992 resultados para Soil erosion indicators


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Numerous studies in the last 60 years have investigated the relationship between land slope and soil erosion rates. However, relatively few of these have investigated slope gradient responses: ( a) for steep slopes, (b) for specific erosion processes, and ( c) as a function of soil properties. Simulated rainfall was applied in the laboratory on 16 soils and 16 overburdens at 100 mm/h to 3 replicates of unconsolidated flume plots 3 m long by 0.8 m wide and 0.15 m deep at slopes of 20, 5, 10, 15, and 30% slope in that order. Sediment delivery at each slope was measured to determine the relationship between slope steepness and erosion rate. Data from this study were evaluated alongside data and existing slope adjustment functions from more than 55 other studies from the literature. Data and the literature strongly support a logistic slope adjustment function of the form S = A + B/[1 + exp (C - D sin theta)] where S is the slope adjustment factor and A, B, C, and D are coefficients that depend on the dominant detachment and transport processes. Average coefficient values when interill-only processes are active are A - 1.50, B 6.51, C 0.94, and D 5.30 (r(2) = 0.99). When rill erosion is also potentially active, the average slope response is greater and coefficient values are A - 1.12, B 16.05, C 2.61, and D 8.32 (r(2) = 0.93). The interill-only function predicts increases in sediment delivery rates from 5 to 30% slope that are approximately double the predictions based on existing published interill functions. The rill + interill function is similar to a previously reported value. The above relationships represent a mean slope response for all soils, yet the response of individual soils varied substantially from a 2.5-fold to a 50-fold increase over the range of slopes studied. The magnitude of the slope response was found to be inversely related ( log - log linear) to the dispersed silt and clay content of the soil, and 3 slope adjustment equations are proposed that provide a better estimate of slope response when this soil property is known. Evaluation of the slope adjustment equations proposed in this paper using independent datasets showed that the new equations can improve soil erosion predictions.

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Date of Acceptance: 05/06/2015 This research was made possible through funding provided by the Leverhulme Trust, the Spanish Ministry of Science and Innovation (Project CGL2010–20672) and Xunta de Galicia (grants R2014/001 and GPC2014/009). N Silva-Sánchez is currently supported by a FPU pre-doctoral grant (AP2010–3264) funded by the Spanish Government. Kirsty Golding, Andy McMullen, and Ian Simpson are thanked for their assistance with fieldwork. Alison Sandison produced the maps. Pete Langdon and two anonymous referees are thanked for comments that helped to improve the paper.

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Date of Acceptance: 05/06/2015 This research was made possible through funding provided by the Leverhulme Trust, the Spanish Ministry of Science and Innovation (Project CGL2010–20672) and Xunta de Galicia (grants R2014/001 and GPC2014/009). N Silva-Sánchez is currently supported by a FPU pre-doctoral grant (AP2010–3264) funded by the Spanish Government. Kirsty Golding, Andy McMullen, and Ian Simpson are thanked for their assistance with fieldwork. Alison Sandison produced the maps. Pete Langdon and two anonymous referees are thanked for comments that helped to improve the paper.

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Date of Acceptance: 05/06/2015 This research was made possible through funding provided by the Leverhulme Trust, the Spanish Ministry of Science and Innovation (Project CGL2010–20672) and Xunta de Galicia (grants R2014/001 and GPC2014/009). N Silva-Sánchez is currently supported by a FPU pre-doctoral grant (AP2010–3264) funded by the Spanish Government. Kirsty Golding, Andy McMullen, and Ian Simpson are thanked for their assistance with fieldwork. Alison Sandison produced the maps. Pete Langdon and two anonymous referees are thanked for comments that helped to improve the paper.

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Site-specific regression coefficient values are essential for erosion prediction with empirical models. With the objective to investigate the surface-soilconsolidation factor, Cf, linked to the RUSLE's prior-land-use subfactor, PLU, an erosion experiment using simulated rainfall on a 0.075 m m-1 slope, sandy loam Paleudult soil, was conducted at the Agriculture Experimental Station of the Federal University of Rio Grande do Sul (EEA/UFRGS), in Eldorado do Sul, State of Rio Grande do Sul, Brazil. Firstly, a row-cropped area was excluded from cultivation (March 1995), the existing crop residue removed from the field, and the soil kept clean-tilled the rest of the year (to get a degraded soil condition for the intended purpose of this research). The soil was then conventional-tilled for the last time (except for a standard plot which was kept continuously cleantilled for comparison purposes), in January 1996, and the following treatments were established and evaluated for soil reconsolidation and soil erosion until May 1998, on duplicated 3.5 x 11.0 m erosion plots: (a) fresh-tilled soil, continuously in clean-tilled fallow (unit plot); (b) reconsolidating soil without cultivation; and (c) reconsolidating soil with cultivation (a crop sequence of three corn- and two black oats cycles, continuously in no-till, removing the crop residues after each harvest for rainfall application and redistributing them on the site after that). Simulated rainfall was applied with a Swanson's type, rotating-boom rainfall simulator, at 63.5 mm h-1 intensity and 90 min duration, six times during the two-and-half years of experimental period (at the beginning of the study and after each crop harvest, with the soil in the unit plot being retilled before each rainfall test). The soil-surface-consolidation factor, Cf, was calculated by dividing soil loss values from the reconsolidating soil treatments by the average value from the fresh-tilled soil treatment (unit plot). Non-linear regression was used to fit the Cf = e b.t model through the calculated Cf-data, where t is time in days since last tillage. Values for b were -0.0020 for the reconsolidating soil without cultivation and -0.0031 for the one with cultivation, yielding Cf-values equal to 0.16 and 0.06, respectively, after two-and-half years of tillage discontinuation, compared to 1.0 for fresh-tilled soil. These estimated Cf-values correspond, respectively, to soil loss reductions of 84 and 94 %, in relation to soil loss from the fresh-tilled soil, showing that the soil surface reconsolidated intenser with cultivation than without it. Two distinct treatmentinherent soil surface conditions probably influenced the rapid decay-rate of Cf values in this study, but, as a matter of a fact, they were part of the real environmental field conditions. Cf-factor curves presented in this paper are therefore useful for predicting erosion with RUSLE, but their application is restricted to situations where both soil type and particular soil surface condition are similar to the ones investigate in this study.

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Soil erosion is one of the chief causes of agricultural land degradation. Practices of conservation agriculture, such as no-tillage and cover crops, are the key strategies of soil erosion control. In a long-term experiment on a Typic Paleudalf, we evaluated the temporal changes of soil loss and water runoff rates promoted by the transition from conventional to no-tillage systems in the treatments: bare soil (BS); grassland (GL); winter fallow (WF); intercrop maize and velvet bean (M+VB); intercrop maize and jack bean (M+JB); forage radish as winter cover crop (FR); and winter cover crop consortium ryegrass - common vetch (RG+CV). Intensive soil tillage induced higher soil losses and water runoff rates; these effects persisted for up to three years after the adoption of no-tillage. The planting of cover crops resulted in a faster decrease of soil and water loss rates in the first years after conversion from conventional to no-tillage than to winter fallow. The association of no-tillage with cover crops promoted progressive soil stabilization; after three years, soil losses were similar and water runoff was lower than from grassland soil. In the treatments of cropping systems with cover crops, soil losses were reduced by 99.7 and 66.7 %, compared to bare soil and winter fallow, while the water losses were reduced by 96.8 and 71.8 % in relation to the same treatments, respectively.

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The description of the fate of fertilizer-derived nitrogen (N) in agricultural systems is an essential tool to enhance management practices that maximize nutrient use by crops and minimize losses. Soil erosion causes loss of nutrients such as N, causing negative effects on surface and ground water quality, aside from losses in agricultural productivity by soil depletion. Studies correlating the percentage of fertilizer-derived N (FDN) with soil erosion rates and the factors involved in this process are scarce. The losses of soil and fertilizer-derived N by water erosion in soil under conventional tillage and no tillage under different rainfall intensities were quantified, identifying the intervening factors that increase loss. The experiment was carried out on plots (3.5 × 11 m) with two treatments and three replications, under simulated rainfall. The treatments consisted of soil with and soil without tillage. Three successive rainfalls were applied in intervals of 24 h, at intensities of 30 mm/h, 30 mm/h and 70 mm/h. The applied N fertilizer was isotopically labeled (15N) and incorporated into the soil in a line perpendicular to the plot length. Tillage absence resulted in higher soil losses and higher total nitrogen losses (TN) by erosion induced by the rainfalls. The FDN losses followed another pattern, since FDN contributions were highest from tilled plots, even when soil and TN losses were lowest, i.e., the smaller the amount of eroded sediment, the greater the percentage of FDN associated with these. Rain intensity did not affect the FDN loss, and losses were greatest after less intense rainfalls in both treatments.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Soil is a critically important component of the earth’s biosphere. Developing agricultural production systems able to conserve soil quality is essential to guarantee the current and future capacity of soil to provide goods and services. This study investigates the potential of microbial and biochemical parameters to be used as early and sensitive soil quality indicators. Their ability to differentiate plots under contrasting fertilization regimes is evaluated based also on their sensitivity to seasonal fluctuations of environmental conditions and on their relationship with soil chemical parameters. Further, the study addresses some of the critical methodological aspects of microplate-based fluorimetric enzyme assays, in order to optimize assay conditions and evaluate their suitability to be used as a toll to asses soil quality. The study was based on a long-term field experiment established in 1966 in the Po valley (Italy). The soil was cropped with maize (Z. mays L.) and winter wheat (T. aestivum L.) and received no organic fertilization, crop residue or manure, in combination with increasing levels of mineral N fertilizer. The soil microbiota responded to manure amendment increasing it biomass and activity and changing its community composition. Crop residue effect was much more limited. Mineral N fertilization stimulated crop residue mineralization, shifted microbial community composition and influenced N and P cycling enzyme activities. Seasonal fluctuations of environmental factors affected the soil microbiota. However microbial and biochemical parameters seasonality did not hamper the identification of fertilization-induced effects. Soil microbial community abundance, function and composition appeared to be strongly related to soil organic matter content and composition, confirming the close link existing between these soil quality indicators. Microplate-based fluorimetric enzyme assays showed potential to be used as fast and throughput toll to asses soil quality, but required proper optimization of the assay conditions for a precise estimation of enzymes maximum potential activity.

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Efficient planning of soil conservation measures requires, first, to understand the impact of soil erosion on soil fertility with regard to local land cover classes; and second, to identify hot spots of soil erosion and bright spots of soil conservation in a spatially explicit manner. Soil organic carbon (SOC) is an important indicator of soil fertility. The aim of this study was to conduct a spatial assessment of erosion and its impact on SOC for specific land cover classes. Input data consisted of extensive ground truth, a digital elevation model and Landsat 7 imagery from two different seasons. Soil spectral reflectance readings were taken from soil samples in the laboratory and calibrated with results of SOC chemical analysis using regression tree modelling. The resulting model statistics for soil degradation assessments are promising (R2=0.71, RMSEV=0.32). Since the area includes rugged terrain and small agricultural plots, the decision tree models allowed mapping of land cover classes, soil erosion incidence and SOC content classes at an acceptable level of accuracy for preliminary studies. The various datasets were linked in the hot-bright spot matrix, which was developed to combine soil erosion incidence information and SOC content levels (for uniform land cover classes) in a scatter plot. The quarters of the plot show different stages of degradation, from well conserved land to hot spots of soil degradation. The approach helps to gain a better understanding of the impact of soil erosion on soil fertility and to identify hot and bright spots in a spatially explicit manner. The results show distinctly lower SOC content levels on large parts of the test areas, where annual crop cultivation was dominant in the 1990s and where cultivation has now been abandoned. On the other hand, there are strong indications that afforestations and fruit orchards established in the 1980s have been successful in conserving soil resources.