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• Classification of eucalyptus species based on their growth using the K-means method. • Obtaining eucalyptus vegetation indexes (VI) using Unmanned Aerial Vehicles. • Selection of the best VI features for classification of eucalyptus using ANOVA statistical test. • Recognition of eucalyptus in relation to its growth using machine learning methods and VI features. Growth and production models can help to simulate the growth of tree dimensions to predict forest productivity at different levels. In this context, the following questions arise: (i) is it possible to recognize the growth pattern of eucalyptus species based on spectral features using machine learning (ML) for data modeling? (ii) what spectral features provides better accuracy? and (iii) what ML algorithms are most accurate for performing this modeling? To answer these questions, the present study evaluated the use of ML techniques using breast height and total plant height to classify the growth of five species of eucalyptus and Corymbria citriodora in an unsupervised learning, and the obtained classes for induce ML algorithms to recognize the species with relation to their growth using vegetation indices (VIs) and spectral bands (SBs). It were evaluated five eucalyptus species (E. camaldulensis , E. uroplylla, E. saligna , E. grandis e E. urograndis) and C. citriodora in experimental design of randomized blocks with four replicates, with 20 plants inside each experimental plot. The diameter at breast height and total plant height at stand level were obtained by measuring five trees in each experimental unit in seven measurements. During this same period, a flight was carried out using a remotely piloted aircraft for the acquisition of spectral variables (SBs and VIs). For recognition of eucalyptus species in relation to their growth two machine learning approaches were employed: supervised and unsupervised. The average accuracy obtained from 10-fold cross-validation, employing Random Forest algorithm and 24 features, was 0.76. This result shows that the proposed approach is appropriate to recognize different eucalyptus species based on their growth. [ABSTRACT FROM AUTHOR]

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

• Forest recomposition to carbon sequestration in degraded areas. • A major challenge of foresters is to restore forest cover in degraded areas. • The adoption of mixed plantations allows obtaining of services and products. • The species used in mixed planting present different growth patterns. A major challenge for the forestry sector is to restore forest cover in degraded areas. In this regard, the adoption of mixed plantations is an opportunity to combine the obtaining of services and products from the forest, providing ecological and socioeconomic advantages. Thus, our objective was to evaluate the growth of native forest species in mixed plantations in Brazilian Savanna. The study was carried out in an area of 4.8 ha with about thirty species planted in spacing 3 × 3 m, located in Chapadão do Sul/MS. At 6.4 years after planting, in 15 randomized plots of 300 m2, the diameter at 1.3 m above ground (DBH), equivalent diameter (DBHeq), total height (H), number of stems (NSI), wood volume (V), biomass (B), carbon (C), and carbon sequestration (CO 2) of the trees were measured. Descriptive statistics of the variables were carried out by using boxplot diagrams and principal component analysis (PCA) to group the species according to their growth. The stocks of V and B were 114.03 m3 ha−1 and 52.99 Mg ha−1, respectively. Thus, the mixed planting of native forest species is efficient for recomposing deforested areas, especially as regards biomass accumulation and carbon stock. The species used in mixed planting present different growth form, which were separated by PCA into four groups. DBHeq was the variable that most influenced the differentiation of species into groups. The species Heliocarpus popayanensis, Croton floribundus, Guazuma ulmifolia, Senegalia polyphylla, Enterolobium contortisiliquum, Anadenanthera colubrina, Ceiba speciosa, Anadenanthera peregrina, Gallesia integrifolia and Peltophorum dubium showed the highest growth in the study area. [ABSTRACT FROM AUTHOR]

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

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]

• 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]

The Guarani Aquifer System is one of the largest freshwater reservoirs in the world and has been widely exploited due to the quantity and quality of the stored water. The outcropping areas are considered highly vulnerable, and anthropic interference can influence the recharge potential and cause changes in water quality. The purpose of this research was to spectrally and temporally analyze the anthropic interference in water production in the Guarani Aquifer in four distinct areas located in the outcrop and recharge areas located in the State of São Paulo, Brazil, from 2012 to 2018. Rainfall data were obtained using the CHIRPS dataset, and the actual evapotranspiration rate was obtained using the MODIS product. The lithology and piezometric level data were obtained through RIMAS of the Geological Service of Brazil. The piezometric level monitoring wells were used as a reference to delimit the study areas and the distance to eliminate overlapping spatial resolutions of the remote sensing products. The area bounded by the buffer is equivalent to 31,412 ha. The recharge was estimated with the ESPERE using the water table fluctuation method. The end classifications of land use were obtained using the normalized difference vegetation index through remote sensing data. The study area located in the municipality of Boa Esperança do Sul - SP had the largest area of exposed soil with a median of 2936 (ha) and the lowest recharge potential with a median of 338 mm/year. The study area located in the municipality of Bofete - SP had the smallest area of exposed soil with a median of 516 (ha) and the highest recharge potential with a median of 502 mm/year. The study area located in the municipality of Brotas - SP showed the lowest evapotranspiration rate, with a median of 639 mm/year. The municipality of São Simão - SP had the largest forest area, with a median of 18,009 ha. Although water resource policies have significantly influenced the valuation of groundwater, studies that assess changes in land use variables using time series analysis and evaluate how these changes influence water dynamics in the hydrological cycle are still lacking. The study aims to contribute technical support to the decision-making process of the National System of Water Resources Management (SINGREH) and State Law 9866/1997 to contribute to the sustainability of the Guarani Aquifer System. • Estimated recharge of the Guarani Aquifer with ESPERE. • Land use in the influence of aquifer recharge. • Spectral models in the distribution of rainfall and evapotranspiration and their influence on the aquifer. [ABSTRACT FROM AUTHOR]

[Display omitted] Grain storage in bag silos has increased in recent years, mainly due to the low initial investment cost. However, there is no control of the ecosystem that involves the biotic and abiotic factors of storage. Thus, the objective was to develop and validate a prototype wireless sensor network and Internet of Things (IoT) platform for real-time monitoring of intergranular equilibrium moisture content and predict through neural network algorithms the physical, physical quality-chemical and microbiological mass of corn stored in bag silos. For an evaluation over three months, the experiments were installed with corn grains with two initial moisture contents of 13 % and 18 % (w.b.), three storage environments with temperatures of 17, 23, and 30 °C in bag silos. It was observed during the monitoring of stored grains, variations of moisture balance hygroscopic that indirectly inferred the quality of corn. The prototype and device with temperature sensors and intergranular relative humidity of the grains stored in bag silos were adjusted, obtaining satisfactory results for the determination of the equilibrium moisture content curves of the mass of corn grains stored, in real-time, connected to an IoT platform, for indirect monitoring of the quality of stored corn grains over time. In the moisture contents of 13 % and the storage condition of 17 °C they had the best quality results, while in the storage in bag silos with moisture contents of 13 % and 18 % showed no differences in the condition of 23 °C. However, at a temperature of 30 °C, the grains suffered a high deterioration. Furthermore, the quality prediction results using Artificial Neural Networks algorithms, indicated a high coefficient of determination of the trained models, presenting itself as a promising perspective, mainly in develop embedded technologies for monitoring and predicting qualitative variables of corn stored in bag silos. [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]

• 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]