Adequate nutritional management of any plant species requires quantifying the nutrients accumulated in the plant and knowing their allocation in the different organs. The objective of this study was to evaluate the partitioning, accumulation, export of macronutrients and the nutritional efficiency of fertigated Prata banana plants. A historical survey of annual fruit productivity was conducted in 66 plots of Prata banana, selecting four that met the annual productivity classes (< 30.0; 30.0 = 37.5; 37.5 = 45.0 and > 45.0 t ha-1). The dry matter weights of the different organs of parent plant and follower, as well as the respective contents of macronutrients, were used to estimate the accumulation, partitioning, export, sequence of accumulation of macronutrients and nutritional efficiency using the coefficient of biological utilization (CBU). The partitioning of macronutrient accumulation varied with productivity class and plant organ. The average distribution of nutrients in the parent plant and in the follower ranged from 67.5 to 82.8 % and from 17.2 to 32.5 %, respectively. Macronutrient exports per ton of bunch were 4.334, 1.660, 0.361, 0.224, 0.148 and 0.119 kg t-1 for K, N, P, Mg, S and Ca, respectively. Banana mats have a nutrient partitioning pattern for different yields, that is, this work provides evidence that there is an “ideal compartmentalization” that promotes higher productivity; however, more research is needed to support this statement. The most exported nutrients per ton of bunch were K and N. The descending order of accumulation was: K > N > Ca > Mg > P > S. It was observed that the CBUs of N and P were less sensitive to productivity variations, while those of Ca and Mg were the most sensitive.

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.

The “Cavendish” and “Prata” subgroups represent respectively 47% and 24% of the world banana production. Compared to world average progressing from 10.6 to 20.6 t ha⁻¹between 1961 and 2016, and despite sustained domestic demand and the introduction of new cultivars, banana yield in Brazil has stagnated around 14.5 t ha⁻¹mainly due to nutrient and water mismanagement. “Prata” is now the dominant subgroup in N-E Brazil and is fertigated at high costs. Nutrient balances computed as isometric log-ratios (ilr) provide a comprehensive understanding of nutrient relationships in the diagnostic leaf at high yield level by combining raw concentration data. Although the most appropriate method for multivariate analysis of compositional balances may be less efficient due to non-normal data distribution and limited nutrient mobility in the plant, robustness of the nutrient balance approach could be improved using Box-Cox exponents assigned to raw foliar concentrations. Our objective was to evaluate the accuracy of nutrient balances to diagnose fertigated “Prata” orchards. The dataset comprised 609 observations on fruit yields and leaf tissue compositions collected from 2010 to 2016 in Ceará state, N-E Brazil. Raw nutrient concentration ranges were ineffective as diagnostic tool due to considerable overlapping of concentration ranges for low- and high-yielding subpopulations at cutoff yield of 40 Mg ha⁻¹. Nutrient concentrations were combined into isometric log-ratios (ilr) and normalized by Box-Cox corrections between 0 and 1 which may also account for restricted nutrient transfer from leaf to fruit. Despite reducedilrskewness, Box-Cox coefficients did not improve model robustness measured as the accuracy of the Cate-Nelson partition between yield and the multivariate distance acrossilrvalues. Sensitivity was 94%, indicating that low yields are attributable primarily to nutrient imbalance. There were 148 false-positive specimens (high yield despite nutrient imbalance) likely due to suboptimal nutrition, contamination, or luxury consumption. The profitability of “Prata” orchards could be enhanced by rebalancing nutrients usingilrstandards with no need for Box-Cox correction.

Melon is one of the most demanding cucurbits regarding fertilization, requiring knowledge of soils, crop nutritional requirements, time of application, and nutrient use efficiency for proper fertilization. Developing support systems for decision-making for fertilization that considers these variables in nutrient requirement and supply is necessary. The objective of this study was parameterization of a fertilizer recommendation system for melon (Ferticalc-melon) based on nutritional balance. To estimate fertilizer recommendation, the system considers the requirement subsystem (REQ), which includes the demand for nutrients by the plant, and the supply subsystem (SUP), which corresponds to the supply of nutrients through the soil and irrigation water. After determining the REQtotal and SUPtotal, the system calculates the nutrient balances for N, P, K, Ca, Mg, and S, recommending fertilizer application if the balance is negative (SUP < REQ), but not if the balance is positive or zero (SUP ≥ REQ). Simulations were made for different melon types (Yellow, Cantaloupe, Galia and Piel-de-sapo), with expected yield of 45 t ha^-1. The system estimated that Galia type was the least demanding in P, while Piel-de-sapo was the most demanding. Cantaloupe was the least demanding for N and Ca, while the Yellow type required less K, Mg, and S. As compared to other fertilizer recommendation methods adopted in Brazil, the Ferticalc system was more dynamic and flexible. Although the system has shown satisfactory results, it needs to be evaluated under field conditions to improve its recommendations.

Soil fertility and nutritional imbalance are among the main limitations in banana production. The present study aimed to diagnose soil fertility and nutrition of bananas trees cultivated in the state of Santa Catarina, southern Brazil. Leaf and soil samples were collected, and banana yield was quantified in 2018 and 2019 in 53 orchards in the regions of ‘Vale do Itajaí’ and northern coast of Santa Catarina. Although orchards had satisfactory yield (42 t /ha/year), limitations related to soil fertility and banana crop nutrition were evident. The observed yield is 59.6 % of its full potential. Liming is still recurrent, mainly before orchard establishment. Fertilizer application is widespread, but in many cases, doses are overestimated. Ca, Zn, Cu, and B contents increased in leaves emitted in periods of higher soil water availability and higher temperatures. The results highlight the influence of weather conditions on the supply of these nutrients to banana plants. Nutritional factors are the main limiting factors in banana production in the state of Santa Catarina, compared to non-nutritional factors.