Soil analysis is used to estimate nutrient availability, but nutrient concentrations are not always related to yield in most fruit plants, including grapevines. Thus, additional multivariate mathematical models, such as the compositional nutrient diagnosis (CND), which takes into account leaves nutrient concentration, and yield, can contribute to estimating critical levels or sufficiency bands of elements, as well as to detect deficiency and/or excess of nutrients. The aim of the present study was to establish CND standards, and the critical level and sufficiency band of nutrients, in the grapevine Vitis vinifera L., grown in a subtropical climate region. Leaves were collected in 81 vineyards in the Campanha Gaúcha do Rio Grande do Sul region, Southern Brazil, and analyzed for macro- and micro-nutrient concentration. The yield of each vineyard was assessed. Grapevine nutritional status was calculated through the CND method. CND-r² indices were effective in establishing the nutritional status of grapevines for macro- and micro-nutrients as sub-optimal, excessive, or balanced. The CND methodology established the critical level and sufficiency bands of nutrients more accurately than the current recommendations for grapevines. Multi-nutrient associations were more effective than the single nutrient determination in defining the threshold of a given nutrient that can reduce grapevine yield.

Regional nutrient ranges are commonly used to diagnose plant nutrient status. In contrast, local diagnosis confronts unhealthy to healthy compositional entities in comparable surroundings. Robust local diagnosis requires well-documented data sets processed by machine learning and compositional methods. Our objective was to customize nutrient diagnosis of peach (Prunus persica) trees at local scale. We collected 472 observations from commercial orchards and fertilizer trials across eleven cultivars of Prunus persica and six rootstocks in the state of Rio Grande do Sul (RS), Brazil. The random forest classification model returned an area under curve exceeding 0.80 and classification accuracy of 80% about yield cutoff of 16 Mg ha⁻¹. Centered log ratios (clr) of foliar defective compositions have appropriate geometry to compute Euclidean distances from closest successful compositions in “enchanting islands”. Successful specimens closest to defective specimens as shown by Euclidean distance allowed reaching trustful fruit yields using site-specific corrective measures. Comparing tissue composition of low-yielding orchards to that of the closest successful neighbors in two major Brazilian peach-producing regions, regional diagnosis differed from local diagnosis, indicating that regional standards may fail to fit local conditions. Local diagnosis requires well-documented Humboldtian data sets that can be acquired through ethical collaboration between researchers and stakeholders.

Brazil is home to 30% of the world’s Eucalyptus trees. The seedlings are fertilized at plantation to support biomass production until canopy closure. Thereafter, fertilization is guided by state standards that may not apply at the local scale where myriads of growth factors interact. Our objective was to customize the nutrient diagnosis of young Eucalyptus trees down to factor-specific levels. We collected 1861 observations across eight clones, 48 soil types, and 148 locations in southern Brazil. Cutoff diameter between low- and high-yielding specimens at breast height was set at 4.3 cm. The random forest classification model returned a relatively uninformative area under the curve (AUC) of 0.63 using tissue compositions only, and an informative AUC of 0.78 after adding local features. Compared to nutrient levels from quartile compatibility intervals of nutritionally balanced specimens at high-yield level, state guidelines appeared to be too high for Mg, B, Mn, and Fe and too low for Cu and Zn. Moreover, diagnosis using concentration ranges collapsed in the multivariate Euclidean hyper-space by denying nutrient interactions. Factor-specific diagnosis detected nutrient imbalance by computing the Euclidean distance between centered log-ratio transformed compositions of defective and successful neighbors at a local scale. Downscaling regional nutrient standards may thus fail to account for factor interactions at a local scale. Documenting factors at a local scale requires large datasets through close collaboration between stakeholders.