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a Dep. of Crop Sciences, Univ. of Illinois, 1102 S. Goodwin Ave., Urbana, IL 61801 USA
dbullock{at}uiuc.edu
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMaterials and methodsResults and discussionConclusionsREFERENCES |
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Abbreviations: CEC, cation exchange capacity • GIS, geographic information system • GPS, global positioning system • OM, organic matter
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMaterials and methodsResults and discussionConclusionsREFERENCES |
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Topography affects yield in a number of ways. First, it influences the redistribution (erosion and/or deposition) of soil particles, OM, and soil nutrients, with resulting changes in physical and chemical properties of uphill and downhill soils (Ovalles and Collins, 1986; Pennock and de Jong, 1990). Second, it affects water availability due to both vertical and horizontal water redistribution (Verity and Anderson, 1990). The amount of plant-available water is an important yield-affecting factor (Afyuni et al., 1993; Daniels et al., 1987; Fiez et al., 1994; Holt et al., 1964; Wright et al., 1990), and water redistribution due to topography can be remarkably significant (Hanna et al., 1982).
Topography–yield relationships have been studied extensively; however, most of the previously reported studies were conducted on a relatively small scale. Landscape position, slope (Changere and Lal, 1997; McConkey et al., 1997), curvature (Sinai et al., 1981; Simmons et al., 1989; Timlin et al., 1998), and total surface area contributing to water inflow (Simmons et al., 1989) were considered to be the most important topographical features. Mahler et al. (1979) used 3- by 30-m plots located at ridgetop, south slope, and bottomland positions to analyze relationships between topography and dry pea (Pisum sativum L.) yield, and observed higher yields as well as higher soil water contents at bottomland landscape position. Ciha (1984), analyzing wheat yields from individual plots located at toe, concave, middle, convex, and interfluve sites, found landscape position to be a significant yield-affecting factor. Higher corn yields at footslope positions were reported by Stone et al. (1985) and Simmons et al. (1989). Miller et al. (1988) observed the highest wheat yields at footslope and backslope positions on a 400- by 200-m study site. Sinai et al. (1981) studied the effect of surface curvature on soil water content and wheat yield on a 70- by 70-m plot and found significantly higher wheat yields in concave positions. Simmons et al. (1989) noticed that yields in concave positions were also more sensitive to changes in surface curvature. Surface curvature was found to be a useful parameter for describing relationships among yield, topography, and weather on a 280- by 150-m field plot (Timlin et al., 1998).
Influence of topography on yield extends from microscales (Wollenhaupt and Richardson, 1982) to watershed scales. On larger scales topographical influence gets inevitably more complex due to an increase in soil and topographical variability, as well as variability in precipitation, temperature, and other climatic factors. Hence, the data obtained from small experimental plots often are not appropriate for predicting field-scale events. This is particularly troublesome, since it is the topography–yield relationship for the whole field that is often of primary interest.
Development of GIS technology and the availability of dense yield data via yield monitors now afford the opportunity to precisely characterize yield variability on large scales. In addition, detailed information on topographical land features can be easily obtained based on dense elevation measurements. Elevation data are particularly useful for relating topography to soil properties (de Bruin and Stein, 1998; Moore et al., 1993; Odeh et al., 1994). This information can be used in an effort to study quantitative relationships between yield and topography, as well as yield and soil physical and chemical properties on large scales.
In this study we used dense corn and soybean yield data, detailed terrain maps, and relatively densely sampled soil properties to study topography–yield relationships on a field scale. We conducted this study with the initial hypothesis that topographical data, in combination with soil information, are useful for explaining yield variability on an agricultural field scale. The objective of this study was to test that hypothesis.
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Materials and methods |
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TOPABSTRACTINTRODUCTIONMaterials and methodsResults and discussionConclusionsREFERENCES |
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Yield data included corn and soybean grain yield collected during 1994 to 1997 using yield monitors (Ag Leader Technology, Ames, IA). Only 1996 and 1997 yield data were available for the WN and WS fields. Yield measurements were taken every second by grain sensors, with each site measurement covering an area of about 2 by 5 m (2 m is an average forward distance traveled by a combine during 1 s, and 5 m is the width of the combine header). Simultaneously, site coordinates were determined by a GPS unit. Statistical summary of the yield data is shown in Table 2 . Full-season cultivars were used during the studied period.
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ArcView Spatial Analyst (ESRI, 1996), a GIS, was used to analyze elevation data and to derive the topographical land features, such as slope, curvature, and flow accumulation. The elevation measurements were converted into cell-based terrain maps. Inverse distance weighting with a power to distance of two and six closest neighboring points was used as an interpolation method for creating the maps. The number of the closest neighboring points was selected so that it would be sufficient for estimation at sparsely sampled level areas and, at the same time, would not produce oversmoothing in densely sampled depressions and hills. Maps of the slope, curvature, and flow accumulation were obtained on the same cell basis as the terrain maps.
Slope describes the rate of elevation change, and it is defined as the first-order derivative of the terrain. The tangent of the slope was calculated as a ratio of difference in elevation between the centers of adjacent cells to the horizontal distance between them. The slope for each cell was obtained based on a set of 3 x 3 neighboring cells using the average maximum technique (Burrough, 1986) and was measured in degrees.
The second derivative of the terrain map describes the curvature of the terrain surface and the acceleration or deceleration of water flow over that surface. Negative curvatures corresponded to concave surfaces or depressions, while convex surfaces, or hills, were described by positive curvatures. The ArcView procedure calculated the curvature at each cell by fitting the fourth-order polynomial to the surface composed of 3 x 3 neighboring cells. The curvature was measured in 10-2 m.
Flow accumulation was defined as the total number of cells contributing to water inflow into a given cell. Prior to calculating the flow accumulation, main flow directions were determined based on elevation differences. The main flow direction corresponded to the direction of the steepest descent in elevation. Based on the main direction map, the flow accumulation was calculated by summing all the cells that flowed into the given cell (Jenson and Domingue, 1988). The absolute value of the flow accumulation depended on the total number of cells in the map, hence, it was a function of both the size of the field and map resolution. The flow accumulation was not applicable for comparing fields of different size. However, within a field, flow accumulation was useful for explaining yield/topography and soil/topography relationships.
Figure 3 presents a measurement scheme with corresponding interpolated terrain map and flow accumulation, slope, and curvature maps for DN field. Original maps (Fig. 3) were derived based on the whole set of elevation measurements. For further analysis we retained only the data from the map cells corresponding to the soil sampling sites. Mean slopes and curvatures for the soil sampling sites (not the whole fields) are shown in Table 1.
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, where Zi is the yield at location i, and Zm and are the field mean yield and the standard deviation, respectively. Only those data points where either negative or positive standardized yields were observed during the entire study period of 1994 to 1997 were selected for further analysis. Separating consistently high- or low-yield locations from the rest of the data allowed us an easier delineation of the factors causing yield variability at those locations.
Pearson correlation coefficients (r) were calculated between soil properties (OM, CEC, P, and K), yields, elevations, slopes, curvatures, and flow accumulations. Multiple linear regression with least square estimation was performed to analyze cumulative effects of soil properties and topographical features on yield. We considered (i) yield versus all measured soil properties, (ii) yield versus topographical data, and (iii) yield versus both soil and topographical data. Forward stepwise linear regression procedure was used (STATISTICA, StatSoft Inc., 1993), and only parameters statistically significant
were retained in the final regression equations.
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Results and discussion |
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TOPABSTRACTINTRODUCTIONMaterials and methodsResults and discussionConclusionsREFERENCES |
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correlation coefficients between soil properties and topographical land features are shown in Table 3
. Slope and elevation were generally better correlated with soil properties than either curvature or flow accumulation. For most of the fields, we detected higher OM contents at lower landscape positions and moderate slopes. These are recognized by negative correlations for elevation–OM and slope–OM. An increase in P and K concentrations and CEC at lower landscape positions was observed for several fields, but for the majority of the fields no significant correlations of P, K, or CEC with elevation or slope were found. For a few fields, we observed higher OM content, CEC, and P concentration in depressions (concave surface), while lower values were detected on hills (convex surface). These are recognized by a negative correlation with curvature. High OM content and P concentration at lower landscape positions have been reported previously (Aguilar and Heil, 1988; Changere and Lal, 1997; Ovalles and Collins, 1986). Insignificant or inconsistent correlations between OM content, P concentration and landscape position have also been reported (Aguilar and Heil, 1988; Day et al., 1987). Frequently, the inconsistent correlations were related to changes in parent material occurring through the landscape. For example, Day et al. (1987) observed low OM contents at lower landscape positions where topsoil was formed in recent overwash deposits. In this study, some of the inconsistent correlations between topography and P, K, CEC values were related to past management practices, such as different fertilizing histories of certain areas within fields.
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are shown in Table 3. All the topographical features (i.e., flow accumulation, slope, elevation, and curvature) were initially included into the multiple regression equation. Forward stepwise multiple regression was then performed, and only the topographical features in which contribution to regression was significant
were retained in the regression equation. Topographical features that appeared in the final multiple regression equations differed from field to field. Slope appeared most frequently in the multiple regression equations. Elevation had less influence, while curvature and flow accumulation seldom had significant effect
. Multiple regression analyses showed that topography explained about 30% of the observed variability in OM content and P and K concentrations for most of the fields.
Yield and Soil Properties
Correlation coefficients between yield and soil properties are shown in Table 4
. Organic matter was the source of the most consistent positive influence on yield among the soil properties studied. Both positive and negative correlations between yield and P and K concentrations and CEC were observed. Since the soils in this study had relatively high P and K concentrations, the effect of P and K probably was not a limiting factor for plant growth; hence, it played a minor role in yield variability. The highest correlation coefficients between yields and OM content observed in FN and FS fields can be related to predominant soils in these fields (Table 1). About 90% of FN field and 40% of FS field were occupied by Alfisols with loamy texture and relatively low OM content, while Mollisols with higher OM content prevailed in the rest of the fields.
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Yield and Topography
Table 4 presents correlation coefficients between topography and yield. Higher yields were frequently observed at lower landscape positions (a negative correlation with elevation). Slope was also negatively correlated with yield for a number of fields. Several significant correlations between yield and flow accumulation and yield and curvature were observed.
Yearly and monthly weather conditions had considerable influence on the yield–topography relationship. Most of the significant negative correlations between curvature and yield were observed in 1996, and it is likely that they were related to extremely low precipitation in August 1996. During periods of drought, areas with concave shape (negative curvature) probably could provide more plant-available water than the areas with convex shape (positive curvature); hence, a negative correlation was observed between yield and curvature. On the other hand, positive correlations were observed when excessive amounts of water accumulated in the areas with concave shape during unusually wet periods, reducing yield. The areas with convex shape did not accumulate moisture and, thus, did not suffer a similar reduction in yield.
Observed results were consistent with those reported in the literature. Changere and Lal (1997), Fahnestock et al. (1996), and McConkey et al. (1997) observed higher yields at lower slope positions and lower yields at higher positions. However, Ebeid et al. (1995) reported that in dry years, eroded areas at higher landscape positions produced better yields than the lower-located sites, which was caused by additional moisture stored in a clay upper layer of eroded soil. Lower yields in lower landscape positions and depressions also were observed in wet years due to poor drainage of lower-located sites (Lindstrom et al., 1986). Yield/topography relationships can be obscured by water accumulation and storage as a result of previous weather conditions, as well as different rates of water consumption by uphill and downhill plants (McConkey et al., 1997).
Relationships between weather conditions and topography/yield reported in literature are rather contradictory. Halvorson and Doll (1991) observed less influence of topography on yield in dry years than in wet. They related it to lesser amounts of water available for topographical redistribution during dry years. In such years water contents could be expected to be more homogeneously distributed through the field. However, Simmons et al. (1989) reported the greatest influence of topography on yield in dry years. More uniform distribution of yields with respect to landscape during years with above-normal precipitation compared with dry years was reported by several other researchers (Afyuni et al., 1993; Daniels et al., 1987; Sinai et al., 1981; Stone et al., 1985). This contradiction can be explained in part by differences in soil and climatic conditions in which the experiments were conducted. Influence of precipitation on yield–topography correlations can also be related to the topographical features of the fields themselves. We noted that correlations between yield and topography in different fields were largely influenced by a field's slope and curvature. Figure 5 shows correlation coefficients between yield and elevation plotted versus average degree of slope for the fields. The negative effect of higher topographical location on yield was more intense in fields with a relatively high degree of slope, while in fields with lower slopes this effect was less noticeable. Outliers observed in Fig. 5 can be explained by precipitation amounts. For example, the high negative correlation (-0.61) between elevation and yield observed in HW field
in 1996 is related to extremely low precipitation (6 mm) in August 1996.
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with May precipitation and positively correlated
with August and September precipitation. This observation implied that the yields suffered from excessive spring precipitation but benefited during dry summers. Locations with low flow accumulation (less than 10 map cells contributing to flow accumulation) were positively correlated with March and April precipitation; i.e., they produced higher yields when sufficient water was obtained during spring months. In general, the greatest effect of topography was observed during extreme weather conditions (either wet or dry years) and at locations with extreme topography (low depressions or high hilltops). For average weather conditions and moderate landscape, the effect of topography was relatively small.
Table 4 shows coefficients of determination obtained from multiple regression analysis between yield and topography (R22) and between yield and all available soil and topographical data (R23). As for soil–topography analysis, all topographical features were initially included in the regression analysis, and after a stepwise multiple regression, only those significantly contributing to the equation were retained. For every year or field that had a significant coefficient of determination between yield and topography, elevation was the major contributor to the multiple regression equation. For half of the fields, curvature was the second-most important parameter. For example, the regression equation for the yield/topography relationship for FN field was
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Coefficients of determination for soil properties were higher than those for topography for most of the fields, signifying that topography by itself was not as informative as soil properties. For some of the fields, soil properties in combination with topography explained as much as 78% of the yield variability; however, for other fields, they explained only about 10%. No dissimilarities between soybean and corn yields in their response to soil properties, topography, and precipitation were observed in the study.
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Conclusions |
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TOPABSTRACTINTRODUCTIONMaterials and methodsResults and discussionConclusionsREFERENCES |
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Received for publication October 16, 1998.
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TOPABSTRACTINTRODUCTIONMaterials and methodsResults and discussionConclusionsREFERENCES |
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