Kinetic parameters related to nitrogen uptake in 'Okinawa' peach rootstocks are altered by 'Chimarrita' scion Nitrogen uptake in 'Okinawa' peach rootstock.

BACKGROUND: The kinetic parameters of peach rootstock are a major factor contributing to its nitrogen (N) uptake efficiency, which directly affects the yield and quality parameters. However, the impact of grafting on N kinetic parameters is not sufficiently known as rootstocks are grafted with other scion cultivars before being transplanted to the field. The objective of this study was therefore to evaluate whether the grafting of 'Okinawa' rootstock with the scion cultivar 'Chimarrita' can alter the kinetic parameters related to N uptake. RESULTS: The plants were acclimatized in Hoagland solution for 21 days and, sequentially, were tested to assess the depletion of the internal reserves at 15 and 30 days in CaSO4 solution. Morphological and physiological parameters were evaluated, and kinetic parameters were calculated. The depletion period for to reach the minimum concentration (Cmin ) of NO3 - and NH4 + in the plant was 62 h and 56 h, respectively. The optimal time for internal reserve depletion for 'Okinawa' rootstock with and without the 'Chimarrita' scion was 30 days. CONCLUSION: 'Okinawa' rootstock grafted with 'Chimarrita' scion cultivar was more efficient in NO3 - and NH4 + uptake. © 2022 Society of Chemical Industry.

Prediction of Nitrogen Dosage in 'Alicante Bouschet' Vineyards with Machine Learning Models.

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.