Effects of traffic control on the soil physical quality and the cultivation of sugarcane

The cultivation of sugarcane with intensive use of machinery, especially for harvest, induces soil compaction, affecting the crop development. The control of agricultural traffic is an alternative of management in the sector, with a view to preserve the soil physical quality, resulting in increased sugarcane root growth, productivity and technological quality. The objective of this study was to evaluate the physical quality of an Oxisol with and without control traffic and the resulting effects on sugarcane root development, productivity and technological quality. The following managements were tested: no traffic control (NTC), traffic control consisting of an adjustment of the track width of the tractor and sugarcane trailer (TC1) and traffic control consisting of an adjustment of the track width of the tractor and trailer and use of an autopilot (TC2). Soil samples were collected (layers 0.00-0.10; 0.10-0.20 and 0.20-0.30 m) in the plant rows, inter-row center and seedbed region, 0.30 m away from the plant row. The productivity was measured with a specific weighing scale. The technological variables of sugarcane were measured in each plot. Soil cores were collected to analyze the root system. In TC2, the soil bulk density and compaction degree were lowest and total porosity and macroporosity highest in the plant row. Soil penetration resistance in the plant row, was less than 2 MPa in TC1 and TC2. Soil aggregation and total organic carbon did not differ between the management systems. The root surface and volume were increased in TC1 and TC2, with higher productivity and sugar yield than under NTC. The sugarcane variables did not differ between the managements. The soil physical quality in the plant row was preserved under management TC1 and TC2, with an improved root development and increases of 18.72 and 20.29 % in productivity and sugar yield, respectively.

Vehicular Traffic Flow Map, 1988

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 1990

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 1992

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 1994

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 1996

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 1998

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 2000

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 2002

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 2004

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 2006

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 2008

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

EFFECTS OF COMPACTION DUE TO MACHINERY TRAFFIC ON SOIL PORE CONFIGURATION

Soil compaction has been recognized as a severe problem in mechanized agriculture and has an influence on many soil properties and processes. Yet, there are few studies on the long-term effects of soil compaction, and the development of soil compaction has been shown through a limited number of soil parameters. The objectives of this study were to evaluate the persistence of soil compaction effects (three traffic treatments: T0, without traffic; T3, three tractor passes; and T5, five tractor passes) on pore system configuration, through static and dynamic determinations; and to determine changes in soil pore orientation due to soil compaction through measurement of hydraulic conductivity of saturated soil in samples taken vertically and horizontally. Traffic led to persistent changes in all the dynamic indicators studied (saturated hydraulic conductivity, K0; effective macro- and mesoporosity, εma and εme), with significantly lower values of K0, εma, and εme in the T5 treatment. The static indicators of bulk density (BD), derived total porosity (TP), and total macroporosity (θma) did not vary significantly among the treatments. This means that machine traffic did not produce persistent changes on these variables after two years. However, the orientation of the soil pore system was modified by traffic. Even in T0, there were greater changes in K0 measured in the samples taken vertically than horizontally, which was more related to the presence of vertical biopores, and to isotropy of K0 in the treatments with machine traffic. Overall, the results showed that dynamic indicators are more sensitive to the effects of compaction and that, in the future, static indicators should not be used as compaction indicators without being complemented by dynamic indicators.

From Empirical Bayes to Full Bayes: Methods for Analyzubg Traffic Safey Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this light, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.