Sistemas de produção de milho sob adubação nitrogenada e plantas de cobertura do solo

The no-tillage system is the predominant model in the agricultural scenario of southern Brazil. Thus, the use of cover crops is significant due to the addition of biomass to protect the soil surface, and contribute to the cycling and/or fixing of nutrients, and in particular nitrogen (N) with liberation for the subsequent culture. Among the cool season species, it was found predominant use of oat to obtain straw to system. Though large quantities input of residue is not the preferred species to precede the corn, cereal with relevant importance in the Paraná Southwest region. It was aimed to evaluate the productivity capacity of corn in no-tillage, in the absence or presence of nitrogen fertilization, on waste of winter cover crops on soil and climatic conditions of the Paraná Southwest region. The installation of no-tillage was held in 2010 in the experimental area belonging to UTFPR, Campus Dois Vizinhos, on a Red Latosol. For the present study, we used data relating to three agricultural years (2012/2013, 2013/2014 and 2014/2015). The experimental design was randomized block design with split plots with three replications. The main plots consisted of systems composed by cover crops (black oat, ryegrass, rye, turnip, vetch, white lupine, aot+vetch consortium and oat+vetch+turnip), preceding corn. In the subplots were used two doses of nitrogen fertilization (0 and 180 kg ha N) coverage in maize.The biggest coverage rates occurred in the consortium with 95% at 62 days after sowing. The residual effect of 180 kg ha cool season plants following year. The residual effect of 180 kg ha systems, reduced in 21% the C/N ratio of poaceae. The common vetch accumulated 32 kg N per ton of MS added. The oat and rye keeps more than 50% waste to the land cover, after 120 days, while the ryegrass and vetch provide low soil protection. Consortium oat+vetch+turnip, vetch and white lupine, released the largest amounts of N, between 52 and 59 kg ha brassica and consortia positively influencing the diameter and length of cobs, number of kernels per row and, total number of grains per ear of corn, in the absence of mineral N. The weight of a thousand grains was increased by 12.4% by the addition of 180 kg ha increase in productivity of grain by the addition of 180 kg ha N, was 2.1 Mg ha 5.6 Mg ha 6.4 Mg ha components when cultivated on vetch. Systems containing fabaceae, brassica and consortium oat+vetch+turnip, predating the corn, in the absence of mineral N, provided similar grain yelds inrelation to the systems with the addition of 180 kg ha Keywords: Cover crops. No-tillage. Grain yield. Zea mays - 1 -1 N, increased 4.8% coverage rate in the of N in corn/cover crops -1 -1 . Fabaceae, -1 N mineral. The average N, in relation to dose 0 kg ha corn kernels on fabaceae, brassica and consortium oat+vetch+turnip, and poaceae the grains in succession. The consortium added amount between 4.0 the DM in the years of study. There was no effect of mineral N rate for corn yield components when cultivated on vetch. Systems containing fabaceae, brassica and consortium oat+vetch+turnip, predating the corn, in the absence of mineral N, provided similar grain yelds inrelation to the systems with the addition of 180 kg ha-1 N.

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.