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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 states of Mato Grosso (MT) and Mato Grosso do Sul (MS) are located in Midwest Brazil and are dependent on agribusiness, which makes their water regimes of fundamental importance. However, the existing weather stations in these states are limited, and rainfall products are therefore a valuable alternative. The objectives of this study are: (a) to validate the CHIRPS datasets for the states of MT and MS in the Brazilian Midwest region; (b) to evaluate the intraseasonal variability of regional rainfall via CHIRPS; and (c) to define the drought and wet periods on the decennial scale, based on the annual SPI. Three seasons were defined (rainy, drought, and transition), and the differences showed that the MS rainy season (56.65%) was reduced due to the veranicos that occur in February, with an increase in the percentages of the drought (13.83%) and transition (29.52%) seasons. The highest monthly rainfall occurs in the north of MS (328–390 mm), while intermediate rainfall (109–273 mm) occurs in the south of MS. This satisfactorily represents the formation of a rainfall gradient in a N–S direction during the rainy season, similar to other characterizations around the world. Based on the annual SPI assessed per decade, there were moderate droughts annually in the 1990s, 2000s, and 2010s, but not in the 1980s. There were significant differences between the decades due to the intensity and categorization of the ENSO phases (strong and moderate), and drier decades with moderate rainfall reduction in both states can be defined in the last 40 years. The data obtained via CHIRPS are satisfactory for the spatio‐temporal evaluation of regional rainfall, and for the use of SPI to detect drought and wet periods in the states of MT and MS. [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]

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In the last 30 years, the growth of the agriculture and livestock industries in the Cerrado biome has caused severe changes in land use and land cover (LULC), and areas previously occupied by native vegetation are changing to agricultural monocultures (e.g., soybean or corn) and/or pastures. Thus, the objective of this study was to analyze the LULC changes for the years 1986, 1999, 2007, and 2016 based on Landsat time series and object-based image analysis (OBIA) for the Prata River Basin. Twelve LULC classes were mapped: riparian forest, cerrado, swampy grasslands, wetlands, semideciduous forest, pasture, agriculture, fallow agricultural land, barren land, eucalyptus, water bodies, and burnt area. The classifications presented results with an overall accuracy of more than 93% and a kappa coefficient of 0.92. In 2007, the pasture class had the highest increase in area (48.5%), with a total area of 118.32 km2 of Cerrado biome vegetation converted to pasture, and the classes banhado, riparian forest, swampy grasslands, and cerrado had the greatest reductions in area (41.58%, 29.67%, 25.44%, and 21.63%, respectively). More precisely, the wetlands class underwent the greatest decrease under the advancement of pasture in the studied period (− 36.2%). These changes are due to factors favorable to agropastoral practices, such as a flat relief and soil with good agricultural suitability. [ABSTRACT FROM AUTHOR]

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]

• The state of Mato Grosso do Sul has three biomes: Cerrado, Mata Atlântica, and Pantanal. • The occurrence of fire foci in this state is higher in the Pantanal. • The variability of the El Niño-Southern Oscillation influences on fire foci dynamics. The state of Mato Grosso do Sul (SMS), located in the Midwest of Brazil, is devoid of climatological studies, mainly on the characterization of rainfall regime and producers' meteorological systems and rain inhibitors. This state has different soil and climatic characteristics distributed among three biomes: Cerrado, Mata Atlântica, and Pantanal. This study aimed to investigate the temporal variability of fire foci in different SMS biomes. Data regarding fire foci and rainfall were obtained on an annual scale, collected from 15 municipalities (nine in Cerrado, four in Mata Atlântica, and two in Pantanal). Boxplot and distribution graphs were constructed for fire foci data and rainfall data associated with the biomes in each year. Subsequently, we applied the Mann–Kendall test to verify if there is a significant trend to the rainfall and fire foci data of the biomes. Pantanal revealed the higher occurrence of fire foci in relation to Cerrado and Mata Atlântica. The highest records of fire foci in Pantanal are caused by the longer drought period and anthropogenic activities (based on extensive agriculture). There is a tendency for positive growth in the occurrence of fire foci in the Pantanal, Cerrado and Mata Atlântica. Therefore, it is necessary to establish public policies to mitigate the occurrence of hot flashes in the SMS biomes, especially in the Pantanal. Image, graphical abstract [ABSTRACT FROM AUTHOR]