This study aimed to select regression equations based on the correlation between chlorophyll index and leaf nutrient contents, for real-time prediction of the nutritional status of 'Prata' banana. Six cultivars of 'Prata' banana were used as treatments, with five replicates and four plants per plot, arranged in a completely randomized design. Nutrient levels were evaluated based on laboratorial analysis and chlorophyll indices using a portable chlorophyll meter, in the third leaf from the apex to the base. Data were subjected to analysis of variance, calculating the correlations between leaf nutrient contents and chlorophyll indices, and the regression equations were adjusted to the associations that were significant and with greater magnitude. The selected models estimate leaf nutrient contents and allow a real-time, low-cost reliable prediction of the nutritional status of 'Prata' banana. It is an auxiliary technique that, combined with leaf and soil analysis, contributes to a more precise and secure nutritional evaluation.

-This assay was conducted in the experimental area of the Federal Institute of Bahia, Campus Guanambi, BA, and aimed to evaluate agronomic traits of Prata, Cavendish, Gros Michel and Maçã banana cultivars three production cycles. The 72 treatments, 24 cultivars and three production cycles were arranged in a split plot scheme in time, in a completely randomized design with five replications and four plants per plot. Plots were arranged in 24 cultivars, Prata-Anã, Maravilha, FHIA-18, FHIA-18 BRS, BRS Platina, JV42-135, Pacovan, Japira, PV79-34, Pacovan-Ken, Preciosa, Guarantida, Maçã, Caipira, BRS Tropical, BRS Princesa, YB42-03, YB42-07, YB42-47, Grande-Naine, Calypso, Buccaneiro, FHIA-23 and FHIA-17; and subplots consisted of three production cycles. Data obtained were submitted to analysis of variance. The average of the cultivars were grouped by Scott-Knott criterion (p<0.05) and production cycles compared by Tukey test (p<0.05). 'JV42-235', 'Japira' and 'Pacovan-Ken' cultivars had larger size and 'Grande Naine' had smaller size. 'Prata-Anã' cultivar had higher number of leaves at harvest, with leaf area index similar to the others. 'BRS Platina' cultivar is earlier at flowering and harvest. 'Maravilha', 'BRS Platina', 'FHIA-23', 'BRS Tropical and BRS Princesa' cultivars presented greater potential for use by farmers.

Proper plant nutrition is critical to increasing the yield of bananas. The objective was to establish the potential nutrient-response curves and sufficiency ranges using the boundary line approach (BLA) and the method proposed by Kenworthy (MK) to assess the nutritional status of 'Prata-Anã' bananas cultivated under two environmental conditions. The study was carried out using a database comprising leaf nutrient concentrations and banana yields grown at Missão Velha, Ceará, and Ponto Novo, Bahia, Brazil. The reference population consisted of high-yielding plants with yields greater than the mean yield plus 0.5 standard deviation. The database was divided into two datasets. One contained 253 leaf analysis results and a reference population with a mean yield greater than 39.81 t ha -1 yr -1 at Missão Velha. The other contained 147 samples and a reference population with a mean yield greater than 41.69 t ha -1 yr -1 at Ponto Novo. The sufficiency ranges obtained by the BLA for 'Prata-Anã' banana in Bahia and Ceará, respectively, are: a) for macronutrients (g kg -1 ):

Fertigation management of banana plantations at a plot scale is expanding rapidly in Brazil. To guide nutrient management at such a small scale, genetic, environmental and managerial features should be well understood. Machine learning and compositional data analysis (CoDa) methods can measure the effects of feature combinations on banana yield and rank nutrients in the order of their limitation. Our objectives are to review ML and CoDa models for application at regional and local scales, and to customize nutrient diagnoses of fertigated banana at the plot scale. We documented 940 “Prata” and “Cavendish” plot units for tissue and soil tests, environmental and managerial features, and fruit yield. A Neural Network informed by soil tests, tissue tests and other features was the most proficient learner (AUC up to 0.827). Tissue nutrients were shown to have the greatest impact on model accuracy. Regional nutrient standards were elaborated as centered log ratio means and standard deviations of high-yield and nutritionally balanced specimens. Plot-scale diagnosis was customized using the closest successful factor-specific tissue compositions identified by the smallest Euclidean distance from the diagnosed composition using centered or isometric log ratios. Nutrient imbalance differed between regional and plot-scale diagnoses, indicating the profound influence of local factors on plant nutrition. However, plot-scale diagnoses require large, reliable datasets to customize nutrient management using ML and CoDa models.

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Este trabalho objetivou avaliar características vegetativas, de rendimento e incidência de maldo-panamá na 'Pacovan', em dois ciclos de produção, nos híbridos 'Japira', 'Pacovan-Ken', 'Preciosa' e 'Garantida', recomendados para produtores pela Embrapa Mandioca e Fruticultura, e no 'PV79-34', em seleção. Utilizou-se o delineamento experimental inteiramente casualizado, com seis tratamentos e cinco repetições. Os dados foram submetidos à análise de variância, e as médias, agrupadas pelo critério de Scott-Knott (P<0,05). Há variabilidade genética entre as cultivares. 'Garantida' apresenta menor rendimento expresso pela massa das pencas e do cacho e pelo número de frutos. 'PV79-34' é a mais vigorosa, de menor porte e com maior rendimento, porém é suscetível ao mal-do-panamá.

Models for estimating leaf area of bananas found in the literature are not suitable for lanceolate type leaves occurring at the vegetative stage of ratoon suckers dependent of mother plant. The objective was to determine equations for estimating the leaf area of ‘Prata-Anã’ and ‘BRS Platina’ banana plants with lanceolate type leaves. 212 and 164 lanceolate type leaves having 10 cm-wide lamina or less were collected from ‘Prata-Anã’ and ‘BRS Platina’ banana plants of 90 days of age or less, respectively. Width (W), length (L), width/length ratio (WLR), and scanner-measured leaf area (LAscanner) were determined. Using the backward elimination procedure, simple and multiple linear regression equations were fitted to the relationship between leaf dimensions (W, L and WLR) and LAscanner. To evaluate how precise the equations are in predicting leaf area (LApredicted), Pearson correlation coefficients were calculated between LA and LApredictedscanner. The models highly correlated with LAscanner at 1% of significance level. The models are and LALL() = Prata-Anã = - 0,0133624 + 0,000489859**L - 0,00183182 **W and LALL(Platina) 0,00237026 + 0,004781**W - 0,096802** WLR.

Information is needed on the characteristics of potential accessions of Spondias tuberosa Arruda Câmara (umbu) and Spondias sp.(umbu-caja) for commercial planting and preservation. Thus, the objective was to evaluate the leaf contents and cycling of nutrients of 15 accessions of umbu and one of umbu -caja. The treatments consisted of 16 accessions: BGU-44, BGU-45, BGU-47, BGU-48, BGU-50, BGU-75, EPAMIG-01, EPAMIG-03, EPAMIG-04, EPAMIG-05, EPAMIG-06, EPAMIG-07, EPAMIG-09, EPAMIG-13, umbu of unknown origin and the umbu-caja Princesa. A completely randomized experimental design was adopted, with three replicates, consisting of one plant each. Nutrient contents in the leaves were measured in the leaf flushing and leaf senescence phases in the 2015/2016 and 2016/2017 seasons, and the N, P, K and Mg retranslocation rates were determined. The accessions showed average leaf nutrient contents of 28.6, 27.6, 9.5, 4.20, 2.5 and 2.0 g kg-1 with the descending order N>Ca>K>Mg>S>P, respectively, and 103.61, 86.22, 82.12, 60.2, 16.0 and 3.52 mg kg-1 of Fe>B>Na>Mn>Zn>Cu, respectively. The accession BGU-48 had higher N and P retranslocation efficiency in the leaves and BGU-50 showed recycling efficiency for K and Mg. The order of retranslocation rates is K>P>N>Mg, being higher in the 2015/2016 season.