Published in Agron. J. 96:252-258 (2004).
© American Society of Agronomy
677 S. Segoe Rd., Madison, WI 53711 USA
SITE-SPECIFIC ANALYSIS
Effect of Soil and Topographic Properties on Crop Yield in a North-Central Corn–Soybean Cropping System
P. Jianga and
K. D. Thelen*,b
a Dep. of Soil and Atmos. Sci., Univ. of Missouri, Columbia, MO 65211
b Dep. of Crop and Soil Sci., 480 Plant and Soil Sci. Bldg., Michigan State Univ., E. Lansing, MI 48824
* Corresponding author (thelenk3{at}msu.edu).
Received for publication February 3, 2003.
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ABSTRACT
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Understanding the variability of soil and landscape properties and their effect on crop yield is a critical component of site-specific management systems. The objectives of this study were to identify yield-limiting soil properties and to investigate the relationship between soil properties and topographical variables and their relationship to crop yield. Two corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] fields in Michigan were sampled, and 23 soil properties from the top two horizons up to 50 cm deep were examined. Corn and soybean yield data were collected from 1996 through 2001 using a combine yield monitor. A multivariate statistical model, principal-component analysis (PCA), was used to identify important soil properties based on their potential to affect crop yield. Soil properties identified by PCA to be important to yield and two topographic variables, elevation and slope, derived from a high-resolution digital elevation model (DEM), were investigated for their effect on crop yield. Correlation analysis was used to examine the relationship between soil properties and field topography and between crop yield and both soil and topographical variables. Principal-component analysis was useful in identifying important soil variables. Slope and very fine sand content were two major yield-limiting factors during the study period. Other soil variables such as base saturation, pH, clay content, and elevation were helpful in explaining yield variability. The combined effect of both soil and topography varied by year and explained 28 to 85% of the observed yield variability.
Abbreviations: CEC, cation exchange capacity • EC, electrical conductivity • PC, principal component • PCA, principal-component analysis
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INTRODUCTION
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IN AGRICULTURAL FIELDS, yield variability is partly caused by soil variability and varying topographic features of the field. Although yield is a function of a host of factors, including soil properties, field topography, climate, biological factors, and management, in certain years as much as 60% or even more of the yield variability can be explained by a combination of soil properties and topographic features (Yang et al., 1998; Kravchenko and Bullock, 2000). Many soil properties such as available water, texture, bulk density, clay content (Stone et al., 1985; Miller et al., 1988; Wright et al., 1990), organic C (Ciha, 1984; Stone et al., 1985; Wright et al., 1990), pH (Kreznor et al., 1989; Moore et al., 1993), subsoil acidity (Wright et al., 1990), fertility, and soil thickness (Kreznor et al., 1989) have been found to affect crop yield.
Crop growth and yield are also affected by field landscape features. Previous studies have looked closely at elevation, slope steepness, slope aspect, and surface curvature (Ciha, 1984; Daniels et al., 1985; Stone et al., 1985; Simmons et al., 1989; Yang et al., 1998; Kravchenko and Bullock, 2000). Field topography can have a direct effect on crop growth and yield by redirecting and changing soil water availability and an indirect effect through its influence on distribution of certain soil chemical and physical properties such as organic matter content, base saturation, soil temperature, and particle size distribution (Franzmeier et al., 1969; Bennett et al., 1972; Stone et al., 1985). Walker et al. (1968) investigated a series of soil properties such as A horizon thickness, distance from surface to mottles, and distance to CO3 and Mn segregations. They concluded that slope position was strongly related to these soil properties. Ovalles and Collins (1986) conducted a study on a broad selection of soil chemical and structural properties, including pH, organic C, total P, coarse sand, medium sand, fine sand, very fine sand, total sand, silt, and clay content from three topographic positions of summit, shoulder, and backslope in north-central Florida. They demonstrated that all of these selected soil properties had a significant dependence on the topographic position of the field. A study by Yang et al. (1998) showed that three topographic variables—elevation, slope, and aspect—alone can explain 15 to 35% of wheat (Triticum aestivum L.) yield variability at the whole-field scale. In addition, they reported that topographic features account for 49 to 84% of the yield variability in some areas of the field. Higher wheat yields were generally found at lower elevation and gentle slope positions. Lower wheat yields were found at higher elevation levels and steep slope positions. However, quite different results were presented in previous studies that found different relationship patterns between soil properties and topographic features across locations and years. Furthermore, the relationship of topographic features to yield can be even more complicated by extreme weather conditions (Simmons et al., 1989; Kravchenko and Bullock, 2000). Fiez et al. (1994) pointed out that due to the inconsistent effect of topographic features on crop yield, research was needed to determine and investigate the soil factors that vary with field topography. This would result in a better understanding of yield variability under varying soil conditions and field topography.
To investigate the complicated interactions between soil variables, a multivariate analysis model is necessary to unbiasedly assess the covariance structure of soil. Principal-component analysis is one of the multivariate models that can use linear combinations of soil variables to explain the covariance structure of a data set with a large number of soil variables. It reduces the dimension of the original data set without losing substantial information and often reveals relationships that were not previously suspected and thereby allows new interpretations or further analysis (Johnson and Wichern, 2000, p. 426–427). Richardson and Bigler (1984) used this model and found that soil electrical conductivity (EC) and soluble Mg and Na were the most important variables in explaining observable differences in wetland soils among clay content, pH, organic C, CaCO3 equivalent, EC, and Mg, Ca, and Na.
The objectives of this study were (i) to identify the important soil variables that define most of the variability within the soil profile, (ii) to examine the correlation between these soil properties and field topographic data, and (iii) to investigate the effect of soil properties and field topography on crop yield.
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MATERIALS AND METHODS
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Study Site Description
The study site was a rotated corn–soybean farm located in Kalamazoo County, MI (42°22' N, 86°36' W), consisting of two adjacent fields—Field 1 and Field 2. The entire study site is approximately 50 ha in size, with Field 1 (east side) about 30 ha and Field 2 (west side) about 20 ha. The two fields were planted with corn and soybean in an alternate-year rotation. The elevation of the study site ranges from approximately 288 to 303 m with near level (0–2%), gentle (2–6%), and moderate (6–12%) slopes (Fig. 1). The soil type of the study site is predominately Kalamazoo (fine-loamy, mixed, mesic Typic Hapludalfs) with Oshtemo (coarse-loamy, mixed, mesic Typic Hapludalfs) at the southwest corner of the study site. The site was nonirrigated, and a minimum-tillage system was employed. During the study period, Field 1 was planted with soybean in 1997 and 1999 and with corn in 1996, 1998, and 2000. This sequence was alternated in Field 2. In 2001, both Field 1 and Field 2 were planted with corn. Weather data were acquired from an adjacent weather station (42°24' N, 85°23' W) operated by the National Climate Data Center (NCDC, 2002). The monthly precipitation during the 1996–2001 growing seasons and the deviation of precipitation from the average are shown in Fig. 2a and 2b, respectively.
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Fig. 2. (a) Growing season precipitation (cm) during 1996–2001 and (b) departure from normal (average of 1971–2000) growing season precipitation (cm) during 1996–2001.
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Field Sample Collection and Laboratory Analyses
Thirty-three soil cores were taken on 1 May 2001. Sixteen of them were taken from Field 1 and 17 from Field 2. The soil probe was 1.2 m long and 4 cm in diameter. Horizon designation and depth were recorded as the soil samples were taken. The number of horizons identified for the soil cores varied from three to six. A 5- to 10-cm undisturbed section from the middle of each horizon was saved for bulk density analysis. Elevation was measured using a real-time kinetic global positioning system (RTK GPS) with a tractor-mounted receiver. The speed of the tractor was 12.8 to 19.2 km/h with a transect spacing of 9 m. Elevation data were recorded at 1-s intervals.
Since the majority of soybean roots are found in the upper 30 cm of soil, with a dominantly large proportion of the root mass in the topmost 16 cm, and corn roots are found in the upper 35 to 40 cm, only the top two horizons were analyzed. The depth of the top two horizons varied from 35 to 85 cm, with an average of 51 cm. The surface horizons were all designated as Ap, whereas the second horizons were mostly B with several A2 or BE designations.
Soil analyses were conducted at the University of Missouri Soil Testing Laboratory. The analyses included soil texture by the pipette method, cations (K+, Mg2+, Ca2+, and Na+) by ammonium acetate extraction, cation exchange capacity (CEC) by base summation plus H ion, pH by soil solution ratio of 1:1, and total C and organic C by emission method. Summary statistics for the analyzed soil variables are presented in Table 1.
Data Retrieved in GIS Environment
Yield Data
Six-year yield data (from 1996–2001) were recorded using a commercial yield monitor mounted on a combine. Yield data were recorded at 1-s intervals. Latitude and longitude for yield data points were recorded simultaneously by a GPS receiver. Questionable and unrealistic yield data points likely caused by significant positional errors—operating errors such as abrupt changes of speed, partial swath entering the combine, and combine stops and starts—were removed from the data set before statistical analyses. Yield values for soil sample locations were calculated by averaging the yield points contained within a 6.7-m diameter around each sample location (GIS Arcview, ESRI, Redlands, CA). Corn yield data from 1996, 1998, 2000, and 2001 and soybean yield data from 1997 and 1999 were available in Field 1. Corn data from 1997, 1999, and 2001 were available in Field 2. No soybean yield data were available in Field 2 during the study period due to malfunction of the yield monitor or unstable GPS signals during harvest.
Slope Derivation
Slope was derived using the Spatial Analyst feature of Arcview GIS (ESRI, Redlands, CA). This process uses a 3 x 3 cell neighborhood around the processing or center cell and the average maximum technique to calculate slope values. It identifies the maximum rate of change in value from each cell to its neighbors (ESRI, 1996). Slopes were measured in degrees. Elevation and slope data for each field are shown in Table 2.
Data Analysis Procedure
Soil data of the first two horizons were averaged and used throughout the analysis. Principal-component analysis was run on each field to investigate soil variance and identify important soil variables to be used as inputs for further analyses. Calculated principal components (PCs) were in the following form:
where Yi is the ith PC; X1, ..., Xp are the original variables; and ai1,..., aip are the coefficients of the ith PC and also an index of relative importance of the variable to that PC. The greater the absolute values of ai, the greater the importance of those soil variables to the PC. A sample correlation matrix instead of a covariance matrix was used in the PC calculation due to differences in order of magnitude between soil variables measured. The PCs with an eigen value greater than 1 (Kaiser's criterion) were retained for further analyses. Soil variables in each retained PC were empirically analyzed and selected based on their loading coefficients (ai). The higher the loadings, the more important the soil variables were considered in a given PC and were believed to have greater effect on yield variability. Correlation coefficients were calculated among the selected soil variables, topographic data, and yield. Stepwise regression was lastly employed to analyze the combined effect of both soil properties and topographic data on crop yield. Variables remaining in final equations had a significance level of 0.05. All statistical analyses were conducted using S-PLUS (Mathsoft, 1999), with the exception of the stepwise regression analyses, which were performed using the SAS (SAS Inst., 1999) software package.
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RESULTS AND DISCUSSION
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Soil Variation and Important Soil Variables
The PCA results for each field are presented in Table 3. In Field 1, the first five PCs all had an eigen value greater than 1 and accumulatively explained 89% of the total sample variance, whereas in Field 2, only the first four PCs had a eigen value greater 1 and explained 88% of the sample variance. In PC1 of both Field 1 and Field 2, variable loadings of absolute dominance were not observed. However, soil texture variables such as total sand (0.308), fine sand (0.292), total silt (–0.292), and fine silt (–0.284) in Field 1 and very similar variables such as total sand (0.280), fine sand (0.271), medium sand (0.274), total silt (–0.275), and fine silt (–0.255) in Field 2 had higher loadings. This suggests that soil texture variables were relatively more important in PC1. These soil texture variables explained 45 and 53% of the total sample variance in Field 1 and Field 2, respectively. In PC2, high loadings were represented by chemistry and fertility variables. For example, Mg concentration (0.351), base saturation (0.347), sum of bases (0.351), and pH (0.354) in Field 1 and Mg concentration (0.411), base saturation (0.372), and sum of bases (0.358) in Field 2 had the highest loadings, which were clearly higher than subsequent variable loadings. Therefore, these variables were also identified as important soil variables based on their relative contribution to overall soil variability and subsequent potential to affect yield. Field 1 and Field 2 had very similar soil variables identified in the first two PCs as important soil properties (Table 3). This similarity may be due to the adjacency of the two fields and similar historical management. For fields that are located further apart, a deviation in PCA results would be expected. Richardson and Bigler (1984) in their wetland soil study using PCA reported that PC1 was "chemical potentiality" dominated by EC and soluble Na and Mg, PC2 was dominated by organic C, and PC3 was dominated by soil texture. The difference between the two studies indicates that soil property PCA results depend on site-specific soil-forming processes and soil conditions.
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Table 3. Variable loading coefficients in the first five principal components (PCs) of Field 2 and first four PCs of Field 2 and their individual and cumulative variance and eigen values.
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In Field 1, clay content (0.478) in PC3, K (–0.728) in PC4, and very find sand content (0.434) and coarse sand content (–0.424) in PC5 were identified as critical variables. In Field 2, very find sand content (–0.442) and EC (0.414) in PC3 and horizon thickness (0.524) and coarse sand content (–0.551) in PC4 were considered important variables based on their high loadings (Table 3). Although only small portions of the total sample variance were explained by the lower PCs (Table 3), it was observed that only a few number of variables were dominant in them.
The Relationship of Soil Properties and Topographic Variables
Eight soil variables (sum of bases, base saturation, pH, K, Mg, very fine sand, coarse sand, and clay content) in Field 1 and seven variables (horizon thickness, sum of bases, base saturation, Mg, EC, very fine sand, and coarse sand content) in Field 2 were identified as important soil variables in the PCAs and were further analyzed for their correlation to topographic features. Correlation coefficients for variables with a 0.05 significance level are presented in Table 4.
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Table 4. Correlation coefficients with a significance level of P  0.05 between topographic data (elevation and slope) and soil properties.
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Elevation had a strong negative correlation with sum of bases in both fields and moderate negative correlation with Mg concentration in Field 2, indicating that lower elevations tended to have higher soil fertility across the study site. These results coincided with the positive correlation between elevation and very fine sand content of Field 1 and the higher organic matter content observed at lower elevations. Elevation was found to be positively correlated with horizon thickness of the first two horizons but not to the A horizon alone (data not shown), which would be expected to be greater at lower elevations. Also, elevation was positively correlated to EC in Field 2.
In both fields, slope was positively correlated to coarse sand content due to more intensive erosion at steeper slope positions. Moore et al. (1993) found that slope was one of the topographic features that most highly correlated with soil properties. In that study, the investigators reported that slope was positively correlated to sand content and negatively correlated to silt content and high organic matter mainly occurred when slopes were less than 2%. Kravchenko and Bullock (2000) found that in more than half of their study sites, slope was negatively correlated to CEC, organic matter, P, and K. The relationship of slope position to soil properties is, to a great degree, controlled by erosion processes in that it alters the distribution of soil particles and water redistribution over the field. The relationship between topographic data and soil properties is often modified or blurred by different parent materials and historical management (Kravchenko and Bullock, 2000).
The Relationship of Soil Properties and Topographic Variables to Crop Yield
Correlation coefficients between soil properties and yield and between topographic data and yield are shown in Table 5. Across years and fields, slope had a significant correlation (negative) to corn yield in four of nine site-years. However, soybean yield was not significantly correlated with slope. Many previous studies have found negative relationships between slope and crop yield (Ciha, 1984; Fahnestock et al., 1996; Changere and Lal, 1997; McConkey et al., 1997; Yang et al., 1998; Kravchenko and Bullock, 2000) due to the soil formation reasons discussed in the preceding section. In general, steep slope positions tend to have more severe erosion, which is characterized by thinner surface horizon, higher clay content, lower infiltration rate, and greater runoff resulting in lower soil productivity (Wright et al., 1990). Slope had a more significant effect on crop yield in Field 2 due to more variable and steep slope gradients (Table 2). Sand fraction was another important yield-limiting soil property. Although coarse sand content accounted for a small portion of total soil particles, it had a consistent negative correlation with corn yield all 3 yr in Field 2. Very fine sand content was also negatively correlated with corn yield in 1997 in Field 1 and in 1999 in Field 2 (Table 5). Elevation had no significant correlation with corn yield throughout the study period. Kravchenko and Bullock (2000) found that elevation was the most important contributing topographic factor in their study and had a fairly consistent negative correlation with crop yield. In most cases, the influence of elevation on yield is reflected through water availability, and this effect is more readily observed under extreme weather conditions and field topography (Kravchenko and Bullock, 2000). The lack of correlation between elevation and yield in this study can be partly explained by the lack of extended extreme weather conditions during the observed growing seasons. The occurrence of certain disease could obscure this effect as well. For example, in 2000, precipitation was slightly above normal for 5 mo in a row (from May to September, Fig. 2), resulting in white mold (Sclerotinia sclerotiorum) infestations in the lower elevations (northwest corner of the field) that affected the potential yield difference between high and low elevations.
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Table 5. Correlation coefficients between crop yield and soil and topographic variables, and with a significance level of P 0.05. Missing values were not significant.
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Other soil properties such as pH, sum of bases, base saturation, and Mg concentration had a significant correlation with either corn or soybean yield in a few specific site years (Table 5). However, this information would be of limited value in developing management zones due to the low incidence of significant correlation with yield. The data suggest that the relatively high nutrient levels present in the field minimized the effect soil nutrient levels ultimately had on yield.
The significant terms retained in stepwise regression analysis are listed in Table 6. In Field 1, clay content explained 32% of the corn yield variability in 1996 and 20% of the soybean yield variability in 1997. Stone et al. (1985), Miller et al. (1988), and Wright et al. (1990) all observed a negative correlation between clay content and crop yield. A decreased drainage rate associated with higher clay content may result in a less favorable soil condition in or near the rooting zone. In Field 2, elevation explained 18% of the corn yield variability in 2001. As much as 85% of corn yield variability was explained by three soil variables (pH, very fine sand, and K) in 1998 in Field 1 (Table 6), and on the contrast, none of the soil properties or topographic data were significant in 1999 and 2001 in Field 1. A range from 28 to 85% of the corn/soybean yield variability in the rest of the site-years were explained (Table 6) by the soil and topographic variables analyzed.
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Table 6. Stepwise regression between yield and soil and topographic variables. Each variable remaining in the equation is significant at P 0.05.
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CONCLUSION
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Principal-component analysis successfully determined soil properties that had a potential to affect crop yield. However, soil properties determined using this method are expected to be different in different agroecological regions. This underscores the importance of identifying soil properties having the greatest affect on crop yield on a site-specific basis. The observed relationship between slope and crop yield indicated that slope can be used as an additional yield indicator and that it will be helpful to incorporate slope information when developing field management zones. This has practical implications for growers as high-resolution digital elevation model (DEM) becomes more affordable and available. In the near future, slope derivation may be done as a common procedure in site-specific management systems. The combined effect of soil properties and topographic data explained approximately 30 to 85% of the corn/soybean yield variability in seven site-years out of nine studied. Unexplained variability, to a great extent, is significantly affected by temporal factors such as plant available water, as reported by Kravchenko and Bullock (2000). This suggests that combining information on identified important soil and topographic properties with a pre-growing-season estimate of plant available water, such as off-season precipitation levels, could significantly improve the accuracy in predicting spatial yield variability. This in turn, would provide growers with a tool for improving the efficiency of input allocations consistent with the goals of precision agriculture management.
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ACKNOWLEDGMENTS
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This research was supported in part by funding from the North Central Soybean Research Program. The authors are grateful for the contributions of Brian Long and Dan Klein for field activities and data collection and Sasha Kravchenko and Wang Yang for constructive discussion on statistical analyses.
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REFERENCES
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TOP
ABSTRACT
INTRODUCTION
