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Eucalyptus species play an important role in the global carbon cycle, especially in reducing the greenhouse effect as well as storing atmospheric CO₂. Thus, assessing the amount of CO₂ released by the soil in forest areas can generate important information for environmental monitoring. This study aims to verify the relation between soil carbon dioxide (CO₂) flux (FCO₂), spectral bands, and vegetation indices (VIs) derived from a UAV-based multispectral camera over an area of eucalyptus species. Multispectral imageries (green, red-edge, and near-infrared) from the Parrot Sequoia sensor, derived vegetation indices, and the FCO₂ data from a LI-COR 8100 analyzer, combined with soil moisture and temperature data, were collected and related. The vegetation indices ATSAVI (Adjusted Transformed Soil-Adjusted VI), GSAVI (Green Soil Adjusted Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index), which use soil correction factors, exhibited a strong negative correlation with FCO₂ for the species E. camaldulensis, E. saligna, and E. urophylla species. A Multivariate Analysis of Variance showed significance (p < 0.01) for the species factor, which indicates that there are differences when considering all variables simultaneously. The results achieved in this study show a specific correlation between the data of soil CO₂ emission and the eucalypt species, providing a distinction of values between the species in the statistical data. [ABSTRACT FROM AUTHOR]

Flavonoids are compounds that result from the secondary metabolism of plants and play a crucial role in plant development and mitigating biotic and abiotic stresses. The highest levels of flavonoids are found in legumes such as soybean. Breeding programs aim to increase desirable traits, such as higher flavonoid contents and vigorous seeds. Soybeans are one of the richest sources of protein in the plant kingdom and the main source of flavonoid derivatives for human health. In view of this, the hypothesis of this study is based on the possibility that the concentration of isoflavones in soybean seeds contributes to the physiological quality of the seeds. The aim of this study was to analyze the content of flavonoids in soybean genotypes and their influence on the physiological quality of the seeds. Seeds from thirty-two soybean genotypes were obtained by carrying out a field experiment during the 2021/22 crop season. The experimental design was randomized blocks with four replications and thirty-two F3 soybean populations. The seeds obtained were subjected to germination, first germination counting, electrical conductivity and tetrazolium vigor and viability tests. After drying and milling the material from each genotype, liquid chromatography analysis was carried out to obtain flavonoids, performed at UPLC level. Data were submitted to analysis of variance and, when significant, the means were compared using the Scott-Knott test at 5% probability. The results found here show the occurrence of genotypes with higher amounts of flavonoids when compared to their peers. The flavonoid FLVD_G2 had the highest concentration and differed from the others. Thus, we can assume that the type and concentration of flavonoids does not influence the physiological quality of seeds from different soybean genotypes, but it does indirectly contribute to viability and vigor, since the genotypes with the highest FLVD_G2 levels had better FGC values. The findings indicate that there is a difference between the content of flavonoids in soybean genotypes, with a higher content of genistein. The content of flavonoids does not influence the physiological quality of seeds, but contributes to increasing viability and vigor. [ABSTRACT FROM AUTHOR]

Boron (B) nutrition can contribute to the conservation of seed quality during storage. Thus, it is crucial to investigate which source and amount of B applied to the soil is most suitable to increase the yield and maintain the quality of germination and carbohydrate content in soybean seeds during 180 days of storage. The cultivar Bonus 8579 Ipro® was evaluated in a field experiment in a 5 × 2 factorial scheme, with five B doses: 0, 1, 2, 4 and 8 kg ha−1, and two sources: boric acid and ulexite, arranged in randomized block design with three replicates. After harvest, the B content and accumulation of B in yield and seed quality were analyzed. Subsequently, the seeds were stored in a cold chamber at 17 °C and controlled relative humidity of 20% for 180 days. Soybean plants responded positively to the boric acid and ulexite with an increase in yield and seed quality up to the doses of 3.17 and 3.36 kg ha−1 of B, respectively. Furthermore, both sources potentiated the carbohydrate content in the seeds over the storage time. Our findings reveal that the application from 3.2 to 4.0 kg ha−1 of B using boric acid maintains high seed germination after 180 days of storage. [ABSTRACT FROM AUTHOR]

Monitoring the intergranular variables of corn grain mass during the transportation, drying, and storage stages it possible to predict and avoid potential grain quality losses. For monitoring the grain mass along the transport, a probe system with temperature, relative humidity, and carbon dioxide sensors was developed to determine the equilibrium moisture content and the respiration of the grain mass. These same variables were monitored during storage. At drying process, the drying air and grain mass temperatures, as well as the relative humidity, were monitored. For the prediction of the physical and physical–chemical quality of the grains, the results obtained from the monitoring were used as input data for the multiple linear regression, artificial neural networks, decision tree, and random forest models. A Pearson correlation was applied to verify the relationship between the monitored and predicted variables. From the results obtained, we verified that the intergranular relative humidity altered the equilibrium moisture content of the grains, contributing to the increased respiration and hence dry matter losses along the transport. At this stage, the artificial neural network model was the most indicated to predict the electrical conductivity, apparent specific mass, and germination. The random forest model satisfactorily estimated the dry matter loss. During drying, the air temperature caused volumetric contraction and thermal damage to the grains, increasing the electric conductivity index. Artificial neural network and random forest models were the most suitable for predicting the quality of dry grains. During storage, the environmental conditions altered the moisture contents causing a reduction in the apparent specific mass, germination, and crude protein, crude fiber, and fat contents. Artificial neural network and random forest were the best predictors of moisture content and germination. However, the random forest model was the best predictor of apparent specific mass, electrical conductivity, and starch content of stored grains. [ABSTRACT FROM AUTHOR]

A current challenge of genetic breeding programs is to increase grain yield and protein content and at least maintain oil content. However, evaluations of industrial traits are time and cost-consuming. Thus, achieving accurate models for classifying genotypes with better industrial technological performance based on easier and faster to measure traits, such as agronomic ones, is of paramount importance for soybean breeding programs. The objective was to classify groups of soybean genotypes to industrial technological variables based on agronomic traits measured in the field using machine learning (ML) techniques. Field experiments were carried out in two sites in a randomized block design with two replications and 206 F2 soybean populations. Agronomic traits evaluated were: days to maturation (DM), first pod height (FPH), plant height (PH), number of branches (NB), main stem diameter (SD), mass of one hundred grains (MHG), and grain yield (GY). Industrial technological variables evaluated were oil yield, crude protein, crude fiber, and ash contents, determined by high-optical accuracy near-infrared spectroscopy (NIRS). The models tested were: support vector machine (SVM), artificial neural network (ANN), decision tree models J48 and REPTree, random forest (RF), and logistic regression (LR, used as control). A genotype clustering was performed using PCA and k-means algorithm, and then the clusters formed were used as output variables of the ML models, while the agronomic traits were used as input variables. ML techniques provided accurate models to classify soybean genotypes for more complex variables (industrial technological) based on agronomic traits. RF outperformed the other models and can be used to contribute to soybean breeding programs by classifying genotypes for industrial technological traits. [ABSTRACT FROM AUTHOR]

Obtaining soybean genotypes that combine better nutrient uptake, higher oil and protein levels in the grains, and high grain yield is one of the major challenges for current breeding programs. To avoid the development of unpromising populations, selecting parents for crossbreeding is a crucial step in the breeding pipeline. Therefore, our objective was to estimate the combining ability of soybean cultivars based on the F2 generation, aiming to identify superior segregating parents and populations for agronomic, nutritional and industrial traits. Field experiments were carried out in two locations in the 2020/2021 crop season. Leaf contents of the following nutrients were evaluated: phosphorus, potassium, calcium, magnesium, sulfur, copper, iron, manganese, and zinc. Agronomic traits assessed were days to maturity (DM) and grain yield (GY), while the industrial traits protein, oil, fiber and ash contents were also measured in the populations studied. There was a significant genotype × environment (G × A) interaction for all nutritional traits, except for P content, DM and all industrial traits. The parent G3 and the segregating populations P20 and P27 can be used aiming to obtain higher nutritional efficiency in new soybean cultivars. The segregating populations P11 and P26 show higher potential for selecting soybean genotypes that combine earliness and higher grain yield. The parent G5 and segregant population P6 are promising for selection seeking improvement of industrial traits in soybean. [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]

Taking into account that the transport of grains can be carried out over long distances and that the mass of grains during transport often has high moisture content, there may be risks of heat and moisture transfer and heating of the grains mass, proving quanti-qualitative losses. Thus, this study aimed to validate a method with probe system for real-time monitoring of temperature, relative humidity and carbon dioxide in the grain mass of corn during transport and storage to detect early dry matter losses and predict possible changes on the grain physical quality. The equipment consisted of a microcontroller, system's hardware, digital sensors to detect air temperature and relative humidity, a non-destructive infrared sensor to detect CO2 concentration. Real-time monitoring system determined early and satisfactorily in an indirect way the changes in the physical quality of the grains confirming by the physical analyses of electrical conductivity and germination. The equipment in real-time monitoring and the application of Machine Learning was effective to predict dry matter loss, due to the high equilibrium moisture content and respiration of the grain mass on the 2-h period. All machine learning models, except support vector machine, obtained satisfactory results, equaling the multiple linear regression analysis. [ABSTRACT FROM AUTHOR]

The current modern mechanized sugarcane harvesting system implies changes in crop management. However, studies on the benefit of planting arrangement associated with seedling types in sugarcane are still scarce. Our hypothesis is that the planting arrangement associated with seedling types can influence the yield and quality of ratoon sugarcane. The objective of this study was to evaluate the effect of planting arrangement associated with seedling types on biometric indices, yield, and juice quality of ratoon cane. The effects of arrangements in double rows using billets, double row using pre-sprouted seedlings (PSS) with 0.50 m and 0.75 m spacing, triple rows using billets, and triple rows using PSS with 0.50 m and 0.75 m spacing were investigated in third, fourth, and fifth ratoon cane. The cuts of each ratoon cane were performed in August for all crop seasons. The experimental design used was randomized blocks with four replicates in a split block design. Biometric, yield, and juice quality variables evaluated in each season were: stalk height (SH), stalk diameter (SD), number of stalks per meter (NSM), mass of ten stalks (MTS), sugarcane yield (THC), apparent juice purity (JP), cane fiber percentage (CF), sugar content (POL), and total recoverable sugar (TRS). The planting arrangement associated with the seedling type did not affect the stalk height and diameter, number of canes per meter, stalk mass, yield, and juice quality of the third, fourth, and fifth ratoon cane. Given these findings, the planting arrangement with triple rows and using PSS with wider spacing between seedlings (0.75 m) is a good management strategy aimed at reducing machine traffic and seedling costs in sugarcane crops. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The monitoring and evaluating the physical and physiological quality of seeds throughout storage requires technical and financial resources and is subject to sampling and laboratory errors. Therefore, machine learning (ML) techniques could help optimize the processes and obtain accurate results for decision-making in the seed storage process. This study aimed to analyze the performance of ML algorithms from variables monitored during seed conditioning (temperature and packaging) and storage time to predict the physical and physiological quality of stored soybean seeds. Data analysis was performed using the Artificial Neural Networks, decision tree algorithms REPTree and M5P, Random Forest, and Linear Regression. In predicting seed quality, the combination of the input variables temperature and storage time for REPTree and Random Forest algorithms outperformed the linear regression, providing higher accuracy indices. Among the most important results, it was observed for apparent specific mass that T + P + ST, T + ST, P + ST, and ST had the highest r means and the lowest MAE means, however, Person's r coefficient for these inputs was 0.63 and the MAE between 9.59 to 10.47. The germination results for inputs T + P + ST and T + ST had the best results (r = 0.65 and r = 0.67, respectively) in the ANN, REPTree, M5P and RF models. Using computational intelligence algorithms is an excellent alternative to predict the quality of soybean seeds from the information of easy-to-measure variables. [ABSTRACT FROM AUTHOR]

Farmers focus on reducing the cost of production and aim to increase profit. The objective of this study was to quantify the reduction of pesticides applied to soybean (Glycine max (L.) Merrill) and maize (Zea mays L.) crops in several stages of the production cycle using a site-specific spraying application based on real-time sensors in the Brazilian Cerrado region. The sprayers were equipped with a precision spraying control system based on a real-time sensor. The spraying operations were performed not only for herbicide, but also for fungicide and insecticides applications. The maps recorded the percentage of the spray boom when the application was turned on (on/off spray system) with nozzle-to-nozzle control. The precision spraying system based on real-time sensors reduced the volume of pesticides (including herbicides, insecticides, and fungicides) applied to soybean and maize crops. There was a more significant reduction in the volume of pesticides applied post-emergence of the crops in the initial stages of soybean and maize when the crops had less leaf area or less foliage coverage between the rows. The cost reduction achieved using this technology was 2.3 times lower than the cost associated with pesticide application over the entire area using a conventional sprayer. Under the experimental conditions, there were no differences in the average crop yield, compared to the historical productivity of soybean and maize crops by applying this technology because the recommended doses were not affected and the site of application was limited to points where the presence of plants was present was detected. [ABSTRACT FROM AUTHOR]

Corn has great relevance worldwide due to its economic and social importance. Its production grows every harvest, and determining traits directly related to the grain yield is essential for crop management. The hypothesis of this study is that structural equation modelling (SEM) and factor analysis allow us to identify agronomic variables and vegetation indices (VIs) that most contribute to grain yield. Thus, the objective was to identify which agronomic traits and VIs are most associated with grain yield in corn using SEM and factor analysis. Field experiments were carried out during the 2017/2018 and 2018/2019 crop seasons. The experimental design was in randomized blocks with three replicates in a factorial scheme. The following agronomic traits were evaluated: ear length (EL), number of rows per ear (NRE), number of grains per row (NGR), plant height, ear insertion height, stem diameter, and grain yield. The VIs evaluated were: Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge (NDRE), Green Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index (SAVI). Data were subjected to factor analysis and structural equation modelling. The EL, NGR, and NRE were the agronomic traits that most related to corn yield. The VIs most related to yield were NDVI, NDRE, and SAVI. By identifying which agronomic variables and VIs are the most related to grain yield in corn, this study contributes to the decision-making process on HTP in corn and consequently to a higher efficiency of corn breeding programs aiming at selecting high-yielding genotypes. [ABSTRACT FROM AUTHOR]

In forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. To do so, using the data obtained by the UAV as input variables, we tested different configurations (number of hidden layers and number of neurons in each layer) of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species. The experimental design was randomized blocks with four replicates, with 20 trees in each experimental plot. The treatments comprised five Eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis, and E. urograndis) and Corymbria citriodora. DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV, so that the multispectral sensor could obtain spectral bands to calculate vegetation indices (VIs). ANNs were then constructed using spectral bands and VIs as input layers, in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first applications of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher estimated r, lower estimated root mean squared error–RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the generated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time. [ABSTRACT FROM AUTHOR]

Macauba palm [Acrocomia aculeata (Jacq.) Lodd. ex Mart.] is a perennial oil, it stands out for having several characteristics of commercial interest, mainly for producing oil for biodiesel, it has high oil productivity, about 2.5 to 4.5 L·year-1. Despite its great potential, o its cultivation is carried out mainly in an extractive way, so the domestication and breeding programs has been incipient. The study hypothesis is that macauba populations collected in different locations have sufficient genetic variability to initiate a breeding program. Thus, the aim of this study was to estimate the genetic diversity and population structure of macauba palm genotypes by using single nucleotide polymorphism (SNP) markers in order to reveal genetic diversity and distribution of genetic variation within and between populations and use the genetic information obtained to assist breeding strategies. Leaf tissues were collected from 566 macauba plants belonging to the Embrapa Cerrados Active Germplasm Bank, composed of genotypes from five states in Brazil: Minas Gerais, Goiás, Pará, São Paulo, and Distrito Federal. Molecular variance analysis estimated, the genetic diversity parameters, the population structure and principal coordinate analysis. The genetic diversity is higher within than between populations. The results provided by PCoA and STRUCTURE were in agreement and indicated that the evaluated genotypes can be grouped into two groups. Genetic diversity parameters reveal the presence of inbreeding and a low number of heterozygotes, evidencing that the reproduction system of the species is mixed. The information revealed of the genotypes using SNP markers will be important for future studies using genome-wide selection and genomic association to develop cultivars of macauba with desirable traits, such as high yield of fruits and oil production. [ABSTRACT FROM AUTHOR]

The study had as objective to evaluate occupational hazards on grain storage unit to define a conceptual model, implemented in an algorithm to manage the grains storage facilities safety standards compliance. Sampling points location were defined for static quantification of noise, dust and heat stress hazards in grains pre-processing operations to indicate the effectiveness of the control measures implemented. Safety standards applied to grain handling and storage facilities were identified and selected. Chart flows were elaborated to the algorithm logics and conceptual modeling. The highest level of noise was present in the grain cleaning operation (99.1 dB), while the expedition operation has the highest level of dust (20.27%). The heat stress was present in the grain drying operation (43.64 WBGT). Noise analysis did not show a difference between grains, only between operations. The flow of corn grain mass caused higher dust concentrations in the expedition operation. The method applied to characterize and quantify the hazards in grain storage units was satisfactory, and it is recommended as standard, for use in corn and soybean grains handling and storage units. The algorithm to manage occupational safety at storage facilities collaborates to monitor the safety compliance on postharvest operations. [ABSTRACT FROM AUTHOR]

Accurate estimation of biomass in natural vegetation sites remains a challenge. Modeling biomass growth and production in Cerrado areas is crucial to understanding the vegetation succession process, especially regarding the changes in biomass accumulation over time. Thus, our objective was to model the growth and production of woody aboveground biomass (living and total) in a cerrado stricto sensu monitored for 27 years after implementing management systems. As expected, the basal area (with a diameter taken at 30 cm from the ground level) is the most important predictor variable and showed a higher correlation with the biomass stocks and allowed accurate and consistent estimates of these accumulated stocks over time. Future estimates of biomass production, generated from growth models that estimate production as a function of parameters observed at previous ages, indicate that maximum stocks of living (25.86 ± 0.15 Mg ha−1 [mean ± standard deviation]) and total aboveground biomass (26.11 ± 0.15 Mg ha−1) are expected for a period between 28 and 30 years after the implementation of the management systems, with maximum mean annual increment between 23 and 27 years. Furthermore, the systems of equations obtained simulated reductions up to 30% of biomass after the occurrence of a forest fire at 23 years. Thus, our study can be useful for the decision-making process and developing public policies and strategies for managing and conserving natural resources in the Cerrado biome. [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]

Jatropha (Jatrophacurcas L.) is an oleaginous potential, howetever, some studies report that there is low genetic diversity in Brazilian genotypes. Estimating genetic diversity are the essential factors to ensure success in the management of genetic resources, planning and adoption of strategies for genetic breeding. The hypothesis of our study is: do Brazilian Jatropha breeding populations have sufficient genetic variability to select individuals within? The objective of this paper is to determine the genetic diversity of 573 genotypes of five populations of Jatropha curcas structured based on the characteristics of yield, resistance to powdery mildew and toxicity, using Single-nucleotide polymorphism molecular markers. The results shows moderate variability among the genotypes analyzed, confirming the initial hypothesis of this study. We recommended using a greater number of individuals per family rather than the number of families in breeding programs in order to exploit the greater variability within populations and hence obtain higher gains with selection. [ABSTRACT FROM AUTHOR]

Canola is a product of traditional plant breeding techniques to remove from rapeseed the antinutritional components erucic acid. This crop proves to be a promising crop due to the diverse purposes of its oil, especially by its potential for biofuel production. This paper aimed to integrate the information available in the literature and report the most promising strategies for genetic and biotechnological progress in canola. Thus, we carried out a detailed review of the origin and uses of canola, its socioeconomic importance in the global and Brazilian aspects, tropicalization, with emphasis on genetic breeding. We demonstrate the main breeding strategies that can be used to increase your oil production. We propose here a breeding strategy for canola, in which some strategies previously mentioned are integrated. The purpose of this strategy is to enhance the selection and efficiency at the beginning of a breeding program. Among these, genome wide selection (GWS) is a suitable tool to help breeders to improve the efficiency selection in a canola breeding program increasing the selection accuracy or even reducing the cycle time. The proposed strategies must be analyzed for each situation, adjusting the GWS model to obtain highest selection accuracy. [ABSTRACT FROM AUTHOR]

Anticipating the harvest period of soybean crops can impact on the post-harvest processes. This study aimed to evaluate early soybean harvest associated drying and storage conditions on the physicochemical soybean quality using of mathematical modeling and multivariate analysis. The soybeans were harvested with a moisture content of 18 and 23% (d.b.) and subjected to drying in a continuous dryer at 80, 100, and 120 °C. The drying kinetics and volumetric shrinkage modeling were evaluated. Posteriorly, the soybean was stored at different packages and temperatures for 8 months to evaluate the physicochemical properties. After standardizing the variables, the data were submitted to cluster analysis. For this, we use Euclidean distance and Ward's hierarchical method. Then defining the groups, we constructed a graph containing the dispersion of the values of the variables and their respective Pearson correlations for each group. The mathematical models proved suitable to describe the drying kinetics. Besides, the effective diffusivity obtained was 4.9 × 10–10 m2 s−1 promoting a volumetric shrinkage of the grains and influencing the reduction of physicochemical quality. It was observed that soybean harvested at 23% moisture, dried at 80 °C, and stored at a temperature below 23 °C maintained its oil content (25.89%), crude protein (35.69%), and lipid acidity (5.54 mL). In addition, it is to note that these correlations' magnitude was substantially more remarkable for the treatments allocated to the G2 group. Furthermore, the electrical conductivity was negatively correlated with all the physicochemical variables evaluated. Besides this, the correlation between crude protein and oil yield was positive and of high magnitude, regardless of the group formed. In conclusion, the early harvest of soybeans reduced losses in the field and increased the grain flow on the storage units. The low-temperature drying and the use of packaging technology close to environmental temperatures conserved the grain quality. [ABSTRACT FROM AUTHOR]

The guidance on decision-making regarding deforestation in Amazonia has been efficient as a result of monitoring programs using remote sensing techniques. Thus, the objective of this study was to identify the expansion of soybean farming in disagreement with the Soy Moratorium (SoyM) in the Amazonia biome of Mato Grosso from 2008 to 2019. Deforestation data provided by two Amazonia monitoring programs were used: PRODES (Program for Calculating Deforestation in Amazonia) and ImazonGeo (Geoinformation Program on Amazonia). For the identification of soybean areas, the Perpendicular Crop Enhancement Index (PCEI) spectral model was calculated using a cloud platform. To verify areas (polygons) of largest converted forest-soybean occurrences, the Kernel Density (KD) estimator was applied. Mann–Kendall and Pettitt tests were used to identify trends over the time series. Our findings reveal that 1,387,288 ha were deforested from August 2008 to October 2019 according to PRODES data, of which 108,411 ha (7.81%) were converted into soybean. The ImazonGeo data showed 729,204 hectares deforested and 46,182 hectares (6.33%) converted into soybean areas. Based on the deforestation polygons of the two databases, the KD estimator indicated that the municipalities of Feliz Natal, Tabaporã, Nova Ubiratã, and União do Sul presented higher occurrences of soybean fields in disagreement with the SoyM. The results indicate that the PRODES system presents higher data variability and means statistically superior to ImazonGeo. [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]

Nutritional deficiency is common in several regions of quinoa cultivation. Silicon (Si) can attenuate the stress caused by nutritional deficiency, but studies on the effects of Si supply on quinoa plants are still scarce. Given this scenario, our objective was to evaluate the symptoms in terms of tissue, physiological and nutritional effects of quinoa plants submitted to nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) deficiencies under Si presence. The experiment consisted of a factorial scheme 6 × 2, using a complete solution (CS), -N, -P, -K, -Ca, -Mg combined with absence and presence of Si (1.5 mmol L−1). Symptomatic, physiological, nutritional and evaluation vegetative were performed in quinoa crop. The deficiencies of N, P, K, Ca and Mg in quinoa cultivation caused visual symptoms characteristic of the deficiency caused by respective nutrients, hence decreasing the plant dry mass. However, Si supply attenuated the deficiency effects by preserving the photosynthetic apparatus, increasing the chlorophyll production, increasing the membrane integrity, and decreasing the electrolyte leakage. Thus, the Si supply attenuated the visual effects provided by deficiency of all nutrients, but stood out for N and Ca, because it reflected in a higher dry mass production. This occurred because, the Si promoted higher synthesis and protection of chlorophylls, and lower electrolyte leakage under Ca restriction, as well as decreased electrolyte leakage under N restriction. [ABSTRACT FROM AUTHOR]

Light is a critical environmental factor that influences nutrient uptake, but understanding how light acts on nutrient uptake is still very elusive. To verify the role of opposite light responses in accumulation, nutrient use efficiency (UE) and growth, a tomato cultivar Micro-Tom (MT), the non-mutant high-pigment-1 (hp1, light exaggerated response) and the mutant aurea (au, phytochrome-deficient) were grown under hydroponic conditions with a complete nutrient solution (CS) or with the individual omission (–) of N, P, K, Ca and Mg. Our results showed that in -N, hp1 exhibited high NUE in the roots and increased root dry mass. In -P, MT compensated the lower accumulation of this nutrient with the increase in PUE and produced shoot dry mass equivalent to the other genotypes with higher PUE and less PUE. In -K, the higher shoot KUE of the au was evidenced by the higher shoot dry weight in relation to the MT. In -Ca, the shoot dry weight loss of hp1 was lower in relation to the other genotypes. In -Mg, hp1 had a low shoot and root MgUE. Finally, we highlighted the perspective of utilizing hp1 in breeding programs as a tool to stimulate root growth under N deprivation conditions, hp1 for poor Ca environments, and au for poor K environments. [ABSTRACT FROM AUTHOR]

Sowing density is one of the most influential factors affecting corn yield. Here, we tested the hypothesis that, according to soil attributes, maximum corn productivity can be attained by varying the seed population. Specifically, our objectives were to identify the soil attributes that affect grain yield, in order to generate a model to define the optimum sowing rate as a function of the attributes identified, and determine which vegetative growth indices can be used to predict yield most accurately. The experiment was conducted in Chapadão do Céu-GO in 2018 and 2019 at two different locations. Corn was sown as the second crop after the soybean harvest. The hybrids used were AG 8700 PRO3 and FS 401 PW, which have similar characteristics and an average 135-day cropping cycle. Tested sowing rates were 50, 55, 60, and 65 thousand seeds ha−1. Soil attributes evaluated included pH, calcium, magnesium, phosphorus, potassium, organic matter, clay content, cation exchange capacity, and base saturation. Additionally, we measured the correlation between the different vegetative growth indices and yield. Linear correlations were obtained through Pearson's correlation network, followed by path analysis for the selection of cause and effect variables, which formed the decision trees to estimate yield and seeding density. Magnesium and apparent electrical conductivity (ECa) were the most important soil attributes for determining sowing density. Thus, the plant population should be 56,000 plants ha−1 to attain maximum yield at ECa values > 7.44 mS m−1. In addition, the plant population should be 64,800 plants ha−1 at values < 7.44 mS m−1 when magnesium levels are greater than 0.13 g kg−1, and 57,210 plants ha−1 when magnesium content is lower. Trial validation showed that the decision tree effectively predicted optimum plant population under the local experimental conditions, where yield did not significantly differ among populations. [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]

The Juruá Watershed region, an important tributary of the Amazon River, lacks scientific information related to climate and environmental variables. In this sense, this study intends to analyze the environmental dynamics of the Juruá Watershed in the Amazon. We used monthly weather data derived from Reanalysis 2. The products fire foci, vegetation index and surface water index derived from the MODIS sensor for a monthly time series from 2001 to 2018. The ARIMA model was used to simulate future changes from NDFI. The lowest rainfall years of the series were 2005, 2010 and 2016 also observed for soil moisture in 2005 and 2016; the most rainfall years were 2009 and 2014, with the highest values of flooded areas in January, 23,772 km2. The highest concentration of fire foci occurred in August and September, coinciding with the lowest NDFI values found (1421 km2 and 890 km2), with the years 2005 and 2010 with highest fire foci. The EVI showed higher values from October to December with values close to 0.57, the lowest values in June and July (0.50). The years 2009 and 2015 showed largest flooded area records, 2005 and 2010 the lowest records. The PCA pointed out the rain as the variable of greatest influence in the basin with 0.98, followed by hot flashes with − 0.90. NDFI was highly correlated with rainfall and ARIMA modeling allowed the generation of a reliable future scenario and a significant downward trend in flooded areas by 2030. [ABSTRACT FROM AUTHOR]

The global demand for renewable sources fuel has rapidly increased in the past years. Sweet sorghum has stood out for being of technically and economically viable alternative for supplying raw materials to distilleries. This research aimed to select, simultaneously, pre-commercial sweet sorghum hybrids with high adaptability and stability, via mixed models, in six different environments. The experiments were conducted in the off-season of 2015/16 and 2016/17 in environments of the Brazilian Cerrado. Sixteen pre-commercial sweet sorghum hybrids were evaluated. The experimental design consisted of randomized blocks with three replications. In each trial, fresh and dry matter yield, soluble solids content, and ethanol yield were evaluated. The variance components were estimated using the restricted maximum likelihood. For each trait, the simultaneous selection for stability and adaptability was performed by the statistics of the harmonic mean of the relative performance of genetic values. Genotype × environment interaction was recorded significant for all the traits. Among the sorghum hybrids evaluated, N43A1001 stood out for obtaining the highest genotypic means for the evaluated traits, besides showing adaptability and stability for these traits. Therefore, this study demonstrated the possibility of obtaining high ethanol yield from sweet sorghum cultivated in the sugarcane off-season, in the Brazilian Cerrado, that would help and reduce the high fluctuation of ethanol's supply and price in the Brazilian market. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Drying and storage methods are fundamental for maintaining the grain quality until processing. Therefore, the aim of this study was to evaluate the associations of the drying temperature with storage systems and conditions as a strategy for preserving the quality of maize grain postharvest on laboratory and field scales. An increase in temperature accelerated the reduction in grain moisture, but increased the deterioration. The wetting during the storage period reduced the grain quality. Hermetic and aerated storage systems maintained the chemical quality of the grains. The control with healthy and whole corn dried at 80 °C and stored in silos with natural aeration provided a satisfactory quality, equivalent to those of controlled drying and storage under airtight conditions and at low temperatures. Different conditions of drying and storage of corn on the laboratory and field scales were evaluated, which provides an appropriate management of these operations to maintain the grain quality. [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]

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]

The objective of this work is to evaluate the occurrence of fire foci during the severe drought that occurred in 2005 in the State of Amazonas. The study was conducted in the State of Amazonas, which is inserted in the northern region of Brazil. The main types of vegetation are Igapó Forest, Várzea Forest and Terra Firme Forest. Kernel density was used to spatialize fire foci to quantify them in seven classes of land use and cover (forest, pasture, exposed soil, urban area, pastoral agroforestry system, agroforestry system and agriculture). Through the regression analysis, the relation among the number of fire foci and four meteorological variables was obtained: rainfall, evapotranspiration, relative humidity and average air temperature. Forest and pasture classes were those with the highest number of fire foci corresponding, respectively, to 58 and 37% of the total number of foci. This can be explained by the greater representativeness of these classes in the State and by the high degree of soil exposure in the case of pasture. The number of fire foci was higher in the dry season, covering approximately 85% of the total fire foci. The variable that had the greatest influence on the occurrence of fire foci in the dry season was evapotranspiration. The study puts on alert the vulnerability of the State of Amazonas to the occurrence of fires and may also suggest actions to mitigate carbon emissions and biomass stock. Research like this one may provide subsidies to region's managers in an attempt to preserve forest areas and a greater controlling in priority areas considered very high. [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]

Genetic study in maize (Zea mays) germplasm development is an important step to understand the genetic variability and complementarity between heterotic groups, as well as additive and dominance genetic effects. Thus, diallel analyses have been widely adopted with the aim of identifying the best parental lines and the best crosses. In addition, different environmental conditions make genetic selection difficult. Thus, the objectives of this study were to compare individual and joint analyses of a diallel design through a mixed model methodology for maize breeding, and to evaluate and interpret the genetic effects and their interaction with the environmental effect. Thirteen F2 hybrids were crossed in an incomplete diallel scheme. Seventy-eight inter-population hybrids, and thirteen self-pollinated parents were evaluated. Eleven traits were evaluated, and emphasis was given to the Grain Yield (GY) trait. The significance of the GY trait, specifically, varied across environments. Joint analysis, in particular, presented significance for dominance and additive by environment interaction effects. Joint analysis had the highest selective accuracies for six traits. The correlation coefficients showed similar results, with values from 0.15 to 0.50 between pairs of environments. Gains with selection, considering each environment, ranged from 9.17 to 20.66%, when five hybrids were selected. When direct gains were compared with indirect gains, combined analysis confirmed the high efficiency of selection. For selection of parents, combined analysis achieves the same results as direct selection when two parents were selected. [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 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. [ABSTRACT FROM AUTHOR]