µg NH4-N g-1 dry soil 3 h-1), amylase (73.84 µg glucose g-1 dry soil 24 h-1), and microbial biomass carbon (41.35 µg C g-1 soil) than R2, which had the lowest emission (1.9 to 2.7 µmol m-2 s-1). In addition, the soil C/N ratio was higher in R2 (15.43) than in R1 (12.18). The spatial pattern of FCO2 in R1 and R2 may not be directly related to the total amount of the microbial community (bacterial 16S rRNA) in the soil but to the specific function that these microorganisms play regarding soil carbon degradation (pmoA).

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.

Correlation study and histopathological description of intestinal alterations in dogs infected with Leishmania infantum / Estudo de correlação e descrição histopatológica das lesões intestinais de cães infectados com Leishmania infantum

Abstract 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.

Resumo O objetivo foi realizar um estudo de correlação e descrição histopatológica das lesões associadas à presença de amastigotas de Leishmania infantum na parede intestinal de cães infectados com leishmaniose visceral canina (LVC). Os cães foram subdivididos em três grupos: G1 (n = 8) cães naturalmente infectados com LVC e com amastigotas de L. infantum no intestino; G2 (n = 9) com LVC, mas sem o parasitismo intestinal; e G3 (n = 3) cães não infectados. Métodos histoquímicos e imunoistoquímicos foram utilizados para a histopatologia e a identificação das amastigotas, respectivamente. 47,1% (8/17) dos cães infectados (grupo G1) apresentavam formas amastigotas na mucosa, submucosa e camada muscular do intestino delgado e grosso, destacando-se uma reação inflamatória caracterizada por infiltrado crônico de células mononucleares; macrófagos, linfócitos e plasmócitos. Observou-se uma diferença significativa somente com relação às alterações estruturais microscópicas intestinais nos cães do G1 quando comparadas com G2 e G3. A intensidade parasitária intestinal teve correlação significativa com as alterações microscópicas e os sinais clínicos dos cães do G1. Concluiu-se que as amastigotas de L. infantum por causarem reações inflamatórias na parede intestinal dos cães podem ter contribuído para as alterações dos processos digestórios, agravando ainda mais o quadro clínico dos animais.

Modelagem da área foliar de duas cultivares de amendoim em função das dimensões lineares dos folíolos / Modelling of leaf area of two peanut cultivars as function of linear dimensions of the leaflets

O objetivo do presente estudo foi determinar um fator de forma para a estimativa da área de folíolos de dois cultivares de amendoim (IAC TATU ST e IAC RUNNER 886). Foram estudadas as correlações entre a área foliar real (SF) e as medidas dimensionais do limbo foliar, como o comprimento ao longo da nervura principal (C), a largura máxima (L) e o produto das duas dimensões (C × L). Para cada cultivar foi determinado um fator de forma (f) por meio da análise de regressão entre o produto do comprimento e largura e área real das folhas e avaliada a correlação entre a área foliar estimada pelo fator de correção e sua medida direta. Todos os modelos avaliados (lineares, exponenciais ou geométricos) permitiram estimativas da área foliar acima de 87%. As equações que apresentaram melhor ajuste foram as lineares, passando ou não pela origem. Do ponto de vista prático, sugere-se optar pela equação linear simples que considera o coeficiente linear igual a zero, com valores de fator f iguais a 0,7111 e 0,7266 para os cultivares IAC RUNNER 886 e IAC TATU ST, respectivamente. O método das dimensões é viável para a estimação de área foliar do amendoim para ambas as cultivares, por apresentar valores de r2 (0,97), com erros inferiores a 3%, inclusive quando utilizado com dados independentes.

The purpose of this study was to determine a shape factor to estimate area of leaflets of two peanut cultivars (IAC TATU ST, IAC RUNNER 886). Correlation studies were conducted involving real leaf area (Sf) and leaf length (C), maximum leaf width (L) and the product between C and L. For each cultivar was determined a form factor (f) by means of regression analysis between the product of the length by the width and the actual area of leaves and the correlation between leaf area estimated by the correction factor and direct measurement. All evaluated models (linear, exponential or geometric) provided good estimates of leaf area (above 87%). Linear models had the best fit, passing or not through the origin. From a practical viewpoint, it is suggested to use the linear model involving the C and L product, using a linear coefficient equal to zero, with values of factor f equal to 0.7111 and 0.7266 for IAC RUNNER 886 and IAC TATU ST, respectively. The method of dimensions is feasible for the estimation of leaf area for both peanut cultivars, for showing good r2 values (0.97), with errors below 3%, even when used with independent data.

Produtividade da cana-de-açúcar: variabilidade linear e espacial entre componentes tecnológicos e da produção / Productivity of cane sugar: linear and spatial variability between components and technological production

Na atualidade, a cultura da cana-de-açúcar desempenha frente à realidade brasileira, sobretudo no aspecto relacionado às fontes energéticas alternativas, importantíssimo papel. No ano de 2009, no município de Suzanápolis (SP), no Cerrado Brasileiro, foi instalado um experimento com a cultura da cana-de-açúcar em um Argissolo Vermelho eutrófico, com o objetivo de selecionar, por meio de coeficientes de correlação de Pearson, da modelagem de regressões lineares simples e de múltiplas e da correlação espacial, os melhores componentes tecnológicos e os de produção, para explicar a variabilidade da produtividade da cana-de-açúcar. Foi instalada a malha geoestatística, para a coleta de dados, com 120 pontos amostrais, numa área de 14,53 ha. Para as regressões lineares simples, a população de plantas é o componente da produção que apresenta a melhor correlação quadrática com a produtividade da cana-de-açúcar, dada por: PRO = -0,553**xPOP2+16,14*xPOP-15,77. Entretanto, para as regressões lineares múltiplas, a equação PRO = - 21,11+4,92xPOP**+0,76xPUR** é a que melhor se apresenta para estimar a referida produtividade. Espacialmente, a melhor correlação com a produtividade da cana-de-açúcar é determinada, também, pelo componente da produção da população de plantas.

Nowadays, the culture of the sugarcane plays an important role regarding the Brazilian reality, especially in the aspect related to the alternative energy sources. In 2009, the municipality of Suzanápolis (SP), in the Brazilian Cerrado, an experiment was conducted with the culture of the sugarcane in a Red eutrophic, with the aim of selecting, using Pearson correlation coefficients, modeling, simple, linear and multiple regressions and spatial correlation, and also the best technological and productive components, to explain the variability of the productivity of the sugarcane. The geostatistical grid was installed in order to collect the data, with 120 sampling points, in an area of 14.53 ha. For the simple linear regressions, the plants population is the component of production that presents the best quadratic correlation with the productivity of the sugarcane, given by: PRO = -0.553**xPOP2+16.14*xPOP-15.77. However, for multiple linear regressions, the equation PRO = -21.11+4.92xPOP**+0.76xPUR** is the one that best presents in order to estimate that productivity. Spatially, the best correlation with yield of the sugarcane is also determined by the component of the production population of plants.

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.