Proposition of critical levels and nutrient sufficiency ranges in leaves of 'White Moscato' (Vitis vinifera 'Muscat') and 'Bordeaux' (Vitis labrusca 'Ives')

The Compositional Nutrients Diagnosis (CND) can establish indexes that establish deficiency, normality or excess, and even critical levels (CL) or sufficiency ranges (SR) in grapevine leaves. However, this information is scarce in 'White Moscato' and 'Bordeaux' cultivars cultivated in subtropical regions of the world. The study aimed to propose the CND, CL and SR indexes of nutrients in leaves of 'Bordeaux' and 'White Moscato' grapevine cultivars, cultivated in subtropical conditions. Leaves were collected from 105 'White Moscato' and 'Bordeaux' vineyards. Leaves were prepared, dried, ground and subjected to chemical nutrient analysis. Productivity was evaluated. The nutritional status of the grapevine was calculated using the CND method. The CND-r2 indexes were effective in establishing the nutritional status of 'White Moscato' and 'Bordeaux' grapevines, in relation to the concentration of nutrients in leaves of N, P, K, Ca, Mg, B, Cu, Fe, Mn and Zn in deficient, adequate and excessive concentrations. The application of the CND method in the grapevine database showed lower SRs for macronutrients N, K, Ca, Mg and S, and the breadth of the nutritional range for nutrients N, K, Mg, and Fe was smaller than reported in literature. The CND methodology established the critical level and nutrient sufficiency ranges suitable under current grapevine production conditions. Multi-nutrient combinations were more effective than the analysis of a single nutrient in expressing that the limitation of a certain element can reduce the productivity of the grapevines

Local Factors Impact Accuracy of Garlic Tissue Test Diagnosis

The low productivity of garlic in Brazil requires more efficient nutritional management. For this, environmental and fertilization-related factors must be adjusted to a set of local conditions. Our objective was to provide an accurate diagnosis of the nutrient status of garlic crops in southern Brazil. The dataset comprised 1024 observations, 962 as field tests conducted during the 2015–2017 period to train the model, and 61 field observations collected during the 2018–2019 period to validate the model. Machine learning models (MLM) related garlic yield to managerial, edaphic, plant, and climatic features. Compositional data analysis (CoDa) methods allowed classification of nutrients in the order of limitation to yield where MLM detected nutrient imbalance. Tissue analysis alone returned an accuracy of 0.750 in regression and 0.891 in classification about the yield cutoff of 11 ton ha−1. Adding all features documented in the dataset, accuracy reached 0.855 in regression and 0.912 in classification. Local diagnosis based on MLM and CoDa and accounting for local features differed from regional diagnosis across features. Local nutrient diagnosis may differ from regional diagnosis because several yield-impacting factors are taken into account and benchmark compositions are representative of local conditions