Mitigation of clayey soil compaction managed under no-tillage.

2015

Soil management, seed treatment and soil compaction on the sowing furrows affect grain yields of upland rice genotypes.

The water availability for flood irrigated rice (Oryza sativa L.) is decreasing worldwide. Therefore, developing technologies to allow growing rice in aerobic condition, such as a no-tillage system (NTS) can contribute to produce upland rice grains without yield losses and also in saving more water. The objective of this study was to determine the effect of soil management, seed treatment and compaction on the sowing furrow on grain yield of upland rice genotypes. We made two trials, one in an NTS and another using conventional tillage, CT (one plowing and two diskings). The field experiments were performed in the Central Region of Brazil in Cerrado soils. For each trial, the experimental design was a randomized block design in a factorial scheme, with three replications. The treatments consisted of a combination of 10 genotypes with 2 compaction pressures on the sowing furrow (25 kPa and 126kPa) and 2 types of seed treatment (with and without pesticide). Under CT, the seed treatment did not contribute to increase upland rice grain yields. However, under NTS the grain yield of some genotypes [BRS Esmeralda (from 723 to 1,766 kg ha-1), BRS Pepita (from 930 to 1,874 kg ha-1), AB072044 (from 523 to 1,579 kg ha-1), and AB072085 (from 632 to 1,636 kg ha-1) at 25 kPA soil compaction pressure, and Sertaneja (from 994 to 2,167 kg ha-1), BRS Pepita (from 1,161 to 2,100 kg ha-1), and AB072085 (from 958 to 2,213 kg ha-1), at 126 kPA soil compaction pressure] increased with the use of this practice. At CT the higher soil compaction pressure on the sowing furrow (from 25 kPa to 126 kPa) increased rice grain yield only when it was used seed treatment and the genotypes Serra Dourada (from 1,239 to 2,178 kg ha-1), Sertaneja (from 1,510 to 2,379 kg ha-1), and Cambará (from 1,877 to 2,831 kg ha-1). On the other hand, under NTS, increasing soil compaction pressure on the sowing furrow allowed for an increased rice grain yield of Serra Dourada (from 1,553 to 2,347 kg ha-1), Esmeralda (from 723 to 1,643 kg ha-1), AB072044 (from 523 to 2,040 kg ha-1), and Cambará (from 1,243 to 2,032 kg ha-1) without seed treatment and Sertaneja (from 1,385 to 2,167 kg ha-1) and AB072044 (from 1,579 to 2,356 kg ha-1) with seed treatment. In CT the most productive genotypes were AB062008 (2,714 kg ha-1) and BRSMG Caravera (2,479 kg ha-1), while at NTS were the genotypes: BRSGO Serra Dourada (2,118 kg ha-1), AB072047 (1,888 kg ha-1), AB062008 (1,823 kg ha-1), BRSMG Caravera (1,737 kg ha-1), Cambará (1,716 kg ha-1), AB072044 (1,625 kg ha-1), BRS Esmeralda (1,604 kg ha-1), and BRS Pepita (1,516 kg ha-1).

Rice affected by seed treatment, soil compaction and nitrogen at no-tillage and conventional tillage.

The objective of this study was to determine the best combination of management options for upland rice production: seed treatment, N management and soil compaction in zero and conventional tillage methods.

Spatial variability of satured soil hydraulic conductivity in the region of Araguaia River - Brazil.

This study evaluates the spatial variability of saturated hydraulic conductivity in the soil in an area of 51,850 ha at the headwaters of the Araguaia River MT/GO. This area is highly vulnerable because it is a location of recharging through natural water infiltration of the Guarani Aquifer System and an area of intense increases in agriculture since its adoption by growers in the last 30 years. Soil samples were collected at 383 points, geographically located by GPS. The samples were collected from depths of 0 - 20 cm and 60 - 80 cm. Exploratory statistics and box-plot were used in the descriptive analysis and semivariogram were constructed to determine the spatial model. The exploratory analysis showed that the mean hydraulic conductivity in the superficial layer was less than at the level of 60-80 cm; however, the greatest variability evaluated with a coefficient of variation also was from this layer. Data tended towards a normal distribution. These results can be explained by the greater soil compaction in the superficial layer. The semivariogram models, adjusted for the two layers, were exponential and demonstrated moderate and strong dependence, with ranges of 5000 and 3000 utm respectively. It was concluded that soil use is influencing the spatial distribution model of the hydraulic conductivity in the region.

Resistência à penetração em um Latossolo Vermelho distrófico em fase inicial de estabelecimento de sistemas intensivos de cultivo de grãos.

2016

Resistência à penetração em área irrigada sob diferentes níveis de investimento, rotação e sucessão e culturas.

2016

Digital soil mapping using reference area and artificial neural networks.

Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN) and environmental variables that express soillandscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was implemented. The neural network simulator used was the Java NNS with the learning algorithm "back propagation." Reference points were collected for evaluating the performance of the digital map produced. The occurrence of soils in the landscape obtained in the reference area was observed in the following digital classification: medium-textured soils at the highest positions of the landscape, originating from sandstone, and clayey loam soils in the end thirds of the hillsides due to the greater presence of basalt. The variables elevation and slope were the most important factors for discriminating soil class through the ANN. An accuracy level of 82% between the reference points and the digital classification was observed. The methodology proposed allowed for a preliminary soil classification of an area not previously mapped using mapping units obtained in a reference area