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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. Image 1 • There was a significant increasing trend in fire foci in Minas Gerais. • The North and Northwest regions of MG, Triângulo Mineiro, and Jequitinhonha have more burned areas. • The proposed theoretical-conceptual model can act in environmental fire risk management. [ABSTRACT FROM AUTHOR]

Under ideal conditions of nitrogen (N), maize (Zea mays L.) can grow to its full potential, reaching maximum plant height (PH). As a rapid and nondestructive approach, the analysis of unmanned aerial vehicles (UAV)-based imagery may be of assistance to estimate N and height. The main objective of this study is to present an approach to predict leaf nitrogen concentration (LNC, g kg−1) and PH (m) with machine learning techniques and UAV-based multispectral imagery in maize plants. An experiment with 11 maize cultivars under two rates of N fertilization was carried during the 2017/2018 and 2018/2019 crop seasons. The spectral vegetation indices (VI) normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), green normalized difference vegetation (GNDVI), and the soil adjusted vegetation index (SAVI) were extracted from the images and, in a computational system, used alongside the spectral bands as input parameters for different machine learning models. A randomized 10-fold cross-validation strategy, with a total of 100 replicates, was used to evaluate the performance of 9 supervised machine learning (ML) models using the Pearson's correlation coefficient (r), mean absolute error (MAE), coefficient of regression (R²), and root mean square error (RMSE) metrics. The results indicated that the random forest (RF) algorithm performed better, with r and RMSE, respectively, of 0.91 and 1.9 g.kg−¹ for LNC, and 0.86 and 0.17 m for PH. It was also demonstrated that VIs contributed more to the algorithm's performances than individual spectral bands. This study concludes that the RF model is appropriate to predict both agronomic variables in maize and may help farmers to monitor their plants based upon their LNC and PH diagnosis and use this knowledge to improve their production rates in the subsequent seasons. [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]

• 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]

The aim of the present study was to evaluate the interactions between Chrysoperla externa (Hagen, 1861) and the eggs and first-instar larvae of Helicoverpa armigera (Hübner 1805) infected by entomopathogenic fungi. The H. armigera eggs and larvae were treated with sterile distilled water + 0.01% Tween 80 (T1, control), Beauveria bassiana (Bals.) Vuill (T2), Metarhizium anisopliae (Metsch.) Sorok (T3), or Metarhizium rileyi (Farlow) Samson. (T4) at different concentrations (1 × 107, 1 × 108, and 1 × 109 con. mL−1). For each treatment, a single third-instar C. externa was offered prey (a combination of 80 eggs and 50 first-instar H. armigera larvae) at 0, 24, and 48 h after inoculation. Ten trials were completed for each treatment, and the entire experiment was repeated three times. Neither the concentrations of fungi nor the application method affected consumption by C. externa. Because all the predator larvae reached the pupal phase, with 100% viability in adults, these results suggest that entomopathogenic fungi and C. externa are compatible and that the simultaneous use of these biological control agents is possible for managing H. armigera. [ABSTRACT FROM AUTHOR]

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., (© 2024. The Author(s).)

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 use of hydrogels helpsthe production of plants in drought-stress environments. Thus, this work evaluated using different hydrogels to minimize drought stress in soybean cultivation. The treatments employed two different hydrogels, one already commercialized and the other produced with cashew gum (Anacardium occidentale), five levels (0, 30, 60, 120, and 240 mg pot -1 ) of the hydrogels, and two levels of drought stress in sandy soil. The growth and yield of soybeans and the levels of macro- and micronutrients in soybeans were evaluated.growth. The use of CG hydrogel promoted 12% increase in protein content in the seeds in the when soybean plants were subjected to drought stress. The levels of 30 mg pot -1 , corresponding to 7.5 kg ha -1 , improved the 'morphological and productive parametersof the soybeans. The increasing levels of hydrogel promoted the increase in P, K, Ca, Mg, and Fe and reduced S and Cu on an exponential scale. The use of cashew gum hydrogel increased the K and Ca contents in soybean seeds compared to commercial hydrogel., (© 2024. The Author(s).)

The 2020 environmental catastrophe in Pantanal has highlighted the fragility of environmental policies and practices for managing and fighting fires in this biome. Therefore, it is essential to know the causes and circumstances that potentiate these fires. This study aimed to: (I) assess the relationship between fire foci and carbon absorption (GPP), precipitation, and carbon dioxide (CO 2 ) flux; (ii) analyze vegetation recovery using the differenced normalized burn ratio (ΔNBR) in Brazilian Pantanal between 2001 and 2022; and (iii) identify priority areas, where the highest intensities of fire foci have occurred, in order to guide public policies in Brazil to maintain local conservation. To this purpose, fire foci were detected using data from the MODIS MOD14/MYD14 algorithm, annual precipitation with CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), and CO 2 flux using the MODIS/MODO9A1 product, and Gross Primary Production (GPP) with the MODIS/MOD17A2 product. The severity of the burned area was also assessed using the ΔNBR index and the risk areas were determined using the averages of these images. During the time series studied, a total of 300,127 fire foci were detected throughout the Pantanal, where 2020 had the highest number of foci and the lowest accumulated precipitation. The years with the highest precipitation were 2014 and 2018. The year 2018 was also the second year with the highest GPP value. The Pettit test showed a trend for 2008 and 2011 as the points of change in the CO 2 flux and GPP variables. Principal component analysis clustered fire foci and precipitation on opposite sides, as well as GPP and CO 2 flux, while ΔNBR clustered HS, MHS and MLS classes with the years 2020, 2019, 2002 and 2021. There was a high negative correlation between fire foci × rainfall and GPP × CO 2 flux. The years with the largest areas of High severity (HS), Moderate-high severity (MHS) and Moderate-low severity (MLS) classes were 2020 and 2019, respectively. The most vulnerable areas for severe fires were the municipalities of Cáceres, Poconé, and Corumbá. The major fire catastrophe in 2020 is correlated with the low precipitation in 2019, the high precipitation in 2018, and the increased GPP, as well government policies unfavorable to the environment., (© 2023. The Author(s).)

Studies of boron (B) and silicon (Si) synergy in cotton crops have shown promising results; however, the focus was on the foliar application of B and Si. Nonetheless, B is an element with little mobility in the plant and its best form of application is in the soil. Thus, the objective of this study was to evaluate the synergistic effect of soil applied B and foliar applied sSi on fiber quality and crop yield of cotton. For this purpose, a field experiment was carried out using cotton cultivar FM 985 GLTP. The soil's B in the experimental site is classified as low for cotton cultivation. The experiment was conducted in a randomized complete-block design, in a 3 × 2 factorial scheme, with three doses of B: 0.0 kg ha -1 (deficiency), 2.0 kg ha -1 (recommended dose), and 4.0 kg ha -1 (high dose) in the absence and presence (920 g L -1 ) of Si, with four replications. One week after the 4 th application of Si, B and Si leaf content was determined. At boll opening, crop yield was estimated, and fiber quality analysis was realized. Boron deficiency reduced cotton yield, in 11 and 9%, compared to the application of 2 and 4 kg ha -1 of B, respectively. The presence of Si, however, increased plant yield in 5% in the treatments with 0 and 2 kg ha -1 of B, respectively. Cotton fiber length and elongation were not influenced by the B doses and Si presence. Fiber breaking strength was increased in 5% by the presence of Si and was not influenced by B deficiency. Micronaire was 8% smaller in the treatment with 0 kg ha -1 of B and 6% smaller in the absence of Si. Short fiber index was 4% greater in the plants of the treatment with 0 kg ha -1 of B. The results of this study reports that the complementation with Si via foliar application increases fiber quality by enhance breaking strength and micronaire. In conclusion, the interaction between soil-applied B and foliar-applied Si is beneficial for cotton cultivation, resulting in high cotton yield with better fiber quality., (© 2023. The Author(s).)

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 F 2 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., (© 2023. Springer Nature Limited.)

Drying rice in a single layer in a silo-dryer-aerator allows uniform drying. The objective of this study was to evaluate the physical, physicochemical, and morphological quality of rice grain cultivars (IRGA 424, BRS Pampeira, and Guri INTA) in the lower (initial time) and upper (final time) layers in a silo-dryer-aerator, employing single-layer loading at low temperatures, using the methods of near-infrared spectroscopy, X-ray diffraction analysis, scanning electron microscopy, and multivariate statistical analysis. Drying rice in silo-dryer-aerator attenuated the moisture diffusivity in the grains, minimizing its effects on the physical, physicochemical, and morphological properties of the grains. However, the physicochemical constituents and morphology of starch were preserved by the low drying temperatures, mainly in the lower layers throughout the 2-month drying. The rice grains of the Guri INTA and BRS Pampeira cultivars were the most resistant to drying and showed greater uniformity on the final quality., Competing Interests: 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., (© 2023 The Authors.)

The genus Gossypium has important ethnobotanical and economic value for Amazonian Native Communities (A.N.C.). However, little research has been undertaken on the distribution and genetic diversity of cotton populations maintained in the Peruvian rainforest. This work aims to present the first report on the genetic diversity of Gossypium spp. populations in the A.N.C. of the province of La Convención, Cusco-Peru. The methodology was based on exploring, collecting, identifying, and characterizing the Gossypium populations present in the A.N.C. Twenty-six descriptors were evaluated (9 quantitative and 17 qualitative), and with this information, distribution, correlation, and principal component (PC) analyses were carried out. As a result, plants of two species [ G. barbadense L. (44 samples) and G. hirsutum L. (19 samples)], one variety [ G. barbadense var. brasiliensis (75 samples)], and three previously unidentified variations (9 samples) were identified. Altogether, 147 samples were collected. G. barbadense var. brasiliensis , which was always found in association with other economic crops within an altitude range of 338 to 1086 m, was the most predominant (51%), distributed in eleven A.N.C. and always in small plots (up to 2 ha). G. barbadense L. was cultivated between 397 and 1137 m of altitude in eight A.N.C. in plots of up to 3 ha in marginal lands. G. hirsutum L., with a smaller distribution (13%), was found between 334 and 497 m of altitude in only three communities; this species is cultivated in marginal areas throughout the year. The variability found for the first two PCs when considering the quantitative and qualitative descriptors was high (74.7%) and moderate (48.2%), respectively. When combining all the descriptors, the analysis showed that the first two PCs accounted for 51.8% of the total variability of the data. The PCs of the two types of data and their combination confirmed that the three populations found were grouped. The nine undefined samples were close to or intermediate between the described ones, showing that these samples may be the result of spontaneous crosses; as such, these samples need to be better evaluated with other tools for further definition. The information obtained shows that in the A.N.C. of Cusco-Peru, there is variability conserved by the inhabitants, who have been able to maintain and use these genotypes, even from their Amazonian indigenous ancestry, and the environment has been able to generate variability among the species, as will be highlighted in future works.

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.

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., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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 (CO 2 Flux) 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., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Genome-wide selection (GWS) has been becoming an essential tool in the genetic breeding of long-life species, as it increases the gain per time unit. This study had a hypothesis that GWS is a tool that can decrease the breeding cycle in Jatropha. Our objective was to compare GWS with phenotypic selection in terms of accuracy and efficiency over three harvests. Models were developed throughout the harvests to evaluate their applicability in predicting genetic values in later harvests. For this purpose, 386 individuals of the breeding population obtained from crossings between 42 parents were evaluated. The population was evaluated in random block design, with six replicates over three harvests. The genetic effects of markers were predicted in the population using 811 SNP's markers with call rate = 95% and minor allele frequency (MAF) > 4%. GWS enables gains of 108 to 346% over the phenotypic selection, with a 50% reduction in the selection cycle. This technique has potential for the Jatropha breeding since it allows the accurate obtaining of GEBV and higher efficiency compared to the phenotypic selection by reducing the time necessary to complete the selection cycle. In order to apply GWS in the first harvests, a large number of individuals in the breeding population are needed. In the case of few individuals in the population, it is recommended to perform a larger number of harvests.

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 (R 2 ) 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.

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.

This work aimed to study different seeding rates in soybean, at management zones determined by the mapping of the soil apparent electrical conductivity (ECa) and its relationships with plant phenology and grain yield (GY). The experiment consisted of a completely randomized design, with six replications. The plant population ranged between 311,000, 360,000, and 422,000 plants ha-1, and the fixed population (360,000 plants ha-1). The management zone with the least yield potential, received the highest seed population. The management zone with the highest plant vigor potential, received the lowest seed population. The variables plant height, Normalized Difference Vegetation Index (NDVI) at 50, 66, and 92 days after emergence (DAE), one hundred-grain weight, and GY were analyzed. ECa maps can be used to decide the seed population of the soybean. The decision strategy of increasing 20% of the seed soybean population on the smaller ECa map zones, and decreasing 20% seed population on higher ECa zones was effective and resulted in similar GY, even with the negative pressure of the high resistance of penetration (RP) values in some zones. GY map variability was influenced by ECa 0-0.2 m, by NDVI at 92 DAE and by RP 0.4-0.6 m soil layer.

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 R 2 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.

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.

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 km 2 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. Graphical abstract.

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: G 1 (North and Central West), G 2 (Southeast and Northeast), and G 3 (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.

[This corrects the article DOI: 10.1371/journal.pone.0192189.].

Jatropha (Jatropha curcas) has become one of the most important species for producing biofuels. Currently, Genotype x Environment (GxE) interaction is the biggest challenge that breeders should solve to increase the section accuracy in the plant breeding. Therefore, the objectives in this study were to estimate the parameters in the 180 half-sib families in Jatropha evaluated for five production years, to verify the significance of the GxE interaction variance, to evaluate the adaptability and stability for each family based on three prediction methods, to select superior half-sib families based on the adaptability and stability analyses, and to predict the accuracy for the sixth production year. Jatropha half-sib families were classified and selected using the follow adaptability and stability methods: linear regression, bi-segmented linear regression and mixed models concepts called harmonic mean of the relative performance of genetic values (HMRPGV). The prediction accuracy was estimated by the Pearson correlation between the predicted genetic values by adaptability and stability methods and the phenotypic value in the sixth production year. In result, most half-sib families were classified as general adaptability and general stability for the evaluated traits. The selection gain obtained via HMRPGV was higher than other methods. The prediction accuracy for the sixth production year was 0.45. Therefore, HMRPGV is efficient to maximize the genetic gain, and it can be a useful strategy to select genotype with high adaptability and stability in Jatropha breeding as well as other species that should be evaluated for many years to take a suitable selection accuracy., Competing Interests: The authors have declared that no competing interests exist.

Asian rust affects the physiology of soybean plants and causes losses in yield. Repeatability coefficients may help breeders to know how many measurements are needed to obtain a suitable reliability for a target trait. Therefore, the objectives of this study were to determine the repeatability coefficients of 14 traits in soybean plants inoculated with Phakopsora pachyrhizi and to establish the minimum number of measurements needed to predict the breeding value with high accuracy. Experiments were performed in a 3x2 factorial arrangement with three treatments and two inoculations in a random block design. Repeatability coefficients, coefficients of determination and number of measurements needed to obtain a certain reliability were estimated using ANOVA, principal component analysis based on the covariance matrix and the correlation matrix, structural analysis and mixed model. It was observed that the principal component analysis based on the covariance matrix out-performed other methods for almost all traits. Significant differences were observed for all traits except internal CO2 concentration for the treatment effects. For the measurement effects, all traits were significantly different. In addition, significant differences were found for all Treatment x Measurement interaction traits except coumestrol, chitinase and chlorophyll content. Six measurements were suitable to obtain a coefficient of determination higher than 0.7 for all traits based on principal component analysis. The information obtained from this research will help breeders and physiologists determine exactly how many measurements are needed to evaluate each trait in soybean plants infected by P. pachyrhizi with a desirable reliability.