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

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]

Wildfires in the Amazon biome of Mato Grosso cause extensive environmental, economic, and health damages, including biodiversity loss and high greenhouse gas emissions. This study used remote sensing to examine the relationship between fire severity and climatic factors, focusing on dNBR (Differenced Normalized Burn Ratio), precipitation, LST (Land Surface Temperature), SPI (Standardized Precipitation Index), NDVI (Normalized Difference Vegetation Index), and VCI (Vegetation Condition Index), analyzing data from 2001 to 2022. Statistical tests included Shapiro-Wilk, Tukey, regression kriging, Mann-Kendall for trend analysis, Pettitt for change points, and canonical variable tests. Regarding trends, only LST showed a significant trend starting in 2009, with the Northeast mesoregion showing the highest impact on temperature. dNBR correlated positively with NDVI and VCI, and negatively with precipitation and SPI. The northern mesoregion had a positive influence on dNBR and NDVI but negative for precipitation, SPI, and VCI. The southwestern mesoregion associated positively with dNBR and LST but negatively with the other variables. The Northeast and South-Central mesoregions showed positive correlations with most variables except dNBR and NDVI. These findings highlight the northern mesoregion's vulnerability due to its proximity to the central Amazon Forest and agri-cultural activity, indicating increased fire susceptibility with reduced humidity. • The northeast mesoregion of the Mato Grosso Amazon had the greatest impact on temperature. • There is vulnerability in the northern mesoregion due to its proximity to the central rainforest and agricultural activity. • The main positive correlations for dNBR were with the variables NDVI and VCI. [ABSTRACT FROM AUTHOR]

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

• Real-time monitoring of boron fertilization in eucalyptus is helpful for guiding precision diagnosis; • The 350–371 nm spectral range can be used for detecting boron-deficient plants; • Adequate boron levels can be identified by using the 426–444, 1811–1910, 1948–2115, and 2124–2208 nm; • The 425–475 nm spectral range can be used to find boron-toxicity plants. Boron (B) is an essential element whose deficiency results in rapid inhibition in the growth of plants, acting on their meristematic growth. Real-time monitoring of B fertilization in eucalyptus is helpful for guiding precision diagnosis and efficient management of plant boron nutrition. This research hypothesizes that different boron levels alter the reflectance of different wavelengths in eucalyptus. In this context, the objective of this study was to identify spectral ranges that can be used to monitor the boron status in eucalyptus plants. The experiment was carried out in a greenhouse, in which the treatments consisted of increasing boron levels in the form of boric acid (17% of B), whose levels varied from deficit to toxicity. Thus, five treatments were established: no boron, 1, 10, 20, and 40 mg/dm3 of boron. The remote sensing data used were bands, heights, and vegetation indices calculated after obtaining the spectral curves in each treatment. Our findings show that it is possible to accurately distinguish the boron levels in eucalyptus using hyper and multispectral bands. The 350–371 nm spectral range can be used for detecting boron-deficient plants. Plants with adequate boron levels can be identified by using the 426–444 nm, 1811–1910 nm, 1948–2115 nm, and 2124–2208 nm spectral ranges. Finally, the 425–475 nm spectral range can be used to find boron-toxicity plants. [ABSTRACT FROM AUTHOR]