Soil and Water Loss Rates in Oxisols Under No-tillage System in Western Paraná, Brazil

The objective of work was to quantify soil and water loss rates as a function of slope variation, correlating these rates with soybean yield. In addition to developing multiple linear regression models that associate water and soil loss rates in function of their physical attributes. The experiment was conducted in an Oxisols under a no-tillage system. The experiment was carried out in Cascavel, PR, Brazil. Four slopes (3.5%; 8.2%; 11.4% and 13.5%) were considered as treatments. The water and soil loss rates were monitored in the rainfall occurring during the crop development cycle. The water drained in each plot was collected in gutters made of polyvinyl chloride and stored in containers for the quantification of soil and water losses. The stepwise backward method was used to identify the variables that had a significant influence on water and soil losses. The unevenness of the terrain did not influence the soil and water loss rates. The maximum soil and water losses during the soybean cycle were, respectively, 0.01962 Mg ha and 4.07 m ha. The maximum soil and water losses occurred when the precipitation volume was up to 82 mm. Soil and water losses showed a higher correlation with macroporosity and bulk density. Soybean grain yield showed a higher linear correlation with water, and soil loss and was higher at the slopes of 8.2% and 13.4%. The low water and soil losses demonstrate the soil capacity, managed under a no-tillage system, to minimize environmental impacts.


Introduction
The basic principle of SPD is the constant coverage in the soil with plant residues from other crops.In addition to the diversification of crops of multiple species, about crop rotation, succession, intercropping and conservative soil management systems (Tiecher et al., 2014).The same authors still report that only a fraction of the cultivated areas in Brazil follow all the fundamental principles of this conservationist system.
As most farmers do not use adequate soil management or conservation techniques, erosion losses are still high in Brazil.Millions of tons of agricultural soil are lost each year due to erosion (Oliveira et al., 2010).Thus, no-tillage system is an alternative to reduce these losses, because the straw that remains on the soil surface acts as a physical protection against water and soil losses, besides improving the chemical, physical and biological characteristics of the soil (Moline et al., 2011).Minella et al. (2007) define erosion as the process in which soil particles are detached from their cohesive matrix and then loaded in the downstream direction by a transport agent.The soil surface flow is the most influential factor for erosion, being determined by soil slope, water infiltration capacity, surface roughness, and soil cover percentage (Carvalho et al., 2012;Fontana et al., 2016).
Attributes such as texture, organic matter content and stability of the aggregates, soil handling, rainfall intensity, etc. may determine soil erodibility (Martins Filho et al., 2001;Cogo, Levien, & Schwarz, 2003;Amaral et al., 2008;Santos et al., 2013) According to Carlesso et al. (2011), the increase of rainfall intensity causes a reduction in time for the beginning of water surface runoff in the soil, causing increase in losses, regardless of soil surface conditions.Cogo, Levien, and Schwarz (2003) and Bertol, González and Vázquez (2007) cite the terrain slope as another factor that influences soil and water losses due to water erosion.According to the authors, as the slope increases, it affects the volume and speed of the runoff, thus reducing the infiltration of water into the soil.
Soil physical attributes such as bulk density, macroporosity and microporosity and saturated hydraulic conductivity (ksat) also influence water and soil loss, since an alteration of these attributes directly implies the infiltration of water in the soil (Mesquita & Moraes, 2004;Sousa, Martins Filho, & Matias, 2012;Primo et al., 2015).
Because of the soil losses caused by erosive processes, there is an impact on the soil thickness, reducing its retention capacity and redistribution of water in its profile, resulting in higher surface flows and, subsequently, higher rates of soil erosion (Hamza & Anderson, 2005;Santos, Griebeler, & Oliveira, 2010).
Losses of soil and water caused by surface runoff may cause major damage to agricultural crops and lead to contamination of watercourses.Thus, studies on water and soil loss can guide decision-making processes on the adoption of conservation practices for erosion control.
Considering the above, this research aimed to quantify soil and water loss rates in an Oxisols under a no-tillage system, relating these rates to soybean grain yield, and to develop models that associate soil and water loss rates as a function of the physical-hydraulic attributes that best correlate with these losses.

Location and Characterization of the Experimental Area
The experiment was carried out in a commercial soybean crop in the municipality of Cascavel, PR at latitude 25º03′28.84″S, longitude 53º26′25.48″W and an average altitude of 655 m.The climate of the region is subtropical, according to the Köppen classification (Cfa) (IPARDES, 2012), with annual average rainfall ranging from 1600 mm to 2000 mm (Caviglione et al., 2000).
The soil was classified as a typical Dystroferric Red Latosol with a clayey texture (EMBRAPA, 2013).For chemical characterization of the soil, samples were collected in the 0-0.1 m layer (Table 1).
Table 1.Chemical characterization of the soil

Field Establishment of Experiment
Four collection gutters with 3 × 3 m in area, delimited with grass edging, were built and buried at a height of up to 0.10 m.In the direction of the greatest terrain slope, the collection gutters were installed to collect the runoff, which flowed into polyvinyl chloride tubes (0.2 m diameter) cut in half, where the volume of the runoff was  According to Figure 2, it is verified that, throughout the development cycle of the soybean crop, no lack of water was evidenced for its full development, given the volume and frequency of rainfall.It was also verified that the maximum precipitated volume was 82 mm.The precipitation during the soybean cycle was 859 mm.This precipitation was higher than that reported by Farias, Neumaier, and Nepomuceno (2015).These authors affirm that, for a good soybean development and productivity, 500 to 700 mm of precipitation is required during the soybean cycle.
The experimental design was completely randomized.The treatments were the four slopes, and the replications were the 29 precipitations occurred during the soybean cycle.

Statistical Analysis of the Data
For the comparison of soil loss, water and soybean grain yield, Tukey's test was used at a significance level of 5%.For the analysis of the data and generation of the regression models and graphs, the statistical program R (R CORE TEAM, 2016) was used.The stepwise backward method was used to evaluate the most significant variables.

Physical-hydric Attributes
The mean values of bulk density, saturated hydraulic conductivity, macroporosity, microporosity and total porosity in the 0-0.1 m, 0.1-0.2m, and 0.2-0.3m layers are shown in Table 2.
Table 2. Values of total porosity (TP), macroporosity (Macro), microporosity (Micro), saturated hydraulic conductivity (Ksat) and bulk density (BD) of the experimental area, in the 0-0.1 m, 0.1-0.2m and 0.2-0.3m layers The mean values of bulk density are within the critical limits for Oxisols (Table 2).According to Reichert, Reinert and Braida (2003) the critical values for clayed soils are from 1.30 to 1.40 Mg m -3 .Argenton et al. (2005) reported that BD values close to 1.30 Mg m -3 for clayey soils limited soil gas exchange, while Klein (2006) found a limiting bulk density of 1.33 Mg m -3 , based on the optimal water range, especially for the development of crops in clayey Latossol.
In general, all slopes present values of BD in the range that requires attention, which, according to Panachuki et al. (2011), may result in a reduction in the rate of water infiltration in the soil, with a subsequent increase in surface runoff rates, as well as interfering with water and soil loss.
As for soil macroporosity, the percentages found are above 10%, considered adequate according to Tormena et al. (2002) and Reichert et al. (2009), as values lower than 10%, according to Beutler and Centurion (2003), interfere in the respiratory demand of the roots, growth and activity of microorganisms, drainage and aeration of the soil, and absorption of water and nutrients.Thus, all treatments present macroporosity above the minimum acceptable range recommended for the good development of the plants.
The Ksat values presented great variability, corroborating the results found by Fontana et al. (2016), in Latosols under soybean cultivation, and Primo et al. (2015), who verified that the Ksat tends to decrease according to the depth due to the densification.

Water and Soil Loss
Soil and water loss presented high variability, making the set of values heterogeneous (CV of 133.98% for soil loss and 74.36% for water loss) (Table 3), according to Vanni (1998).
Soil loss varied from 0.029 to 19.62 kg ha -1 , while the average loss in the four soil differences was 1.27 kg ha -1 .Water loss, in turn, varied from 35 to 4074 L ha -1 , while the average loss in the four slopes was 1040 L ha -1 (Table 3).
In the analysis of variance, it was verified that there was no significant difference at 5% probability between the slopes for water loss.According to Leite et al. (2009), the water loss results have not presented consistency, with similar water losses between the soil management systems depending on the rain regime, soil type, topography, and management systems.Bertol (1994) also states that water losses may be similar among different soil preparation methods, because the soil presents limited water infiltration capacity.Bertol, Cogo, and Levien (1989), Levien et al. (1990), Schick et al. (2000), Cogo, Levien, and Schwarz (2003) also stated that water losses were little influenced by soil management systems, regardless of soil slope.Although it is considerably low, water loss can be detrimental to the environment, mainly linked to the area of a river basin, as the flood constitutes the main transport agent for nutrients and sediment to the water sources (Bertol, González, & Vázquez, 2007).
The variables that influenced water loss the most were bulk density (BD), macroporosity (Macro) and saturated hydraulic conductivity (ksat), according to the Stepwise Method.
Thus, water loss graphs were generated as a function of BD and Macro, BD and ksat, and Macro and ksat.
Figure 3 shows the relationship of water loss involving BD, ksat, and Macro, two by two. jas.ccsenet.

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There    Such models can demonstrate that, as the crop did not suffer any drought during its cycle, having availability of water, other variables did not interfere in productivity.An explanation may be the fact that in the period 2015-2016, the soil had water available due to the high rainfall rates in the western region of the state of Paraná, in which, based on a series of studies, Den Biggelaar et al. (2001) determined that the yield decline in dry years is more significant than in years with abundant rainfall.
Several factors can affect crop yield because of the interaction of the effects of surface soil loss (Brunel et al., 2011).Oyedele and Aina (2006) concluded that the primary impacts of soil removal affect soil physical properties and organic matter content significantly more than other chemical properties.

Conclusion
The unevenness of the terrain did not influence soil and water loss rates.
The maximum soil and water losses during the soybean cycle were, respectively, 0.01962 Mg ha -1 and 4.07 m 3 ha -1 The maximum soil and water losses occurred when the precipitation volume was up to 82 mm.Soil and water loss showed a higher correlation with macroporosity and bulk density.
Soybean grain yield showed a higher linear correlation with water soil loss and was higher in slopes of 8.2 and 13.4%.
The low water and soil losses demonstrate the soil capacity, managed under a no-tillage system, to minimize environmental impacts Figure 3.

Figure
Figure 3(a Figure 3(b 3(c), it is v values gen the results