An investigation of spatial variation in soil erosion, soil properties, and crop production within an agricultural field in Devon, United Kingdom.
Both the existence and the importance of spatial variation in soil properties and crop production are axiomatic; nevertheless, current understanding of the causes and sources of variation is incomplete. This understanding is essential for development of cost-effective and environmentally-sustainable farming practices. It has been recognized that variation is the product of the interaction of natural properties and processes (intrinsic variability) with human practices (management-induced variability) and developments in precision farming have sought to overcome the former through spatial variation in the latter. This approach assumes that "natural" soil variation is a static variable on the time scales of concern to land managers. This assumption is called into question if a pedo-geomorphological perspective is taken in which the pattern of soil variation is considered to be the product of continuing landform evolution processes, in particular soil erosion and aggradation. The potential importance of this per spective has become more evident with the growing recognition of tillage erosion as an important erosion process per se rather than a contributor to water erosion (cf. review by Govers et al. 1999). The aim of this study was, therefore, to employ a pedo-geomorphological perspective in the investigation of interrelationships and potential causal links between processes of landscape change and the spatial distributions of soil properties and crop production.
This study builds on progress in the understanding of soil and crop variability and of soil erosion processes. Early studies of the relationships between soil properties and crop variability reflect recognition of catenary variation in soils and were often based on qualitative subdivision of the landscape (Stone et al. 1985; Ovalles and Collins 1986). More recently developments in terrain analysis have permitted quantitative description of the landscape and landscape elements through the use of grid-based terrain models and derived attributes (Miller et al. 1988; Moore et al. 1993; Franzen et al. 1997; Yang et al. 1998; Timlin et al. 1998). Although slope descriptions including gradient, aspect, and curvature are the most commonly used attributes, Moore et al. (1993) demonstrated the potential of using process-based compound terrain attributes in the prediction of spatial variation in the properties of surface soil materials. This study employs these developments in quantitative landscape analysis.
Early studies of the effect of erosion on crop production relied on qualitative or indirect measures of erosion, such as profile truncation and soil color (Stone et al. 1985; Daniels et al. 1985). More recently, advances in the measurement and assessment of erosion, in particular the development of the caesium-137 ([Cs.sup.137]) technique (Ritchie and McHenry 1975; Loughran 1989; Walling and Quine 1991), have facilitated the collection of spatially distributed erosion rate data that may be compared to soil property and crop production data (Moulin et al. 1994). Most studies of erosion-productivity relationships have focused either explicitly or implicitly on water erosion, but there is a growing recognition of the potential importance of tillage erosion especially in topographically complex terrain (Miller et al. 1988; Moulin et al. 1994). More recently, attempts have been made to separate the effects of tillage and water erosion on soil variability (Quine, Walling, and Zhang 1999). This latter development h as been made possible through the growing understanding of tillage erosion derived from experimental investigations (Lindstrom et al. 1990, 1992; Govers et al. 1994; Lobb et al. 1995; Poesen et al. 1997; Quine, Covers, et al. 1999; Van Muysen et al. 1999) and the resultant progress made in the simulation of tillage erosion (Govers et al. 1993, 1994, 1996; Quine et al. 1994, 1997; Quine, Govers, et al. 1999; Lobb et al. 1995; Lobb and Kachanoski 1999).
In a recent review, Goderya (1998) suggested that the soil properties influencing crop yield may be divided into three groups: static soil properties, water transport properties, and fertility properties. In this study, spatially-distributed data were collected for soil properties from the first and third of these groups, in addition to data concerning net soil redistribution, water erosion, and crop production. This study aims to advance understanding by employing this wide range of detailed geo-referenced spatially distributed data in the investigation and explanation of spatial variation in soil properties and crop production.
Methods and Materials
Study area. The field under study is located in the southwest of England 13 km (8.1 mi) from Exeter (3[degrees]35'46" W 50[degrees]51'28" N). The soils have developed from non-calcareous Permian Breccia and Conglomerate and, throughout the field, are classified as typical Brown Earths (Clayden 1971; Findlay et al. 1984) or Dystric Cambisols in the United Nations Food and Agriculture Organization system (Dystric Eutrochrept in the United States Department of Agriculture soil classification system). The only variation mapped by Clayden (1971) within the field is a division according to the texture of the parent material into Shaldon series developed from "Clayey" breccia over the upper part of the field (above the 114 m [347 ft] contour - Fignre 1a) and the Crediton series developed from "Loamy" breccia on the lower part of the field. Despite this mapped variation and the complex relief, the soils of the field show very little physical variation. In particular, no systematic variation in texture of the Ap or t he B horizons was observed, and there was no evidence for higher clay contents over the upper part of the slope (the weak, statistically insignificant trend was the reverse).
The climate is maritime, but the study area has a long-term mean annual rainfall of 845 mm (33.3 in) (Applegarth and Cornish 1982) that is amongst the lowest in the region because it lies in the rain-shadow of the Dartmoor upland to the south. The area is well suited to agricultural cropping with its mild temperatures and high sunshine rates, although frost incidence and occasional summer drought affect crop growth on certain topographic positions. Water erosion of soil on the arable land is usually associated with low-intensity, long-duration rainfall during winter months.
Coombe Barton Farm is part of a typical family-run, heavily mechanized farm. Cereal production has been practiced for several decades with a rotation system including barley, wheat, oat, and green cover grass. The fields are delineated by well-maintained living hedges. The annual tillage cycle includes one pass of a moldboard plow and two or more passes of a disk or cultivator harrow Composite chemical fertilizer and organic manure are uniformly applied throughout the field. In the two years preceding the study the sampled field had been planted to winter wheat.
Field survey and sampling strategies. In the autumn of 1997, the field was surveyed and soil samples obtained. Crop samples were collected during the autumn of 1998. The field terrain was surveyed on an approximate 10-m grid using a TOTAL station (Wild, Leica Geosystems, Heerbrugg Switzerland) composed of an electronic theodolite (Wild T1000), infrared distomat, and a data logger (GRE3); the same equipment was used to record all sample locations. Soil core samples were collected using steel core tubes with an internal diameter of 6.9 cm (2.7 in) and length of 65 cm (25.6 in). These tubes were driven into the ground using a motorized percussion corer. In total, 245 soil samples were collected from 19 transects parallel with the tramlines (to facilitate later collection of crop samples from the same locations). Each sample was separated in situ into current plow layer ([A.sub.p] samples) and subplow layer (B samples) based on knowledge of the plow depth and on contrasts in color, structure, texture, and compac tness. The same sampling locations were revisited in the autumn of 1998 to collect crop samples. In practice, relocation of some sample points was problematic because of the standing crop; however, all data used are from crop samples located within 3 m of the associated soil sample. Figure 1 shows the field topography and locations of soil samples. For each crop sample, the wheat within a 1-x-1-[m.sup.2] area was cut with garden shears immediately above the ground and returned to the laboratory for measurement. During November 1998, after seeding of the study field, a prolonged period of rainfall resulted in extensive water erosion. The extent of the rill network and location of the major channels were recorded using the TOTAL station.
Crop sample treatment. For each crop sample, the grain was separated from the stalk, and both were weighed to provide field-dry weights per 1-[m.sup.2] sampled area of grain ([F.sub.g]) and stalk ([F.sub.s]). A subsample of each grain and stalk sample was then oven-dried to establish field moisture content of grain ([W.sub.g]) and stalk ([W.sub.s]). These data were used to derive dry aboveground biomass and dry yield:
AGB = ([F.sub.g](1 - [W.sub.g])) + ([F.sub.s](1 - [W.sub.s])) (1)
Y = ([F.sub.g](1-[W.sub.g])) (2)
AGB is aboveground biomass (kg [m.sup.-2]).
F is field dry weight per unit area (kg [m.sup.-2]).
W is moisture content of field dry samples (kg [kg.sup.-1]).
Y is yield (kg [m.sup.-2]).
Soil sample treatment and analysis. All soil samples were oven-dried for 24 hours at 105[degrees]C and then disaggregated by hand and machine and sieved through a 2-mm mesh. The >2-mm fraction was weighed and stored, and the <2-mm fraction was weighed (a kg for plow - [A.sub.p] - samples; b kg for sub-plow - B - samples) and used in all subsequent analyses.
A subsample of the total core was then prepared for [Cs.sup.137] analysis by careful mixing of (a/(a+b)) kg of the plow layer <2-mm sample with (b/(a+b)) kg of the sub-plow layer <2-mm sample This total core subsample was then packed into a marinelli beaker, and [Cs.sup.137] was measured by gamma spectrometry, using a coaxial germanium detector and multi-channel analyzer system (EG&G ORTEC, PerkinElmer, Inc., Shelton, CT). Counting time of ca. 30,000s gave an analytical precision of 10%. These measurements of [Cs.sup.137] activity were converted into [Cs.sup.137] inventories as follows:
[C.sub.p] = [C.sub.a] (a+b) / [X.sub.c] (3)
[C.sub.p] is the [Cs.sup.137] inventory at the sampling point (Bq [m.sup.-2]).
[C.sub.a] is the measured [Cs.sup.137] activity of the analyzed subsample (Bq [kg.sup.-1]).
[X.sub.c] is the cross-sectional area of the core tube ([m.sup.2]).
Plow and sub-plow samples (<2-mm fraction) were analyzed for total carbon and total nitrogen with an NA2500 C/N Analyzer (CE Instruments, Milan, Italy) and for total phosphate and inorganic phosphate using a SP6 UV/VIS Spectrophotometer (UNICAM, Thermo ONIX Corp., Angleton, TX) following extraction with 1.0 N sodium hydroxide and 1.0 N hydrochloric acid. Samples analyzed for particle size composition were pretreated with hydrogen peroxide and 0.4% sodium hexametaphosphate and then measured using a Malvern Mastersizer (Malvern Instruments Ltd., Worcestershire, UK). The percentages of sand (<2-mm and >0.06mm), silt (.06 mm and >0.002-mm), and clay (<0.002-mm) fractions were calculated according to the British Standard.
Pattern simulation, data integration, and data analysis. All the statistical analyses were carried out using MINITAB software (version 13.1). The TBGIS interface (Zhang 1996) was used to access ARC/INFO sub-routines in the analysis of the topographic data and in the management and integration of soil property and crop production data. A digital terrain model (DTM) was created from the topographic survey data using the spline interpolation method, and the DTM was used to derive the following topographic attributes: slope, profile curvature, planform curvature, and upslope contributing area. TBGIS was also used to simulate tillage erosion using the simple diffusive model that was proposed by Govers et al. (1993, 1994, 1996) and has since been used extensively (Quine et al., 1994, 1996, 1997; Quine, Walling, and Zhang 1999; Quine, Walling, et al. 1999):
Qt = [k.sub.1]S (4)
Qt is the soil flux due to tillage (kg [m.sup.-1]).
[k.sub.1] is the tillage flux coefficient (kg [m.sup.-1]).
S is the local slope gradient (tangent).
Where only the pattern of tillage erosion is of interest, the value of [k.sub.1] may be set to 1 to provide relative values of Qt.
TBGIS was also used to simulate the pattern of potential overland flow erosion based on the stream power index used by Moore et al. (1993):
[OMEGA]=S [A.sub.s] (5)
[OMEGA] is the stream power index.
[A.sub.s] is the specific catchment area ([m.sup.2] [m.sup.-1]).
The [Cs.sup.137] data may be used directly to examine qualitative patterns of soil redistribution by calculating [Cs.sup.137] percentage residuals:
[C.sub.r] = 100 ([C.sub.p] - [C.sub.f])/[C.sub.f] (6)
[C.sub.r] is the [Cs.sup.137] percentage residual
[C.sub.f] is a measure of the fallout inventory of [Cs.sup.137] (in this case 250[+ or -]10 Bq [m.sup.-2]), derived by sampling an undisturbed, uneroded site adjacent to the sampled field.
Negative percentage residuals indicate erosion, and positive residuals indicate aggradation. The magnitude of the residual is approximately proportional to the severity of erosion or aggradation. In this study, the [Cs.sup.137] residuals have been used directly as measures of net soil redistribution. This avoids the need to make assumptions regarding process and erosion rate calculation.
A range of approaches has been proposed for the conversion of percentage residuals into quantitative estimates of erosion and these have been reviewed elsewhere (e.g., Walling and Quine 1990; Quine 1995; Quine et al. 1996). In this study, the only use of [Cs.sup.137] for erosion rate estimation was in the calibration of the tillage erosion model to derive the appropriate value of [k.sub.1]., which followed the approach explained in detail in Quine et al. (1997). The measured [Cs.sup.137] data were compared with [Cs.sup.137] data derived as a result of simulated tillage erosion using TBGIS for a range of values of [k.sub.1] (Equation 4) until optimum agreement was obtained between simulated and measured [Cs.sup.137] residuals. Optimum agreement was obtained with a [k.sub.1] value of 520 kg [m.sup.-1] [year.sup.-1] (350 lb [ft.sup.-1] [year.sup.-1]), and this was used to produce a spatial distribution of tillage erosion rates over the last four decades. However, it is important to emphasize that in the analysi s that follows, the use of [Cs.sup.137] residuals as indicators of net soil redistribution makes no assumption regarding erosion process, and the simulated tillage erosion pattern is independent of the magnitude of the [k.sub.1] value.
Results and Discussion
The integrated site-specific data were divided into four broad groups: terrain attributes, soil properties, crop production parameters, and measures of soil redistribution. Only the crop production parameters satisfied the Anderson-Darling test for normality, and therefore, non-parametric statistical tests were used in the exploration of interrelationships. A list of summary statistics for terrain attributes and measured data can be seen in Table i.
Topography and soil variability. The terrain attributes derived from the DEM provide a satisfactory basis for the subsequent non-parametric analysis, even though the absolute values are scale-dependent. The range of slope angles cultivated in the region is well represented by the large variation encountered at the field site, and the topographic complexity of the landscape is reflected in the very high variability in plan and profile curvature.
The soil physical properties, such as bulk density and particle size composition, demonstrate lower variability than the chemical properties. The calculated coefficients of variance range from 7-18% and 17-25%, respectively, in the plow soil. Variability is higher and differences between physical and chemical properties are more pronounced in the sub-plow layer, where variance ranges are 20-30% and 45-80%, respectively. Crop production parameters exhibit similar variability to the chemical properties of the plow soil. When the data for this site are compared to the studies reviewed by Goderya (1998), variability in this field is seen to be higher for hulk density (6-14%) and crop yield (8-15 and 29%), lower for sand (17-83%) and clay (9-45%), and within the range of previously observed values for the other properties.
The soil and terrain variables have been identified as non-normal in distribution, and it has been suggested by Ovalles and Coffins (1986) that this is an indication of systematic rather than random variation. Further investigation of potential systematic variation was undertaken by examination of bivariate relations between soil properties and topographic attributes. The Spearman's correlation coefficients for these relations are shown in Table 2. Profile curvature is seen to be strongly related (95%) to all of the soil properties and very strongly related (99%) to total N, total C, and total P. In profile curvature, a negative sign for the correlation coefficient indicates depletion on convexities and enrichment in concavities, while for plan curvature this association is reflected in a positive sign for the coefficient. The relationships between the soil properties and plan curvature are weak, but the topographic association indicated by the two measures of curvature is consistent. Convexities are associa ted with depletion of total N, total C, total P, inorganic P, and silt, and with enrichment of sand. It is interesting to note that slope is only statistically significantly related to P (total and inorganic)--the sign of the relationship indicating enrichment in areas of low slope angle and depletion on steep slopes.
While the terrain and soil data and the associated relations provided evidence for the existence of systematic variation in soil properties, it remained necessary to identify the cause of this variation. This was investigated by examining relations between the soil properties and measures of erosion.
Soil redistribution and variability of soil properties. The most direct evidence for soil redistribution within the field site is provided by the [Cs.sup.137] percentage residuals. Comparison of these with the soil properties makes no assumption regarding erosion process, but allows evaluation of a possible link between soil variability and spatial variation in erosion and deposition intensity. Since non-parametric correlation analysis is used, the [Cs.sup.137] percentage residual is a particularly appropriate and robust indicator of soil redistribution intensity, as no assumptions need to be made concerning conversion of [Cs.sup.137] data to erosion rate estimates.
The correlation of [Cs.sup.137] with the measured soil properties can be seen in Table 2. These data show that all the measured soil properties are significantly ([alpha] = 0.05) correlated to the [Cs.sup.137] residual, and all except inorganic P are strongly ([alpha] = 0.01) correlated. This indicates that the variation of soil properties within the field is significantly related to the pattern of net soil redistribution that has occurred over the last four decades. Furthermore, for all soil properties except inorganic P, the correlation coefficients suggest that interrelations with [Cs.sup.137], and therefore net soil redistribution, are stronger than with individual terrain attributes. The nature of these interrelations is illustrated in Figure 2 for total P, N, and C.As would be expected from the positive correlation coefficients, the scatter plots show a gradual increase in the P, N and C with increasing [Cs.sup.137] In the case of total P, there is relatively little scatter but a marked change in gradi ent of the relationship for [Cs.sup.137] residuals above 100%. However, caution is needed in interpreting this because of the small number of sites with such high aggradation rates. More scatter is seen in the plots of total N and total C, but both evidence consistently low levels in the most eroded locations ([Cs.sup.137] <-50%).
In summary, eroded areas tend to be nutrient-poor and aggraded areas relatively nutrient-rich. This pattern is clearly seen when the spatial distributions of total N (Figure 3) and [Cs.sup.137] residuals (Figure 4a) are compared. Although the [Cs.sup.137] residual data provide evidence for a link between soil redistribution and variability in soil properties, without further analysis they do not identify the erosion process or processes responsible. This can be investigated from two directions: first, by identifying the erosion process that best explains the [Cs.sup.137] residual pattern, and second, by examining relations between soil properties and process-specific measures of soil redistribution.
The spatial distributions of [Cs.sup.137] residuals, simulated tillage, and simulated overland flow erosion potential are shown in Figure 4. The location of the rill network surveyed in 1998 is shown in Figure 5. The rill network and overland flow potential distribution show good qualitative agreement, indicating that the latter may be used as a reliable indicator of spatial variation in water erosion intensity in the non-parametric analysis. Nevertheless, comparison of the single-process distributions with the pattern of [Cs.sup.137] residuals suggests that tillage erosion is the dominant soil redistribution process within the field. This is further supported by quantitative analysis, which indicates that 40% of the observed variance in [Cs.sup.137] residuals can be explained by simulated tillage erosion, but only 10% by simulated water erosion. This close agreement between [Cs.sup.137] and simulated tillage suggests that tillage erosion may be identified as the dominant soil redistribution process causing variation in soil properties. This suggestion is supported by the strong statistically significant relationships between measured soil properties and simulated tillage erosion (Table 2). In contrast, the water erosion index exhibits no statistically significant (95%) relationships with soil properties.
Qualitative interpretation of crop variability. The spatial distributions of aboveground biomass and crop yield are shown in Figure 6. Over the whole field except the lowest margin, the crop production distributions are similar to those of [Cs.sup.137] residuals and soil redistribution by tillage (Figures 4a and 4b, respectively). The crop production distributions exhibit low values where highest erosion rates were noted on the upper field perimeter and on the mid-field convexities. Furthermore, high yield and biomass are noted where soil aggradation was identified in the mid-field concavities. Therefore, over most of the field, variability in biomass and yield appears to be closely correlated with and, by implication at least, partially controlled by soil redistribution. Nevertheless, the relationship between soil redistribution and crop production is not simple in this field. At the base of the field, especially in the lowest corner, sample sites with high rates of soil aggradation and associated nutrient e nrichment are not characterized by elevated rates of crop production. Field observations suggest that the area is subject to water-logging, weed infestation, and occasional ground frost. Low crop yields in similar areas have been reported elsewhere (Franzen et al. 1997; Schumacher et al. 1999). The presence of low crop production in this area of high aggradation weakens the relationship between soil redistribution and crop production (Figure 7). In light of this complexity, it is not surprising that non-parametric correlation analysis tends to show weaker correlation between crop variability and soil properties and erosion measures than has been seen between the latter groups. Nevertheless, statistically significant ([alpha] = 0.05) correlation coefficients are found between above ground biomass and both planform curvature and simulated soil redistribution by tillage. Both aboveground biomass and crop yield also exhibit statistically significant ([alpha] = 0.05) correlation coefficients with total phosphorus.
Despite the close visual similarity between patterns of crop production parameters and measures of tillage erosion and total soil redistribution ([Cs.sup.137]), there remains much unexplained variability in crop production. Even when the data are log-transformed, variation in [Cs.sup.137] explains only 16% and 11%, respectively, of the variation in aboveground biomass and yield. This reflects the fact that the growth and development status of a crop population depends on the coordination of soil-plant-air continuity, which is temporally and spatially variable (Franzen et al. 1997; Timlin et al. 1998). Under conditions of relatively uniform management and with the adoption of the same crop cultivar, crop variability at the field scale is most likely to be determined by the interaction of soil properties and microclimates. The growing season for the winter wheat under study was the wettest for 20 years, with a recorded rainfall of 959 mm (37.8 in), compared with the average of 845 mm (33.3 in). Therefore, this single-year dataset only represents the crop variability in a wet year, under conditions in which nonlinear relationships between crop production and soil properties (and erosion) are likely.
High rates of soil redistribution within agricultural fields have been identified in earlier studies using (137) Cs (Martz and de Jong 1987; Walling and Quine 1990; Quine and Walling 1991; Quine et al. 1994). This study is consistent with these earlier investigations, evidencing patterns and rates of soil redistribution than cannot be explained by water erosion. The pattern of soil redistribution, characterized by loss from convexities and deposition in concavities, is typical of tillage erosion and agrees with earlier studies that have indicated the dominance of tillage in soil redistribution on rolling topography subject to mechanical cultivation (Covers et al. 1993, 1996; Quine et al. 1994, 1996, 1997). Tillage erosion operates like a conveyor belt, transferring soil and associated constituents from convexities to concavities. On this field, rates of erosion and accumulation of as much as 4 kg [m.sup.2] [year.sup.-1] or 40 t [ha.sup.-1] [year.sup.-1] (16 tons [acre.sup.-1] [year.sup.-1]) (Figure 4b) were noted, equivalent to 1.5% of the plow layer per year. During cultivation, plow layer depth is maintained on the convex slope elements by incorporation of nutrient-poor and, on this field, usually more coarse subsoil into the plow soil. Therefore, the plow soil on these eroded Convexities becomes depleted in surface-applied or surface-immobilized nutrients, the products of weathering, and fine soil fractions (Table 2). (It is interesting to note that, on this field, soil modification through tillage erosion results in whole-field correlation between nutrients and texture that would probably be attributed to size-selective transport of fines and associated nutrients, if water erosion was assumed to be dominant). The depleted plow soil is also translocated away from the convexities, and therefore, areas of no (or limited) net soil loss on linear slope elements below convexities may also be characterized by nutrient depletion of the plow soil. Conversely, plow soil accumulates in concavities through downslope tra nslocation from the upslope landscape elements. The significance of these processes for profile anisotropy may be seen in the ratio of the total carbon concentration in the plow soil to the concentration in the sub-plow soil (Figure 8). In areas of aggradation, most of the ratios lie between 1.5 and 3, with a decline towards 1 as aggradation rates increase. This reflects the presence in the sub-plow soil of increasing quantities of former plow soil (chemically similar to the current plow soil) isolated below the plow layer by gradual surface elevation due to aggradation. In contrast, in eroding areas ratios are much higher, reflecting the surface origin and, therefore, surface concentration of carbon and the pure sub-soil origin of the sub-plow soil. It is suggested that translocation of soil by tillage erosion is, therefore, a major contributor to within-field variability in soil properties. The strong correlation between soil properties (C, N and P) and both net soil redistribution ([Cs.sup.137]) and simula ted tillage erosion supports this hypothesis.
Clearly, continuing tillage erosion will result in more extreme spatial variability in soil properties. In particular, greater depletion of the soils on the convexities and an expansion of the area of depleted soils is anticipated. In order to evaluate the severity of this phenomenon, TBGIS was used to simulate [Cs.sup.137] and soil redistribution due to tillage from 1997-2040. The frequency distributions of [Cs.sup.137] residuals over the sampled area for 1997 and 2040 are shown in Figure 9a. These distributions show an increase in skewness and in the proportion of the field that is severely depleted ([Cs.sup.137] residuals <-50%), from 13-38%. The area subject to very high rates of aggradation ([Cs.sup.137] residuals >100%) also increases from less than 1% to over 7%. This represents a significant increase in within-field variability. Evolution of variability in other properties will be more complex, due to artificial and "natural" additions over the time period, but the pattern is likely to be similar.
The economic implications of continually-developing soil variability are more difficult to gauge because the data obtained in this study represent a single exceptionally wet year, and the relationship between erosion and crop production has been observed to be more complex than that between erosion and the soil properties investigated. Nevertheless, a qualitative evaluation may be made on the basis of the operation of tillage erosion discussed above. On the basis of the increase in the proportion of the field in the most denuded areas (Figure 9a) and the low yields seen in such areas at present (Figures 9b and 7), it is anticipated that significant yield decline may occur on both the currently eroded zones and on the adjacent downslope landscape elements. Areas of current aggradation will continue to be receptor sites for soil and nutrients. As has been seen, these aggradation areas may not be optimal for crop production, and it is anticipated that, at best, these areas will not produce sufficient additional yield to compensate for eroded zones and, at worst, that continued soil accumulation may contribute to yield decline in these areas as well (cf. Figures 7 and 9).
Summary and Conclusion
This study employed a pedo-geomorphological approach to the investigation of soil and crop variability. A close relationship between spatial patterns of soil properties and the spatial distribution of net erosion and tillage erosion was demonstrated. It was suggested that soil redistribution by tillage is the most important erosion process contributing to within-field soil variability and that, due to its continuous operatino and high magnitude, higher within-field soil variability can be expected to develop. This has the important implication for precision farming approaches that "natural" soil variation can no longer be considered a static variation on the time scales of concern to land managers--indeed the management of the land is contributing to the pattern of soil variability that managers attempt to overcome. It is clear that attention must be given to tillage erosion in the development of strategies to manage soil variability.
Patterns of soil redistribution by tillage also showed close visual similarity to patterns of crop production. However, the strength of the statistical relationship between these parameters was influenced by lower than expected yields in low-lying aggradational zones near the field boundary. More data for years with different weather conditions are required to explore these trends more fully. Nevertheless, it is apparent that yields in eroded zones are unlikely to match those in uneroded areas and are unlikely to be fully compensated for by elevated yields in aggradational areas. It is suggested that tillage erosion represents a threat to cost-effective and environmentally-sustainable agriculture in areas of rolling terrain because it results in the transfer of resources from areas where they are needed to areas where they are surplus to requirements and may have a deleterious effect.
This study was undertaken as part of the TERON (Tillage Erosion: current state, future trends and prevention) project (FAIR CT96 1478), and the financial support provided by the European Commission is gratefully acknowledged. We are also very grateful to Mr. John Lee for providing ready access to the field site on several occasions and for his assistance with field logistics.
[Figure 2 omitted]
[Figure 7 omitted]
[Figure 8 omitted]
[Figure 9 omitted]
Table 1 Descriptive statistics of measured field and crop properties. Properties min. max. median Terrain slope (degree) 1.4 19.8 8.3 plan curvature (m/100 m) (+) -0.905 1.11 0.02 profile curvature (m/100 m) (+) -1.639 1.023 0.034 Plow soil bulk density (g/[m.sup.3]) 1.046 2.352 1.478 sand fraction (%) 28.7 53.2 41.5 silt fraction (%) 35.1 58.9 43.8 clay fraction (%) 11.2 24.0 14.6 total C (%) 0.64 3.37 1.36 total N (%) 0.09 0.31 0.15 total P (ppm) 385 1392 590 inorganic P (ppm) 280 1200 439 Sub-plow soil sand fraction (%) 25.3 60.1 43.4 silt fraction (%) 29.9 55.3 42.1 clay fraction (%) 7.7 20.1 14.3 total N (%) 0.02 0.23 0.10 total C (%) 0.07 2.56 0.69 Crop AGB (g/[m.sup.2]) 658 2444 1583 yield (g/[m.sup.2]) 270 1051 587 ratio of grain to stalk 0.37 1.32 0.60 Erosion Caesium-137 residual (%) -79.2 148.0 -3.4 Properties Q1 (a) Q3 (a) C.V. (b) Terrain 6.5 9.8 38.9 -0.112 0.225 > 100 -0.135 0.211 > 100 Plow soil 1.364 1.624 17.6 39.4 43.7 10.5 42.1 45.5 7.7 13.8 15.5 12.0 1.24 1.5 18.8 0.13 0.17 24.2 523 667 24.5 396 472 17.1 Sub-plow soil 39.0 52.0 29.8 36.2 44.9 20.7 11.8 16.1 30.1 0.07 0.12 46.8 0.39 0.94 78.6 Crop 1376 1795 26.5 484 671 31.9 0.54 0.66 20.6 Erosion -24.0 15.8 > 100 (a)Q1 and Q3 are the 25th and 75th of quartile, respectively. (b)Coefficient of variance (C.V. %) is calculated as: ((Q3. Q1)/ median) (*) 100 (+)Concavity is negative and convexity is positive for plan curvature. The opposite sign is adopted for profile curvature Table 2 Tabular list of Spearman's correlation coefficients Total N Total C Plow soll properties Total N 0.876 (**) Total C Total P Inorganic P Sand Silt Topographic attributes Slope -0.111 -0.174 Plan curvature -0.376 (*) -0.367 (*) Profile curvature 0.464 (**) 0.585 (**) Erosion measures [Cs.sup.137] residual (- = erosion) 0.613 (**) 0.671 (**) Simulated Tillage redistribution (- = erosion) 0.512 (**) 0.586 (**) Simulated water erosion (+ = erosion) 0.180 0.168 Inorganic Total P P Plow soll properties Total N 0.576 (**) 0.264 Total C 0.622 (**) 0.356 (*) Total P 0.652 (**) Inorganic P Sand Silt Topographic attributes Slope -0.435 (**) -0.493 (**) Plan curvature -0.226 -0.018 Profile curvature 0.461 (**) 0.314 (*) Erosion measures [Cs.sup.137] residual (- = erosion) 0.605 (**) 0.407 (*) Simulated Tillage redistribution (- = erosion) 0.481 (**) 0.238 Simulated water erosion (+ = erosion) 0.146 0.006 Sand Silt Plow soll properties Total N -0.516 (**) 0.466 (**) Total C 0.524 (**) 0.516 (**) Total P 0.570 (**) 0.566 (**) Inorganic P -0.214 0.231 Sand 0.948 (**) Silt Topographic attributes Slope 0.199 -0.203 Plan curvature 0.344 (*) -0.326 (*) Profile curvature -0.362 (*) 0.38 (*) Erosion measures [Cs.sup.137] residual (- = erosion) -0.518 (**) 0.503 (**) Simulated Tillage redistribution (- = erosion) -0.470 (**) 0.458 (**) Simulated water erosion (+ = erosion) -0.170 0.159 Above Ground Crop Biomas Yield Plow soll properties Total N 0.120 0.157 Total C 0.165 0.205 Total P 0.303 (*) 0.354 (*) Inorganic P 0.036 0.164 Sand -0.288 -0.286 Silt 0.237 0.243 Topographic attributes Slope -0.057 0.002 Plan curvature 0.345 (*) -0.262 Profile curvature 0.272 0.212 Erosion measures [Cs.sup.137] residual (- = erosion) 0.283 0.254 Simulated Tillage redistribution (- = erosion) 0.370 (*) 0.310 Simulated water erosion (+ = erosion) -0.096 -0.133 (*)statistically significant at [alpha] = 0.05 (**)statistically significant at [alpha] = 0.01
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Dr Timothy A. Quine is a reader in Earth Surface Processes and Dr. Zhang Yusheng is a research fellow with the Department of Geography, University of Exeter, Exeter, Devon, United Kingdom.
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|Author:||Quine, T.A.; Zhang, Y.|
|Publication:||Journal of Soil and Water Conservation|
|Date:||Jan 1, 2002|
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