The soil physical quality is a way of evaluating the current condition of forest plantations that is growing in the southeast region of Mato Grosso do Sul State. In this sense, this work aimed to evaluate the impact of the forest plantations on the physical quality of an Oxisol (Haplic Acrustox) in Cerrado Reforested ciliary forest, being used a completely randomized design, with 25 replications and 3 treatments. The analyzed attributes of the soil was: macroporosity (Ma), microporosity (Mi), total porosity (TP), bulk density (BD), real particle (RP), soil resistance to penetration (PR), gravimetric moisture (GM), volumetric moisture (VM) and sand, silt and clay contents. The three evaluated areas presented macroporosity below the critical limit (0.100 m³ m -³), thereby impairing the root development. The three evaluated areas affected the physical quality of the soil. Being the physical attributes that most influenced in the reduction of the soil physical quality was the bulk density, total porosity, microporosity, macroporosity and soil resistance to penetration.
In no-tillage (NT) and minimum tillage (MT) areas, spatial variability of soil physical properties may affect crop yield. The aim of this study was to assess the spatial distribution of soil physical properties, as well as the yield components and grain yield of soybean (GY), based on the mapping of areas under soil conservation farming systems. We assessed yield components, GY and the physical properties of an Oxisol, under NT and MT using the t-student test, and geostatistics to assess spatial variability. The largest population of NT plants showed no spatial dependence and did not influence GY, but the components related to plant height and soil properties differed between systems. From a spatial standpoint, the kriging maps demonstrated that mass of one thousand grains (MOG), total porosity (TP) and soil bulk density (BD) influenced GY under NT, whereas TP1 exerted the most influence under high soil moisture conditions and MT. The maps make it possible to assess the spatial distribution of soil physical properties and the influence on GY, making them an important tool for more accurate production planning in soil conservation systems.
The purpose of this study was to identify, through multivariate analyzes, the physical and chemical characteristics of the soil that best explain the initial development of eucalyptus treated with limestone and lime sludge. This study was conducted in 2014 in the municipality of Três Lagoas (MS). Three treatments were experimented corresponding to the applications of (i) limestone, (ii) lime sludge, and (iii) control or application of neither limestone nor lime sludge. The principal components analysis identified that Group 1 assembling gravimetric moisture, hydrogen potential, calcium and aluminum and, Group 2 composed of contents of sand, clay, phosphorus and organic matter explained 54.96% of the total variability that showed higher discriminatory power of eucalyptus height and diameter variation at breast height. The treatment with limestone showed a contrast between high pH and high Ca content, but low levels of aluminum.
The spatial variability of soil physical attributes is important to indicate management practices that best suit agricultural areas. This study aimed to analyze spatial correlations between soybean grain yield and soil mass-volume relationships, in order to select which attribute is correlated with yield, as well as to evaluate the spatial variability of soil attributes and yield components of this crop, in an Oxisol under no-tillage system. The soil attributes analyzed (0.0-0.10 m and 0.10-0.20 m) were the following ones: soil bulk density (paraffin-coated clod and volumetric ring methods), particle density (volumetric flask and modified volumetric flask methods) and total porosity. The soybean yield components were evaluated as it follows: grain yield, number of pods per plant, number of grains per pod, mass of 100 grains, grain mass per plant, plant population and plant height. The total soil porosity, calculated by the relations between the bulk density (volumetric ring method) and particle density (volumetric flask), in the 0.10-0.20 m layer, was the best indicator of soybean grain yield under no-tillage conditions.
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