Soil tillage affects soybean growth and promotes heavy metal accumulation in seeds.

When soybean is grown in soils with high heavy metal concentrations, it may introduce those contaminants into the human food chain, posing risks to human health. This study evaluated the effect of tilling the soil with high Cu, Zn, and Mn levels on soybean physiology and metal accumulation in seeds. Disturbed and undisturbed soil samples were collected in two different sites: a vineyard with high heavy metal concentration and a grassland area, containing natural vegetation. Two soybean cultivars were sown and grown in the greenhouse. Photosynthetic parameters and biochemical analysis of oxidative stress were performed. Cu, Zn, and Mn in leaves and seeds, dry mass, and weight of seeds were evaluated. Soil structure had a high influence on plant growth and physiology, while soil site had a high impact on heavy metal accumulation in leaves and seeds. Soybean plants that grown in vineyard soils with high heavy metal concentrations, accumulated 50% more Zn in leaves and seeds, 70% more Cu in leaves, and 90% more Cu in seeds, than those plants grown in grassland soils. Besides, Zn concentration in seeds was higher than the permissible limit. Moreover, the disturbance of both vineyard soil and grassland soil was not good for plant growth and physiology, which have increased TBARS and H2O2 concentration in plants, transpiration rate, metal concentration in leaves and seeds. Soil disturbance may have caused organic matter oxidation and changes in the composition and quantity of soil microorganisms and it affects the availability of other nutrients in the soil.

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.