Agricultural practices that allow a productive increase in a sustainable manner are becoming increasingly necessary to feed an ever-growing global population. The inoculation with Azospirillum brasilense has the potential to reduce the use of synthetic mineral fertilizers with efficient capacity to promote plant growth and increase nutrition. Therefore, this research was developed to investigate the potential use of A. brasilense to increase the accumulation of macro- and micronutrients and its influence on grain yield, plant height, and spike population in two wheat cultivars (CD1104 and CD150), under irrigated conditions in the Brazilian Cerrado. The study was carried out in a Rhodic Hapludox under a no-tillage system. The experiment was designed in randomized blocks with six replications, arranged in a 2 × 2 factorial scheme: two cultivars (CD150 and CD1104) and two levels of inoculation (control and with A. brasilense). The inoculation with A. brasilense provided greater accumulation of micronutrients in the aerial part of the wheat. In the cultivar CD1104, B and Cu had an accumulation 27.7 and 57.4% higher compared with those of the control without bacteria. In the cultivar CD150, Azospirillum increased the accumulation of B, Fe, and Mn by 43.8, 49.9, and 22%, respectively, and reduced Cu by 21.9%, compared with those of the control. The cultivar CD150 has greater efficiency to accumulate N (+35.5 kg N ha−1) as compared with the cultivar CD1104. Interactions between inoculation and cultivars resulted in greater accumulation of S and K in the shoot of the wheat cultivar CD150, as well greater accumulation of Cu in CD1104. In growth assessments, inoculation or cultivars did not statistically influence wheat grain yield and spike population. Howevere, for plant height, the CD1104 genotype has 13.1% bigger plant height on average than that of the CD150 genotype. Inoculation can contribute more sustainably to wheat nutrition.

Aluminum (Al) toxicity is a major abiotic constraint for agricultural production in acidic soils that needs a sustainable solution to deal with plant tolerance. Silicon (Si) plays important roles in alleviating the harmful effects of Al in plants. The genus Urochloa includes most important grasses and hybrids, and it is currently used as pastures in the tropical regions. Xaraés palisadegrass (Urochloa brizantha cv. Xaraés) is a forage that is relatively tolerant to Al toxicity under field-grown conditions, which might be explained by the great uptake and accumulation of Si. However, studies are needed to access the benefits of Si application to alleviate Al toxicity on Xaraés palisadegrass nutritional status, production, and chemical–bromatological composition. The study was conducted under greenhouse conditions with the effect of five Si concentrations evaluated (0, 0.3, 0.6, 1.2, and 2.4 mM) as well as with nutrient solutions containing 1 mM Al in two sampling dates (two forage cuts). The following evaluations were performed: number of tillers and leaves, shoot biomass, N, P, K, Ca, Mg, S, B, Cu, Fe, Mn, Zn, Al, and Si concentration in leaf tissue, Al and Si concentration in root tissue, neutral detergent fiber (NDF), and acid detergent fiber (ADF) content in Xaraés palisadegrass shoot. Silicon supply affected the relation between Si and Al uptake by increasing root Al concentration in detriment to Al transport to the leaves, thereby alleviating Al toxicity in Xaraés palisadegrass. The concentrations between 1.4 and 1.6 mM Si in solution decreased roots to shoots Al translocation by 259% (from 3.26 to 1.26%), which contributed to a higher number of leaves per plot and led to a greater shoot dry mass without affecting tillering. Xaraés palisadegrass could be considered one of the greatest Si accumulator plants with Si content in leaves above 4.7% of dry mass. In addition, Si supply may benefit nutrient-use efficiency with enhanced plant growth and without compromising the chemical–bromatological content of Xaraés palisadegrass.

RESUMOReuso de água é o termo utilizado quando se faz o aproveitamento de águas previamente utilizadas, uma ou mais vezes, em alguma atividade humana, para suprir as necessidades de outros usos benéficos. A água é um recurso natural finito e essencial à vida, é o insumo básico de quase todos os processos industriais, é de vital importância para a produção de alimentos. Muitos dos mananciais utilizados estão cada vez mais poluídos e deteriorados, seja pela falta de controle, de investimento em coleta, tratamento e disposição final de esgoto e aterro sanitário. A disponibilidade de água de boa qualidade torna-se cada vez mais onerosa induzindo à priorização do abastecimento para consumo humano. O termo reuso da água remonta à década de 1980 quando as indústrias passaram a procurar alternativas para a redução do custo de fabricação de seus produtos, tentando reaproveitar ao máximo seus próprios efluentes. O presente trabalho tem como objetivo quantificar e reutilizar a água desperdiçada nos bebedouros por 250 alunos/dia, na Universidade Estadual Paulista Júlio de Mesquita Filho -Ilha Solteira -SP, onde são utilizados bebedouros coletivos do tipo calha e com torneira de jato inclinado. A captação do excedente para reuso não requer nenhum tratamento, apenas investimento em uma caixa de 150 litros, cano para captação da água desperdiçada e mangueira gotejadora para irrigação de jardim, economizando 1.000 litros de água por mês.Palavras-chave: Abastecimento. Reuso. Irrigação. Água. INTRODUÇÃO"O reuso de água é o aproveitamento de águas previamente utilizadas, uma ou mais vezes, em alguma atividade humana, para suprir as necessidades de outros usos benéficos, inclusive o original" (BREGA FILHO; MANCUSO, 2003).A água é um recurso natural finito e essencial à vida, considerando que os recursos hídricos acessíveis ao consumo humano direto constituem uma pequena fração do capital hidrológico, observa-se que a água doce, em escala mundial, é um recurso cada vez mais escasso, seja pelo aumento populacional e das atividades econômicas, seja pela redução da oferta. Em consequência disto, o preço teórico da água tende a elevar-se, tendo em vista que a demanda está aumentando e a oferta diminuindo (BERNARDI, 2003).Segundo o Centro internacional de referência em reuso da água (2004), a

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.