RESUMEN
Vineyard soils normally do not provide the amount of nitrogen (N) necessary for red wine production. Traditionally, the N concentration in leaves guides the N fertilization of vineyards to reach high grape yields and chemical composition under the ceteris paribus assumption. Moreover, the carryover effects of nutrients and carbohydrates stored by perennials such as grapevines are neglected. Where a well-documented database is assembled, machine learning (ML) methods can account for key site-specific features and carryover effects, impacting the performance of grapevines. The aim of this study was to predict, using ML tools, N management from local features to reach high berry yield and quality in 'Alicante Bouschet' vineyards. The 5-year (2015-2019) fertilizer trial comprised six N doses (0-20-40-60-80-100 kg N ha-1) and three regimes of irrigation. Model features included N dosage, climatic indices, foliar N application, and stem diameter of the preceding season, all of which were indices of the carryover effects. Accuracy of ML models was the highest with a yield cutoff of 14 t ha-1 and a total anthocyanin content (TAC) of 3900 mg L-1. Regression models were more accurate for total soluble solids (TSS), total titratable acidity (TTA), pH, TAC, and total phenolic content (TPC) in the marketable grape yield. The tissue N ranges differed between high marketable yield and TAC, indicating a trade-off about 24 g N kg-1 in the diagnostic leaf. The N dosage predicted varied from 0 to 40 kg N ha-1 depending on target variable, this was calculated from local features and carryover effects but excluded climatic indices. The dataset can increase in size and diversity with the collaboration of growers, which can help to cross over the numerous combinations of features found in vineyards. This research contributes to the rational use of N fertilizers, but with the guarantee that obtaining high productivity must be with adequate composition.
RESUMEN
This study quantified K uptake kinetic parameters in grass species Paspalum notatum, Paspalum urvillei and Axonopus affinis to associate them with growth. Ten milliliters (10 ml) of nutrient solution were collected in two-liter pots - with five repetitions in 24 hours - in order to determine K concentrations in the samples. Shoot and root dry matter, root length, volume, diameter and surface area, as well as kinetic parameters associated with nutrient uptake (Vmax, Km, Cmin and I) were determined. Species P. notatum was the one presenting the highest root, shoot and total dry matter production, as well as the highest root volume. P. urvillei recorded the highest K content, which may be associated with thinner roots and greater root surface area. A. affinis recorded the highest Vmax value, which led to greater K uptake efficiency.
O estudo quantificou os parâmetros cinéticos de captação de K nas gramíneas Paspalum notatum, Paspalum urvillei e Axonopus affinis associados com o crescimento. Dez mililitros (10 ml) de solução nutritiva foram coletados em vasos de dois litros - com cinco repetições em 24 horas - para determinação das concentrações de K nas amostras. A matéria seca da parte aérea e raízes; comprimento, volume, diâmetro e área superficial radicular, bem como parâmetros cinéticos associados à absorção de nutrientes (Vmax, Km, Cmin e I) foram avaliados. A espécie P. notatum apresentou a maior produção de raiz, parte aérea e MS total, bem como o maior volume de raiz. P. urvillei apresentou o maior teor de K, que pode ser associado a raízes mais finas e resultando em maior área superficial. A. affinis registrou o maior valor de Vmax, o que levou a uma maior eficiência de absorção de K.