Efeitos diretos e indiretos de parâmetros dos semivariogramas referentes aos atributos químicos do solo sobre a produtividade de grãos de soja

In order to add value to soybens crops, and hence the marketing, medium and large producers have been using precision agriculture techniques (PA), as the Remote Sensing, Geographic Information Systems (GIS) and positioning satellite, to assist the management of crops. Thus, given the economic relevance of that culture to the southwest of Paraná State and Brazil, scientific studies to increase their productivity and profitability are of main importance. The objective of this study was to evaluate the correlation between the chemical soil properties and soybean yield for each estimated parameter of semivariogram (range, nugget and level effect), and the deployment of these correlations in direct and indirect effects, aiming to improve the mapping process of spatial variability of soil chemical properties for use in PA. The hypothesis is that not all attributes of soil used to estimate the semivariogram parameters has a direct effect on productivity, and that even in groups of plants within a larger area it is possible to estimate the parameters of the semivariograms. The experiment was conducted in a commercial area of 19.7 ha, located in the city of Pato Branco - PR, central geographic coordinates 26º 11 '35 "South, 52 43' 05" West longitude, and average altitude of 780 m. The area is planted with soybeans for over 30 years, currently being adopted to cultivate Brasmax Target RR - Don Mario 5.9i, with row spacing of 0.50 m and 13 plants m-1, totaling 260,000 plants ha-1. For georeferencing of the area of study and sampling points was used a couple of topographic ProMarkTM3 receptors, making a relative positioning to obtain the georeferenced coordinates. To collect data (chemical analyzes of soil and crop yield) were sampled 10 blocks in the experimental area, each with an area of 20 m2 (20 meters long x 1 meter wide) containing two spaced adjacent rows of 0.5 m. Each block was divided into 20 portions of 1 m2, and from each were collected four subsamples at a distance of 0.5 m in relation to the lines of blocks, making up a sample depth for 0-10 cm a sample to 10-20 cm for each plot, totaling 200 samples for each depth. The soybean crop was performed on the blocks depending on maturity, and in each block was considered a bundle at each meter. In the data analysis, it was performed a diagnosis of multicollinearity, and subsequently a path analysis of the main variables according to the explanatory variables (range of chemical attributes: pH, K, P, Ca, etc.). The results obtained by the path analysis of the parameters of the semivariogram of soil chemical properties, indicated that only the Fe, Mg, Mn, organic matter (OM), P and Saturation by bases (SB) exerted direct and indirect effects on soybean productivity, although they have not presented spatial variability, indicating that the distribution of blocks in the area was unable to identify the spatial dependence of these elements, making it impossible to draw up maps of the chemical attributes for use in PA.

Interação genótipo-ambiente na densidade de gemas e comprimento de ramos de pessegueiro

The instability of environment between years in climates of subtropical regions difficult to obtain peach trees genotypes with wide adaptation and stable production, contributing to poor crop. The climate instability can affect development stages as flower bud and vegetative bud formation. The factors understanding that control the bud formation, presents elementary importance for effective solutions search to these problems. The objective this work is verify the temperature effect, relative humidity and rainfall on bud density and length shoot (Brindilas) and identify genotypes with more adaptability and stability for this character. Was used 12 peach trees genotypes growing in experimental orchard in the Technology Federal of Paraná State University, Campus Pato Branco with Cfa Köppen climate according to the classification. Data of rainfall, hourly temperature were collected by the weather station of Simepar. They were used three plants for genotype (rehearsal), identify five shoots per tree, in May of each year. Were carried analyzes of length shoot CR (cm), count number of flower bud (GF) and vegetative bud (GV). Also calculated the relationship between GF/GV and flower bud density and vegetative bud density. Evaluations were performer annual 2007-2014. With these data adaptability and stability analyzes were performed using Biplot methodology and correlations analyzes (Pearson) with climates variables. They used the weather data to calculate the sums of hours with temperatures below 20 °C, temperatures between 20-25 °C, temperature between 25-30 °C and temperature above 30 °C, considering the period of August 1fst of the previous period to February 28 of the following year. Pearson correlation coefficients were used for path analysis, GF and DGF as basic variables. For CR, GV and GF the highest average occurred in 2009/10 period. The genotypes ‘BRS Kampai’ and ‘BRS Libra’ highest CR. They are considered stable and adapted as the CR genotypes ‘Casc. 967’ and ‘BRS Kampai’. There was negative correlation between CR and GV for Σh <20 ° C, Σh> 30 °C and Σh with URA <50% and positive correlation between these variables and Σh 25-30 °C and Σh with URA> 70%. The evaluation of GV ‘Cons. 681’ and ‘Casc. 1055’ can be considered adapted and stable. The lowest average was presented by the genotype ‘Sta. Áurea’ though the genotype is also stable. In GF evaluation genotypes are considered adapted ‘BRS Bonão’, ‘Casc. 1055’, ‘Cons. 681’ with adaptability to all evaluated period. In path analysis was direct effect Σh 25-30 °C on flower bud density. In evaluating DGV and DGF and the variations are due to genetic effect. The most adapted and stable genotypes for DGV were ‘T. Beauty’, ‘T. Snow’, ‘Casc. 1055’ and ‘Cons. 681’. CR and GV variables are strongly affected by environment. GF is strongly affected by genetic conditions and moderately affected by environment. DGV and DGF are affected basically by genetic conditions.