Cause and Effect Estimates on Corn Yield as a Function of Tractor Planting Speed

Abstract

The objective of this work was to explain the cause and effect estimates on corn yield as a function of planting speed. The study was conducted from January 21 to June 15, 2019, at Fazenda Invernadinha, rural area of ​​the municipality of Mineiros, GO, Brazil. The experimental design consisted of randomized blocks, corresponding to five tractor planting velocities (4, 6, 8, 10 and 12 km h-1), in 4 replicates, where each plot consisted of 8 300 m long lines spaced at each 0.9 m, for a population of 60.000 plants ha-1. Sowing was carried out in no-till system after soybean harvest. A John Deere tractor and planter assembly were used, models 7715 (182 hp horsepower) and 2115 CCS Vacumeter (8 rows of 0.9 m respectively) with 30-hole 30 mm seed discs. Data were submitted to the assumptions of the statistical model, verifying the normality and homogeneity of the residual variances, as well as the additivity of the model. The analyzes were performed in the Rbio R interface, besides the Genes Software. It was concluded that the plant population is the variable that most influences the cause and effect estimates on corn yield. For high corn yields it is recommended that the tractor planting speed be adjusted to 6 km h-1.

Genetic variability and effect of plant arrangement on corn

“…Although the correlation analysis is important to understand the interrelationships between the characteristics, high values do not necessarily imply a cause and effect relationship. As well as, in this work where high correlations can be observed, corroborating with Melo et al (2019) when working with corn genotypes, as well as in Ferreira et al (2019a) and Ferreira et al (2019b). In the analysis of simple correlation, the formation of 2 significant pairs was observed for the spacing between lines of 0.45 m, being the YI x TGM (0.85) and NGR x NGE (0.85) (Figure 3 E45), as well as, TGM x YI (0.85), NGR x NGE (0.76) and NGE x NRE (0.66) for lines spaced at 0.90 m (Figure 3 E90).…”

Genetics, phosphorus and correlations in soybean yield

“…In the analysis of principal components, the sum of the pairs was of the order of 76% of explanation of the total variation of the data (Figure 3). The analysis of canonical variables is still scarce in the literature, and works with good responses in the Cerrado are reported, such as Ferreira et al (2019a) and Ferreira et al (2019b). The TMG1180 genotype at all concentrations showed similarities for the characters In the direct and indirect effects of the explanatory characters on those of production, the regression coefficients (r) were adjusted and validated by the probabilities (p) of both canonical pairs.…”

“…In breeding programs, generally, in addition to aiming to improve a main character, we also seek to maintain or improve the expression of other characters simultaneously. In soybeans, studies on correlations involving their characters with different focuses are reported by Bárbaro et al (2007), Sha et al (2016), Zhang et al (2016, Kong et al (2019), Ferreira et al (2019a), Ferreira et al (2019b and Kurt et al (2020), however, there are no references to the correlations that describe their magnitudes with phosphate fertilization.…”

Desempenho da cultura de milho em função de arranjos espaciais

“…A semeadura de milho é uma das práticas de manejo que mais afetam a produtividade (FERREIRA et al 2019). Como os custos de produção têm aumentado nos últimos anos, é necessário que os produtores busquem o arranjo espacial (taxa de semeadura x espaçamento entre linhas) que maximize a produtividade.…”