Strategies to Increase the Phosphorus Content in the Soil Profile of Vineyards Grown in Subtropical Climates.

Phosphate fertilizers are applied to the soil surface, especially in vineyards in production in subtropical regions. Nowadays, phosphorus (P) is not incorporated into the soil to avoid mechanical damage to the root system in orchards. However, over the years, successive surface P applications can increase the P content only in the topsoil, maintaining low P levels in the subsurface, which can reduce its use by grapevines. For this reason, there is a need to propose strategies to increase the P content in the soil profile of established orchards. The study aimed to evaluate the effect of management strategies to (i) increase the P content in the soil profile; (ii) enhance the grape production; and (iii) maintain the grape must composition. An experiment on the 'Pinot Noir' grape in full production was carried out over three crop seasons. The treatments were without P application (C), P on the soil surface without incorporation (SP), P incorporated at 20 cm (IP20), P incorporated at 40 cm (IP40), and twice the P dose incorporated at 40 cm (2IP40). The P concentration in leaves at flowering and veraison, P content in the soil, grape production and its components, and chemical parameters of the grape must (total soluble solids, total polyphenols, total titratable acidity, total anthocyanins, and pH) were evaluated. The P concentration in leaves did not differ among the P application modes. The application of P associated with soil mobilization, especially at 20 cm depth, increased grape production. The P application modes did not affect the values of the chemical parameters of the grape must except for the total anthocyanins, which had the highest values when the vines were subjected to 2IP40. Finally, the P application and incorporation into the soil profile was an efficient strategy for increasing the grape production in full production vineyards.

Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models.

Brazil presents large yield gaps in garlic crops partly due to nutrient mismanagement at local scale. Machine learning (ML) provides powerful tools to handle numerous combinations of yield-impacting factors that help reducing the number of assumptions about nutrient management. The aim of the current study is to customize fertilizer recommendations to reach high garlic marketable yield at local scale in a pilot study. Thus, collected 15 nitrogen (N), 24 phosphorus (P), and 27 potassium (K) field experiments conducted during the 2015 to 2017 period in Santa Catarina state, Brazil. In addition, 61 growers' observational data were collected in the same region in 2018 and 2019. The data set was split into 979 experimental and observational data for model calibration and into 45 experimental data (2016) to test ML models and compare the results to state recommendations. Random Forest (RF) was the most accurate ML to predict marketable yield after cropping system (cultivar, preceding crops), climatic indices, soil test and fertilization were included features as predictor (R2 = 0.886). Random Forest remained the most accurate ML model (R2 = 0.882) after excluding cultivar and climatic features from the prediction-making process. The model suggested the application of 200 kg N ha-1 to reach maximum marketable yield in a test site in comparison to the 300 kg N ha-1 set as state recommendation. P and K fertilization also seemed to be excessive, and it highlights the great potential to reduce production costs and environmental footprint without agronomic loss. Garlic root colonization by arbuscular mycorrhizal fungi likely contributed to P and K uptake. Well-documented data sets and machine learning models could support technology transfer, reduce costs with fertilizers and yield gaps, and sustain the Brazilian garlic production.

Development and validation of a siphoning prototype for surface runoff evaluation.

The degradation of soil and water quality encourages research to assess the effects of rainfall on the losses of soil and chemical elements that result from surface runoff. In seasons of high surface runoff, the collectors must support the total volume drained or allow its correct estimation to avoid misinterpretation of the data. The present investigation aimed to develop and validate a compact and low-cost system to quantify surface runoff, sediments, and chemical elements losses using the bucket-siphon sampler system (BS3) siphoning method. The tests performed within the system used the runoff collected in a Nitisol or solutions constructed at the laboratory through mixing soil (i.e., Nitisol or Cambisol) with tap water. The BS3 method was efficient in estimating the total volume of water runoff and the concentration of sediments and P in the surface runoff. The maximum flow rate supported by the BS3 method, as presented here, is 0.035 L s-1 , which considering a 10-m² plot is equivalent to 12.6 mm h-1 . Due to the craft characteristics of the system, we recommend the calibration of each unit built to obtain a precise ratio between the volume stored and discarded by the siphoning. The prototype developed here is suitable for quantifying runoff volume, sediment, and P losses in field plots and has been manufactured under US$15.00.