A forte relação das pessoas com o Rio Doce, bem como suas enchentes regulares, representam fator contínuo de exposição ao risco de doenças hídricas. Dada sua relevância epidemiológica, este estudo analisa a associação entre percepção de contaminação e uso do rio, bem como os mecanismos heurísticos empregados na formação da percepção de risco. Utilizou-se um modelo probit ordenado com variável instrumental e análise de redes temáticas aplicados a uma base de dados primária de 352 domicílios, representativa dos moradores de Tumiritinga, Minas Gerais, Brasil, para o ano de 2012. Os resultados indicam que embora a maioria (92,6%) dos moradores de Tumiritinga perceba o risco de contrair algum tipo de doença quando nada no Rio Doce, somente 11,4% informam não entrar na água. A análise de conteúdo sugere que esse paradoxo advém da falta de compreensão populacional sobre os mecanismos de transmissão de doenças hídricas, criando viés otimista sobre as chances de contaminação. Campanhas para promoção de comportamento preventivo devem, portanto, enfatizar as formas de contrair doenças hídricas na região.

Durante as últimas décadas, com as fortes evidências do aquecimento global, ganham importância os estudos sobre o comportamento e as mudanças dos padrões em séries climáticas. Para a coerência nas análises de dados, o processo probabilístico gerador dos valores da variável aleatória ao longo do tempo deve permanecer o mesmo. No caso de dados de estações meteorológicas, este pressuposto de homogeneidade no processo estocástico pode ser violado a partir de perturbações como mudanças de posição da estação, mudança de instrumentos ou mudança climática.
Neste trabalho, o pacote Climatol-R foi aplicado a 122 séries mensais de temperatura mínima, cobrindo o interior do estado de São Paulo para os anos 1940 até 2012. Todas as estações estudadas apresentaram não homogeneidades. Uma das consequências deste trabalho de homogeneização foi a melhora da homogeneidade dos dados de temperatura mínima para a estação meteorológica IAC.00015 onde se obteve redução do desvio padrão de 0,86ºC para 0,81ºC. O pacote Climatol-R tende a melhorar espacialmente e temporalmente a série original, apresentando melhores resultados para a análise de variabilidade climatológica.

Research networks provide a framework for review, synthesis and systematic testing of theories by multiple scientists across international borders critical for addressing global-scale issues. In 2012, a GHG research network referred to as MAGGnet (Managing Agricultural Greenhouse Gases Network) was established within the Croplands Research Group of the Global Research Alliance on Agricultural Greenhouse Gases(GRA). With involvement from 46 alliance member countries, MAGGnet seeks to provide a platform for the inventory and analysis of agricultural GHG mitigation research throughout the world. To date, metadata from 315 experimental studies in 20 countries have been compiled using a standardized spreadsheet. Most studies were completed (74%) and conducted within a 1–3-year duration (68%). Soil carbon and nitrous oxide emissions were measured in over 80% of the studies. Among plant variables, grain yield was assessed across studies most frequently (56%), followed by stover (35%) and root (9%) biomass. MAGGnet has contributed to modeling efforts and has spurred other research groups in the GRA to collect experimental site metadata using an adapted spreadsheet. With continued growth and investment, MAGGnet will leverage limited-resource investments by any one country to produce an inclusive, globally shared meta-database focused on the science of GHG mitigation.

The recent article in this journal of Cerri et al. (2016) gives estimates of greenhouse gas (GHG) emissions for cattle production on extensively managed farms in the State of Mato Grosso, Brazil. The results are expressed as GHG emission per kg of animal in the herd, and not per kg of product. These estimates should not be referred to as “carbon footprints” as they do not reflect the estimate of the GHG emissions utilized in the production of 1 kg of animal or carcass, but the total emission per kg of the total mass of animals in the herd, whether calves, heifers, steers or mature bulls and cows. This leads to a large underestimation of the true GHG emissions for the production of Brazilian beef.

Hourly fCO2 is recorded at a time series at the PIRATA buoy located at 6°S 10°W in the eastern tropical Atlantic since June 2006. This site is located south and west of the seasonal Atlantic cold tongue and is affected by its propagation from June to September. Using an alkalinity–salinity relationship determined for the eastern tropical Atlantic and the observed fCO2, pH and the inorganic carbon concentration are calculated. The time series is investigated to explore the intraseasonal, seasonal and interannual timescales for these parameters, and to detect any long-term trends. At intraseasonal timescales, fCO2 and pH are strongly correlated. On seasonal timescales, the correlation still holds between fCO2 and pH and their variations are in agreement with those of sea surface salinity. At interannual timescales, some important differences appear in 2011–2012: lower fCO2 and fluxes are observed from September to December 2011 and are explained by higher advection of salty waters at the mooring, in agreement with the wind. In early 2012, the anomaly is still present and associated with lower sea surface temperatures. No significant long-term trend is detected over the period 2006–2013 on CO2 and any other physical parameter. However, as atmospheric fCO2 is increasing over time, the outgassing of CO2 is reduced over the period 2006–2013 as the flux is mainly controlled by the difference of fCO2 between the ocean and the atmosphere. A longer time series is required to determine if any significant trend exists in this region.

Few grass genotypes with high yield potential are used as fodder in Brazil, making it necessary to develop new cultivars. In this study, we compared tiller density, forage mass, and agronomic traits across 12 Elephant grass (Pennisetum purpureum Schum.) genotypes under free-growth and conventional cutting systems at the Agência Paulista de Técnologia dos Agronegócios (APTA), Andradina, São Paulo, Brazil. The experimental design of the two trials was three replicates of a randomized complete block, each experimental unit consisting of 37.5-m2 plots with five rows spaced 1.5 m apart. We estimated tiller density 30, 60, and 150 days after planting (DAP), and 90 days after cut (DAC), and harvested the grass 150 DAP and 90 DAC for determination of forage mass and agronomic traits. In the free-growth system, forage mass was highest in genotypes CNPGL 00-155-2, CNPGL 92-41-1, CNPGL 92-51-1, and CNPGL 96-21-1 and in cultivar Guaçu. After cutting and regrowth, forage mass was highest in genotypes CNPGL 93-41-1, CNPGL 92-41-1, and CNPGL 91-11-2 and in cultivars Mineiro IPEACO, Guaçu, and Pioneiro. Stem mass was higher than leaf mass in the two trials, with leaf/stem ratio in most genotypes below 1.0. Aerial tiller density was highest for genotypes Pioneiro and CNPGL 00-155-2, and stem diameter was smallest in cv. Pioneiro in the free-growth and in the cutting and regrowth systems (9.7 and 7.62 mm, respectively). After regrowth, plant height ranged from 0.56 m in cv. Guaçu to 2.0 m in cv. Mineiro IPEACO and the mean number of internodes.stem-1 ranged from 2.1 in cv. Guaçu to 7.4 in cv. Pioneiro. Forage mass correlated positively with basal tiller density (r =0.4096) and negatively with stem diameter (r =-0.4879) in the free-growth trial, but relationships were not significant after regrowth.

The intense rainfall in the tropical Atlantic spatially overlaps with the spread of the Amazon plume. Based on remote-sensed sea surface salinity and rainfall, we removed the contribution of rainfall to the apparent Amazon plume area, thus refining the quantification of its extension (0.84 ± 0.06 × 106 km2 to 0.89 ± 0.06 × 106 km2). Despite the previous overestimation of the Amazon plume area due to the influence of rainfall (>16%), our calculated annual CO2 flux based on rainfall-corrected sea surface CO2 fugacity confirms that the Amazon River plume is an atmospheric CO2 sink of global importance (−7.61 ± 1.01 to −7.85 ± 1.02 Tg C yr−1). Yet we show that current sea-air CO2 flux assessments for the tropical Atlantic could be overestimated in about 10% by neglecting the CO2 sink associated to the Amazon plume. Thus, including the Amazon plume, the sea-air CO2 exchange for the tropical Atlantic is estimated to be 81.1 ± 1.1 to 81.5 ± 1.1 Tg C yr−1.

The variability of sea surface Total Alkalinity (TA) and sea surface Total Inorganic Carbon (CT) is examined using all available data in the western tropical Atlantic (WTA: 20°S-20°N, 60°W-20°W). Lowest TA and CT are observed for the region located between 0°N-15°N/60°W-50°W and are explained by the influence of the Amazon plume during boreal summer. In the southern part of the area, 20°S-10°S/40°W-60°W, the highest values of TA and CT are linked to the CO2–rich waters due to the equatorial upwelling, which are transported by the South Equatorial Current (SEC) flowing from the African coast to the Brazilian shore. An increase of CT of 0.9 ± 0.3 μmol kg−1yr−1 has been observed in the SEC region and is consistent with previous published estimates. A revised CT-Sea Surface Salinity (SSS) relationship is proposed for the WTA to take into account the variability of CT at low salinities. This new CT-SSS relationship together with a published TA-SSS relationship allow to calculate pCO2 values that compare well with observed pCO2 (R2 = 0.90).

Tropical forests harbor a significant portion of global biodiversity and are a critical component of the climate system. Reducing deforestation and forest degradation contributes to global climate-change mitigation efforts, yet emissions and removals from forest dynamics are still poorly quantified. We reviewed the main challenges to estimate changes in carbon stocks and biodiversity due to degradation and recovery of tropical forests, focusing on three main areas: (1) the combination of field surveys and remote sensing; (2) evaluation of biodiversity and carbon values under a unified strategy; and (3) research efforts needed to understand and quantify forest degradation and recovery. The improvement of models and estimates of changes of forest carbon can foster process-oriented monitoring of forest dynamics, including different variables and using spatially explicit algorithms that account for regional and local differences, such as variation in climate, soil, nutrient content, topography, biodiversity, disturbance history, recovery pathways, and socioeconomic factors. Generating the data for these models requires affordable large-scale remote-sensing tools associated with a robust network of field plots that can generate spatially explicit information on a range of variables through time. By combining ecosystem models, multiscale remote sensing, and networks of field plots, we will be able to evaluate forest degradation and recovery and their interactions with biodiversity and carbon cycling. Improving monitoring strategies will allow a better understanding of the role of forest dynamics in climate-change mitigation, adaptation, and carbon cycle feedbacks, thereby reducing uncertainties in models of the key processes in the carbon cycle, including their impacts on biodiversity, which are fundamental to support forest governance policies, such as Reducing Emissions from Deforestation and Forest Degradation.

The objective of this study was to evaluate the effect of seasons under a tropical climate on forage quality, aswell the effect of an Urochloa brizantha cv. Marandu grazing system on enteric methane (CH4) emissions fromNellore cattle in the Southeast region of Brazil. Sixteen Nellore steers (18 months old and initial weight 318.0 ± 116.59 kg of LW; final weight 469 ± 98.50 kg of LW) were used for a trial period of 10 months, with four collection periods in winter (August), spring (December), summer (February) and autumn (May). Each collection period consisted of 28 days, corresponding to the representative month of each season where the last six days were designed for methane data collection. Animals were randomly distributed within 16 experimental plots, distributed in four random blocks over four trial periods. CH4 emissions were determined using the sulphur hexafluoride (SF6) tracer gas technique measured by gas chromatography and fluxes of CH4 calculated. The forage quality was characterized by higher CP and IVDMD and lower lignin contents in spring, differing specially from winter forage. Average CH4 emissions were between 102.49 and 220.91 g d-1 (37.4 to 80.6 kg ani-1 yr-1); 16.89 and 30.20 g kg-1 DMI; 1.35 and 2.90 Mcal ani-1 d-1; 0.18 and 0.57 g kg-1 ADG-1 and 5.05 and 8.76% of GE. Emissions in terms of CO2 equivalents were between 4.68 and 14.22 g CO2-eq-1 g-1 ADG. Variations in CH4 emissions were related to seasonal effect on the forage quality and variations in dry matter intake.

We aimed to quantify nitrous oxide (N₂O) and methane (CH₄) emissions as a function of the addition of different quantities of bovine faeces and urine on soil under pasture. Two experiments were performed in randomised complete blocks with five replicates. In the first experiment, the emissions of CH₄ and N₂O were evaluated for 14 days after the addition of four amounts of faeces (0.0, 1.2, 1.8 and 2.4 kg of fresh faeces per plot), and in a second experiment, N₂O emissions were evaluated for 43 days after addition of four volumes of urine (0.0, 1.0, 1.5 and 2.0 L). Urine and faeces came from crossbred (Fresian × Gir) dairy cows fed on pasture and concentrates. N₂O emissions from faeces did not alter the emission factor (EF) according to the faeces weight (P = 0.73). N₂O-N EF from faeces-N averaged 0.18% (±0.05) of total applied N. The volume of urine applied influenced N₂O losses. The EF decreased linearly (P = 0.015) with increasing volumes of urine, being 4.9% (±0.75), 3.36% (±0.7) and 2.43% (±0.46) of N applied emitted as N₂O for the 1.0, 1.5 and 2.0 L volumes of urine respectively. The EF from urine was significantly (P < 0.0001) higher than the EF from faeces. There was no change to the CH₄ emissions per kilogram of excreta when the amount of faeces added was varied (P = 0.87). However, the CH₄ emitted increased linearly with the amount of faeces (P = 0.02). The CH₄ EF was estimated to be 0.95 (±0.38) kg/head.year.

As a contribution to an Integrated Water Resources Management (IWRM) project in Distrito Federal, Brazil, we address several aspects for a credible downscaling of near-surface air temperature and precipitation using the Statistical DownScaling Model (SDSM4.2). For instance, we apply a detailed screening of predictors, consider the end user needs in the validation procedure, assess the added value of the downscaling model and include several sources of uncertainties until the downscaling step. Results suggest that the interpolation of large-scale predictors to the target site is a reasonable alternative to predictors derived from grid-boxes. The validation metrics, measures (i.e. bias, root-mean-square error, and Pearson’s correlation coefficient) and quantile–quantile plots reveal that model tends to underestimate near-surface temperature and precipitation; whereas extreme values are subject of considerable uncertainties. Single-site projections at daily scale are derived from 27 climate models from the Coupled Model Intercomparison Project phase 5 (CMIP5) forced by Representative Concentration Pathways (i.e. RCP2.6, RCP4.5, RCP6.0 and RCP8.5) scenarios. The downscaling model adds substantial value in terms of amplitude of variability when compared to the host coarse-resolution projections. Its performance is higher than a quantile-mapping bias correction technique, particularly in reproducing observed trends. In spite of the elevated level of uncertainties in the magnitude of change, most of the downscaled projections agree on positive changes in near-surface temperature and precipitation for the period of 2036–2055 when compared to the reference period (i.e. 1986–2005). The massive amount of downscaled projections is of limited application in hydrological studies and, therefore, we suggest a summarized group of projections which are representative to the central tendency and spread of the ensemble.