Maria Inês Diel

Nonlinear regression for description of strawberry (Fragaria x ananassa) production

ABSTRACT The aim of this study was to select growth models to describe strawberry (Fragaria x ananassa)fruit production. To do this, data on 16 treatments (combination of 2 cultivars [Albion and Camarosa], 2 origins [National and Imported], and 4 organic substrates mixed [70% crushed sugar cane residue + 30% organic compost, 70% crushed sugar cane residue + 30% commercial substrate, 70% burnt rice husk + 30% organic compost, and 70% burnt rice husk + 30% commercial substrate]) conducted in a Randomized Complete Block Design (RCBD) with 4 replicates were used. Different parameterizations of the Logistic, Gompertz, and von Bertalanffy models were adjusted for number and weight of fruits per plant (g) accumulated in multiple harvests. The model adjustment and parameter estimation were obtained by ordinary least squares, using a Gauss Newton algorithm. The selection of the best model was chosen by intrinsic and parametric nonlinearity. Among the adjusted nonlinear models, the best adjustment for both variables was achieved by the parameterization 2 of the Logistic model and parameterization 1 of the Gompertz model, because they had lower results with less parametric and intrinsic nonlinearity. However, care should be taken when using the Gompertz model because it tends to overestimate the production estimate and may cause misunderstandings in interpretation.

Cultivation of strawberry in substrate: Productivity and fruit quality are affected by the cultivar origin and substrates

The production and quality of strawberries may vary according to cultivar, transplant origin, and the system of plant cultivation. The objective was to evaluate the effect of different organic substrates and different transplant origins on strawberry production and fruit quality. The experiment was conducted in a randomized complete block design with a 2x2x4 factorial scheme using two cultivars (Albion and Camarosa) obtained from different origins (National-Brazilian and Imported-Argentine) and four combinations of substrates: (crushed sugarcane bagasse [70%] + organic compost [30%], crushed sugarcane bagasse [70%] + Carolina® commercial substrate [30%], burnt rice husks [70%] + organic compost [30%], and burnt rice husks [70%] + Carolina® commercial substrate [30%]). The experimental unit was composed of eight plants, with repetition in four blocks. The quality parameters of titrable total acidity, total soluble solids, ratio of sugar / acidity, firmness, and pulp coloration (chroma, hue angle, and L*, a* and b*) were evaluated, as were the production parameters of total number of fruits, total fruit production, average fruit mass, non-commercial fruit production, commercial fruit production and total productivity. The substrate influences the strawberry production variables. Fruit yield was highest in the Camarosa cultivar grown on the S3 substrate mixture. The cultivar, the transplant origin and the cultivation substrate influence fruit AT. SST and AT / SST are influenced only by cultivar and transplant origin. Chroma differs between plants with different transplant origins. Camarosa of National origin cultivated in burnt rice husks + organic compost presented the best production results and fruit quality.

The effect of types and split of urea on yield indicators and yield components of maize

Maize is one of the most important cereals in the world. The productive potential of this crop is closely associated with nitrogen (N) fertilization, thus, studies focused on this subject are important in the development of cropping strategies. The aim of this work was to evaluate the effects of N split and different type of urea on important agronomic traits of the maize crop. A randomized complete block design in a 2×2+1 factorial treatment design with four replications was used. The factorial levels were composed of two types of urea (common and coated) and two nitrogen splits (V3 and V3+V8), plus the control treatment (without urea application). Important agronomic traits such as grain yield, biological productivity, and yield components were assessed. It was verified that there is no difference between the common or coated urea on grain yield and its components. On the other hand, the split of nitrogen into V3 and V8 stages is an efficient strategy to improve grain yield as well as important features as the number of rows per ears and harvest index. Thus, by using this management system farmers can achieve a more efficient nitrogen use.