National Soil Erosion Research Laboratory

Occasionally, the classic understanding of the effect of pH on P uptake from soils is questioned through the claim that maximum P uptake occurs at a pH much lower than 6.5–7. The purpose of this paper was to thoroughly examine that claim and provide a critical review on soil processes that control how soil pH affects P solubility and availability. We discuss how individual P retention mechanisms are affected by pH in isolation and when combined in soils, and how both real and apparent exceptions to the classic view can occasionally occur due to dynamics between mechanisms, experimental techniques (equilibration time, method of soluble P extraction, and pH adjustment), and plant species that thrive under acidic conditions. While real exceptions to the rule of thumb of maximum P availability at near neutral pH can occur, we conclude that the classic textbook recommendation is generally sound.

Increased impervious surface area is a consequence of urbanization, with correspondent and significant effects on the hydrologic cycle. It is intuitive that an increased proportion of impervious surface brings with it shorter lag times between onset of precipitation and subsequently higher runoff peaks and total volume of runoff in receiving waters. Yet, documentation on quantitative relationships between the extent and type of impervious area and these hydrologic factors remains dispersed across several disciplines. We present a literature review on this subject to better understand and synthesize distinctions among different types of impermeable surface and their relative impacts, and describe the manner in which these surfaces are assessed for their putative impacts on landscape hydrology.

The NASA Soil Moisture Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the soil moisture retrieval algorithm performance. Those sites provide wellcalibrated in situ soil moisture measurements within SMAP product grid pixels for diverse conditions and locations. The estimation of the average soil moisture within the SMAP product grid pixels based on in situ measurements is more reliable when location specific calibration of the sensors has been performed and there is adequate replication over the spatial domain, with an up-scaling function based on analysis using independent estimates of the soil moisture distribution. SMAP fulfilled these requirements through a collaborative Cal/Val Partner program. This paper presents the results from 34 candidate core validation sites for the first eleven months of the SMAP mission. As a result of the screening of the sites prior to the availability of SMAP data, out of the 34

This chapter focuses on physical and chemical indicators of soil quality as related to soil erosion by water. It illustrates a procedure that can be tailored to site-specific situations and used to quantify soil quality impacts, even when tradeoffs between short-term economics vs. long-term sustainability, or water erosion vs. deep percolation of chemicals are considered. The chapter proposes a framework for evaluating soil quality. The framework and procedure are demonstrated using information collected from an alternative and conventional farm in central Iowa. The framework may be useful as a model for developing quantitative assessments of soil quality at various scales of evaluation. The Water Erosion Prediction Project significantly changed soil erosion prediction technology that is available for use in soil and water conservation planning and assessment. Soil with high quality must accommodate water entry, facilitate water transfer and absorption, resist physical degradation, and sustain plant growth.

Abstract The effects of electrolyte concentration and soil sodicity on the infiltration rate and extent of crust formation of a calcareous and a noncalcareous soil were studied using a rain simulator. The infiltration rate was more sensitive to the sodicity of the soil and to the electrolyte concentration of the applied water than was the permeability of the underlying soil. The mechanical impact of the raindrops and the relative freedom for particle movement at the soil surface may account for the greater sensitivity of the infiltration rate. These observations suggest that crust formation is due to two mechanisms: (i) a physical dispersion of soil aggregates caused by the impact action of the raindrops, and (ii) a chemical dispersion which depends on the soil exchangeable sodium percentage (ESP) and the electrolyte concentration of the applied water.

Our objective is to provide an optimistic strategy for reversing soil degradation by increasing public and private research efforts to understand the role of soil biology, particularly microbiology, on the health of our world’s soils. We begin by defining soil quality/soil health (which we consider to be interchangeable terms), characterizing healthy soil resources, and relating the significance of soil health to agroecosystems and their functions. We examine how soil biology influences soil health and how biological properties and processes contribute to sustainability of agriculture and ecosystem services. We continue by examining what can be done to manipulate soil biology to: (i) increase nutrient availability for production of high yielding, high quality crops; (ii) protect crops from pests, pathogens, weeds; and (iii) manage other factors limiting production, provision of ecosystem services, and resilience to stresses like droughts. Next we look to the future by asking what needs to be known about soil biology that is not currently recognized or fully understood and how these needs could be addressed using emerging research tools. We conclude, based on our perceptions of how new knowledge regarding soil biology will help make agriculture more sustainable and productive, by recommending research emphases that should receive first priority through enhanced public and private research in order to reverse the trajectory toward global soil degradation.

Empirical soil erosion models continue to play an important role in soil conservation planning and environmental evaluations around the world. The effect of hillslope length on soil loss, often termed the slope length factor , is one of the main and most variable components of any empirical model. In the most widely used model, the Universal Soil Loss Equation (USLE), normalized soil loss, L , is expressed as a power function of slope length, λ, as , in which the slope exponent, m , is 0.2, 0.3, 0.4, and 0.5 for different, increasing slope gradients. In the Revised Universal Soil Loss Equation (RUSLE), the exponent, m , is defined as a continuous function of slope gradient and the expected ratio of rill to interrill erosion. When the slope gradient is 60% and the ratio of rill to interrill erosion is classified as moderate, the exponent m has the value of 0.71 in RUSLE, as compared with 0.5 for the USLE. The purpose of this study was to evaluate the relationship between soil loss and slope length for slopes up to 60% in steepness. Soil loss data from natural runoff plots at three locations on the Loess Plateau in China and data from a previous study were used. The results indicated that the exponent, m , for the relationship between soil loss and the slope length for the combined data from the three stations in the Loess Plateau was For the data as a whole, the exponent did not increase as slope steepness increased from 20 to 60%. We also found that the value of m was greater for intense storms than for less intense storms. These experimental data indicate that the USLE exponent, , is more appropriate for steep slopes than is the RUSLE exponent, and that the slope length exponent varies as a function of rainfall intensity.

Abstract The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

Abstract Although nearly three decades of widespread use have confirmed the reliability of the Universal Soil Loss Equation (USLE), very little work has been done to assess the error associated with it. This study was conducted to develop a set of statistics that would measure the performance of the USLE. Estimates of soil loss using the USLE were compared with measured values on 208 natural runoff plots, representing >1700 plot years of data, to assess the error associated with the USLE predictions. The overall Nash‐Sutcliffe model efficiency was determined to be 0.75 on an average annual basis and 0.58 when compared on a yearly basis. The USLE overpredicted soil loss on plots with low erosion rates while the plots with higher rates were underpredicted. Of the USLE parameters, the topographic factor ( LS ) and the cover and management factor ( C ) had the most influence on the model efficiency. Confidence intervals for USLE predictions were developed and showed that the accuracy of the USLE in terms of percentage difference between predicted and expected values increases with increasing values of total soil loss. It was also shown that there was no significant difference between the average magnitude of error for pre‐ and post‐1960 data sets and that the use of rainfall and runoff factor ( R ) values instead of calculated erosion index (EI) values resulted in a drop in model efficiency of 0.02. One must use caution in applying the results of this error analysis to conditions in which they may not be applicable, due to the limited nature of this data set.

The midwestern United States offers some of the most productive agricultural soils in the world. Given the cool humid climate, much of the region would not be able to support agriculture without subsurface (tile) drainage because high water tables may damage crops and prevent machinery usage in fields at critical times. Although drainage is designed to remove excess soil water as quickly as possible, it can also rapidly transport agrochemicals, including phosphorus (P). This paper illustrates the potential importance of tile drainage for P transport throughout the midwestern United States. Surface runoff and tile drainage from fields in the St. Joseph River Watershed in northeastern Indiana have been monitored since 2008. Although the traditional concept of tile drainage has been that it slowly removes soil matrix flow, peak tile discharge occurred at the same time as peak surface runoff, which demonstrates a strong surface connection through macropore flow. On our research fields, 49% of soluble P and 48% of total P losses occurred via tile discharge. Edge-of-field soluble P and total P areal loads often exceeded watershed-scale areal loadings from the Maumee River, the primary source of nutrients to the western basin of Lake Erie, where algal blooms have been a pervasive problem for the last 10 yr. As farmers, researchers, and policymakers search for treatments to reduce P loading to surface waters, the present work demonstrates that treating only surface runoff may not be sufficient to reach the goal of 41% reduction in P loading for the Lake Erie Basin.

Abstract Detachment of soil particles by shallow surface flow is influenced by soil cohesion, soil aggregate properties, and hydraulic flow characteristics. This study was conducted to evaluate relationships between soil detachment rates, soil aggregate size, soil tensile strength, flow shear stress, and flow stream power. Detachment rates were measured in a hydraulic flume on three aggregate size classes of two soils. Tensile strength and wet aggregate stability were measured to characterize each aggregate class. Flow depths ranged from 0.5 to 2.0 cm and slopes ranged from 0.5 to 2%. Detachment rates increased with both increasing flow depth and increasing bed slope. Multiple linear regression analyses indicated that the logarithm of detachment rate vs. flow depth, slope, and either mean weight diameter or tensile strength were good models ( R 2 = 0.94 and 0.91, respectively) for predicting detachment rates. Detachment rate for a given soil material was not a function of either shear stress or streampower of the flow. The largest size class of aggregates were detached at a faster rate than the smaller two aggregate size classes.

The Soil Management Assessment Framework (SMAF) was developed to assess conservation effects on soil, and uses multiple soil quality indicator measurements to compare soil functioning. Our objective was to develop a SMAF‐compatible scoring equation for soil β‐glucosidase (BG) activity using published data sets representing different soils and management. The resulting equation was an S‐shaped curve: y = a /[1 + b exp(− cx )], where x is the measured BG activity (mg p ‐nitrophenol [PNP] released kg −1 soil h −1 ), a and b are constants, and c is a factor modified by soil classification, texture, and climate. Data from a study conducted near Mandan, ND were used to test the model for sensitivity to crop management systems. Soil organic C (SOC) content at the site measured 247 to 687 g kg −1 , while BG activity ranged from 33 to 675 mg kg −1 h −1 Using SMAF, SOC indicator scores ranged from 0.25 to 0.73, while BG activity scores varied from 0.17 to 0.93. As the work progressed, it became apparent that when BG activity values were normalized to the SOC content, the resulting ratio could indicate C sequestration trends, with ratios of 10 to 17 g PNP kg −1 SOC h −1 reflective of systems in equilibrium. Ratios >17 were mostly from recently altered management systems with SOC contents trending upward, while ratios <10 were generally from soils that were expected to continue to lose soil C. The application of a sensitive C cycling enzyme activity such as BG should improve the SMAF soil quality assessments for soil functions where soil metabolic activity or C‐cycle enzyme activity play a role.

Soil erosion on hillslopes occurs by processes of soil splash from raindrop impacts and sediment entrainment by surface water flows. This study investigates the process of soil erosion by surface water flow on a stony soil in a semiarid environment. A field experimental method was developed whereby erosion by concentrated flow could be measured in predefined flow areas without disturbing the soil surface. The method allowed for measurements in this study of flow erosion at a much wider range of slopes (2·6 to 30·1 per cent) and unit discharge rates (0·0007 to 0·007 m2 s−1) than have been previously feasible. Flow velocities were correlated to discharge and hydraulic radius, but not to slope. The lack of correlation between velocity and slope might have been due to the greater rock cover on the steeper slopes which caused the surface to be hydraulically rougher and thus counteract the expected effect of slope on flow velocity. The detachment data illustrated limitations in applying a linear hydraulic shear stress model over the entire range of the data collected. Flow detachment rates were better correlated to a power function of either shear stress (r2 = 0·51) or stream power (r2 = 0·59). Published in 1999 by John Wiley & Sons, Ltd.

Abstract Recent breakthroughs in remote‐sensing technology have led to the development of high spectral resolution imaging sensors for observation of earth surface features. This research was conducted to evaluate the effects of organic matter content and composition on narrow‐band soil reflectance across the visible and reflective infrared spectral ranges. Organic matter from four Indiana agricultural soils, ranging in organic C content from 0.99 to 1.72%, was extracted, fractionated, and purified. Six components of each soil were isolated and prepared for spectral analysis. Reflectance was measured in 210 narrow (10‐nm) bands in the 400‐ to 2500‐nm wavelength range. Statistical analysis of reflectance values indicated the potential of high dimensional reflectance data in specific visible, near‐infrared, and middle‐infrared bands to provide information about soil organic C content, but not organic matter composition. Although reflectance in the visible bands (425–695 nm) had the highest correlation ( r = −0.991 or better) with organic C content among the soils having the same parent material, these bands also responded significantly to Fe‐ and Mn‐oxide content. For soils formed on different parent materials, five long, middle‐infrared bands (1955–1965, 2215, 2265, 2285–2295, and 2315–2495 nm) gave the best correlation ( r = −0.964 or better) with organic C content. Several wavebands were identified in which the soils were separable, but the reflectance response was dominated by soil factors other than organic matter content, indicating that choice of wavebands should not be based on spectral curve separability alone.

Understanding and quantifying the large, unexplained variability in soil erosion data are critical for advancing erosion science, evaluating soil erosion models, and designing erosion experiments. We hypothesized that it is possible to quantify variability between replicated soil erosion field plots under natural rainfall, and thus determine the principal factor or factors which correlate to the magnitude of the variability. Data from replicated plot pairs for 2061 storms, 797 annual erosion measurements, and 53 multi‐year erosion totals were used. Thirteen different soil types and site locations were represented in the data. The relative differences between replicated plot pair data tended to be lesser for greater magnitudes of measured soil loss, thus indicating that soil loss magnitude was a principal factor for explaining variance in the soil loss data. Using this assumption, we estimated the coefficient of variation of within‐treatment, plot replicate values of measured soil loss. Variances between replicates decreased as a power function of measured soil loss, and were independent of whether the measurements were event‐, annual‐, or multi‐year values. Coefficients of variation ranged on the order of 14% for a measured soil loss of 20 kg/m 2 to greater than 150% for a measured soil loss of less than 0.01 kg/m 2 These results have important implications for both experimental design and for using erosion data to evaluate prediction capability for erosion models.

Abstract Recently proposed relationships for the effect of slope steepness on soil loss by water are linear functions of the sine of the slope angle. The Revised Universal Soil Loss Equation (RUSLE) uses two such functions: one for slopes <9% and another for slopes >9%. Recent research indicates that yet a different linear function is necessary for slopes greater than approximately 22%. The objective of this study was to develop a single slope steepness function that is representative of the data for all slopes. The resultant equation takes the form of a logistic function. It closely follows the RUSLE relationships for the slope steepness factor for slopes up to 22%, and also fits existing data for slopes greater than those from which the RUSLE relationships were derived.

Climate in the United States is expected to change during the 21st century, and soil erosion rates may be expected to change in response to changes in climate for a variety of reasons. This study was undertaken to investigate potential impacts of climate change on soil erosion by water. Erosion at eight locations in the United States was modeled using the Water Erosion Prediction Project model modified to account for the effects of atmospheric CO 2 concentrations on plant growth. Simulated climate data from the U.K. Meteorological Office's Hadley Centre HadCM3 Global Circulation Model were used. The results indicated a complex set of interactions between the several factors that affect the erosion process. Direct effects of rainfall increases and decreases to runoff and erosion increases and decreases were observed but were often not dominant. One of the key factors of change in the system was the biomass production. Changes in soil moisture, atmospheric CO 2 concentration, temperature, and solar radiation each impacted the biomass production at differing levels at the eight different sites. Different types of changes occurring at different periods of the year also complicated the response of the system. Overall, these results suggest that where precipitation increases are significant, erosion can be expected to increase. Where precipitation decreases occur, the results may be more complex due largely to interactions of plant biomass, runoff, and erosion, and either increases or decreases in overall erosion may be expected.

Abstract Many transport processes on or across the soil surface boundary are controlled by surface microtopography, or roughness. How roughness affects the transport process depends on the length scale of the process. The most commonly used method of expressing soil surface roughness, the roughness length or random roughness, is contrained by the measurement technique and does not embody the concept of scale. The structural function, or variogram, plotted on a log‐log scale was used in this study to express the surface roughness at different scales. With the aid of a laser scanner, surface topography was measured down to 0.5‐mm grid spacing. Data collected from a variety of surface conditions showed that soil roughness can be quantified by a combination of fractal and Markov‐Gaussian processes at different scales. Potential applications of the roughness quantification were also discussed.

Abstract A 0.5‐ha watershed of Rayne silt loam on 9% slope at Coshocton, Ohio was farmed for 20 yr in continuous no‐till corn ( Zea mays L.). With average rainfall >1 m/yr, runoff from this mulch‐covered surface averaged <2 mm/yr. Previous dye studies show that even at low rainfall rates, water moves rapidly through vertically continuous macropores (mainly earthworm burrows) in this field that hasn't been tilled since 1960. To characterize the distribution of these pores, we photographed cleaned, horizontal 30.5‐ by 30.5‐cm 2 surfaces at depths of 2.5, 7.5, 15, and 30 cm. The images were scanned with an image analyzer to count and determine the size of open pores. With eight replications at each depth, total number of pores >0.4 mm in diameter per m 2 of surface area ranged from 3369 to 21 151 in the 2.5‐cm depth and from 5673 to 28 966 at the 30‐cm depth. The overall average was 14 576 pores per m 2 , 160 of which were >5 mm in diameter. Mean pore diameter ranged from 1 to 2 mm at all depths and the number of pores was inversely proportional to pore diameter. There were more pores at the lower depth than near the surface. Pores >0.4 mm in diameter accounted for approximately 1.4% of the total area.

Abstract The effects of six rates of applied wheat straw mulch on infiltration and erosion were studied on a highly permeable Wea silt loam with 5% slope. Series of simulated rainstorms totaling 6.25 inches at an intensity of 2.5 inches per hour were used to evaluate the treatments. Mulch applications of 1, 2, and 4 tons per acre maintained very high infiltration rates resulting in essentially no erosion. The ¼‐ and ½‐ton mulch application lost 3 tons and 1 ton of soil per acre, respectively, whereas the check (no mulch) treatment lost 12 tons per acre. Benefits which were indicated from the mulching were: (1) reduced soil surface sealing as evidenced by higher infiltration rates, and (2) decreased rainfall and runoff energy for particle detachment and transport as evidenced by reduced soil content in the runoff.