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Agronomic properties are more likely to be impacted by water deficits that affect the nutrient uptake and production of secondary metabolites based on their timing and intensity. The aim of this study was to assess the effects of the water deficit on the nutritional quality of forage sorghum (Sorghum bicolor) hybrids. For that purpose, a factorial, completely randomized experiment was conducted by considering three forage sorghum hybrids (AGRI 002-E, BREVANT SS318, and BRS 658) and two levels of evapotranspiration water replacement (50% and 100% of ETc). Parameters relating to water consumption, secondary metabolites (isoflavones daidzein, daidzin, genistein, and genistin), leaf nutrients (P, K, Ca, Mg, S, Mn, and Zn), and bromatological attributes (dry matter, crude protein, neutral detergent fiber, and mineral material) were evaluated at the end of the crop cycle. Isoflavone levels differed between the hybrids and were highest in water-deficient sorghum. There was a significant interaction between the factors only for the daidzin. The leaf content of the other compounds was influenced either by hybrids (genistein) or by the replacement of evapotranspired water levels (daidzein). The leaf content of P and S was influenced by the interaction between the factors, while the levels of K, Ca, and Mg were influenced by the effect of a single factor. The leaf contents of Mn and Zn were not influenced by the treatments. There was a difference between the hybrids for dry mass and crude protein contents, and hybrids x water deficit was only significant for dry mass. The hybrids Brevant SS318 and BRS 658 had the highest crude protein. The presented results are novel and demonstrate that water deficits can significantly affect the levels of secondary metabolites and the nutritional quality of forage sorghum, depending on the hybrid. The mentioned indices are important parameters for evaluating the nutritional quality and development of agricultural crops, particularly in response to adverse environmental conditions such as water stress. [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. [ABSTRACT FROM AUTHOR]

Flavonoids are compounds that result from the secondary metabolism of plants and play a crucial role in plant development and mitigating biotic and abiotic stresses. The highest levels of flavonoids are found in legumes such as soybean. Breeding programs aim to increase desirable traits, such as higher flavonoid contents and vigorous seeds. Soybeans are one of the richest sources of protein in the plant kingdom and the main source of flavonoid derivatives for human health. In view of this, the hypothesis of this study is based on the possibility that the concentration of isoflavones in soybean seeds contributes to the physiological quality of the seeds. The aim of this study was to analyze the content of flavonoids in soybean genotypes and their influence on the physiological quality of the seeds. Seeds from thirty-two soybean genotypes were obtained by carrying out a field experiment during the 2021/22 crop season. The experimental design was randomized blocks with four replications and thirty-two F3 soybean populations. The seeds obtained were subjected to germination, first germination counting, electrical conductivity and tetrazolium vigor and viability tests. After drying and milling the material from each genotype, liquid chromatography analysis was carried out to obtain flavonoids, performed at UPLC level. Data were submitted to analysis of variance and, when significant, the means were compared using the Scott-Knott test at 5% probability. The results found here show the occurrence of genotypes with higher amounts of flavonoids when compared to their peers. The flavonoid FLVD_G2 had the highest concentration and differed from the others. Thus, we can assume that the type and concentration of flavonoids does not influence the physiological quality of seeds from different soybean genotypes, but it does indirectly contribute to viability and vigor, since the genotypes with the highest FLVD_G2 levels had better FGC values. The findings indicate that there is a difference between the content of flavonoids in soybean genotypes, with a higher content of genistein. The content of flavonoids does not influence the physiological quality of seeds, but contributes to increasing viability and vigor. [ABSTRACT FROM AUTHOR]

In recent decades, the main commercial crops of Mato Grosso, such as soybeans, corn, and cotton, have been undergoing transformations regarding the adoption of new technologies to increase production. However, regardless of the technological level, the climate of the region, including the rainfall regime, can influence the success of crops and facilitate, or not, the maximum production efficiency. This study aimed to define the behavior of the variability in monthly and annual rainfall and its probability of monthly occurrence and calculate the drought index for the northwestern region of Mato Grosso, in the southern region of the Brazilian Amazon. To carry out the study, daily rainfall records were collected, calculating the totals for each month of the historical series for each of the four National Water and Sanitation Agency (ANA) rain gauge stations, Aripuanã (1985–2020), Colniza (2001–2020), Cotriguaçu (2004–2020), and Juína (1985–2020), representing the northwestern region. The annual distribution of rainfall during the periods studied ranged from 1376.2 to 3017.3 mm. The monthly distribution indicated a typical water shortage in the months of June, July, and August. The probability of rainfall near the average for each month was more than 50%. The monthly SPI-1 index revealed a total of 56 months affected by very dry events and 34 extreme dry events. The annual SPI-12 index pointed to seven very dry years and five extremely dry years. Therefore, the region presented high rainfall rates in most years; however, a significant process of drought was also observed, including in rainy months, which are the periods with the greatest demand for the main agricultural crops. [ABSTRACT FROM AUTHOR]

Boron (B) nutrition can contribute to the conservation of seed quality during storage. Thus, it is crucial to investigate which source and amount of B applied to the soil is most suitable to increase the yield and maintain the quality of germination and carbohydrate content in soybean seeds during 180 days of storage. The cultivar Bonus 8579 Ipro® was evaluated in a field experiment in a 5 × 2 factorial scheme, with five B doses: 0, 1, 2, 4 and 8 kg ha−1, and two sources: boric acid and ulexite, arranged in randomized block design with three replicates. After harvest, the B content and accumulation of B in yield and seed quality were analyzed. Subsequently, the seeds were stored in a cold chamber at 17 °C and controlled relative humidity of 20% for 180 days. Soybean plants responded positively to the boric acid and ulexite with an increase in yield and seed quality up to the doses of 3.17 and 3.36 kg ha−1 of B, respectively. Furthermore, both sources potentiated the carbohydrate content in the seeds over the storage time. Our findings reveal that the application from 3.2 to 4.0 kg ha−1 of B using boric acid maintains high seed germination after 180 days of storage. [ABSTRACT FROM AUTHOR]

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]

Monitoring the intergranular variables of corn grain mass during the transportation, drying, and storage stages it possible to predict and avoid potential grain quality losses. For monitoring the grain mass along the transport, a probe system with temperature, relative humidity, and carbon dioxide sensors was developed to determine the equilibrium moisture content and the respiration of the grain mass. These same variables were monitored during storage. At drying process, the drying air and grain mass temperatures, as well as the relative humidity, were monitored. For the prediction of the physical and physical–chemical quality of the grains, the results obtained from the monitoring were used as input data for the multiple linear regression, artificial neural networks, decision tree, and random forest models. A Pearson correlation was applied to verify the relationship between the monitored and predicted variables. From the results obtained, we verified that the intergranular relative humidity altered the equilibrium moisture content of the grains, contributing to the increased respiration and hence dry matter losses along the transport. At this stage, the artificial neural network model was the most indicated to predict the electrical conductivity, apparent specific mass, and germination. The random forest model satisfactorily estimated the dry matter loss. During drying, the air temperature caused volumetric contraction and thermal damage to the grains, increasing the electric conductivity index. Artificial neural network and random forest models were the most suitable for predicting the quality of dry grains. During storage, the environmental conditions altered the moisture contents causing a reduction in the apparent specific mass, germination, and crude protein, crude fiber, and fat contents. Artificial neural network and random forest were the best predictors of moisture content and germination. However, the random forest model was the best predictor of apparent specific mass, electrical conductivity, and starch content of stored grains. [ABSTRACT FROM AUTHOR]

A current challenge of genetic breeding programs is to increase grain yield and protein content and at least maintain oil content. However, evaluations of industrial traits are time and cost-consuming. Thus, achieving accurate models for classifying genotypes with better industrial technological performance based on easier and faster to measure traits, such as agronomic ones, is of paramount importance for soybean breeding programs. The objective was to classify groups of soybean genotypes to industrial technological variables based on agronomic traits measured in the field using machine learning (ML) techniques. Field experiments were carried out in two sites in a randomized block design with two replications and 206 F2 soybean populations. Agronomic traits evaluated were: days to maturation (DM), first pod height (FPH), plant height (PH), number of branches (NB), main stem diameter (SD), mass of one hundred grains (MHG), and grain yield (GY). Industrial technological variables evaluated were oil yield, crude protein, crude fiber, and ash contents, determined by high-optical accuracy near-infrared spectroscopy (NIRS). The models tested were: support vector machine (SVM), artificial neural network (ANN), decision tree models J48 and REPTree, random forest (RF), and logistic regression (LR, used as control). A genotype clustering was performed using PCA and k-means algorithm, and then the clusters formed were used as output variables of the ML models, while the agronomic traits were used as input variables. ML techniques provided accurate models to classify soybean genotypes for more complex variables (industrial technological) based on agronomic traits. RF outperformed the other models and can be used to contribute to soybean breeding programs by classifying genotypes for industrial technological traits. [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]

Plants respond to biotic and abiotic pressures by changing their biophysical and biochemical aspects, such as reducing their biomass and developing chlorosis, which can be readily identified using remote-sensing techniques applied to the VIS/NIR/SWIR spectrum range. In the current scenario of agriculture, production efficiency is fundamental for farmers, but diseases such as target spot continue to harm soybean yield. Remote sensing, especially hyperspectral sensing, can detect these diseases, but has disadvantages such as cost and complexity, thus favoring the use of UAVs in these activities, as they are more economical. The objectives of this study were: (i) to identify the most appropriate input variable (bands, vegetation indices and all reflectance ranges) for the metrics assessed in machine learning models; (ii) to verify whether there is a statistical difference in the response of NDVI (normalized difference vegetation index), grain weight and yield when subjected to different levels of severity; and (iii) to identify whether there is a relationship between the spectral bands and vegetation indices with the levels of target spot severity, grain weight and yield. The field experiment was carried out in the 2022/23 crop season and involved different fungicide treatments to obtain different levels of disease severity. A spectroradiometer and UAV (unmanned aerial vehicle) imagery were used to collect spectral data from the leaves. Data were subjected to machine learning analysis using different algorithms. LR (logistic regression) and SVM (support vector machine) algorithms performed better in classifying target spot severity levels when spectral data were used. Multivariate canonical analysis showed that healthy leaves stood out at specific wavelengths, while diseased leaves showed different spectral patterns. Disease detection using hyperspectral sensors enabled detailed information acquisition. Our findings reveal that remote sensing, especially using hyperspectral sensors and machine learning techniques, can be effective in the early detection and monitoring of target spot in the soybean crop, enabling fast decision-making for the control and prevention of yield losses. [ABSTRACT FROM AUTHOR]

In recent years, agricultural remote sensing technology has made great progress. The availability of sensors capable of detecting electromagnetic energy and/or heat emitted by targets improves the pre-harvest process and therefore becomes an indispensable tool in the post-harvest phase. Therefore, we outline how remote sensing tools can support a range of agricultural processes from field to storage through crop yield estimation, grain quality monitoring, storage unit identification and characterization, and production process planning. The use of sensors in the field and post-harvest processes allows for accurate real-time monitoring of operations and grain quality, enabling decision-making supported by computer tools such as the Internet of Things (IoT) and artificial intelligence algorithms. This way, grain producers can get ahead, track and reduce losses, and maintain grain quality from field to consumer. [ABSTRACT FROM AUTHOR]

Using multispectral sensors attached to unmanned aerial vehicles (UAVs) can assist in the collection of morphological and physiological information from several crops. This approach, also known as high-throughput phenotyping, combined with data processing by machine learning (ML) algorithms, can provide fast, accurate, and large-scale discrimination of genotypes in the field, which is crucial for improving the efficiency of breeding programs. Despite their importance, studies aimed at accurately classifying sorghum hybrids using spectral variables as input sets in ML models are still scarce in the literature. Against this backdrop, this study aimed: (I) to discriminate sorghum hybrids based on canopy reflectance in different spectral bands (SB) and vegetation indices (VIs); (II) to evaluate the performance of ML algorithms in classifying sorghum hybrids; (III) to evaluate the best dataset input for the algorithms. A field experiment was carried out in the 2022 crop season in a randomized block design with three replications and six sorghum hybrids. At 60 days after crop emergence, a flight was carried out over the experimental area using the Sensefly eBee real time kinematic. The spectral bands (SB) acquired by the sensor were: blue (475 nm, B_475), green (550 nm, G_550), red (660 nm, R_660), Rededge (735 nm, RE_735) e NIR (790 nm, NIR_790). From the SB acquired, vegetation indices (VIs) were calculated. Data were submitted to ML classification analysis, in which three input settings (using only SB, using only VIs, and using SB + VIs) and six algorithms were tested: artificial neural networks (ANN), support vector machine (SVM), J48 decision trees (J48), random forest (RF), REPTree (DT) and logistic regression (LR, conventional technique used as a control). There were differences in the spectral signature of each sorghum hybrid, which made it possible to differentiate them using SBs and VIs. The ANN algorithm performed best for the three accuracy metrics tested, regardless of the input used. In this case, the use of SB is feasible due to the speed and practicality of analyzing the data, as it does not require calculations to perform the VIs. RF showed better accuracy when VIs were used as an input. The use of VIs provided the best performance for all the algorithms, as did the use of SB + VIs which provided good performance for all the algorithms except RF. Using ML algorithms provides accurate identification of the hybrids, in which ANNs using only SB and RF using VIs as inputs stand out (above 55 for CC, above 0.4 for kappa and around 0.6 for F-score). There were differences in the spectral signature of each sorghum hybrid, which makes it possible to differentiate them using wavelengths and vegetation indices. Processing the multispectral data using machine learning techniques made it possible to accurately differentiate the hybrids, with emphasis on artificial neural networks using spectral bands as inputs and random forest using vegetation indices as inputs. [ABSTRACT FROM AUTHOR]

Assessing different levels of red gum lerp psyllid (Glycaspis brimblecombei) can influence the hyperspectral reflectance of leaves in different ways due to changes in chlorophyll. In order to classify these levels, the use of machine learning (ML) algorithms can help process the data faster and more accurately. The objectives were: (I) to evaluate the spectral behavior of the G. brimblecombei attack levels; (II) find the most accurate ML algorithm for classifying pest attack levels; (III) find the input configuration that improves performance of the algorithms. Data were collected from a clonal eucalyptus plantation (clone AEC 0144—Eucalyptus urophilla) aged 10.3 months old. Eighty sample evaluations were carried out considering the following severity levels: control (no shells), low infestation (N1), intermediate infestation (N2), and high infestation (N3), for which leaf spectral reflectances were obtained using a spectroradiometer. The spectral range acquired by the equipment was 350 to 2500 nm. After obtaining the wavelengths, they were grouped into representative interval means in 28 bands. Data were submitted to the following ML algorithms: artificial neural networks (ANN), REPTree (DT) and J48 decision trees, random forest (RF), support vector machine (SVM), and conventional logistic regression (LR) analysis. Two input configurations were tested: using only the wavelengths (ALL) and using the spectral bands (SB) to classify the attack levels. The output variable was the severity of G. brimblecombei attack. There were differences in the hyperspectral behavior of the leaves for the different attack levels. The highest attack level shows the greatest distinction and the highest reflectance values. LR and SVM show better accuracy in classifying the severity levels of G. brimblecombei attack. For the correct classification percentage, the RL and SVM algorithms performed better, both with accuracy above 90%. Both algorithms achieved F-score values close to 0.90 and above 0.8 for Kappa. The entire spectral range guaranteed the best accuracy for both algorithms. [ABSTRACT FROM AUTHOR]

Knowing nutrient allocation dynamics in the tissues and the characteristics related to growth in different forest species is crucial to fertilization management and selecting better species for specific environments, ensuring greater fertilization efficiency and consequent sustainability in the forestry sector through the rational use of fertilizers. The objectives of this study were (i) to evaluate the content of macro- and micronutrients in different tissues of eucalyptus species and (ii) to relate them with their growth. The treatments were composed of six eucalyptus species (Eucalyptus camaldulensis Dehnh., Corymbia citriodora Hook., E. saligna Sm., E. grandis W. Hill ex Maiden, E. urograndis, and E. urophylla S. T. Blake). Macro- (nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur) and micronutrient (boron, copper, iron, manganese, and zinc) contents were determined in the leaves, bark, and sapwood. To study the functional patterns in macro- and micronutrient contents, Canonical Variable Analysis (CVA) was performed. The first two canonical variables in nutrient content of leaves, bark, and sapwood and the growth variables of eucalyptus species accumulated values greater than 80% of variance. The species E. grandis and E. urograndis showed the highest means for volume and total height but showed no differences regarding the concentration of major elements in the tissues, except the iron content in the bark, which was higher compared to other species. CVA proved to be an excellent tool for understanding, identifying, and classifying the strategies of Eucalyptus sp. regarding the content of nutrients in the shoot biomass tissues and may support genetic improvement programs aiming at identifying potential species. Future research involving the use of remotely piloted aircraft and remote sensors could be a strategy to monitor nutrient contents in different parts of trees throughout the cycle of different eucalyptus species. [ABSTRACT FROM AUTHOR]

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 F2 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. [ABSTRACT FROM AUTHOR]

The definition of the distance between sampling grid points directly impacts the development of fertility maps because it affects the spatial dependence of geostatistics and the estimates for locations not sampled in the interpolation. Based on geostatistical concepts, it is common to recommend one or more soil samples per hectare. However, there is a need to understand how a cost-effective grid-sampling scheme can be developed to produce accurate digital maps. This study aimed to assess how + 5% and + 10% additional random points in the original sample grids affect the development of soil fertility maps under Brazilian Cerrado conditions. Four agricultural areas located in different states of Brazil were analyzed by applying the various sampling techniques. In total, 625 points were sampled, and ten sub-samples within 5 m of the central point were collected. Additional sampling points (+ 5% and + 10%) were randomly placed in the four quadrants of the field boundary following the original grid scheme. The soil attributes evaluated were pH, cation exchange capacity, base saturation, and Ca, Mg, K, and P contents. Geostatistical variograms were modeled for each field, attribute, and statistical sampling treatment. The best variogram model was selected based on the minimal difference between the root mean square error and average standard error of the cross-validation procedure, along with an evaluation of the root mean square standardized error value. The maps were compared using the relative deviation coefficients. The inclusion of additional 5% and 10% sampling points in the original grid was found to be effective in generating soil fertility maps, resulting in improved root mean square and average standard error values. [ABSTRACT FROM AUTHOR]

Soil salinity affects plant growth, compromising sugarcane cultivation in regions with great production potential. Saccharum complex genotypes that respond positively to growth under saline environment can be used in the diversification of sugarcane cultivars to obtain greater economic returns. The objective of this study was to evaluate growth‐related traits of Saccharum genotypes grown under the presence and absence of salinity. The experiment was carried out in a 32 × 2 factorial scheme in a randomized block design with three replicates. The first factor consisted of 32 genotypes of the Saccharum complex and the second factor consisted of the presence and absence of salinity. The salinity provided higher mean values than the environment without salinity for plant height in the genotypes G9, G11, G13, G22 and G28, leaf number for G9 and G24, leaf area index for G9 and stem diameter for G1, G11 and G24. Among the genotypes tested, G1, G9, G11, G13, G22, G24 and G28 were the most promising genotypes and could be used for breeding new sugarcane cultivars of enhanced salinity tolerance. [ABSTRACT FROM AUTHOR]

Physical classification is the procedure adopted by the rice unloading, delivery, storage, and processing units for the commercial characterization of the quality of the grains. This step occurs mostly by the conventional method, which demands more time and specialized labor, and the results are subjective since the evaluation is visual. In order to make the operation faster, more accurate, and less dependent, non-destructive technologies and computational intelligence can be applied to characterize grain quality. Therefore, this study aimed to characterize and predict the quality of whole, processed rice grains, as well as classify any defects present. This was achieved by sampling from the upper and lower points of four silo dryers with capacities of up to 40,000 sacks. The grain samples had moisture contents of 16%, 17%, 18%, and 19% and were subjected to drying-aeration until reaching 12% moisture content (w.b.). Near-infrared spectroscopy technology and Machine Learning algorithm models (Artificial Neural Networks, decision tree algorithms Quinlan's algorithm, Random Tree, REPTree, and Random Forest) were employed for this purpose. By analyzing Pearson's correlation statistics, a strong negative correlation (R2 = 0.98) was found between moisture content and the yield of whole grains. Conversely, a strong positive correlation (R2 = 0.97) was observed between moisture content and classified physical defects across the various characterized physicochemical constituents. These findings indicate the effectiveness of near-infrared spectroscopy technology. The Random Tree model (RandT) successfully predicted the grain quality outcomes and is therefore recommended as the model of choice, obtained Pearson's correlation coefficient (r = 0.96), mean absolute error (MAE = 0.017), and coefficient of determination (R2 = 0.92). The results obtained here reveal that the combination of near-infrared spectroscopy technology and Machine Learning algorithm models is an excellent non-destructive alternative to manual physical classification for characterizing the physicochemical quality of whole and defective rice grains. [ABSTRACT FROM AUTHOR]

Tempering-drying time may affect the quality of polished rice. Hence, this study aims to evaluate the effect of drying-tempering time and air temperature relationships on the physical, physicochemical, and morphological qualities of polished rice. Herein, paddy with 20–24% (w.b.) moisture were subjected to drying and tempering with air temperatures of 75–115 °C, 115–130 °C, and 75–130 °C over 14 h to reduce their moisture content up to 12% (w.b.). With the increase in the paddy mass temperature to 49 °C, the whole grain yield and the percentages of protein, crude fiber, fat, and ash reduced; additionally, the morphology of starch and the composition of minerals were altered. The cumulative effects and prolonged time of the drying-tempering influenced the physical, physicochemical, and morphological qualities of the rice. In conclusion, paddy drying technologies with tempering periods should not be designed for temperatures not exceeding 75 °C and drying times not longer than 8 h for good system performance and rice quality. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms can be used to predict wood volume in a faster and more accurate way, providing reliable answers in forest inventories. The objective of this work was to evaluate the performance of different ML techniques to predict the volume of eucalyptus wood, using diameter at breast height (DBH) and total height (Ht) as input variables, obtained by measuring DBH and Ht of 72 trees of six eucalyptus species (Eucalyptus camaldulensis, E. uroplylla, E. saligna, E. grandis, E. urograndis, and Corymbria citriodora). The trees were cut down in two different epochs, rendering 48 samples at 24 months and 24 samples at 48 months, and the volume of each tree was measured using the Smailian method. This research explores five machine learning models, namely artificial neural networks (ANN), K-nearest neighbor (KNN), multiple linear regression (LR), random forest (RF) and support vector machine (SVM), to estimate the volume of eucalyptus wood using DBH and Ht. Artificial neural networks achieved higher correlations between observed and estimated wood volume values. However, the RF outperformed all models by providing lower MAE and higher correlations between observed and estimated wood volume values. Therefore, RF is the most accurate for predicting wood volume in eucalyptus species. [ABSTRACT FROM AUTHOR]

The Amazon region comprises the largest tropical forest on the planet and is responsible for absorbing huge amounts of CO2 from the atmosphere. However, changes in land use and cover have contributed to an increase in greenhouse gas emissions, especially CO2, and in endangered indigenous lands and protected areas in the region. The objective of this study was to detect changes in CO2 emissions and removals associated with land use and land cover changes in the Brazilian Legal Amazon (BLA) through the analysis of multispectral satellite images from 2009 to 2019. The Gross Primary Production (GPP) and CO2Flux variables were estimated by the MODIS sensor onboard Terra and Aqua satellite, representing carbon absorption by vegetation during the photosynthesis process. Atmospheric CO2 concentration was estimated from the GOSAT satellite. The variables GPP and CO2Flux showed the effective flux of carbon in the BLA to atmosphere, which were weakly correlated with precipitation (r = 0.191 and 0.133). The forest absorbed 211.05 TgC annually but, due to its partial conversion to other land uses, the loss of 135,922.34 km2 of forest area resulted in 5.82 TgC less carbon being absorbed. Pasture and agriculture, which comprise the main land conversions, increased by 100,340.39 km2 and absorbed 1.32 and 3.19 TgC less, and emitted close to twice more, than forest in these areas. Atmospheric CO2 concentrations increased from 2.2 to 2.8 ppm annually in BLA, with hotspots observed in the southeast Amazonia, and CO2 capture by GPP showed an increase over the years, mainly after 2013, in the north and west of the BLA. This study brings to light the carbon dynamics, by GPP and CO2Flux models, as related to the land use and land cover in one of the biggest world carbon reservoirs, the Amazon, which is also important to fulfillment of international agreements signed by Brazil to reduce greenhouse gas emissions and for biodiversity conservation and other ecosystem services in the region. [ABSTRACT FROM AUTHOR]

Taking into account that the transport of grains can be carried out over long distances and that the mass of grains during transport often has high moisture content, there may be risks of heat and moisture transfer and heating of the grains mass, proving quanti-qualitative losses. Thus, this study aimed to validate a method with probe system for real-time monitoring of temperature, relative humidity and carbon dioxide in the grain mass of corn during transport and storage to detect early dry matter losses and predict possible changes on the grain physical quality. The equipment consisted of a microcontroller, system's hardware, digital sensors to detect air temperature and relative humidity, a non-destructive infrared sensor to detect CO2 concentration. Real-time monitoring system determined early and satisfactorily in an indirect way the changes in the physical quality of the grains confirming by the physical analyses of electrical conductivity and germination. The equipment in real-time monitoring and the application of Machine Learning was effective to predict dry matter loss, due to the high equilibrium moisture content and respiration of the grain mass on the 2-h period. All machine learning models, except support vector machine, obtained satisfactory results, equaling the multiple linear regression analysis. [ABSTRACT FROM AUTHOR]

Using spectral data to quantify nitrogen (N), phosphorus (P), and potassium (K) contents in soybean plants can help breeding programs develop fertilizer-efficient genotypes. Employing machine learning (ML) techniques to classify these genotypes according to their nutritional content makes the analyses performed in the programs even faster and more reliable. Thus, the objective of this study was to find the best ML algorithm(s) and input configurations in the classification of soybean genotypes for higher N, P, and K leaf contents. A total of 103 F2 soybean populations were evaluated in a randomized block design with two repetitions. At 60 days after emergence (DAE), spectral images were collected using a Sensefly eBee RTK fixed-wing remotely piloted aircraft (RPA) with autonomous take-off, flight plan, and landing control. The eBee was equipped with the Parrot Sequoia multispectral sensor. Reflectance values were obtained in the following spectral bands (SBs): red (660 nm), green (550 nm), NIR (735 nm), and red-edge (790 nm), which were used to calculate the vegetation index (VIs): normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), green normalized difference vegetation index (GNDVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), modified chlorophyll absorption in reflectance index (MCARI), enhanced vegetation index (EVI), and simplified canopy chlorophyll content index (SCCCI). At the same time of the flight, leaves were collected in each experimental unit to obtain the leaf contents of N, P, and K. The data were submitted to a Pearson correlation analysis. Subsequently, a principal component analysis was performed together with the k-means algorithm to define two clusters: one whose genotypes have high leaf contents and another whose genotypes have low leaf contents. Boxplots were generated for each cluster according to the content of each nutrient within the groups formed, seeking to identify which set of genotypes has higher nutrient contents. Afterward, the data were submitted to machine learning analysis using the following algorithms: decision tree algorithms J48 and REPTree, random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR, used as control). The clusters were used as output variables of the classification models used. The spectral data were used as input variables for the models, and three different configurations were tested: using SB only, using VIs only, and using SBs+VIs. The J48 and SVM algorithms had the best performance in classifying soybean genotypes. The best input configuration for the algorithms was using the spectral bands as input. [ABSTRACT FROM AUTHOR]

Predicting maize yield using spectral information, temperature, and different irrigation management through machine learning algorithms provide information in a fast, accurate, and non-destructive way. The use of multispectral sensor data coupled with irrigation management in maize allows further exploration of water behavior and its relationship with changes in spectral bands presented by the crop. Thus, the objective of this study was to evaluate, by means of multivariate statistics and machine learning techniques, the relationship between irrigation management and spectral bands in predicting maize yields. Field experiments were carried out over two seasons (first and second seasons) in a randomized block design with four treatments (control and three additional irrigation levels) and eighteen sample repetitions. The response variables analyzed were vegetation indices (IVs) and crop yield (GY). Measurement of spectral wavelengths was performed with the Sensefly eBee RTK, with autonomous flight control. The eBee was equipped with the Parrot Sequoia multispectral sensor acquiring reflectance at the wavelengths of green (550 nm ± 40 nm), red (660 nm ± 40 nm), red-edge (735 nm ± 10 nm), and NIR (790 nm ± 40 nm). The blue length (496 nm) was obtained by additional RGB imaging. Data were subjected to Pearson correlations (r) between the evaluated variables represented by a correlation and scatter plot. Subsequently, the canonical analysis was performed to verify the interrelationship between the variables evaluated. Data were also subjected to machine learning (ML) analysis, in which three different input dataset configurations were tested: using only irrigation management (IR), using irrigation management and spectral bands (SB+IR), and using irrigation management, spectral bands, and temperature (IR+SB+Temp). ML models used were: Artificial Neural Network (ANN), M5P Decision Tree (J48), REPTree Decision Tree (REPT), Random Forest (RF), and Support Vector Machine (SVM). A multiple linear regression (LR) was tested as a control model. Our results revealed that Random Forest has higher accuracy in predicting grain yield in maize, especially when associated with the inputs SB+IR and SB+IR+Temp. [ABSTRACT FROM AUTHOR]

In Brazil, grain sorghum has been mainly grown in Cerrado biome during the off‐season, as a succession crop, after the soybean harvest. At this period, rainfall is erratic and insufficient, causing drought stress in pre‐ or post‐flowering of grain sorghum crops. In this scenario, the identification of both drought‐tolerant and irrigation responsive hybrids has been one of the main targets of sorghum breeding programmes. The hypothesis of this research was that grain sorghum genotypes respond differently to different moments of water deficit. This study aimed to evaluate the agronomic performance of sorghum hybrids under full irrigation and drought stress at pre‐ and post‐flowering, in order to identify hybrids tolerant to drought stress and responsive to the water supply. Field trials were carried out in Nova Porteirinha during the 2017/2018 and 2018/2019 crop seasons. Twelve grain sorghum single‐hybrids were evaluated for days to flowering and grain yield in three water availability conditions: drought stress at pre‐flowering, drought stress at post‐flowering and full‐irrigation (control). Tolerant hybrids were identified under both pre‐ and post‐flowering drought stress, as well as hybrids responsive to irrigation. The hybrids 1G282 and 116,748 stood out in the 2017/18 season for high yield and earliness under both drought‐stress conditions, while the hybrid 1G282, AG1090, AS4625 and DKB590 stood out for the 2018/19 season in both stress condition. The hybrid A9904 and 1G282 stood out for its responsiveness to water supply, being recommended to high‐tech farming systems that employ irrigation or for in‐season cultivation. [ABSTRACT FROM AUTHOR]

The search for cotton (Gossypium hirsutum L.) genotypes gathering high yield and fiber quality is the main objective of breeding programs. The objectives of this study were: (a) to verify which effects contribute the largest fraction of the total phenotypic variance of cotton fiber quality traits and (b) to recommend the best genotypes for these traits. Eight competition trials of cotton cultivars were carried out between the 2017 and 2019 seasons. Field trials were carried out in Alagoinha (crops seasons 2017, 2018, and 2019) and Barbalha (2017 and 2018) in a randomized block design with three repetitions. Fifteen genotypes developed by the cotton plant breeding program of Embrapa Algodão were evaluated. The following fiber quality traits were evaluated: fiber percentage (FP), average boll weight (BW), upper‐half mean length, fiber uniformity (UNIF), short fiber index (SFI), fiber strength (STR), elongation (ELG), micronaire (MIC), and fiber maturity. Genotypic values (GVs) for the evaluated traits were estimated. The total phenotypic variance of the fiber quality traits evaluated is mostly composed of genotypic effects. Only the traits FP, BW, and ELG showed significant genotypes × sites interaction. The genotype G1 has high GVs for the traits FP, BW, fiber length, and fiber uniformity. The G15 stood out by gathering high values for UNIF, SFI, and STR. Core Ideas: Cotton genotypes gathering high yield and fiber quality is the main objective of breeding programs.The total phenotypic variance of the fiber quality traits evaluated is mostly composed of genotypic effects.Only the traits fiber percentage, boll weight, and elongation showed significant genotypes—sites interaction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The current modern mechanized sugarcane harvesting system implies changes in crop management. However, studies on the benefit of planting arrangement associated with seedling types in sugarcane are still scarce. Our hypothesis is that the planting arrangement associated with seedling types can influence the yield and quality of ratoon sugarcane. The objective of this study was to evaluate the effect of planting arrangement associated with seedling types on biometric indices, yield, and juice quality of ratoon cane. The effects of arrangements in double rows using billets, double row using pre-sprouted seedlings (PSS) with 0.50 m and 0.75 m spacing, triple rows using billets, and triple rows using PSS with 0.50 m and 0.75 m spacing were investigated in third, fourth, and fifth ratoon cane. The cuts of each ratoon cane were performed in August for all crop seasons. The experimental design used was randomized blocks with four replicates in a split block design. Biometric, yield, and juice quality variables evaluated in each season were: stalk height (SH), stalk diameter (SD), number of stalks per meter (NSM), mass of ten stalks (MTS), sugarcane yield (THC), apparent juice purity (JP), cane fiber percentage (CF), sugar content (POL), and total recoverable sugar (TRS). The planting arrangement associated with the seedling type did not affect the stalk height and diameter, number of canes per meter, stalk mass, yield, and juice quality of the third, fourth, and fifth ratoon cane. Given these findings, the planting arrangement with triple rows and using PSS with wider spacing between seedlings (0.75 m) is a good management strategy aimed at reducing machine traffic and seedling costs in sugarcane crops. [ABSTRACT FROM AUTHOR]

The use of silo and raffia bags for the temporary grain storage has been increasing in recent years. However, the methods for monitoring a stored product are limited to visual inspections and sampling. Thus, this research aimed to real‐time equilibrium moisture content monitoring to predict grain quality of corn stored in different conditions in silo and raffia bags using wireless sensor network prototype, Internet of Things (IoT) platform, and neural network algorithms. Experiments were conducted using corn grain with two initial water contents of 13% and 18% (w.b.), three storage environments with temperatures of 30, 23, and 17°C, and two types of packaging, that is, silo and raffia bags, for a 3‐month storage evaluation. During the monitoring of stored grain, variations in equilibrium moisture hygroscopic content were observed, which inferred changes in the corn quality. Water contents of 13% under a storage condition of 17°C showed the highest quality results, whereas storage in silo bags with water contents of 13% and 18% showed no differences at 23°C; however, at a temperature of 30°C, the grain suffered a high level of deterioration. The storage time influenced the reduction of grain quality for all factors. The physicochemical quality prediction results indicated a high coefficient of determination of the trained models, presenting itself as a promising perspective, mainly in developing embedded technologies for monitoring and predicting the qualitative variables of grain stored in silo and raffia bags. Practical Applications: The application of sensor technology and the Internet of Thing (IoT) to monitor the temperature and relative humidity of intergranular air in real time for the determination of equilibrium moisture content became possible to predict the physical and physicochemical quality of grains stored in bag silos and raffia bags using artificial neural networks (ANN) algorithms. The results obtained were satisfactory and can replace the punctual sampling of the grains mass stored in hermetic packages. The application of a set of technologies possible to monitor the grain quality in real time and predict the grain storage time in bag and raffia silos to reduce losses. [ABSTRACT FROM AUTHOR]

The Pantanal is the world's largest and most biodiverse continental sheet-flow wetland. Recently, vast tracts of the Pantanal have succumbed to the occurrence of fires, raising serious concerns over the future integrity of the biodiversity and ecosystem services of this biome, including revenues from ecotourism. These wildfires degrade the baseline of natural ecosystems and the ecotourism economy across the region. Local residents ("Pantaneiros") anecdotally state that extensive cattle herbivory can solve the contemporary flammability problem of the Pantanal by controlling vegetation biomass, thereby preventing or reducing both fuel loads and fires across the region. Here, we examine the covariation between the presence and density of cattle and the incidence of fires across the Brazilian Pantanal. Variables assessed included bovine cattle density, SPI (Standardized Precipitation Index), GPP (Gross Primary Productivity)/biomass estimate, and fire foci along a 19-year time series (2001 to 2019). Our findings show that fire foci across the Pantanal biome are related to climatic variables, such as lower annual precipitation and higher annual drought indices (SPI) rather than to cattle stocking rates. Therefore, the notion of "cattle firefighting", a popular concept often discussed in some academic circles, cannot be validated because cattle numbers are unrelated to aboveground phytomass. Gross primary productivity further invalidated the "cattle herbivory" hypothesis because GPP was found to be strongly correlated with cattle density but not with the spatial distribution of fires. Fires throughout the Pantanal are currently aggravated by the presence of livestock and result from a combination of extreme weather events and outdated agricultural practices. [ABSTRACT FROM AUTHOR]

Using remote sensing combined with machine learning (ML) techniques is a promising approach to classify soybean cultivars. Therefore, the objectives of this study are (i) to verify which input dataset configuration (using only spectral bands, only vegetation indices, or both) is more accurate in the identification of soybean cultivars, and (ii) to verify which ML technique is more accurate in the identification of soybean cultivars. Information was extracted from five central irrigation pivots in the same region and with the same sowing date in the 2015/2016 crop year, in which each pivot was cultivated with a different cultivar, in which the cultivars used were: CV1—P98y12 RR, CV2—Desafio RR, CV3—M6410 IPRO, CV4—M7110 IPRO, and CV5—NA5909 RR. A cloud-free orbital image of the site was acquired from the Google Earth Engine platform. In addition to the spectral bands alone, a total of 13 vegetation indices were calculated. The models tested were: artificial neural networks (ANN), radial basis function network (RBF), decision tree algorithms J48 (DT) and reduced error pruning tree (REP), random forest (RF), and support vector machine (SVM). The five soybean cultivars were classified by the six-machine learning (ML) models in stratified randomized cross-validation with k-fold = 10 and 10 repetitions (100 runs for each model). After obtaining the r and MAE statistics, analysis of variance was performed considering a 6 × 3 factorial scheme (models versus inputs) with 10 repetitions (folds). The means were grouped by the Scott–Knott test at 5% probability. The spectral bands were the most accurate among the tested inputs in the identification of soybean cultivars. ANN was the most accurate model in identifying soybean cultivars. [ABSTRACT FROM AUTHOR]

The monitoring and evaluating the physical and physiological quality of seeds throughout storage requires technical and financial resources and is subject to sampling and laboratory errors. Therefore, machine learning (ML) techniques could help optimize the processes and obtain accurate results for decision-making in the seed storage process. This study aimed to analyze the performance of ML algorithms from variables monitored during seed conditioning (temperature and packaging) and storage time to predict the physical and physiological quality of stored soybean seeds. Data analysis was performed using the Artificial Neural Networks, decision tree algorithms REPTree and M5P, Random Forest, and Linear Regression. In predicting seed quality, the combination of the input variables temperature and storage time for REPTree and Random Forest algorithms outperformed the linear regression, providing higher accuracy indices. Among the most important results, it was observed for apparent specific mass that T + P + ST, T + ST, P + ST, and ST had the highest r means and the lowest MAE means, however, Person's r coefficient for these inputs was 0.63 and the MAE between 9.59 to 10.47. The germination results for inputs T + P + ST and T + ST had the best results (r = 0.65 and r = 0.67, respectively) in the ANN, REPTree, M5P and RF models. Using computational intelligence algorithms is an excellent alternative to predict the quality of soybean seeds from the information of easy-to-measure variables. [ABSTRACT FROM AUTHOR]

Wood volume is the variable that best represents the yield of planted forests, and several regression models are used in its estimation. Artificial neural networks (ANNs) are recognised for their accuracy and generalisation capacity associated with the quality and quantity of data in training and validation. Box–Müller transformation generates random variables from the original data and provides a consistent dataset. Given the above, the hypothesis of this research is that the expansion of data by the Box–Müller theorem provides more accurate estimates for predicting wood volume in eucalyptus species. The objectives were to (i) to evaluate the efficiency of the Box–Müller method for expanding the dataset of eucalyptus sample tree cubing, (ii) use different ANN topologies to predict the wood volume of different Eucalyptus species, and (iii) compare the estimates with those obtained by using the Schumacher and Hall model. The experimental design used randomised blocks with four replicates, composed of the following treatments: Corymbia citriodora and different Eucalyptus species. Sample trees were cubed at ages 2 years and 4.5 years. The estimated volume was obtained using the Schumacher and Hall non-linear regression model for each species and compared with the ANNs through Pearson's correlation, and root mean square error at the steps training, validation, and utilisation. Two ANN architectures were tested, multilayer perceptron (MLP) and radial basis function (RBF). Dataset expansion of cut-down sample trees for cubing is efficient and can be used for ANNs training when there are cubing restrictions of sample size. The topology with seven neurons in the first hidden layer and 12 in the second with expanded data of RBF showed better performance for predicting wood volume. When evaluating all species, the accuracy of the estimates provided by ANNs was higher than that obtained with non-linear regression. [ABSTRACT FROM AUTHOR]

Establishing the optimal time for rooting permanence and evaluation of the rooting quality of eucalypt clones allows us to optimise the production process and increase the quality of the seedlings produced. This study aimed to determine the optimal time for rooting permanence and the rooting quality of three eucalypt clones. Minicuttings from the clones AEC 0144, GG100 and VM01 were propagated in a greenhouse and evaluated for six weeks. The percentages of callus- rooted, rooted and live minicuttings, the number of roots, root system length, and root dry mass were evaluated. The selected clones differed in the speed and rate of rooting, reflecting differences in the optimal time in the rooting house for the minicuttings. Clone VM01 presented the lowest rate and speed of rooting. We conclude that optimisation of the rooting process can be achieved using logistic model fitting and noting the intercept between the daily current increment and daily average increment curves of the rooting percentage, since the other variables are dependendent on this. [ABSTRACT FROM AUTHOR]

In environmental research, remote sensing techniques are mostly based on orbital data, which are characterized by limited acquisition and often poor spectral and spatial resolutions in relation to suborbital sensors. This reflects on carbon patterns, where orbital remote sensing bears devoted sensor systems for CO2 monitoring, even though carbon observations are performed with natural resources systems, such as Landsat, supported by spectral models such as CO2Flux adapted to multispectral imagery. Based on the considerations above, we have compared the CO2Flux model by using four different imagery systems (Landsat 8, PlanetScope, Sentinel-2, and AisaFenix) in the northern part of the state of Mato Grosso, southern Brazilian Amazonia. The study area covers three different land uses, which are primary tropical forest, bare soil, and pasture. After the atmospheric correction and radiometric calibration, the scenes were resampled to 30 m of spatial resolution, seeking for a parametrized comparison of CO2Flux, as well as NDVI (Normalized Difference Vegetation Index) and PRI (Photochemical Reflectance Index). The results obtained here suggest that PlanetScope, MSI/Sentinel-2, OLI/Landsat-8, and AisaFENIX can be similarly scaled, that is, the data variability along a heterogeneous scene in evergreen tropical forest is similar. We highlight that the spatial-temporal dynamics of rainfall seasonality relation to CO2 emission and uptake should be assessed in future research. Our results provide a better understanding on how the merge and/or combination of different airborne and orbital datasets that can provide reliable estimates of carbon emission and absorption within different terrestrial ecosystems in southern Amazonia. [ABSTRACT FROM AUTHOR]

Farmers focus on reducing the cost of production and aim to increase profit. The objective of this study was to quantify the reduction of pesticides applied to soybean (Glycine max (L.) Merrill) and maize (Zea mays L.) crops in several stages of the production cycle using a site-specific spraying application based on real-time sensors in the Brazilian Cerrado region. The sprayers were equipped with a precision spraying control system based on a real-time sensor. The spraying operations were performed not only for herbicide, but also for fungicide and insecticides applications. The maps recorded the percentage of the spray boom when the application was turned on (on/off spray system) with nozzle-to-nozzle control. The precision spraying system based on real-time sensors reduced the volume of pesticides (including herbicides, insecticides, and fungicides) applied to soybean and maize crops. There was a more significant reduction in the volume of pesticides applied post-emergence of the crops in the initial stages of soybean and maize when the crops had less leaf area or less foliage coverage between the rows. The cost reduction achieved using this technology was 2.3 times lower than the cost associated with pesticide application over the entire area using a conventional sprayer. Under the experimental conditions, there were no differences in the average crop yield, compared to the historical productivity of soybean and maize crops by applying this technology because the recommended doses were not affected and the site of application was limited to points where the presence of plants was present was detected. [ABSTRACT FROM AUTHOR]

Corn has great relevance worldwide due to its economic and social importance. Its production grows every harvest, and determining traits directly related to the grain yield is essential for crop management. The hypothesis of this study is that structural equation modelling (SEM) and factor analysis allow us to identify agronomic variables and vegetation indices (VIs) that most contribute to grain yield. Thus, the objective was to identify which agronomic traits and VIs are most associated with grain yield in corn using SEM and factor analysis. Field experiments were carried out during the 2017/2018 and 2018/2019 crop seasons. The experimental design was in randomized blocks with three replicates in a factorial scheme. The following agronomic traits were evaluated: ear length (EL), number of rows per ear (NRE), number of grains per row (NGR), plant height, ear insertion height, stem diameter, and grain yield. The VIs evaluated were: Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge (NDRE), Green Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index (SAVI). Data were subjected to factor analysis and structural equation modelling. The EL, NGR, and NRE were the agronomic traits that most related to corn yield. The VIs most related to yield were NDVI, NDRE, and SAVI. By identifying which agronomic variables and VIs are the most related to grain yield in corn, this study contributes to the decision-making process on HTP in corn and consequently to a higher efficiency of corn breeding programs aiming at selecting high-yielding genotypes. [ABSTRACT FROM AUTHOR]

One of the main objectives of soybean breeding programs is the search for genotypes that are both high yielding and tolerant to abiotic stresses. Brazilian Cerrado, the main grain‐producing region in the country, is characterised by naturally acidic and low fertility soils that consequently have low base saturation. Therefore, identifying genotypes with good performance for favourable and low base saturation stress conditions is of fundamental importance for crop improvement. The objective of this study was to select F3 soybean populations for multiple traits under contrasting base saturation conditions. Field experiments were carried out in two crop seasons in randomised block design with three replicates and 10 F3 populations (P1, P2, P3, P4, P5, P6, P7, P8, P9 and P10). In the first experiment, F3 populations were evaluated without base saturation correction (V = 30%, low saturation), while in the second experiment, limestone was applied 3 months before the start to raise the base saturation to 60% (recommended saturation). We evaluated physiological, nutritional, agronomic and spectral variables. Multivariate analysis was performed to know the populations' performance and the interrelationship between variables and populations in the saturation conditions evaluated. The F3 populations showed differential performance for all traits across the conditions tested, enabling the identification of the best populations for each specific condition and both base saturation environments. Population P2 stood out under both low and recommended base saturation conditions and can be used in F4 plant selection to obtain superior lines for physiological and agronomic traits and vegetation indices. [ABSTRACT FROM AUTHOR]

In forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. To do so, using the data obtained by the UAV as input variables, we tested different configurations (number of hidden layers and number of neurons in each layer) of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species. The experimental design was randomized blocks with four replicates, with 20 trees in each experimental plot. The treatments comprised five Eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis, and E. urograndis) and Corymbria citriodora. DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV, so that the multispectral sensor could obtain spectral bands to calculate vegetation indices (VIs). ANNs were then constructed using spectral bands and VIs as input layers, in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first applications of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher estimated r, lower estimated root mean squared error–RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the generated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time. [ABSTRACT FROM AUTHOR]

Macauba palm [Acrocomia aculeata (Jacq.) Lodd. ex Mart.] is a perennial oil, it stands out for having several characteristics of commercial interest, mainly for producing oil for biodiesel, it has high oil productivity, about 2.5 to 4.5 L·year-1. Despite its great potential, o its cultivation is carried out mainly in an extractive way, so the domestication and breeding programs has been incipient. The study hypothesis is that macauba populations collected in different locations have sufficient genetic variability to initiate a breeding program. Thus, the aim of this study was to estimate the genetic diversity and population structure of macauba palm genotypes by using single nucleotide polymorphism (SNP) markers in order to reveal genetic diversity and distribution of genetic variation within and between populations and use the genetic information obtained to assist breeding strategies. Leaf tissues were collected from 566 macauba plants belonging to the Embrapa Cerrados Active Germplasm Bank, composed of genotypes from five states in Brazil: Minas Gerais, Goiás, Pará, São Paulo, and Distrito Federal. Molecular variance analysis estimated, the genetic diversity parameters, the population structure and principal coordinate analysis. The genetic diversity is higher within than between populations. The results provided by PCoA and STRUCTURE were in agreement and indicated that the evaluated genotypes can be grouped into two groups. Genetic diversity parameters reveal the presence of inbreeding and a low number of heterozygotes, evidencing that the reproduction system of the species is mixed. The information revealed of the genotypes using SNP markers will be important for future studies using genome-wide selection and genomic association to develop cultivars of macauba with desirable traits, such as high yield of fruits and oil production. [ABSTRACT FROM AUTHOR]

Defining the ideal soybean stand is essential for obtaining maximum grain yield. As a hypothesis of the work, it was questioned whether it is possible to vary seeding population of soybean based on the physical–chemical attributes of soil related to grain yield. The objectives of this work were (i) to identify soil attributes that help obtain the maximum grain yield grown in the Cerrado region of Brazil; and (ii) to generate a model to define the seeding rate according to these attributes. The experiment was carried out in the 2017/2018 and 2018/2019 crop seasons. In the first year, plots with different rate-seeding were sown on site one and site two. In the second crop season, site three was sown at a variable rate based on the results found in 2017/2018. The pH, calcium, magnesium, phosphorus, potassium, organic matter and clay contents, cation exchange capacity, base saturation and apparent electrical conductivity of the soil were evaluated. The results were subjected to Pearson's correlation network, followed by path analysis and decision tree. The variable-rate seeding suggested by the decision tree was applied on site 3 and the similarity between the soil attributes contained in the decision tree and the grain yield was verified using the relative deviation coefficient technique. The apparent electrical conductivity of the soil and the clay contents are the soil attributes that should be used to determine the soybean seeding rate. Areas with the highest values of electrical conductivity and clay contents should receive the higher soybean rate seeding in the Cerrado region. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Accurate estimation of biomass in natural vegetation sites remains a challenge. Modeling biomass growth and production in Cerrado areas is crucial to understanding the vegetation succession process, especially regarding the changes in biomass accumulation over time. Thus, our objective was to model the growth and production of woody aboveground biomass (living and total) in a cerrado stricto sensu monitored for 27 years after implementing management systems. As expected, the basal area (with a diameter taken at 30 cm from the ground level) is the most important predictor variable and showed a higher correlation with the biomass stocks and allowed accurate and consistent estimates of these accumulated stocks over time. Future estimates of biomass production, generated from growth models that estimate production as a function of parameters observed at previous ages, indicate that maximum stocks of living (25.86 ± 0.15 Mg ha−1 [mean ± standard deviation]) and total aboveground biomass (26.11 ± 0.15 Mg ha−1) are expected for a period between 28 and 30 years after the implementation of the management systems, with maximum mean annual increment between 23 and 27 years. Furthermore, the systems of equations obtained simulated reductions up to 30% of biomass after the occurrence of a forest fire at 23 years. Thus, our study can be useful for the decision-making process and developing public policies and strategies for managing and conserving natural resources in the Cerrado biome. [ABSTRACT FROM AUTHOR]

Background: Precision agriculture techniques are widely used to optimize fertilizer and soil applications. Furthermore, these techniques could also be combined with new statistical tools to assist in phenotyping in breeding programs. In this study, the research hypothesis was that soybean cultivars show phenotypic differences concerning wavelength and vegetation index measurements. Results: In this research, we associate variables obtained via high-throughput phenotyping with the grain yield and cycle of soybean genotypes. The experiment was carried out during the 2018/2019 and 2019/2020 crop seasons, under a randomized block design with four replications. The evaluated soybean genotypes included 7067, 7110, 7739, 8372, Bonus, Desafio, Maracai, Foco, Pop, and Soyouro. The phenotypic traits evaluated were: first pod height (FPH), plant height (PH), number of branches (NB), stem diameter (SD), days to maturity (DM), and grain yield (YIE). The spectral variables evaluated were wavelengths and vegetation indices (NDVI, SAVI, GNDVI, NDRE, SCCCI, EVI, and MSAVI). The genotypes Maracai and Foco showed the highest grain yields throughout the crop seasons, in addition to belonging to the groups with the highest means for all VIs. YIE was positively correlated with the NDVI and certain wavelengths (735 and 790 nm), indicating that genotypes with higher values for these spectral variables are more productive. By path analyses, GNDVI and NDRE had the highest direct effects on the dependent variable DM, while NDVI had a higher direct effect on YIE. Conclusions: Our findings revealed that early and productive genotypes can be selected based on vegetation indices and wavelengths. Soybean genotypes with a high grain yield have higher means for NDVI and certain wavelengths (735 and 790 nm). Early genotypes have higher means for NDRE and GNDVI. These results reinforce the importance of high-throughput phenotyping as an essential tool in soybean breeding programs. [ABSTRACT FROM AUTHOR]

Knowledge of the physiological mechanisms in saline environment may boost sugarcane breeding programmes targeting abiotic stresses. Our hypothesis is that the physiology of Saccharum genotypes responds differently under salt stress. Thus, the objective of the study was to evaluate the physiological performance of Saccharum complex genotypes grown under presence and absence of saline stress. The experimental design used was randomized blocks arranged in a 32 × 2 factorial scheme (32 genotypes × 2 salinity levels). The presence of salinity provided higher mean values for photosynthetic rate in genotypes G4, G18, G22, G25 and G29 compared with the environment without salinity, with mean values (17.26, 21.49, 24.22 and 26.19 µmol CO2m−2s−1), respectively, for internal CO2 concentration in G2, G6, G9, G14, G17, G19, G23 and G29, with mean values (323.45, 399.64, 386.88, 412.14, 366.31, 250.48, 379.10 and 380.75 µmol CO2 mol air−1), respectively, for transpiration in G18, G24, G25 and G29, with mean values (5.05, 3.30, 4.39 and 4.01 mmol H2O m−2s−1), respectively, and for chlorophyll content in G3, G5, G6, G8, G10, G13, G20, G22, G23, G25, G31 and G32, with mean values (34.18, 43.01, 38.08, 32.38, 37.09, 37.18, 32.47, 38.38, 38.04, 36.95 and 33.32 SPAD units) respectively. Genotypes that under salt stress increase their physiological performance demonstrate superiority over others and should be considered in breeding programmes. Photosynthesis and transpiration is the most adequate combination for screening, but the spad index is the most viable tool because of its ease of determination and cost. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Jatropha (Jatrophacurcas L.) is an oleaginous potential, howetever, some studies report that there is low genetic diversity in Brazilian genotypes. Estimating genetic diversity are the essential factors to ensure success in the management of genetic resources, planning and adoption of strategies for genetic breeding. The hypothesis of our study is: do Brazilian Jatropha breeding populations have sufficient genetic variability to select individuals within? The objective of this paper is to determine the genetic diversity of 573 genotypes of five populations of Jatropha curcas structured based on the characteristics of yield, resistance to powdery mildew and toxicity, using Single-nucleotide polymorphism molecular markers. The results shows moderate variability among the genotypes analyzed, confirming the initial hypothesis of this study. We recommended using a greater number of individuals per family rather than the number of families in breeding programs in order to exploit the greater variability within populations and hence obtain higher gains with selection. [ABSTRACT FROM AUTHOR]

Canola is a product of traditional plant breeding techniques to remove from rapeseed the antinutritional components erucic acid. This crop proves to be a promising crop due to the diverse purposes of its oil, especially by its potential for biofuel production. This paper aimed to integrate the information available in the literature and report the most promising strategies for genetic and biotechnological progress in canola. Thus, we carried out a detailed review of the origin and uses of canola, its socioeconomic importance in the global and Brazilian aspects, tropicalization, with emphasis on genetic breeding. We demonstrate the main breeding strategies that can be used to increase your oil production. We propose here a breeding strategy for canola, in which some strategies previously mentioned are integrated. The purpose of this strategy is to enhance the selection and efficiency at the beginning of a breeding program. Among these, genome wide selection (GWS) is a suitable tool to help breeders to improve the efficiency selection in a canola breeding program increasing the selection accuracy or even reducing the cycle time. The proposed strategies must be analyzed for each situation, adjusting the GWS model to obtain highest selection accuracy. [ABSTRACT FROM AUTHOR]

The guidance on decision-making regarding deforestation in Amazonia has been efficient as a result of monitoring programs using remote sensing techniques. Thus, the objective of this study was to identify the expansion of soybean farming in disagreement with the Soy Moratorium (SoyM) in the Amazonia biome of Mato Grosso from 2008 to 2019. Deforestation data provided by two Amazonia monitoring programs were used: PRODES (Program for Calculating Deforestation in Amazonia) and ImazonGeo (Geoinformation Program on Amazonia). For the identification of soybean areas, the Perpendicular Crop Enhancement Index (PCEI) spectral model was calculated using a cloud platform. To verify areas (polygons) of largest converted forest-soybean occurrences, the Kernel Density (KD) estimator was applied. Mann–Kendall and Pettitt tests were used to identify trends over the time series. Our findings reveal that 1,387,288 ha were deforested from August 2008 to October 2019 according to PRODES data, of which 108,411 ha (7.81%) were converted into soybean. The ImazonGeo data showed 729,204 hectares deforested and 46,182 hectares (6.33%) converted into soybean areas. Based on the deforestation polygons of the two databases, the KD estimator indicated that the municipalities of Feliz Natal, Tabaporã, Nova Ubiratã, and União do Sul presented higher occurrences of soybean fields in disagreement with the SoyM. The results indicate that the PRODES system presents higher data variability and means statistically superior to ImazonGeo. [ABSTRACT FROM AUTHOR]

The search for high‐yielding genotypes and that are tolerant to abiotic stresses has been a major goal in plant breeding. Thus, the use of technologies such as precision agriculture associated with remote sensing tools for plant phenotyping has increased. The hypothesis of this research was that soya bean genotypes respond differently to low and adequate base saturation levels in the soil and that vegetation indexes can be efficient auxiliary tools in plant phenotyping for this purpose. The objective of this study was to evaluate the nutritional status and agronomic performance of soya bean genotypes grown in low and recommended base saturation conditions using high‐throughput phenotyping. The research was carried out in 2017/2018 and 2018/2019 crop seasons, in which two field experiments in each season were installed. In experiment I, genotypes (P1, P2, P3, P4, P5, P6, P7, P8, P9 and P10) were evaluated without soil correction (low saturation condition). In experiment II, liming was performed three months before sowing of the genotypes to raise the base saturation to 60% (recommended saturation). Canopy spectral behaviour at the following wavelengths was evaluated: green (550 nm), red (660 nm), red edge (735 nm) and near‐infrared (790 nm), and the vegetation indices (Vis) NDVI, SAVI, EVI and MSAVI were calculated. The variables evaluated were leaf calcium and magnesium contents and grain yield. The use of VI's was efficient in assessing the performance of genotypes soya bean at different base saturation levels. The EVI showed moderate correlation with the nutritional and agronomic variables measured in each base saturation level. The approach used enabled both to identify genotypes tolerant to low base saturation soils and the ones with better response to liming. [ABSTRACT FROM AUTHOR]