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Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field. [ABSTRACT FROM AUTHOR]

Mato Grosso state is the biggest maize producer in Brazil, with the predominance of cultivation concentrated in the second harvest. Due to the need to obtain more accurate and efficient data, agricultural intelligence is adapting and embracing new technologies such as the use of satellites for remote sensing and geographic information systems. In this respect, this study aimed to map the second harvest maize cultivation areas at Canarana-MT in the crop year 2019/2020 by using geographic object-based image analysis (GEOBIA) with different spatial, spectral, and temporal resolutions. MSI/Sentinel-2, OLI/Landsat-8, MODIS-Terra and MODIS-Aqua, and PlanetScope imagery were used in this assessment. The maize crops mapping was based on cartographic basis from IBGE (Brazilian Institute of Geography and Statistics) and the Google Earth Engine (GEE), and the following steps of image filtering (gray-level co-occurrence matrix—GLCM), vegetation indices calculation, segmentation by simple non-iterative clustering (SNIC), principal component (PC) analysis, and classification by random forest (RF) algorithm, followed finally by confusion matrix analysis, kappa, overall accuracy (OA), and validation statistics. From these methods, satisfactory results were found; with OA from 86.41% to 88.65% and kappa from 81.26% and 84.61% among the imagery systems considered, the GEOBIA technique combined with the SNIC and GLCM spectral and texture feature discriminations and the RF classifier presented a mapping of the corn crop of the study area that demonstrates an improved and aided the performance of automated multispectral image classification processes. [ABSTRACT FROM AUTHOR]

The definition of the distance between sampling grid points directly impacts the development of fertility maps because it affects the spatial dependence of geostatistics and the estimates for locations not sampled in the interpolation. Based on geostatistical concepts, it is common to recommend one or more soil samples per hectare. However, there is a need to understand how a cost-effective grid-sampling scheme can be developed to produce accurate digital maps. This study aimed to assess how + 5% and + 10% additional random points in the original sample grids affect the development of soil fertility maps under Brazilian Cerrado conditions. Four agricultural areas located in different states of Brazil were analyzed by applying the various sampling techniques. In total, 625 points were sampled, and ten sub-samples within 5 m of the central point were collected. Additional sampling points (+ 5% and + 10%) were randomly placed in the four quadrants of the field boundary following the original grid scheme. The soil attributes evaluated were pH, cation exchange capacity, base saturation, and Ca, Mg, K, and P contents. Geostatistical variograms were modeled for each field, attribute, and statistical sampling treatment. The best variogram model was selected based on the minimal difference between the root mean square error and average standard error of the cross-validation procedure, along with an evaluation of the root mean square standardized error value. The maps were compared using the relative deviation coefficients. The inclusion of additional 5% and 10% sampling points in the original grid was found to be effective in generating soil fertility maps, resulting in improved root mean square and average standard error values. [ABSTRACT FROM AUTHOR]

The height, diameter, and leaf area of trees influence the anatomical structures because rapid growth requires maintenance of the water conduction in sapwood, despite environmental variations that may limit water supply. Our objective was to evaluate the influence of water availability conditions on the soil in humid and dry sites, on the percentage of sapwood along the trunk, and on the hydraulic properties of the wood of eucalyptus clones. The experimental plantings were carried out in Brazil in humid and dry sites. Six-year-old E. urophylla and E. grandis × E. camaldulensis clones were evaluated under 100% water (W) at each site considering local precipitation and 33% rainfall exclusion (WE) conditions. The wood was evaluated for heartwood and sapwood percentage, apparent sapwood density, diameter, wall thickness and vessel area, potential hydraulic conductivity, and Huber value. Rainfall exclusion at the dry site promoted an increase of 9% of sapwood when compared to the humid site, with an impact on diameter, area, wall thickness and vessel composition index, and lower Huber value for E. grandis × E. camaldulensis wood. The rainfall exclusion increased the sapwood percentage and reduced the growth of E. urophylla. E. grandis × E. camaldulensis had a higher sapwood apparent density at the dry site, while E. urophylla had a higher density at the humid site. The genetic diversity of the clones was expressed in the tree growth and in the sapwood content and was modified by humid and dry sites, which demonstrates different rusticity levels of the clones. [ABSTRACT FROM AUTHOR]

The Amazon region comprises the largest tropical forest on the planet and is responsible for absorbing huge amounts of CO2 from the atmosphere. However, changes in land use and cover have contributed to an increase in greenhouse gas emissions, especially CO2, and in endangered indigenous lands and protected areas in the region. The objective of this study was to detect changes in CO2 emissions and removals associated with land use and land cover changes in the Brazilian Legal Amazon (BLA) through the analysis of multispectral satellite images from 2009 to 2019. The Gross Primary Production (GPP) and CO2Flux variables were estimated by the MODIS sensor onboard Terra and Aqua satellite, representing carbon absorption by vegetation during the photosynthesis process. Atmospheric CO2 concentration was estimated from the GOSAT satellite. The variables GPP and CO2Flux showed the effective flux of carbon in the BLA to atmosphere, which were weakly correlated with precipitation (r = 0.191 and 0.133). The forest absorbed 211.05 TgC annually but, due to its partial conversion to other land uses, the loss of 135,922.34 km2 of forest area resulted in 5.82 TgC less carbon being absorbed. Pasture and agriculture, which comprise the main land conversions, increased by 100,340.39 km2 and absorbed 1.32 and 3.19 TgC less, and emitted close to twice more, than forest in these areas. Atmospheric CO2 concentrations increased from 2.2 to 2.8 ppm annually in BLA, with hotspots observed in the southeast Amazonia, and CO2 capture by GPP showed an increase over the years, mainly after 2013, in the north and west of the BLA. This study brings to light the carbon dynamics, by GPP and CO2Flux models, as related to the land use and land cover in one of the biggest world carbon reservoirs, the Amazon, which is also important to fulfillment of international agreements signed by Brazil to reduce greenhouse gas emissions and for biodiversity conservation and other ecosystem services in the region. [ABSTRACT FROM AUTHOR]

In Brazil, grain sorghum has been mainly grown in Cerrado biome during the off‐season, as a succession crop, after the soybean harvest. At this period, rainfall is erratic and insufficient, causing drought stress in pre‐ or post‐flowering of grain sorghum crops. In this scenario, the identification of both drought‐tolerant and irrigation responsive hybrids has been one of the main targets of sorghum breeding programmes. The hypothesis of this research was that grain sorghum genotypes respond differently to different moments of water deficit. This study aimed to evaluate the agronomic performance of sorghum hybrids under full irrigation and drought stress at pre‐ and post‐flowering, in order to identify hybrids tolerant to drought stress and responsive to the water supply. Field trials were carried out in Nova Porteirinha during the 2017/2018 and 2018/2019 crop seasons. Twelve grain sorghum single‐hybrids were evaluated for days to flowering and grain yield in three water availability conditions: drought stress at pre‐flowering, drought stress at post‐flowering and full‐irrigation (control). Tolerant hybrids were identified under both pre‐ and post‐flowering drought stress, as well as hybrids responsive to irrigation. The hybrids 1G282 and 116,748 stood out in the 2017/18 season for high yield and earliness under both drought‐stress conditions, while the hybrid 1G282, AG1090, AS4625 and DKB590 stood out for the 2018/19 season in both stress condition. The hybrid A9904 and 1G282 stood out for its responsiveness to water supply, being recommended to high‐tech farming systems that employ irrigation or for in‐season cultivation. [ABSTRACT FROM AUTHOR]

Wood volume is the variable that best represents the yield of planted forests, and several regression models are used in its estimation. Artificial neural networks (ANNs) are recognised for their accuracy and generalisation capacity associated with the quality and quantity of data in training and validation. Box–Müller transformation generates random variables from the original data and provides a consistent dataset. Given the above, the hypothesis of this research is that the expansion of data by the Box–Müller theorem provides more accurate estimates for predicting wood volume in eucalyptus species. The objectives were to (i) to evaluate the efficiency of the Box–Müller method for expanding the dataset of eucalyptus sample tree cubing, (ii) use different ANN topologies to predict the wood volume of different Eucalyptus species, and (iii) compare the estimates with those obtained by using the Schumacher and Hall model. The experimental design used randomised blocks with four replicates, composed of the following treatments: Corymbia citriodora and different Eucalyptus species. Sample trees were cubed at ages 2 years and 4.5 years. The estimated volume was obtained using the Schumacher and Hall non-linear regression model for each species and compared with the ANNs through Pearson's correlation, and root mean square error at the steps training, validation, and utilisation. Two ANN architectures were tested, multilayer perceptron (MLP) and radial basis function (RBF). Dataset expansion of cut-down sample trees for cubing is efficient and can be used for ANNs training when there are cubing restrictions of sample size. The topology with seven neurons in the first hidden layer and 12 in the second with expanded data of RBF showed better performance for predicting wood volume. When evaluating all species, the accuracy of the estimates provided by ANNs was higher than that obtained with non-linear regression. [ABSTRACT FROM AUTHOR]

Caatinga biome, located in the Brazilian semi-arid region, is the most populous semi-arid region in the world, causing intensification in land degradation and loss of biodiversity over time. The main objective of this paper is to determine and analyze the changes in land cover and use, over time, on the biophysical parameters in the Caatinga biome in the semi-arid region of Brazil using remote sensing. Landsat-8 images were used, along with the Surface Energy Balance Algorithm for Land (SEBAL) in the Google Earth Engine platform, from 2013 to 2019, through spatiotemporal modeling of vegetation indices, i.e., leaf area index (LAI) and vegetation cover (VC). Moreover, land surface temperature (LST) and actual evapotranspiration (ETa) in Petrolina, the semi-arid region of Brazil, was used. The principal component analysis was used to select descriptive variables and multiple regression analysis to predict ETa. The results indicated significant effects of land use and land cover changes on energy balances over time. In 2013, 70.2% of the study area was composed of Caatinga, while the lowest percentages were identified in 2015 (67.8%) and 2017 (68.7%). Rainfall records in 2013 ranged from 270 to 480 mm, with values higher than 410 mm in 46.5% of the study area, concentrated in the northern part of the municipality. On the other hand, in 2017 the lowest annual rainfall values (from 200 to 340 mm) occurred. Low vegetation cover rate was observed by LAI and VC values, with a range of 0 to 25% vegetation cover in 52.3% of the area, which exposes the effects of the dry season on vegetation. The highest LST was mainly found in urban areas and/or exposed soil. In 2013, 40.5% of the region's area had LST between 48.0 and 52.0 °C, raising ETa rates (~4.7 mm day−1). Our model has shown good outcomes in terms of accuracy and concordance (coefficient of determination = 0.98, root mean square error = 0.498, and Lin's concordance correlation coefficient = 0.907). The significant increase in agricultural areas has resulted in the progressive reduction of the Caatinga biome. Therefore, mitigation and sustainable planning is vital to decrease the impacts of anthropic actions. [ABSTRACT FROM AUTHOR]

In recent years, Brazil has become a major global contributor to the occurrence of national fires and greenhouse gas emissions. Therefore, this study aimed to evaluate the fire foci data of the past 20 years to determine their relationship with climatic variables in various Brazilian regions. The variables evaluated included fire foci, land surface temperature, rainfall, and standardized precipitation index, which were obtained via remote sensing from 2000 to 2019. The data were subjected to trend analyses (Mann–Kendall and Pettitt tests) and a multivariate analysis of canonical variables for evaluation. The results showed that the Midwest and North regions had the highest occurrence of fire foci throughout the study period, and that the North region had the highest accumulated annual rainfall. Thus, these regions require specific public policies to prevent future fires. Overall, the Midwest, Southeast, and South regions exhibit significant increasing fire foci tendencies. Our results reveal that this trend is related to the El Niño-Southern Oscillation (ENSO) phenomena, which alter climatic variables such as precipitation, land surface temperature, and the standardized precipitation index. Finally, the sugarcane growing area had a significant linear relationship with fire foci in the Southeast region, especially in the state of São Paulo, the major national sugarcane producer. [ABSTRACT FROM AUTHOR]

Defining the ideal soybean stand is essential for obtaining maximum grain yield. As a hypothesis of the work, it was questioned whether it is possible to vary seeding population of soybean based on the physical–chemical attributes of soil related to grain yield. The objectives of this work were (i) to identify soil attributes that help obtain the maximum grain yield grown in the Cerrado region of Brazil; and (ii) to generate a model to define the seeding rate according to these attributes. The experiment was carried out in the 2017/2018 and 2018/2019 crop seasons. In the first year, plots with different rate-seeding were sown on site one and site two. In the second crop season, site three was sown at a variable rate based on the results found in 2017/2018. The pH, calcium, magnesium, phosphorus, potassium, organic matter and clay contents, cation exchange capacity, base saturation and apparent electrical conductivity of the soil were evaluated. The results were subjected to Pearson's correlation network, followed by path analysis and decision tree. The variable-rate seeding suggested by the decision tree was applied on site 3 and the similarity between the soil attributes contained in the decision tree and the grain yield was verified using the relative deviation coefficient technique. The apparent electrical conductivity of the soil and the clay contents are the soil attributes that should be used to determine the soybean seeding rate. Areas with the highest values of electrical conductivity and clay contents should receive the higher soybean rate seeding in the Cerrado region. [ABSTRACT FROM AUTHOR]

The collapse of mining tailing dams in Brumadinho, Minas Gerais, Brazil, that occurred in 2019 was one of the worst environmental and social disasters witnessed in the country. In this sense, monitoring any impacted areas both before and after the disaster is crucial to understand the actual scenario and problems of disaster management and environmental impact assessment. In order to find answers to that problem, the aim of this study was to identify and analyze the spatiality of the impacted area by rupture of the tailing dam of the Córrego do Feijão mine in Brumadinho, Minas Gerais, by using orbital remote sensing. Land use and land occupation, phytoplankton chlorophyll-a, water turbidity, total suspended solids on water, and carbon sequestration efficiency by vegetation (CO2Flux) were estimated by orbital imagery from the Landsat-8/OLI and MSI/Sentinel-2 sensors in order to assess the environmental impacts generated by the disaster. Data were extracted from spectral models in which the variables that best demonstrated the land use variation over the years were sought. Mean comparison by t-test was performed to compare the time series analyzed, that is, before and after the disaster. Through the analysis of water quality, it was observed that the environmental impact was calamitous to natural resources, especially water from Córrego do Feijão. [ABSTRACT FROM AUTHOR]

High fiber quality is one of the most desired traits in the upland cotton breeding. To this end, breeding programs have developed hybrids with high heterotic effect, which provide genetic combinations and segregating populations from which superior genotypes can be selected. The objectives of this study were: (1) to identify superior genitors based on the general combining ability (GCA), (2) to indicate the best crossbreeds through the specific combining ability (SCA), and (3) to estimate phenotypic and genotypic correlations for the following fiber traits: length, uniformity, short fiber index, strength, elongation, micronaire, maturity, reflectance, degree of yellowing, and spinning index. The experiments were carried out in two environments in the semi-arid region of Brazil and two harvests (2015 and 2019) to evaluate six parents and fifteen F1's hybrids obtained by diallel crossings. The design used was random blocks with three replicates. The following traits were evaluated: length, uniformity, short fiber index, strength, elongation, micronaire, maturity, reflectance, degree of yellowing, and spinning index. Analysis of variance, diallel analysis and genetic correlations were performed. The parents with higher GCA were: TAMB 139-17, PSC 355, IAC 26, and FM 993. The crossings with highest SCA were: TAMB 139-17 × TAMCOT CAMD-E, CNPA 04 2080 × PSC 355, TAMB 139-17 × IAC 26, FM 993 × CNPA 04 2080. Positive genetic correlations were found between length, uniformity, strength, and spinning index. Micronaire had positive correlations with maturity. The short fiber index showed negative correlations with length, uniformity, strength, elongation, degree of yellowing, and spinning index. [ABSTRACT FROM AUTHOR]

The aims of this study were: i) to compare no-till areas in two municipalities located in different regions of Brazil, along with the influence on CO2Flux and GPP, and ii) to verify the difference between environmental factors followed by the trends of these variables regarding future scenarios (ARIMA time-series model number). The study was carried out in two areas with different latitudes in the municipalities of Sinop-MT and Passo Fundo-RS, both in Brazil. A time series of 19 years was performed with data acquired by remote sensing from the following satellites: i) Landsat-8 (OLI and TIRS), and ii) TERRA/AQUA (MODIS). The results propound that the spectro-temporal variables are directly influenced by soil management and agricultural practices over the observation time, with a satisfactory correlation in future predictions of the variables for the next ten years, in which presented that the variation of GPP and albedo values for the two study sites would gradually increase until 2028 and the temperature remained constant between the range of its seasonality, and CO2Flux tends to decrease in its seasonality, indicating a higher CO2 absorption. [ABSTRACT FROM AUTHOR]

The performance of genotypes in a wide range of environments can be affected by extensive genotype × environment (G × E) interactions, making the subdivision of the testing environments into relatively more homogeneous groups of locations (mega‐environments) a necessary strategy. The genotype main effects + genotype × environment interaction biplot method (GGE) allows identification of mega‐environments and selection of stable genotypes adapted to specific environments and mega‐environments. The objectives of this study were to identify mega‐environments regarding sorghum [Sorghum bicolor (L.) Moench] grain yield and demonstrate that the GGE biplot method can identify essential locations for conducting tests in different mega‐environments. A total of 22 competition trials of grain sorghum genotypes were conducted over three crop seasons across several production locations in Brazil. A total of 25, 22, and 30 genotypes were evaluated during the first, second, and third crop seasons, respectively. After identifying the presence of G × E interactions, the data were subjected to adaptability and stability analyses using the GGE biplot method. A phenotypic correlation network was used to express functional relationships between environments. The GGE biplot was found to be an efficient approach for identifying three mega‐environments in grain sorghum in Brazil, selecting representative and discriminative environments, and recommending more adaptive and stable grain sorghum genotypes. Core Ideas: Identifying mega‐environments in Brazil for sorghum planting, during the off‐season.Selecting representative and discriminative environments for sorghum in Brazil.Recommending more adapted and stable grain sorghum genotypes for the off‐season in Brazil. [ABSTRACT FROM AUTHOR]

Rainfall is a climatic variable that dictates the daily rhythm of urban areas in Northeastern Brazil (NEB) and, therefore, understanding its dynamics is fundamental. The objectives of the study were (i) to validate the CHELSA product with data in situ, (ii) assess the spatial-temporality of the rains, and (iii) assess the trends and socio-environmental implications in the Metropolitan Region of Maceió (MRM). The monthly rainfall data observed between 1960 and 2016 were flawed and were filled with the imputation of data. These series were subjected to descriptive and exploratory statistics, statistical indicators, and the Mann–Kendall (MK) and Pettitt tests. CHELSA product was validated for MRM, and all stations obtained satisfactory determination coefficients (R2) and Pearson correlation (r). The standard error of the estimate (SEE), root mean square error (RMSE), and mean absolute error (MAE) were satisfactory. The highest annual rainfall accumulated occurred near the Mundaú and Manguaba lagoons. The Pettitt test identified that abrupt changes occur in El Niño and La Niña years (strong and weak). The monthly rain boxplots showed high variability in the rainy season (April–July). Outliers have been associated with extreme rainfall at MRM. The drought period was 5 months in all MRM seasons, except in Satuba and Pilar. The Mann–Kendall test and the Sen method showed a tendency for a significant increase in rainfall in Satuba and not significant in the Pilar, while in the others, there was a tendency for a decrease in rainfall. The MRM rainfall depends on physiographic factors, multiscale meteorological systems, and the coastal environment. These results will assist in planning conservationist practices, especially in areas of socio-environmental vulnerability. [ABSTRACT FROM AUTHOR]

Understanding the variability of soil CO 2 emission across several land use and cover (LULC) classes and biomes and its relationship with climate variables is important to drive strategies that contribute to meeting local and international demands for sustainable development and low carbon agriculture. The hypothesis of this research is that soil CO 2 emission in situ (FCO 2) is variable between LULCs across different biomes and that there may be an association between soil CO 2 flux and environmental variables such as temperature and soil moisture. This study evaluated FCO 2 , measured by a portable EGM-5 CO 2 gas analyzer, CO 2 Flux model (obtained by remote sensing approach), soil moisture (SM), soil temperature (ST) and relationship between these variables in different LULC classes. We identified LULCs can contribute to carbon neutralization actions over the Cerrado, Atlantic Forest and Pantanal biomes located in State of Mato Grosso do Sul (MS), Brazil. Four LULC classes were evaluated in each biome: agriculture (soybean cultivation), pasture, eucalyptus plantation, and native vegetation. A principal component analysis (PCA) was performed to verify the relationship between biomes and LULC classes with the variables evaluated, and a Pearson correlation plot was created to assess the relationship between the variables evaluated. The lowest FCO 2 values were found in eucalyptus and soybean crops, regardless of biome. Our findings reveal the existence of soil CO 2 flux variability between the different LULCs and biomes. Pasture in Pantanal and Atlantic Forest biomes exhibited the highest FCO 2 values. Eucalyptus cultivation and native forest showed negative CO 2 Flux values, regardless of biome. Lower FCO 2 values were also observed for soybean cultivation. Such findings reinforce that native vegetation function as carbon sinks and that, therefore, their conservation is vital for the mitigation of CO 2 emissions. However, soybean and eucalyptus farming can be strategic for low carbon agriculture in MS and carbon neutralization projects by simultaneously contribute to economic and sustainable development of the regions covered by the biomes evaluated here. [Display omitted] • Multivariate analysis in understanding CO 2 soil emission. • Higher FCO 2 values were observed in pastures in the Pantanal and Atlantic Forest. • CO 2 absorptions were observed in eucalyptus and native forest áreas. [ABSTRACT FROM AUTHOR]

The effects of water and salt stress on rate of germination and seedling growth were investigated under laboratory conditions in 46 soya bean genotypes from Central‐West region of Brazil to verify how these stresses may limit crop establishment during the initial growth stage and also to identify the most tolerant genotypes to drought and salinity. Mild water and salt stresses were imposed by seed exposure to –0.20 MPa iso‐osmotic solutions with polyethylene glycol—PEG 6000 (119.57 g/L) or NaCl (2.357 g/L) for 12 days at 25°C. The germination percentage, seedling length and seedling dry matter were measured, and then, salt or drought tolerance indexes were calculated. The "NS 5909 RG," "NS 7000 IPRO," "NS 7338IPRO," "FPS Solimões RR," "NS 5151 IPRO," "SYN 13610 IPRO," "LG 60177 IPRO," "NS 6909 IPRO" and "BMX Desafio RR" were identified as the most drought‐tolerant genotypes, whereas under salinity conditions, the genotypes "5D 615 RR," "BMX Desafio RR," "5D 6215 IPRO" and "BMX Ponta IPRO" were identified as tolerant. The "BMX Desafio RR" is the genotype most adapted to both stress conditions and, therefore, should be used under conditions of water shortage and excess salt in the soil at sowing time. [ABSTRACT FROM AUTHOR]

The need to validate the quality of evapotranspiration estimates is essential for this parameter which has extended its use. For this, it is necessary to evaluate both new remote sensing products that expand the areas of estimated evapotranspiration and empirical equations that provide estimates with different data requirements. In order to examine this problem, the present study compared the estimates of evapotranspiration obtained by remote sensing of the MOD16A2 product and seven empirical equations with the estimates obtained through the FAO-56 reference method, with data obtained from six meteorological stations in the State of Rio de Janeiro, Brazil. Data cover the period from 2007 to 2013, which contains different phases of the El Niño–Southern Oscillation phenomenon. The methods proposed by Valiantzas were those that obtained the best performances when compared with FAO-56 with R2 over 90%. The non-parametric analysis of Mann-Kendall for the six seasons was mostly not significant; only the station of Resende showed a tendency of significant growth during the El Niño episode (Z = 0.283 and p value = 0.050). The mangrove and forest classes were the ones that obtained the highest averages (3.75 mm d−1 and 3.62 mm d−1), where the gradient of evapotranspiration can be observed in the South-Northeast portions. The MOD16A2 orbital product was inferior to the methods that used surface meteorological station data. [ABSTRACT FROM AUTHOR]

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Several environmental impacts are resulting from the process of anthropization and climate variability that have caused degradation of biomes and humid environments. Thus, the objective of this study was to evaluate the effect of the anthropic process and the variation of climatic conditions on the dynamics of the marsh vegetation in the Pandeiros River preservation area in the north of Minas Gerais, Brazil. The Enhanced Vegetation Index (EVI) of product MOD13Q1 and the gross primary productivity (GPP) of product MOD17A2 of the Moderate Resolution Imaging Spectroradiometer (MODIS) were used for the period from 2001 to 2017 were used in this process. Rain and air temperature data were obtained from the Conventional Weather Station of Januária-MG. The time series were submitted to the nonparametric statistical test of Mann-Kendall. The process of anthropization of the swamp area in the environmental protection area of the Pandeiros River/MG (EPA) showed a pattern of expansion of vegetation cover associated with the reduction of the water table, a phenomenon resulting from the silting process and reduction of the incidence of rain in this region, which contributes to the reduction of ecosystem services. Thus, understanding the influence of climatic variables on the dynamics of vegetation in humid environments, such as the EPA swamp area on the Pandeiros River, is essential for the preservation and recovery of these ecosystems and for the implementation of public policies for preservation and conservation. [ABSTRACT FROM AUTHOR]

In the last 30 years, the growth of the agriculture and livestock industries in the Cerrado biome has caused severe changes in land use and land cover (LULC), and areas previously occupied by native vegetation are changing to agricultural monocultures (e.g., soybean or corn) and/or pastures. Thus, the objective of this study was to analyze the LULC changes for the years 1986, 1999, 2007, and 2016 based on Landsat time series and object-based image analysis (OBIA) for the Prata River Basin. Twelve LULC classes were mapped: riparian forest, cerrado, swampy grasslands, wetlands, semideciduous forest, pasture, agriculture, fallow agricultural land, barren land, eucalyptus, water bodies, and burnt area. The classifications presented results with an overall accuracy of more than 93% and a kappa coefficient of 0.92. In 2007, the pasture class had the highest increase in area (48.5%), with a total area of 118.32 km2 of Cerrado biome vegetation converted to pasture, and the classes banhado, riparian forest, swampy grasslands, and cerrado had the greatest reductions in area (41.58%, 29.67%, 25.44%, and 21.63%, respectively). More precisely, the wetlands class underwent the greatest decrease under the advancement of pasture in the studied period (− 36.2%). These changes are due to factors favorable to agropastoral practices, such as a flat relief and soil with good agricultural suitability. [ABSTRACT FROM AUTHOR]

The genotype by environment interaction is essential in any plant breeding program. Methodologies allowing the evaluation of nonlinear genotype responses to environmental variation allied to prior beliefs on unknown parameters bring new insights for breeders. In this context, we aimed to propose a Bayesian segmented regression model to infer on phenotypic adaptability and stability of cotton (Gossypium L.) cultivars. The efficiency of using informative and minimally informative prior distributions in the selection of cultivars was also investigated. Randomized complete-block design experiments to study fiber yield (kg/ha) of 16 cotton genotypes were carried out in eight different environments in the State of Mato Grosso, Brazil. The proposed methodology was implemented using the free software R through the rbugs package. Bayesian segmented regression model was able to recommend cotton genotypes for cultivation, allowing to exploit the nonlinear pattern of genotype responses to environmental variation to find out the "ideal" genotype. The use of suitable prior information reduces the ranges of the credibility intervals, implying in higher precision of parameter estimates and, consequently, in reliable genotypes selection. [ABSTRACT FROM AUTHOR]

• Remotely sensed data in establishment of new boundary between biomes. • Land Use and Land Cover Change and the advance of the Cerrado biome on the edge of the Brazilian Amazonia. • Biophysical-atmospheric aspects altered with the anthropic advance and its influence on public environmental policies. The Amazonia is the largest tropical forest in the world, with approximately 5 million km2 occupying about 49.3% of Brazil in the North and Central-West regions and bordering the Cerrado sensu lato (Brazilian savannah). The border area between these two biomes is called the Cerrado-Amazonia transition that extends approximately 6240 km. Currently, these limits, as well as other areas of ecotonal stress, are not clearly defined mainly due to intense Land Use and Land Cover Change (LULCC). Remote sensing is a tool that tries to define and update these limits. In this context, the objective of this study was to use remote sensing tools to determine the transition between the two large Brazilian biomes. The study area comprised the transition between the Cerrado-Amazonia biomes in Brazil and an area composed of a buffer of 300 km was created around the official line of the abovementioned biomes. We used data from some remote sensors to calculate the composition of the previous year's pixel means for the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) (-1˜1), CO 2 Flux (μmol m−2 s-1), evaporative efficiency (mm yr-1), and Gross Primary Production (GPP) (g C m−2 d−1) variables. The statistical analyses were performed by softening the pixels of all the variables by means of the Kernel density (KD). Later, the analysis of the main components and the cluster between the biomes and the remote sensing variables were applied. Therefore, we sought to standardize the results and to find an overall mean between the standards to delimit a new area of the Amazonia and Cerrado biomes, called the Transition Area between Biomes (TAB). The variables analyzed by remote sensing were effective for identifying the Amazonia and Cerrado biomes characteristics that highlighted the evaporative efficiency variable in relation to other variables in both biomes. Thus, from the results achieved, a new boundary was determined that delimits the transition of the biomes that is different than the official government boundary. The analyses used in this study constitute important tools to foment the creation of public policies able to curb the anthropic actions on these biomes. [ABSTRACT FROM AUTHOR]

Understanding of how crop yield is influenced by interactions between the soil and environment is needed to minimize agricultural inputs and reduce the impacts of agriculture on the environment. The objective of this study was to determine the effect of soil chemical attributes, soil type, and rainfall on cotton (Gossypium hirsutum L.) yields and yield variability. In this study, the average cotton fiber yield was collected from 101 fields over nine crop seasons. In these fields, located in central Brazil, the average and standard deviation of normalized difference vegetation index (NDVI) were determined. Growing season rainfall and soil nutrients were measured. The variables analyzed were: average cotton fiber yield, NDVI from Landsat images calculated at the boll development phenological stage (enlargement and fiber filling phases), CV of the NDVI, precipitation during the cycle, and soil chemical attributes. The average field NDVI values collected at the boll development phenological stage were correlated to yield (r = 0.50; p < 0.05). Fields with a higher CV for the NDVI values generally had lower yields. The effect of precipitation depends on the cotton phenological stage and soil type. A high rainfall volume at the beginning of the cropping cycle increased plant height variability, which harmed the cotton fiber yield. The highest average values of aluminum content, aluminum saturation, and potential acidity promoted variability in cotton plant growth. Soil chemical attributes related to aluminum were negatively correlated with yield (r = -0.34; p > 0.05). [ABSTRACT FROM AUTHOR]

Cotton (Gossypium spp.) provides ~90% of the world's textile fibre. The aim of this study was to use the principal additive effects and multiplicative interaction (AMMI) model under the Bayesian approach to recommend cotton genotypes for the Central-West region of Brazil. Eight trials with upland cotton genotypes were conducted during the 2008–09 harvest in the State of Mato Grosso, Brazil. The experiment included a randomised block design with 16 genotypes. The genotypes were evaluated for fibre yield, length and strength. Chains were simulated via the Markov chain Monte Carlo method with 300 000 iterations for the parameters of the Bayesian AMMI model. From the chains generated, the first 20 000 burn-in observations were discarded and samples were taken by jumping every 20 observations (thin). Bayesian analysis provided additional results to those obtained by the frequentist approach, highlighting the credibility regions in the biplot for the genotypic and environmental scores. Bayesian AMMI model allowed identification of a genotype that can be widely recommended; this genotype has genotypic values above the overall mean for the three evaluated traits and did not contribute to the genotype × environment interactions observed in these traits. In addition, adaptability of genotypes to specific environments was observed, which makes it possible to capitalise the positive effect of the genotype × environment interaction. Cotton is a crop of great industrial importance. However, cotton cultivars have different productivities and fibre qualities when submitted to different environments. In this work, we use modern statistical techniques to identify cotton cultivars that have stable behaviour for these characteristics throughout Brazilian environments [ABSTRACT FROM AUTHOR]

Cotton has high production costs compared to other annual crops because large numbers of plant protection product (PPP) applications can be needed to control insect pests, diseases, and growth. The hypothesis underlying this study was that vegetation indices (VIs) could be used to estimate application rates for cotton. Our objectives were to (i) evaluate the relationship between different VIs and the application rates for cotton; (ii) propose a modification to the canopy chlorophyll content index (CCCI); and (iii) to develop a VI based equation that will indicate the ideal application rate needed to maximize deposition in the middle layer of a cotton crop. The experiments were carried out during the crop seasons 2017/18, and 2018/19 in the State of Mato Grosso do Sul, Brazil. A multispectral sensor installed in an unmanned aerial vehicle (UAV) was used to obtain the VIs, and the application rates evaluated were 40, 70, 100, and 130 L ha−1. The spray deposits on cotton leaves were measured using the mass balance analysis method. Our findings revealed that an increase in the VIs led to a rise in the application rate needed to maintain spray deposition on the middle layer of cotton plants. The CCCI is related to the rate variation in the cotton crop. However, our results showed that the proposed modified equation (the simplified modified canopy chlorophyll content index), which is based on the relative deposition, improves the estimation of the application rate that will optimize spray deposition in the middle layer of cotton plants. [ABSTRACT FROM AUTHOR]

Brazil is one of the world's biggest emitters of greenhouse gases (GHGs). Fire foci across the country contributes to these emissions and compromises emission reduction targets pledged by Brazil under the Paris Agreement. In this paper, we quantify fire foci, burned areas, and carbon emissions in all Brazilian biomes (i.e., Amazon, Cerrado, Caatinga, Atlantic Forest, Pantanal and Pampa). We analyzed these variables using cluster analysis and non-parametric statistics to predict carbon and CO2 emissions for the next decade. Our results showed no increase in the number of fire foci and carbon emissions for the evaluated time series, whereby the highest emissions occur and will persist in the Amazon and Cerrado biomes. The Atlantic Forest, Pantanal, Caatinga and Pampa biomes had low emissions compared to the Amazon and Cerrado. Based on 2030 projections, the sum of emissions from fire foci in the six Brazilian biomes will exceed 5.7 Gt CO2, compromising the national GHG reduction targets. To reduce GHG emissions, Brazil will need to control deforestation induced by the expansion of the agricultural frontier in the Amazon and Cerrado biomes. This can only be achieved through significant political effort involving the government, entrepreneurs and society as a collective. [ABSTRACT FROM AUTHOR]

Simple Summary: Agricultural crop systems have adopted integrated management as a model of success in pest control; however, chemical control is still prioritized. The use of pesticides incorrectly and excessively has provided a reduction in natural enemies, selection of resistant populations and resurgence of pests. Chrysoperla externa is a predator found in several regions in Brazil that preys on different pest insects. Entomopathogenic fungi Beauveria and Metarhizium also stand out for causing epizootics on pests. Both predators and entomopathogens can simultaneously act as pest control; thus, verifying the selectivity of entomopathogenic fungi to predators increases the potential for biological control through synergism and conservation of natural enemies in the agroecosystem. Considering the control potential of these different biological control agents, in this study we evaluated the selectivity of Beauveria bassiana, Metarhizium anisopliae and Metarhizium rileyi to the larvae of this predator. The results provide evidence that the biological development of larvae of C. externa is not influenced by the entomopathogenic fungus. These species of fungi can be recommended, aiming at a management of populations of arthropod pests, with low effect on C. externa when it is present in the agrosystem. We aimed to evaluate the selectivity of entomopathogenic fungi to larvae of Chrysoperla externa (Neuroptera: Chrysopidae). For this purpose, Beauveria bassiana (strain ESALQ PL63), Metarhizium anisopliae (strain ESALQ E9) and Metarhizium rileyi (strain UFMS 03) were assessed at different concentrations (1 × 107, 1 × 108 and 1 × 109 conidia mL−1). The control treatment consisted of distilled water and Tween80 0.01. The treatments were applied with a Potter spray tower using two different methodologies: direct application (DA) and dry film (DF). Up to 96 h after application, no treatment provided a larval mortality above 3%. After 120 h, only B. bassiana induced significant mortality in all instars, with rates of 26%, 17% and 10% for first, second and third instar larval periods, respectively. There was no difference regarding to the application method or concentration of conidia. The percentage of individuals that revealed changes in the length of the larval and pupal periods varied among different treatments with entomopathogenic fungi and control treatments, application methodologies and concentrations. Despite B. bassiana revealing a higher mortality than M. anisopliae and M. rileyi on larvae of C. externa, these three entomopathogenic fungi may be used in association with C. externa for sustainable pest management. [ABSTRACT FROM AUTHOR]

• The state of Mato Grosso do Sul has three biomes: Cerrado, Mata Atlântica, and Pantanal. • The occurrence of fire foci in this state is higher in the Pantanal. • The variability of the El Niño-Southern Oscillation influences on fire foci dynamics. The state of Mato Grosso do Sul (SMS), located in the Midwest of Brazil, is devoid of climatological studies, mainly on the characterization of rainfall regime and producers' meteorological systems and rain inhibitors. This state has different soil and climatic characteristics distributed among three biomes: Cerrado, Mata Atlântica, and Pantanal. This study aimed to investigate the temporal variability of fire foci in different SMS biomes. Data regarding fire foci and rainfall were obtained on an annual scale, collected from 15 municipalities (nine in Cerrado, four in Mata Atlântica, and two in Pantanal). Boxplot and distribution graphs were constructed for fire foci data and rainfall data associated with the biomes in each year. Subsequently, we applied the Mann–Kendall test to verify if there is a significant trend to the rainfall and fire foci data of the biomes. Pantanal revealed the higher occurrence of fire foci in relation to Cerrado and Mata Atlântica. The highest records of fire foci in Pantanal are caused by the longer drought period and anthropogenic activities (based on extensive agriculture). There is a tendency for positive growth in the occurrence of fire foci in the Pantanal, Cerrado and Mata Atlântica. Therefore, it is necessary to establish public policies to mitigate the occurrence of hot flashes in the SMS biomes, especially in the Pantanal. Image, graphical abstract [ABSTRACT FROM AUTHOR]

• Indigenous lands account for 23% of Amazonian forests in Brazil. • Brazilian government policies threaten forests and resources for their protection. • Illegal activities such as mining, logging and soybeans are allowed by indigenous people. • The liberation of exploitation by the capital of wealth in these territories will compromise biodiversity and climate. The Indigenous Lands in Brazil today resemble a steam locomotive, where the government and the indigenous add wood to the boiler and the conservationists push the brakes. Arranged on the rails and along the way of this locomotive are more than 100 million hectares of the largest tropical forest on the planet. In this article we evaluate the advance of soybean cultivation and fire foci in indigenous lands using remote sensing and discuss the possible effects of a liberation of the economic exploitation in these territories intended by the Brazilian government and requested by the indigenous people themselves. We present that even with the prohibition of economic exploitation on indigenous lands, there are large concentrations of fires in those territories which undermine public policies in Brazil, both in maintaining biodiversity as the assumed climate agreements. [ABSTRACT FROM AUTHOR]

• Natural regeneration recovers biomass stocks. • Silvicultural systems influences aerial biomass. • Forest fires reduces biomas of fire-sensitive species. The Brazilian Cerrado is the second largest biome in Brazil, and its natural landscapes have undergone transformations, due in large part to anthropic activities. However, little is known about the effects of these activities on the natural regeneration of Cerrado vegetation. Thus, the objective of this study was to evaluate the accumulation of woody biomass in an area of a Cerrado sensu stricto that was monitored for 27 years after the implementation of six silvicultural systems. A period of 17 years was sufficient for the woody community of the Cerrado sensu stricto to naturally recover its original stock of aerial biomass after the application of silvicultural systems. A Few woody species contribute to the total aerial biomass accumulated over time, and such contribution is variable with time and with the type of silvicultural system. At 27 years after cutting, the largest fraction of biomass accumulated in the area were represented by the species Miconia leucocarpa , Blepharocalyx salicifolius , Schefflera macrocarpa , Qualea grandiflora , Dalbergia miscolobium , Eriotheca pubescens , Qualea parviflora and Caryocar Brasiliense , which also demonstrated higher fire resistance. A period of 17 years after the implementation of silvicultural systems is considered sufficient for an area to recover naturally to the original stock of woody biomass at the community level. This result causes a greater reduction in the biomass of fire-sensitive species and transforms a Cerrado from a carbon sink into a source of CO 2 emissions to the atmosphere. [ABSTRACT FROM AUTHOR]

Dengue is among the largest public health problems in Brazil. Reported dengue cases via DATASUS were correlated with reanalysis data from NCEP (rainfall and air temperature) and Brazil's population data (2000 and 2010) from 1994 to 2014. The aim of this study was to evaluate relational patterns between climate variables together with population data from the last census and reported cases of dengue in Brazil from 1994 to 2014 by using statistical techniques. Several statistical methods [descriptive and exploratory statistics; simple and multiple linear regressions; Mann–Kendall (MK), Run, and Pettit nonparametric tests; and multivariate statistics via cluster analysis (CA)] were applied to time series. The highest percentages of Dengue cases were in Brazil's Southeast (47.14%), Northeast (29.86%), and Central West (13.01%). Upon CA of the Brazilian regions, three homogeneous dengue groups were formed: G1 (North and Central West), G2 (Southeast and Northeast), and G3 (South). Run testing indicated that the time series is homogenous and persistence free. MK testing showed a nonsignificant trend of increase of dengue cases in 23 states with positive trends and in four states with negative trends of Brazil. A significant increase in the magnitude of dengue at the regional level was recorded in the North, Southeast, South, and Central West regions. Statistical methods showed that dengue variability in Brazil is cyclical (2- to 3-year cycles), but not repetitive of El Niño-Southern Oscillation (ENSO) in the moderate, strong, and neutral categories. ENSO interferes with the action of weather systems, changing or intensifying rainfall and air temperatures in Brazil. The population increase in recent decades and the lack of effective public policies together with the action of ENSO contributed to the increase in dengue cases reported in Brazil. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the adaptability and phenotypic stability of cowpea genotypes using a nonlinear regression analysis and multivariate analysis. Experiments were performed at four sites in Brazil using a randomized blocks design with 20 treatments and four replications. The adaptability and stability of genotypes were evaluated by Toler nonlinear regression and genotype plus genotype 3 environment (GGE) biplot methodologies. Most of the genotypes revealed linear response patterns, with no differences regarding the favorable and unfavorable environments. Regarding the genotype classification for stability and adaptability, the Toler and GGE biplot methodologies are congruent. Genotypes MNC99-537F-4, MNC00-561G-6, MNC99542F-5, and Patativa have high overall adaptability and adequate yield. Therefore, they should be recommended for cultivation in the tested environments. Genotypes closer to the ideotype by the GGE biplot method are considered doubly desirable by the nonlinear method. [ABSTRACT FROM AUTHOR]

Bird species have been widely used as suitable bioindicators of environmental mercury (Hg). However, there is still some debate about the most suitable tissue to indicate Hg body burden in birds. For a long time, blood and feathers have proved to be relevant to monitor Hg at different time scales, and recently, bill sheath has been suggested as a potential tissue to this end. In the present study, we evaluated THg in muscle, liver, feathers, claws, and bill sheath in two waterbird species (i.e. the ringed and the Amazon kingfishers) from the Teles Pires, Juruena and Paraguay rivers. Considering all species and sites, feathers (5.47 ± 2.15 μg/g) and bill sheath (3.39 ± 1.37 μg/g) had mean THg concentrations about 2-, 3- and 10-times higher than claws, liver and muscle, respectively. When bird species were segregated, the ringed kingfisher showed THg values 1.8 times higher than the Amazon kingfisher in all tissues. Moreover, results showed that the Amazon kingfisher from the Juruena and Teles Pires rivers was clearly separated from the Paraguay River (control site), and was associated with higher THg values in the claws and feathers. Results obtained for the THg concentrations in bill sheath, muscle and liver tissues of the Amazon kingfisher using multivariate analysis of canonical variates (CVA) showed a pattern of segregation between the sampling areas, being the highest THg values in Teles Pires River samples. The largest bill sheath vector in the CVA suggests that this tissue is a key variable in the segregation of the samples. Overall, feathers may be useful for effects monitoring or spatial patterns, whereas bill sheath, which are more invasive, may be advantejous for temporal trends and retrospective studies of Hg pollution. [Display omitted] • We assessed THg in muscle, liver, feathers, claws and bill sheath in two kingfishers. • We studied the ringed and Amazon kingfishers at three Brazilian rivers. • THg concentration decreased as follows: feathers ≥ bill sheath > claws ≥ liver > muscle. • Bill sheath/rhamphotheca is a key parameter for Hg variation among organs and sites. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) sources and sinks are an important global concern. Monitoring the spatiotemporal variations of GHG concentrations, particularly carbon dioxide (CO 2), is crucial for identifying potential sources and sinks and moving toward a sustainable future. Therefore, via a time-series of remote data and multispectral images, this study evaluates the CO 2 spatiotemporal dynamics and related factors during 2015–2018 in one of the world's main agricultural frontier areas, the state of Mato Grosso (SMT), Brazil, which is both experiencing continued deforestation and attempting to achieve sustainable food production. In this study, data was obtained from the measurement of column-averaged carbon dioxide (CO 2) dry air mole fraction in the atmosphere, set as X CO2 from Orbiting Carbon Observatory-2 satellite from January 2015 to December 2018. The enhanced vegetation index data were obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor, and rainfall data were obtained from the Climate Hazards Group InfraRed Precipitation with Station dataset. From a series of Landsat-8 satellite images, it was possible to distinguish land use and land cover classes and estimate the CO 2 flux in the SMT. The results showed that the temporal variability of CO 2 flux is correlated positively with rainfall, while X CO2 is negatively correlated with rainfall. Regarding spatial variability, we observed that forest areas that were converted to other land uses resulted in higher values that characterize with sources, and that the highest and lowest average concentrations of CO 2 occurred in the dry and rainy months, respectively, for X CO2 , which might be the result of differences in the vertical resolution of the CO 2 column and scale. In contrast, areas with large continuous forest areas tended to have lower values and contribute positively to the carbon balance as sinks, thereby mitigating climate change impacts. Therefore, not only X CO2 but also CO 2 flux are directly related to changes in land use and land cover (LULC) in complex systems that are affected by climatic variables and processes, such as photosynthesis and soil respiration. • X CO2 is inversely related to rainfall, with highest concentration in drier periods. • Human actions in land use and land cover change increase atmospherical CO 2. • Remote sensing to locate and understand the sources and sinks of carbon dioxide. [ABSTRACT FROM AUTHOR]

Fire is used in the management of pastures, renewal and expansion of areas, and agricultural activities in South America (SA). The objectives of this study were: i) to identify the countries and regions with the highest number of fire foci in SA, and ii) to evaluate the spatial dynamics of fire foci based on the Meteorological Fire Danger Index (MFDI) and future scenarios through numerical simulations. Fire foci time series comprised 21 years (1998–2018) from the BDQueimadas database. Cluster Analysis (CA), descriptive and exploratory statistics were employed. Fire foci maps for SA were made in 10-km pixel dimensions. MFDI was used to assess fire danger via SPEEDY (Simplified Parametrizations, primitivE-Equation DYnamics) model simulations. Three simulations were performed: control scenario (1980–2015), RCP2.6 scenario (optimistic - 2016 and 2050), and RCP8.5 scenario (pessimistic - 2015 and 2050). Regionally, three homogeneous groups of fire foci (G1, G2 and G3) and one atypical (NA - Not Grouped) were identified for Brazil via CA. The highest fire foci occurred in Brazil (62.72%), followed by Bolivia (9.03%), Argentina (8.28%), Venezuela (6.11%), Paraguay (5.94%), and Colombia (3.87%), respectively. The highest density of fire foci occurred in the MATOPIBA region, the confluence of Maranhão, Tocantins, Piauí, and Bahia, - (agricultural frontier), and also in the Cerrado-Amazon transition and the states of Mato Grosso and Mato Grosso do Sul in Brazil, followed by Paraguay, Bolivia, Venezuela, and Argentina. The countries and regions of Brazil do not change, only intensify from year to year, and such fire foci variability may be associated with the El Niño-Southern Oscillation (ENSO) phases. The control scenario identified in east-central Brazil, western Bolivia, Paraguay, and northern Argentina. The optimistic scenario showed an improvement in some countries and a worsening in the territorial distribution in Brazil, Venezuela, and Colombia. The pessimistic scenario identified increased degradation compared to the previous scenarios in almost all SA countries. • Brazil, Bolivia, Argentina, Venezuela, and Paraguay largest records of fire foci in South America. • Projections of future fire risk scenarios indicate central-eastern Brazil, Bolivia, Paraguay, and Argentina. • The integrated analysis identified land use and climatic variability as the main factors. [ABSTRACT FROM AUTHOR]

• Paricá is a native species of Amazon used in the laminated wood industry. • Brazilian half-sib progenies of paricá has high genetic variability. • Combined selection strategy provides the greatest genetic gains for paricá. Paricá (Schizolobium parahyba var. amazonicum) is a tropical species native to the Amazon region and is continually growing for wood exploitation potential, especially in the laminated wood industry. In Brazil, there are still no breeding programs for this species. Therefore, it is necessary to assess its genetic diversity and use appropriate selection strategies to develop genotypes that serve the consumer market. This research hypothesized that half-sib families of paricá have genetic variability for characteristics favorable to wood production. Thus, the aim was to evaluate different selection strategies in paricá genotypes for growth traits. Half-sib progenies from 58 paricá matrices were planted in the experimental area of Embrapa Agrossilvipastoril in the municipality of Sinop-MT. The seeds used for obtaining the seedlings were collected in the states of Mato Grosso, Rondônia, Acre, Tocantins, and Pará. The traits evaluated were: diameter at breast height and plant height. Variance components and heritability were estimated, and subsequently, the genetic gains with the selection were predicted by four selection strategies: combined selection, among and within-families selection, mass selection, and stratified mass selection. The results obtained by analysis of variance showed the existence of genetic variability among families for the traits evaluated, which revealed the possibility of obtaining genetic gains and thereby success with the selection. All selection strategies showed to be suitable for obtaining genetic gains with a selection of the best paricá progenies aimed at wood production. However, the combined selection provided the highest genetic gains. The results reported in this research made it possible to direct the strategies of the first genetic breeding program for paricá in Brazil. [ABSTRACT FROM AUTHOR]

• Automated identification of soybean plants via orbital sensors. • EOS-MODIS, MSI and OLI multitemporal images. • Soybean discrimination by phenological phases. • Spatial distribution of soybean for public policy purposes. Soybean is the main crop of the Brazilian agribusiness. The near-real-time monitoring of this crop is important in the production estimate, identification of the progress, and location of the crops. It is also crucial for governmental surveillance institutions regarding sanitary break. Thus, this study aimed to estimate and map soybean areas in almost real time using temporal series multispectral images and vegetation indices (near-infrared and red) in the Google Earth Engine system in the state of Mato Grosso, Brazil. A multitemporal algorithm of the Perpendicular Vegetation Index (PVI) of MODIS, OLI, and MSI images of the 2016/2017 crop yr−1 was created from the identification of soybean areas using the Perpendicular Crop Enhancement Index (PCEI). The use of the MODIS images for the monitoring of soybean areas using the Google Earth Engine platform was a viable and promising automated alternative for large-scale soybean area estimates. [ABSTRACT FROM AUTHOR]

Yield multi-location trials associated to geostatistical techniques with environmental covariables can provide a better understanding of G x E interactions and, consequently, adaptation limits of soybean cultivars. Thus, the main objective of this study is understanding the environmental covariables effects on soybean adaptation, as well as predicting the adaptation of soybean under environmental variations and then recommend each soybean cultivar to favorable environments aiming maximize the average yield. The trials were carried out in randomized block design (RBD) with three replicates over three years, in 28 locations. Thirty-two genotypes (commercial and pre-commercial) representing different maturity groups (7.5-8.5) were evaluated in each trial were covering the Edaphoclimatic Region (REC) 401, 402 and 403. The covariables adopted as environmental descriptors were accumulated rainfall, minimum temperature, mean temperature, maximum temperature, photoperiod, relative humidity, soil clay content, soil water avaibility and altitude. After fitting means through Mixed Linear Model, the Regression-Kriging procedure was applied to spacialize the grain yield using environmental covariables as predictors. The covariables explained 32.54% of the GxE interaction, being the soil water avaibility the most important to the adaptation of soybean cultivars, contributing with 7.80%. Yield maps of each cultivar were obtained and, hence, the yield maximization map based on cultivar recommendation was elaborated., (© 2024. The Author(s).)

Failure in irrigation management of grapevines grown in the Brazilian semiarid region can affect bud fertility. Adequate irrigation, considering both the development of bunches in the current cycle and the formation of fertile buds for subsequent cycles, can bring significant advances to viticulture. Therefore, the objective of this research was to investigate the effect of different irrigation levels during flowering on the formation of buds and potential bunches of 'Arra 15' grapevine and its relationship with metabolic processes. A field experiment was carried out in a commercial vineyard in Petrolina, Pernambuco, Brazil, during the 2021 and 2022 seasons. The experiment was designed in randomized blocks with four replications and five irrigation levels (70; 85; 100; 115 and 130% of crop evapotranspiration - ETc) during three production cycles. The variables fertile bud, vegetative bud, dead bud, potential fertility of the basal, median, and apical regions of the branches, number of potential bunches, reducing sugar, total soluble sugar, net photosynthesis, stomatal conductance, transpiration, and relative chlorophyll index were evaluated. The 115% ETc irrigation level improved the number of fertile buds and number of potential bunches. Irrigation level above 115% ETc increased gas exchange and relative chlorophyll index, while 70% ETc increased leaf sugar content. The most appropriate irrigation strategy is the application of 115% ETc during the flowering stage, for the increase of fertile buds and potential bunches of the next cycle, without influencing the vine metabolism. Total soluble sugars are a promising indicator of water deficit during flowering and as an indicator of vegetative bud formation for the next cycle., (© 2024. The Author(s).)

The emission of soil carbon dioxide (CO 2 ) in agricultural areas is a process that results from the interaction of several factors such as climate, soil, and land management practices. Agricultural practices directly affect the carbon dynamics between the soil and atmosphere. Herein, we evaluated the temporal variability (2020/2021 crop season) of soil CO 2 emissions and its relationship with related variables, such as the CO 2 flux model, enhanced vegetation index (EVI), gross primary productivity (GPP), and leaf area index (LAI) from orbital data and soil temperature, soil moisture, and soil CO 2 emissions from in situ collections from native forests, productive pastures, degraded pastures, and areas of high-yield potential soybean and low-yield potential soybean production. A significant influence (p < 0.01) was observed for all variables and between the different land uses and occupation types. September and October had lower emissions of soil CO 2 and low means of soil moisture and soil temperature, and no differences were observed among the treatments. On the other hand, there was a significant effect of the CO 2 flux model in productive pastures, high-yield potential soybean areas, and low-yield potential soybean areas. The months with the highest CO 2 flux values in the model, regardless of land use and land cover, were October and November, which is the beginning of the rainy season. There were positive correlations between soil CO 2 emissions and GPP (0.208), LAI (0.354), EVI (0.363), and soil moisture (0.280) and negative correlations between soil CO 2 emissions and soil temperature (-0.240) and CO 2 flux model (-0.314) values. Land use and land cover showed negative correlations with these variables, except for the CO 2 flux model variable. Soil CO 2 emission values were lower for high-yield potential soybean areas (averages from 0.834 to 6.835 μmol m -2 s -1 ) and low-yield potential soybean areas (from 0.943 to 5.686 μmol m -2 s -1 ) and higher for native forests (from 2.279 to 8.131 μmol m -2 s -1 ), whereas the opposite was true for the CO 2 flux model., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The growth of the world population has led to the expansion of agricultural areas to produce food that meets world demand, making it necessary to increase productivity and maintain environmental sustainability in these areas. Seeking sustainable food production, the agricultural use of soil must be assessed in view of optimal use or land as natural resource, as well as minimize the effects of global warming related to land use and land cover (LULC). We hypothesize that different LULC affects Amazonian soil attributes. In this study, the effect of different LULC in the southern Brazilian Amazon, namely, native forest, pasture, and rice and soybean crops, on the spatial variability of soil fertility and texture was assessed, seeking to obtain information that will guide farmers in the near future to better exploit their areas and contribute to a more sustainable agriculture. Descriptive statistical analysis was performed for the pH, H + Al, Al, Ca, Mg, P, K, Cu, Fe, Mn, Zn, V, m, organic matter, clay, silt, and sand values from soil samples under different LULC. To verify the data normality, the Shapiro-Wilk test at 5% significance was performed. Outlier analysis using boxplot graphics, principal component analysis (PCA), and cluster analysis was performed. Data were submitted to geostatistical analysis to verify the spatial dependence degree of the variables through semivariograms for interpolated kriging maps. Except for silt, all variables were well represented in the factor map. PCA revealed that the data variability can be explained mainly by pH, V, Ca, K, and Zn values, which are inversely proportional to m, P, and sand. Through geostatistical analysis, spatial dependence ranging from moderate to strong was observed, generating reliability in the prediction of most attributes in pasture, rice, and soybean areas. Yet, a spatial dependence ranging from moderate to strong was found, generating reliability in the prediction of most attributes in pasture, rice, and soybean areas. Our findings reveal a lower fertility and higher acidity in forest areas, whereas crop areas presented the opposite result., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Several studies have reported the relationship of deforestation with increased incidence of infectious diseases, mainly due to the deregulation caused in these environments. The purpose of this study was to answer the following questions: a) is increased loss of vegetation related to dengue cases in the Brazilian Cerrado? b) how do different regions of the tropical savanna biome present distinct patterns for total dengue cases and vegetation loss? c) what is the projection of a future scenario of deforestation and an increased number of dengue cases in 2030? Thus, this study aimed to assess the relationship between loss of native vegetation in the Cerrado and dengue infection. In this paper, we quantify the entire deforested area and dengue infection cases from 2001 to 2019. For data analyses, we used Poisson generalized linear model, descriptive statistics, cluster analysis, non-parametric statistics, and autoregressive integrated moving average (ARIMA) models to predict loss of vegetation and fever dengue cases for the next decade. Cluster analysis revealed the formation of four clusters among the states. Our results showed significant increases in loss of native vegetation in all states, with the exception of Piauí. As for dengue cases, there were increases in the states of Minas Gerais, São Paulo, and Mato Grosso. Based on projections for 2030, Minas Gerais will register about 4,000 dengue cases per 100,000 inhabitants, São Paulo 750 dengue cases per 100,000 inhabitants, and Mato Grosso 500 dengue cases per 100,000 inhabitants. To reduce these projections, Brazil will need to control deforestation and implement public health, environmental and social policies, requiring a joint effort from all spheres of society., Competing Interests: The authors have declared that no competing interests exist.

The objectives of this study are: (i) to evaluate the space-temporal variability of fire foci by environmental satellites, CHIRPS and remote sensing products based on applied statistics, and (ii) to identify the relational pattern between the distribution of fire foci and the environmental, meteorological, and socioeconomic variables in the mesoregions of Minas Gerais (MG) - Brazil. This study used a time series of fire foci from 1998 to 2015 via BDQueimadas. The temporal record of fire foci was evaluated by Mann-Kendall (MK), Pettitt (P), Shapiro-Wilk (SW), and Bartlett (B) tests. The spatial distribution by burned area (MCD64A1-MODIS) and the Kernel density - (radius 20 km) were estimated. The environmental variables analyzed were: rainfall (mm) and maximum temperature (°C), besides proxies to vegetation canopy: NDVI, SAVI, and EVI. PCA was applied to explain the interaction between fire foci and demographic, environmental, and geographical variables for MG. The MK test indicated a significant increasing trend in fire foci in MG. The SW and B tests were significant for non-normality and homogeneity of data. The P test pointed to abrupt changes in the 2001 and 2002 cycles (El Niño and La Niña moderated), which contributes to the annual increase and in winter and spring, which is identified by the Kernel density maps. Burned areas highlighted the northern and northwestern regions of MG, Triângulo Mineiro, Jequitinhonha, and South/Southwest MG, in the 3rd quarter (increased 17%) and the 4th quarter (increased 88%). The PCA resulted in three PCs that explained 71.49% of the total variation. The SAVI was the variable that stood out, with 11.12% of the total variation, followed by Belo Horizonte, the most representative in MG. We emphasize that the applied conceptual theoretical model defined here can act in the environmental management of fire risk. However, public policies should follow the technical-scientific guidelines in the mitigation of the resulting socioeconomic - environmental damages., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Forest canopies have an important influence on the global climate balance. Through the analysis of the temperature of the canopy, it is possible to infer about the physiological aspects of the plants, helping to understand the behavior of the vegetation and, consequently, in the environmental monitoring and management of green areas. This study aims to validate the MOD11A2 V006 product from canopy surface temperature data obtained by an infrared radiation sensor. For the validation of the MOD11A2 product, a comparative analysis was performed between the land surface temperature (LST) data, obtained by the MODIS sensor, and the canopy temperature data, obtained by the SI-111 infrared radiation sensor coupled to the Itatiaia National Park (PNI) micrometeorological tower. Meteorological variables and land surface temperature collected from January to December 2018 in the PNI were also analyzed. The results reveal that the MOD11A2 product overestimates the canopy temperature in the daytime (MB ranging from 1.56 to 3.57 °C) and underestimates in the night time (MB ranging from - 0.18 to - 4.22 °C). During daytime, the months corresponding to the dry season presented a very high correlation (r = 0.74 and 0.86) and the highest values for the Willmott index (d = 0.70 and 0.64). At nighttime, the MOD11A2 product did not present a good performance for the LST estimation, especially in the rainy season. Therefore, we observed that the MOD11A2 product has limitations to estimate the land surface temperature and that possible changes in the algorithm of this product can be performed for high atmospheric humidity conditions.

Pesticides aerial application may results in the drift to neighboring areas if some application technology is not well executed. This phenomenon should be minimized to reduce environmental risks and agricultural production costs. This work aimed to investigate the interaction of environmental conditions, surrounding distance, and application conditions influencing spray drift in aerial applications. Sampling data from aerial sprays were collected during three agricultural years (from 2012 to 2014) in fields cultivated with sorghum, millet, soybean, corn, and cotton. The following variables were evaluated: application swath width, application rate, distance from the applied field, wind speed, relative humidity, and temperature. The estimated Pearson's correlations and path analysis identified that application rate and distance from the applied field and application were the variables that most influenced drift. Equations relating spray drift in function of distance from the applied field and application rate were adjusted in function of the variable, and a response surface model was constructed to estimate drift. The major pesticide class sprayed with aircraft in the Brazilian Cerrado was insecticide, followed by fungicide. This scenario shows the potential hazard risk of spray drift over the environment. The concentration of the drift deposits decreased as surrounding distance and application rate were increased. A mathematical equation of drift prediction was established, where the variables that contributed most to drift deposits were surrounding distance and wind speed. Thus, it is very important to monitor and respect the wind speed limits during the aerial spraying, mainly when there is any risk potential associated with pesticide exposure over the downwind direction., Competing Interests: The authors have declared that no competing interests exist.

This study aimed to analyze the spectral trend of vegetation with rainfall in El Niño-Southern Oscillation events (ENSO) in the Atlantic Forest, Brazil. Monthly rainfall data were collected from 85 conventional meteorological stations (EMC), data from the Enhanced Vegetation Index 2 (EVI2) and ENSO events (El Niño, La Niña, and Neutral) in the period from 2001 to 2013. Afterwards, state cluster analysis was performed using the results of non-parametric tests. The Mann-Kendall (MK) non-parametric test did not identify a trend pattern in rainfall distribution in the Atlantic Forest. The results for EVI2 by state and region showed that the trend is decreasing in the Northeast Region, except for the states of Alagoas and Pernambuco. Southeast region showed an increasing trend of EVI2 (except for Rio de Janeiro and São Paulo), while the South region showed a decreasing trend. In the Midwest, the trend was significantly decreasing. In the prognosis elaborated for the future, the regions with significant declines of the vegetation were the Northeast and Midwest. This study shows that the Atlantic Forest in some regions of Brazil has been suffering from the growing urbanization process and there is a trend of soil degradation.