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a USDA–ARS, P.O. Box 459, Mandan, ND 58554 USA
b USDA–ARS retired, 226 E. Circle Dr., Canyon City, CO 81212 USA
c USDA–ARS, 3810 4th St., Lubbock, TX 79415 USA
d Custom Products & Consultants, 7204 S. Frontage Rd., Big Spring, TX 79720 USA
e Box T0410, Tarleton State Univ., Stephenville, TX 76402 USA
f USDA–ARS, P.O. Box E, Ft. Collins, CO 80522 USA
merrills{at}mandan.ars.usda.gov
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ABSTRACT |
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 TOPNOTES ABSTRACT INTRODUCTIONMaterials and methodsResults and discussionREFERENCES |
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Abbreviations: ASD, aggregate size distribution • CC, fraction of canopy cover • COG, RWEQ combined residue–plant materials factor • Cpar, soil chain roughness measured parallel to tillage orientation • Cper, soil chain roughness perpendicular to tillage orientation • CT, conventional-till • EF, erodible fraction • GMD, geometric mean diameter • H, ridge height • K, RWEQ soil roughness factor • Kr, ridge roughness factor • LR, low-residue (tillage) • MT, minimal-till • NT, no-till • Q, transported soil • Qmax, wind transport capacity (maximum transported soil) • Rc, rotational coefficient for roughness factor • RWEQ, revised wind erosion equation (model) • s, RWEQ critical field length • S, average interval between ridges • SA, standing residue silhouette area per ground area • SC, percent residue cover • SCF, RWEQ soil crust factor • SIWE, soil-inherent wind erodibility • SLR, soil-loss ratio • SLRC, RWEQ soil loss ratio for plant canopies • SLRF, RWEQ soil loss ratio for flat residue • SLRS, RWEQ soil loss ratio for standing residue • WF, RWEQ weather factor
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INTRODUCTION |
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TOPNOTESABSTRACTINTRODUCTIONMaterials and methodsResults and discussionREFERENCES |
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To what extent can conservation tillage, either no-till or minimal till, reduce the vulnerability of wheat–fallow cropping to wind erosion? Adequate research to answer this question has not been available previously because two necessary elements were lacking: (i) quantitative measurements of both the crop residue and soil factors of wind erodibility in a wheat–fallow system over a multi-year wet–dry weather cycle, and (ii) application of a functional model containing modern observations of wind erosion in the field so that wind erosion hazards may be quantified.
Soil wind erodibility is the tendency of surface soil to resist or be vulnerable to being transported by wind. The ability of wind to cause erosion is strongly and nonlinearly dependent on wind energy and is termed wind erosivity (Lyles et al., 1983). Soil wind erodibility has both soil and plant components. The soil components include (i) soil-inherent wind erodibility (SIWE; Merrill et al., 1999); (ii) soil surface microtopography, often termed soil roughness; and (iii) soil wetness, which includes soil water content and snow coverage. SIWE is defined as the resultant of all properties of soil, excluding soil microtopography and soil wetness, that relate to its wind erodibility. The plant components of wind erodibility include (i) the areal coverage effect of plant residues, and (ii) the profile effect of standing plant residues and plants. Although we define components of wind erodibility for scientific discussion, in nature they interact and are difficult or impossible to separate. For example, aerodynamic roughness depends on both soil roughness and the profile of dead and live plant material. Soil aggregation, which dominates SIWE, affects soil roughness.
Although SIWE includes an array of surface soil properties, as reviewed by Zobeck (1991), it appears to be dominated by the combination of aggregate size distribution (ASD) and aggregate stability. Dry aggregate size distribution was related to wind erosion, according to Chepil (1950), who observed that soil aggregates <0.8-mm diam. were highly erodible. The principal instrument used to produce a measurement of dry ASD is the rotary sieve (Chepil, 1962). However, rotary sieves abrade aggregates and thus, for most soils, sieve measurements include some significant component of aggregate stability. This was acknowledged by Chepil (1952), who prescribed measurement of aggregate stability by multiple repassage of soil separates >0.84-mm diam. through the rotary sieve. The erodible fraction (EF) of soil (percentage of aggregates <0.84 mm, based on rotary sieve determination of dry ASD) is currently the principal functional measure of SIWE.
A variety of devices have been used to measure soil roughness, and a number of indices have been prescribed to summarize the data, as reviewed by Zobeck and Onstad (1987). In the past, pin meters have been the dominant devices used for roughness. Saleh (1993) has described roughness measurement by use of a roller chain, which is more convenient than using a pin meter. The validity of roughness measurement using a single roller chain has been discussed by Skidmore (1997) and Saleh (1997). Merrill (1998) has shown theoretically that using a set of chains with different linkage lengths will overcome any problems of invalid measurements due to possible scale insensitivity arising from the use of a single, finely linked chain.
The effect of soil roughness on wind erosion has been studied using wind tunnels (Fryrear, 1984; Hagen and Armbrust, 1992).
The plant residue and plant factors of wind erodibility are measured as percent surface coverage for flat residue and as area of profile per unit land area for standing residue (stubble) and plants. Bilbro and Fryrear (1994) have summarized wind tunnel research relating crop plant residue factors to wind-erosion potential.
Long-term studies of SIWE have been carried out in spring wheat–fallow systems using rotary-sieve measurements of ASD. Larney et al. (1994) studied effects of tillage on over-winter ASD changes during the fallow phase of spring wheat–fallow cropping for 5 yr. The study that most clearly demonstrated effects of a multi-year wet–dry weather cycle on the SIWE aspect of wind erodibility was conducted by Bisal and Ferguson (1968) in Saskatchewan on spring wheat–fallow. Their ASD measurements on three soil types over a 12-yr period showed that multi-year drought caused large increases in erodible fraction. Moulin and Townley-Smith (1993) used 14 years of ASD data from Saskatchewan spring wheat–fallow to show that year-to-year variations due to weather changes were greater than the effects of tillage or herbicide usage.
The wind-erosion hazards implied by measured soil wind erodibility values in a cropping system may be estimated by two current wind erosion models. Scientists of the USDA–ARS have developed a deterministic and process-oriented model known as the Wind Erosion Prediction System (WEPSWEPS: Hagen, 1991). USDA–ARS scientists have also developed another process-based but more empirical model using a factor multiplication approach that is known as the Revised Wind Erosion Equation (RWEQRWEQ: Fryrear et al., 1998). Both models had available for their development numerous wind-tunnel studies of erodibility factors and field measurements of wind erosion during storm events (Fryrear et al., 1991; Fryrear and Saleh, 1996; Fryrear et al., 1998).
We present here a report on the measurement of soil and crop residue factors of wind erodibility over a major portion of a significant Great Plains weather cycle. Measurements were made on a normally non-wind-erodible soil under a spring wheat–fallow rotation using a spectrum of modern tillage practices. The wind-erosion hazard has been estimated by using the RWEQ model.
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Materials and methods |
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TOPNOTESABSTRACTINTRODUCTIONMaterials and methodsResults and discussionREFERENCES |
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Tillage was conducted during the fallow year and immediately before seeding during the crop year. Tillage treatments (Table 1) were managed to attain various levels of residue cover after seeding: (i) low-residue (LR) tillage—10% or less cover by disk and undercutter (sweeps); (ii) conventional-till (CT)—0 to 30% cover by tandem disk and undercutter; (iii) minimal-till (MT)—30 to 60% cover primarily by undercutter; and (iv) no-till (NT)—60% or greater cover. Herbicides were used in all treatments, but were used most often in MT and NT treatments.
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Two series of plots existed—Series A, fallowed in even-numbered years, and Series B, fallowed in odd-numbered years. Besides tillage, there were two crop cultivar treatments and three N fertilization levels. The experimental design was strip-strip-split plot, with tillage and N-fertilization stripped, and cultivar as subplots. Cultivar and N-fertilization appeared to have relatively low effect on residue production and other wind-erodibility factors compared with the effects of tillage and climate variance. Thus, wind-erodibility measurements were taken without regard to crop cultivar or N-fertilization. There were three replications of treatments. The CT, MT, and NT treatments were randomized within one set of 73- by 137-m field blocks initiated in 1984. The LR treatment was carried out on another 73- by 34-m set of field blocks initiated in 1987 that were interspersed among the blocks carrying the other three tillage treatments. Field blocks were contained within a 22-ha area.
Wind-Erodibility Measurements
Surface soil was sampled about every 30 d when soil was unfrozen and not covered by snow. Two triple-composited samples were taken from each replicate tillage plot to a depth of 
3 cm with a flat-bottomed shovel fitted with depth guides. After air drying at 30°C, ASD was determined by passing 1.1 kg of soil through a rotary sieve described by Chepil (1962). This sieve produces size fractions of <0.42 mm, 0.42 to 0.84 mm, 0.84 to 2 mm, 2 to 6 mm, 6 to 19.2 mm, and >19.2 mm. We have analyzed and displayed ASD data as EF and as geometric mean diameter (GMD), which assumes aggregate weight is distributed log-normally with size (Gardner, 1956). Geometric mean diameter has a nonlinear, inverse relationship to EF.
Soil surface roughness was measured in 1989 and 1990 with a pin meter (Zobeck and Potter, 1988), an instrument in which the heights of 40 pins in a 1-m wide rack were electronically logged at 20 positions in a 1-m2 area. The 40-pin rows of readings were perpendicular to the north-south tillage direction. One set of pin meter readings was made in each replicate tillage plot on a given date. During the period of this study, pin meter readings were not made in 1988 nor in 1991 to 1992.
Starting in 1993, surface roughness was measured by the chain method (Saleh, 1993), using a 1-m roller chain with 0.95-cm links. Five parallel- and perpendicular-to-tillage pairs of chain readings were taken in each tillage plot, with nonburied surface residues gently removed before laying the chain. Soil roughness, Cr, measured by chain, is expressed as a percentage value in Eq. [1]:
 | (1) |
Pin meter measurements were converted to chain roughness values by analysis of simultaneous pin meter and chain measurements taken on plots of the LR, MT, and NT treatments. For each correlative set (used for pin-to-chain conversion) of pin meter readings, 12 parallel and perpendicular chain measurements were made in the same 1-m2 area. Tortuosity index value was calculated for each row and each column of both experimental and correlative pin meter data sets by dividing the distance defined by the line through the ends of the pins by the horizontal span of the pin row or column. Tortuosity values for pin elevation columns were related to parallel-to-tillage chain readings and values for pin elevation rows to perpendicular-to-tillage readings by linear regression (with intercepts fixed at zero, R2 = 0.96 for the parallel set, and R2 = 0.82 for the perpendicular set).
Crop residue coverage was measured by evaluating 1-m2 areas in downward-view photograph slides taken with a wide-angle lens. Three photographs were usually taken in each tillage plot, and the residue cover percentage was evaluated by scoring 50 points on each slide. Weed plants were counted as residue.
Standing residue profile was measured by horizontal-view photographs taken with a telephoto lens at a distance of 5 m from a backboard placed parallel to seeding direction. Usually three photograph slides per plot were taken, and each slide was evaluated at 500 points. Residue silhouette area per unit of ground area was calculated from a calibration of stem number, width, and height.
The RWEQ Model and Its Application to Wind-Erodibility Measurements
The RWEQ model (Fryrear et al., 1998) is an estimator of long-term soil loss due to wind erosion. Based on field observations of soil movement resulting from windstorms (Chepil, 1946; Fryrear et al., 1991; Fryrear and Saleh, 1996), soil movement is represented by a steady state equation that assumes the existence of a wind transport capacity. If properties are assumed to be uniform, then the amount of soil transported past a point x downwind of the edge of an erodible area may be expressed as shown in Eq. [2] (Fryrear and Saleh, 1996):
 | (2) |
Average soil loss (kg/m2) for a field of length y is calculated as E = Q(y)/y. The parameters Qmax and s are determined by equations (Eq. [3] and [4]) carrying wind-erosivity and wind-erodibility factors (Fryrear et al., 1998):
 | (3) |
 | (4) |
The weather factor, WF, is the product of a wind-erosivity factor and two wind-erodibility factors, one for soil water content and the other for snow cover. Wind erosivity in the WF is a wind energy value derived from a statistical distribution of recorded wind speeds at a location (Skidmore and Tatarko, 1990; Fryrear et al., 1998). All of the wind-erodibility factors in RWEQ, with the exception of EF, are used in the form of a soil-loss ratio (SLR), which is the value of soil loss with the factor present divided by soil loss without the factor (0 
SLR 1). We used the RWEQ (Fryrear et al., 1998) to calculate WF for Bismarck, ND, which is <10 km from the field site. Equations specified in RWEQ (see below) were used with measured data to calculate the other wind-erodibility factors that appear in Eq. [3] and [4] for dry (1989–1990) and wet (1992–1994) periods (Table 5)
. Estimated soil losses were computed for 15-d or lesser periods (following RWEQ practice) for a flat field 400 m on a side with uniform soil properties.
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EF 1). The soil crust factor (SCF) is set to a value of 1 immediately after tillage. Once 12 mm or more precipitation had accumulated since tillage, SCF was calculated with an equation (Eq. [5]) largely based on research by Hagen et al. (1992):
 | (5) |
The RWEQ soil roughness factor, K, was calculated from chain roughness values in the following manner: (i) Oriented chain roughness values were determined as differences between perpendicular-to-tillage and parallel-to-tillage chain readings (Cper - Cpar; Table 3)
. (ii) Ridge heights, H, were calculated by assuming that oriented roughness could be represented by triangular tillage ridges and that the chain value for oriented roughness represented the surface tortuosity of such ridges (distance along the surface perpendicular to the ridges), and by using the field observation that the average interval between ridges, S, was approximately 25 cm. (iii) Values of H were then used (Eq. [6]) to determine Kr, the ridge roughness factor as:
 | (6) |
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 | (7) |
 | (8) |
The RWEQ combined residue–plant materials factor, COG, is the product of factors for flat residue (SLRF), standing residue (SLRS), and crop plants (SLRC). The equation for flat residue (Eq. [9]) depends on percent cover, SC:
 | (9) |
The equation for standing residue (Eq. [10]) depends on the silhouette area of residue per unit of ground area (SA in cm2/m2):
 | (10) |
Both residue equations are from Bilbro and Fryrear (1994).
The crop factor is found in Eq. [11] as
 | (11) |
 | (12) |
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Results and discussion |
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TOPNOTESABSTRACTINTRODUCTIONMaterials and methodsResults and discussionREFERENCES |
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Soil Surface Roughness
Roughness measurements made by pin meter in 1989 to 1990 and by chain in 1993 to 1994 are shown in Table 3. Pin meter measurements have been converted to chain roughness values. Measurements made perpendicular to tillage and seeding direction (Cper ) represent the combination of oriented (ridge) and random roughness, while parallel measurements (Cpar) indicate random roughness alone. Thus, Cper values were greater than or equal to Cpar values in 41 out of 43 pairs of values in Table 3.
Roughness values for LR, CT, and MT treatments were greater than values for the NT treatment, as would be expected. Tillage was generally not deeper than 5 to 10 cm, and this is reflected in moderate to low roughness values. The highest roughness values in 1989 to 1990 were measured in May 1989, and primarily reflected effects of seed drill disturbance. Roughness values were higher in 1993 to 1994, and measurements made after seeding were similar in value, compared with those made in the first-year-fallow period.
The K-factor values (Table 3) calculated according to RWEQ model practice indicate the degree of protection against erosion attributable to soil roughness (K = 1.0 means no protection). K-factor values ranged from 0.87 to 0.16 in 1989 to 1990, and from 0.40 to 0.10 in 1993 to 1994.
The higher roughness values measured in 1993 to 1994 compared with 1989 to 1990 appear to be due to the fact that (i) less tillage was performed in 1989 to 1990 because of drier conditions (Table 1), and tillage caused less clod formation because of observably lower aggregate stability; and (ii) residue amounts were greater in 1993 to 1994, and the incomplete removal of flat residues before chain measurements could have inflated their values. The accuracy of converting pin meter readings to chain roughness is indicated by the fact that roughness values measured by pin meter in the after-seeding period in May 1989 were approximately comparable to chain measurements made during after-seeding periods in 1993 to 1994. Flat residue levels were lowest in the after-seeding period (Table 4) , and chain readings should have been least affected by residue then.
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Reduced crop plant growth in 1988, with average residue yields at harvest of 930 kg/ha (Table 1), resulted in reduced residue protection in NT and MT plots fallowed in 1989. Residue cover percentages in 1989-fallowed plots were 60 and 40% in NT plots during first-year-fallow and second-year-fallow periods, respectively, compared with average values of 84% in 1988 and 83% in 1991 to 1993. However, in plots fallowed in 1990, flat and standing residue values were not greatly different from overall averages, even though they were based on average residue yields of 2440 kg/ha at harvest in 1989. This was considerably lower than residue yields in 1987 and 1991 to 1994, which were all >4100 kg/ha.
Soil-loss ratio (SLR) values calculated from residue measurements (Table 4) show that standing residue was generally more protective (lower SLR values) than flat residue during the first-year-fallow management period. However, during the second-year-fallow period, standing residue SLR values were less protective than flat residue values. This reflects over-winter decay and flattening of standing residues (Steiner et al., 1994). For the NT and MT treatments, standing residue was more protective than flat, or the same, during the first-year-fallow period in 12 out of 14 cases; but flat residue was more protective, or the same as standing in 7 out of 8 cases during the second-year-fallow period. For the LR and CT treatments, standing residue was more protective than flat, or the same, during first-year-fallow in 9 out of 14 cases, but flat residue was more protective than standing during second-year-fallow in 6 out of 8 cases. This indicates that over-winter attenuation of the erosion protective effect is greater for standing residue than for flat.
Further disappearance, decay, and flattening of standing and flat residues occurred in the after-seeding management period, leading to increases in SLR values (Table 4). In the spring of 1990, standing residue SLR values in the NT and MT treatments increased to a significantly lower level of protection (SLR = 0.35 for both treatments). This compares with measurements made after seeding in 1993, which had considerably more protective standing residue SLR values of 0.09 and 0.19 for NT and MT treatments, respectively.
Application of the RWEQ Model
Wind-erosion hazards were quantified by soil losses calculated by applying the RWEQ model equations to measurements (Table 5). Soil losses are summed for crop-management periods. Estimated losses in the drought period 1989 to 1990 were from 11 to 6100 times greater than those estimated to occur in the wetter 1992 to 1994 period.
The much larger soil losses calculated under drought are the result of both soil and residue wind-erodibility factors being higher. The EF as a fraction averaged 0.53 under drought and 0.26 in the wetter period. Soil roughness K-factor for first- and second-year-fallow periods in 1989 to 1990 averaged 0.62, compared with 0.26 for 1992–1994. K-factor values for the after-seeding period, under droughted vs. wetter conditions, were 0.37 vs. 0.14. Crop residue factor values were more protective than roughness K-factor values in first- and second-year-fallow periods, and the products of flat- and standing-residue SLRs (SLRF x SLRS) ranged from 0.34 to <0.01 in 1989 to 1990 and from 0.06 to <0.01 in 1992 to 1994.
The aggregate size distribution (ASD) aspect of SIWE is represented in RWEQ by erodible fraction (EF). The dependence of erosion on EF is assumed to be linear, and as previously noted, EF is used as a decimal fraction in RWEQ. The EF is an indicator of loose erodible material that wind turbulence above the threshold level moves so that the soil surface is abraded during a windstorm (Chepil, 1946; Hagen et al., 1992). Abrasion near the upwind boundary of a field generates more abrader particles, producing intermittent cascades of saltating particles near the soil surface (Stout and Zobeck, 1996).
Because of the nature of the wind-erosion process, and because EF determined by rotary sieve represents aggregate stability as well as a strictly natural ASD, the dependence of soil loss on EF is probably generally nonlinear. According to the earlier Wind Erosion Equation (WEQ) model (Woodruff and Siddoway, 1965), soil loss is nonlinearly dependent on EF to a power greater than two. Thus, potential soil loss during the drought period in our study may have been relatively higher than the 53 vs. 26% difference in measured EF would indicate.
Residues conserved with no-till ordinarily protect soil from wind erosion, and calculated soil losses for the NT treatment were 230 to 3000 times lower than losses for the LR treatment during all periods in 1992 to 1994; NT soil loss was 1400 times lower than that for LR during first-year-fallow in 1989 (Table 5). The MT treatment also offered considerable protective effect, with estimated soil losses 2500 times lower than LR in first-year fallow in 1992 to 1993 and 19 times lower than LR in first-year fallow in 1989. However, low crop plant growth in 1988 (930 vs. 3640 kg/ha 1986–1994 residue yield average: Table 1) led to an insufficient amount of residue remaining in 1990 after progressive losses due to fallowing tillage, over-winter decay, and disturbance by seed drilling (Table 4). Thus, the 0.03 vs. 40 Mg/ha estimated soil loss difference of NT vs. LR in the first-year-fallow period of 1989 was lessened to a 2 vs. 18 Mg/ha difference during the January to May second-year-fallow period in 1990. Disturbance by seeding resulted in effective equality in estimated soil losses in the after-seeding period, 12.5 vs. 8.0 Mg/ha for LR vs. NT. Multi-year drought interacted with the long fallow period of the rotation (21-mo total) to remove protective effects of no-till management.
Thus, in a drought period, the wind-erosion hazard is accelerated (i) because the lack of precipitation leads to lowered residue production and subsequent increased residue-based wind erodibility, and (ii) because SIWE itself is increased. While the influences of environment and management on disappearance and decay of crop plant residues is relatively well understood (Steiner et al., 1994; Schomberg and Steiner, 1997), the relationships of environment to the dynamics of SIWE is currently under-researched and poorly understood (Merrill et al., 1999).
Our study was conducted on a productive, ordinarily nonerodible soil. On less productive, more fragile soils, crop plant growth will be lowered even more under chaotically occurring drought periods, and the protective effects of NT and MT practices will be significantly diminished. A fundamental contradiction exists in the use of crop–fallow rotations in dryland agriculture: the practice is widely believed to produce its greatest economic advantage during drought, when it becomes most dangerous to conservation of surface soil, which is the natural resource of highest societal value.
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ACKNOWLEDGMENTS |
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NOTES |
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TOPNOTESABSTRACTINTRODUCTIONMaterials and methodsResults and discussionREFERENCES |
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Received for publication May 13, 1997.
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TOPNOTESABSTRACTINTRODUCTIONMaterials and methodsResults and discussionREFERENCES |
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