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The compositional nutrient diagnosis (CND) method considers the multiple relationships among nutrients and has been proposed to evaluate the nutritional status of plants in place of the univariate and bivariate methods. As it is mathematically based and considers the interactions among all nutrients at the same time, it avoids the errors and trends observed in the calculations of other methods estimating nutritional status, enabling a greater relationship with productivity. The objective of this study was to obtain the CND norms for high-yielding populations of potato crops. For this, 587 samples were used from 21 experimental areas in the state of São Paulo, Brazil to correlate the leaf nutrient contents and the yields of potato crops. Crops with yields higher than 48,993.24 kg ha−1 were considered to have high yields, and the Mahalanobis distance separated the balanced samples from the nutritionally unbalanced ones. Thus, the CND-ilr method generated the norms and classified the 587 samples as nutritionally balanced with a high yield (5% of the total), nutritionally unbalanced with a low yield (92%), nutritionally unbalanced with a high yield (0.3%), or nutritionally balanced with a low yield (2.7%), with accuracy, sensitivity, specificity, NPV, and PPV scores of 96.9, 97.1, 93.6, 64.4, and 99.6%, respectively.
The compositional nutrient diagnosis (CND) method considers the multiple relationships among nutrients and has been proposed to evaluate the nutritional status of plants in place of the univariate and bivariate methods. As it is mathematically based and considers the interactions among all nutrients at the same time, it avoids the errors and trends observed in the calculations of other methods estimating nutritional status, enabling a greater relationship with productivity. The objective of this study was to obtain the CND norms for high-yielding populations of potato crops. For this, 587 samples were used from 21 experimental areas in the state of São Paulo, Brazil to correlate the leaf nutrient contents and the yields of potato crops. Crops with yields higher than 48,993.24 kg ha−1 were considered to have high yields, and the Mahalanobis distance separated the balanced samples from the nutritionally unbalanced ones. Thus, the CND-ilr method generated the norms and classified the 587 samples as nutritionally balanced with a high yield (5% of the total), nutritionally unbalanced with a low yield (92%), nutritionally unbalanced with a high yield (0.3%), or nutritionally balanced with a low yield (2.7%), with accuracy, sensitivity, specificity, NPV, and PPV scores of 96.9, 97.1, 93.6, 64.4, and 99.6%, respectively.
Soil fertility evaluation is important for adopting conservation management and adequate nutrient supply. The objective of this study was to identify critical levels and soil fertility classes using the boundary line method for rainfed crops (corn, cowpea and sabiá [Mimosa caesalpiniifolia]) in the Brazilian semi-arid region. A database of 226 soil fertility analyses of samples from the 0.00-0.20 m soil layer, and corn, cowpea and sabiá yields from Ceará State was used to generate interpretation classes (at 80 and 95 % of maximum yield). In a scatter plot, soil nutrient concentrations (x-axis) and relative crop yields (y-axis) were correlated, and the border points fitted to a quadratic model. Proposed interpretation classes were classified as very low, low, adequate, high and very high, except for Na + , whose adequate class was considered tolerable. Generated models showed coefficients of determination (R 2 ) for the chemical properties ranging from 0.54 to 0.92. Based on the interpretation classes, the critical level was determined as 6.3 for pH, 10.8 g dm -3 for OM, 20.9 mg dm -3 for P, 81 mg dm -3 for K, 55 mmol c dm -3 for Ca 2+ , 24 mmol c dm -3 for Mg 2+ and 8 mg dm -3 for S-SO 4 2- . Interpretation classes for soils with high-activity clay in the Brazilian semi-arid region were superior to those in the reference literature. Boundary line method established fertility classes and critical levels for soil chemical properties in more than one crop, using the concept of relative yield.
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