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−1 between 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−1 mainly 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 reduced ilr skewness, Box-Cox coefficients did not improve model robustness measured as the accuracy of the Cate-Nelson partition between yield and the multivariate distance across ilr values. 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 using ilr standards 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

In banana cultivation, fertilization recommendations are almost exclusively based on soil chemical analysis, without considering leaf analysis and expected yield, which can help in the adjustment of fertilization programs. The aim of this study was to develop a method to recommend macronutrient fertilization rates which integrates data on leaf analysis, soil chemical analysis, and yield. Yield, soil chemical analysis, and leaf analysis data of fertigated plantations of 'Prata' banana were obtained for the first and second halves of the years from 2010 to 2015. Yield was correlated with soil organic matter (SOM) and soil contents of macronutrients (P, K, Ca, and Mg) to obtain the critical level (CL Nu i). Then, leaf nutrient contents were plotted on a dispersion graph as a function of soil contents using the method of Quadrant Diagram of the Plant-Soil Relationship (QDpsR). Based on leaf analysis, recommended rates were simulated for four plots and compared with rates recommended by other methods. The values of CL Nu i obtained were 13.2 g dm-3 for SOM; 97.5 and 91.5 mg dm-3 for P and K; and 2.71 and 0.61 cmol c dm-3 for Ca 2+ and Mg 2+. The rates recommended based on leaf analysis diverged from the recommendations of Ferticalc ®-Bananeira and the Recommendation Table for Banana Fertilization; in plots for which recommendations were made, there were higher rates of P 2 O 5 and Ca and lower rates of K 2 O. However, in most cases, applications were not recommended, either because contents in leaves and soil were adequate or because yield was being limited by non-nutritional factors or, if nutritional, related to other nutrient(s). Leaf analysis satisfactorily adjusts the recommended rates of nutrients and has advantages if incorporated in nutritional balance models.

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.

aridiano lima de deus (6) sUmmarY nitrogen is the most important nutrient for rice (Oryza sativa l) yields. this study aimed to evaluate the response of upland rice cultivars to n rate and application times in a randomized block design, in subdivided plots with four replications. the studied factors were five rice cultivars (Brs mg curinga, Brs monarca, Brs Pepita, Brs Primavera, and Brs sertaneja), three application times (100 % at planting, 50 % at planting -50 % at tillering and 100 % at tillering) and four n rates (0, 50, 100, and 150 kg ha -1 ). all cultivars responded to increased rates and different times of n application, especially Brs Primavera and Brs sertaneja, which were the most productive when 50 % n rates were applied at sowing and 50 % at tillering. the response of cultivar Brs monarca to n fertilization was best when 100 % of the fertilizer was applied at tillering. index terms: Oryza sativa l, nitrogen fertilization, production components.(1) Part of monograph the first author, presented the course in agronomy, federal University of ceará -Ufc. received for publication in may 4, 2011 and approved in January 24, 2012. (2) agronomist, federal University of ceará -Ufc. Post office Box 12168, ceP 60356-000 fortaleza (ce). e-mail: jhbbarreto@ yahoo.com.br (3) associate Professor of soil science department, Ufc. e-mail: ismail@ufc.br (4) researcher of emBraPa -agricultural center of the mid-north. Post office Box 01, ceP 64006-220 teresina (Pi). e-mail: almeida@cpamn.embrapa.br (5)