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The delineation of management zones is one of the ways to enable the spatially differentiated management of plots using precision agriculture tools. Over the years, the spatial variability of data collected from soil and plant sampling started to be replaced by data collected by proximal and orbital sensors. As a result, the variety and volume of data have increased considerably, making it necessary to use advanced computational tools, such as machine learning, for data analysis and decision-making support. This paper presents a methodology used to establish management zones (MZ) in precision agriculture by analyzing data obtained from soil sampling, proximal sensors and orbital sensors, in experiments carried out in four plots featuring soybean–cotton and soybean–corn crops, in Mato Grosso and Paraná states, Brazil. Four procedures were evaluated, using different input data sets for the MZ delineation: (I) soil attributes, including clay content, apparent electrical conductivity or fertility, along with elevation, yield maps and vegetation indices (VIs) captured during the peak crop biomass period; (II) soil attributes in conjunction with VIs demonstrating strong correlations; (III) solely VIs exhibiting robust correlation with soil attributes and yield; (IV) VIs selected via random forests to identify the importance of the variable for estimating yield. The results showed that the VIs derived from satellite images could effectively replace other types of data. For plots where the natural spatial variability can be easily identified, all procedures favor obtaining MZ maps that allow reductions of 40% to 70% in yield variance, justifying their use. On the other hand, in plots with low natural spatial variability and that do not have reliable yield maps, different data sets used as input do not help in obtaining feasible MZ maps. For areas where anthropogenic activities with spatially differentiated treatment are already present, the exclusive use of VIs for the delineation of MZs must be carried out with reservations. [ABSTRACT FROM AUTHOR]

Mineral prospectivity mapping (MPM), like other geoscience fields, is subject to a variety of uncertainties. When data about unfavorable sites to find deposits (i.e., drill intersections to barren rocks) is lacking in MPM using machine learning (ML) methods, the synthetic generation of negative datasets is required. As a result, techniques for selecting point locations to represent negative examples must be employed. Several approaches have been proposed in the past; however, one can never be certain that the points chosen are true negatives or, at the very least, optimal for training. As a consequence, methodologies that account for the uncertainty of the generation of negative datasets in MPM are needed. In this paper, we compare two criteria for selecting negative examples and quantify the uncertainty associated with this process by generating 400 potential maps for each of the three ML methods utilized (200 maps for each criterion), which include random forest (RF), support vector machine (SVM), and k-nearest neighbors (KNC). The results showed that applying a geological constraint to the creation of negative datasets reduced prediction uncertainty and improved overall model performance but produced larger areas of very high probability (i.e., >0.9) when compared to using only the spatial distribution of known deposits and occurrences as a constraint. SHAP values were used to find approximations for the importance of features in nonlinear methods, and kernel density estimations were used to examine how they varied depending on the negative dataset used to train the ML models. Prospectivity models for magmatic-hydrothermal gold deposits were generated using data from the shuttle radar terrain mission, gamma-ray, magnetic lineaments, and proximity to dykes. The Juruena Mineral Province, situated in Northern Mato Grosso, Brazil, represented the case study for this work. [ABSTRACT FROM AUTHOR]

Tectona grandis L. f. (teak) is highly valued in the international market, but its volume and properties vary depending on its genetic material and planting site. Evaluating these factors is crucial for promoting new plantations. Therefore, this study aimed to assess the impact of genetic material (clones TG1 and TG3 and seminal material) and planting site (Nova Maringá and Água Boa, Mato Grosso, Brazil) on morphological parameters (heartwood, sapwood, bark, pith proportions, and pith eccentricity), physical properties (shrinkage and air-dry density), and mechanical properties (static bending strength—fm, compressive strength—fc0, Janka hardness—fH90, and shear strength—fv0). For this purpose, we sampled five trees aged 13 years per genetic material from commercial plantations. In Nova Maringá, trees exhibited, on average, 56.07% heartwood, while in Água Boa, this value was less than 50%. Seminal material showed the lowest percentage of heartwood (49.2%). The pith percentage was significantly greater in Água Boa than in Nova Maringá, regardless of the genetic material. We observed the highest standard deviation (5.61) in pith eccentricity for the seminal material. Both the planting site and genetic material influenced the air-dry density (~12% moisture content), which ranged from 0.535 to 0.618 g·cm−3. Trees grown in Nova Maringá produced wood with higher dimensional stability than those from Água Boa, exhibiting a 14% lower radial shrinkage and a 6% lower volumetric variation. In Nova Maringá, the wood from the seminal material exhibited greater resistance. On the other hand, in Água Boa, that material showed lower resistance (fv0, fm, and fc0), or there was no significant difference (fH90) compared to the clonal materials. When comparing the clonal materials (TG1 and TG3) at each planting site, they demonstrated similar mechanical properties. The variability in physical and mechanical properties among different genetic materials and planting locations highlights the need to select appropriate teak genetic materials for each region. We concluded that more productive teak clones can be selected without compromising the physical and mechanical properties of the wood. [ABSTRACT FROM AUTHOR]

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

Large-scale land use/land cover changes have occurred in Mato Grosso State (hereafter MT), Brazil, following the introduction of extensive mechanized agriculture and pastoral activities since the 1980s. Author investigated what kind of agro-pastoral activities which are both cattle ranching and top five crops (soybean, sugarcane, corn, cotton and rice) that are closely related to land use change on lands experiencing conversion land use change (such as deforestation and the increase in deeply anthropogenically influenced areas) at each municipal district in MT. Then, this study identifies the volume of exports including contribution ratio by municipal districts where land use changed due to agro-pastoral activities. The patterns of vegetation change indicated that cattle ranching, corn, cotton, rice croplands in the northwest, and soybean and sugarcane fields in the central areas are the main contributors to deforestation. It is shown that land use change due to soybean or corn cultivation occurs mainly in the west and the southeast, respectively. Corn cultivation is associated with a greater increase in anthropogenically influenced areas than soybean cultivation. The municipal districts that export each agro-pastoral product with land use change are limited. Exports of soybeans, corn, and cotton in the municipal districts associated with deforestation had increased dramatically after experienced land use change. For example, Sapezal, which has experienced deforestation, was the only municipal district associated with export of corn to only Switzerland. Since 2007, the number of export partners has increased to 56 countries with the export volume increased 2300 times. These findings highlight the overall non-sustainability of environmental resource development activities in MT. [ABSTRACT FROM AUTHOR]

Understanding how climatic variables impact the reference evapotranspiration (ETo) is essential for water resource management, especially considering potential fluctuations due to climate change. Therefore, we used the Sobol' method to analyze the spatiotemporal variations of Penman–Monteith ETo sensitivity to the climatic variables: downward solar radiation, relative humidity, maximum and minimum air temperature, and wind speed. The Sobol' indices variances were estimated by Monte Carlo integration, with sample limits set to the 2.5th and 97.5th percentiles of the daily data of 33 automatic weather stations located in the state of Mato Grosso, Brazil. The results of the Sobol' analysis indicate considerable spatiotemporal variations in the sensitivity of ETo to climatic variables and their interactions. The dominant climatic variable responsible for ETo fluctuations in Mato Grosso is incident solar radiation (53% to 93% of annual total sensitivity—Stot), which has a more significant impact in humid environments (70% to 90% of Stot), as observed in the areas of the Amazon biome in the state. Air relative humidity and wind speed have higher sensitivity indices during the dry season in the Cerrado biome (savanna) areas in Mato Grosso (20% and 30% of the Stot, respectively). Our findings show that changes in solar radiation, relative humidity, and wind speed are the main driving forces that impact the reference evapotranspiration. [ABSTRACT FROM AUTHOR]

Lichens were investigated in Brazil in a small area along the Roosevelt River in Amazonas; 25 species are first reports for Brazil, and 190 additional species are first records for Amazonas state. As many as 24 species are described that are new to science: Allographa lineatipruinosa, Allographa variopruinata, Arthonia xanthopycnidiata, Astrothelium aurantioseptemseptatum, Astrothelium bulbosum, Astrothelium coloratum, Astrothelium inspersonovemseptatum, Astrothelium insulare, Astrothelium laureroides, Astrothelium marjoleinae, Astrothelium meandratum, Astrothelium multireflexum, Astrothelium myopicum, Astrothelium parabathelium, Astrothelium stellare (also known from Mato Grosso state), Astrothelium suprainspersum, Astrothelium xanthocavatum, Ocellularia fuscolichexanthonica, Ocellularia lichexanthocavata, Pertusaria amazonica, Phaeographis xantholirellinata, Porina ramiisidiata, Pseudopyrenula connexa, and Sprucidea squamulosa. [ABSTRACT FROM AUTHOR]

An increasing amount of Brazilian rainforest is being lost or degraded for various reasons, both anthropogenic and natural, leading to a loss of biodiversity and further global consequences. Especially in the Brazilian state of Mato Grosso, soy production and large-scale cattle farms led to extensive losses of rainforest in recent years. We used a spectral mixture approach followed by a decision tree classification based on more than 30 years of Landsat data to quantify these losses. Research has shown that current methods for assessing forest degradation are lacking accuracy. Therefore, we generated classifications to determine land cover changes for each year, focusing on both cleared and degraded forest land. The analyses showed a decrease in forest area in Mato Grosso by 28.8% between 1986 and 2020. In order to measure changed forest structures for the selected period, fragmentation analyses based on diverse landscape metrics were carried out for the municipality of Colniza in Mato Grosso. It was found that forest areas experienced also a high degree of fragmentation over the study period, with an increase of 83.3% of the number of patches and a decrease of the mean patch area of 86.1% for the selected time period, resulting in altered habitats for flora and fauna. [ABSTRACT FROM AUTHOR]

Due to different combinations of agriculture, livestock and forestry managed by rotation, succession and intercropping practices, integrated agriculture production systems such as integrated crop–livestock systems (iCL) constitute a very complex target and a challenge for automatic mapping of cropping practices based on remote sensing data. The overall objective of this study was to develop a classification strategy for the annual mapping of integrated Crop–Livestock systems (iCL) at a regional scale. This strategy was designed and tested in the six agro-climatic regions of Mato Grosso, the largest Brazilian soybean producer state, using MODIS satellite time-series images acquired between 2012 and 2019, ground data with heterogeneous distribution in space and time and a Random Forest classifier. The results showed that: 1. the use of unbalanced training samples with a class composition close to the real one was the right classifier training strategy; 2. the use of a single training database (pooling samples from different years and regions) to classify each region and year individually proved to be robust enough to provide similar classification accuracies in comparison to those based on the use of a database acquired for each region and for each year. The final hierarchical classification overall accuracy was 0.89 for Level 1, the cropping pattern level (single and double crops DC); 0.84 for Level 2, the DC category level (integrated system iCL soy-pasture/brachiaria, soy-cotton and soy-cereal); 0.77 for Level 3, the iCL level (iCL1 soy-pasture and iCL2 soy-pasture mixed with corn). The F-scores for DC, iCL and iCL1 cropping systems presented high accuracy (0.89, 0.85 and 0.84), while iCL2 was more difficult to classify (0.63). This approach will next be applied across the entire Brazilian soybean corridor, leading to an operational tool for monitoring the adoption of sustainable intensification practices recognized by Brazil's Agriculture Low Carbon Plan (ABC PLAN). [ABSTRACT FROM AUTHOR]

The city of Aripuana is one of the largest wood producer in the state of Mato Grosso, Brazil. Wood residues are used in the electricity generation at three thermoelectric plants in this region. However, the plants have high costs in transporting the wood residues (due to poor road conditions). Hence, this paper compares the energy performance of wood residues in natura and compacted as briquettes by calculating the heating value and determining the influence of moisture content on the energy characteristics of wood residues. The goal is to demonstrate the viability of using briquettes in order to improve thermoelectric generation. The wood residues from this region are affected by the high humidity of the biome. An alternative to improve the use of energy contained in the wood residues is to produce briquettes with lower humidity. This allows one to maintain high levels of heat energy in a lower volume, facilitating handling and storage. The results show that the use of briquettes improved the performance of thermoelectric plants, generating 1 MW of electricity power with less than 1 ton of briquettes. This contributes to the preservation of the environment, reducing operating costs, transportation and storage of the raw materials. [ABSTRACT FROM AUTHOR]

The Cerrado biome is being gradually reduced. Remote sensing has been widely used to investigate spatio-temporal changes in the landscape, which are frequently limited to mapping with orbital sensors, while the Remotely Piloted Aircraft System (RPAS) proved to be advantageous in terms of spatial resolution and the application of advanced digital processing techniques. In this study, we investigated a vereda (humid area) of a conservation unit in the state of Mato Grosso, Brazil. Object-Based Image Analysis (OBIA) was applied to images obtained by RPAS to distinguish the phytophysiognomies of plant strata from the vereda and to diagnose the recovery of native and invasive vegetation after prescribed burning. The study was carried out in the following five stages: biomass collection; quality analysis of the land cover; phytosociological survey; collection of control points using a GNSS receiver (type L1/L2); and the capture of aerial images with an RGB camera coupled to a DJI Phantom 4 Pro, which was performed through overflights in three different periods. Object–Based Image Analysis was subsequently performed using the Nearest Neighbor classifier combined with Feature Space Optimization, obtaining classifications with accuracy and Kappa indexes greater than 80% and 0.80, respectively. The results of image processing allowed us to infer that fire acted as a renewing agent for native vegetation and as an inhibiting agent for invasive vegetation. The classification analyses combined with the phytosociological analysis allowed us to infer that the vereda is in the process of maturation. Therefore, the study demonstrated the potential of data obtained by RPAS for the diagnosis and analysis of vegetation dynamics in small wetlands submitted to Integrated Fire Management (IFM). [ABSTRACT FROM AUTHOR]

Soil turning during pre-seeding preparation is meant to loosen and promote physical characteristics responsible for the development of crop roots in soil. In this study, we demonstrate effects of three methods of pre-seeding preparation at two depths of irrigation on carbon dioxide (CO2) efflux as well as the temporal variability in the distribution of soil carbon. This experiment was conducted at the rural cooperative extension research station in Tangará da Serra, Mato Grosso, Brazil, for two wheat production years in 2011 and 2012. The experimental design was randomized in strips, with four repetitions. Wheat was irrigated during the June to early October dry season. The treatments include two methods of soil preparation: traditional preparation with one plowing and two disc harrowings and minimal preparation with one seed bed conditioner pass. A third treatment of direct seeding was also evaluated. Soil CO2 efflux, soil temperature, air-filled soil porosity, and labile carbon were measured. We found soil preparation affects CO2 efflux in irrigated cultivation systems within Brazil's Cerrado savannah, with conventional tillage having greater CO2 emissions than either reduced tillage or direct seeding. Soil CO2 emissions were lower at 508 mm versus 698 mm irrigation depth, but wheat yields were significantly lower. [ABSTRACT FROM AUTHOR]