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Dep. of Agron., Iowa State Univ., Ames, IA 50011
* Corresponding author (apmallar{at}iastate.edu)
Received for publication October 8, 2001.
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ABSTRACT |
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 TOPNOTES ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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0.05) corn yield in one site-year (230 kg ha-1), and liming methods did not differ. The VR method applied less lime (56–61%) and reduced pH variability in one field. The lack of response was explained by high subsoil pH and high small-scale variation of topsoil pH. Sampling schemes based on 0.7-ha cells or zones identified smaller acid and alkaline areas than schemes based on small cells. Results suggest that yield response from lime is not likely when calcareous subsoils are present and topsoil pH is as low as 5.5. A VR liming method would apply less lime than a FR method in soils similar to those in this study.
Abbreviations: CCE, calcium carbonate equivalent • EC, electrical conductivity • DGPS, differential global positioning system • FR, fixed rate • NNA, nearest-neighbor analysis • RCBD, randomized complete block design • SD, standard deviation • VR, variable rate • VRT, variable-rate technology
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INTRODUCTION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Zone sampling has recently been suggested to reduce number of samples and sampling costs while maintaining acceptable information about nutrient variation within fields. Sampling by zone assumes that sampling areas can be identified on the basis of zones with different soil or crop characteristics across a field and that patterns are likely to remain temporally stable (Franzen et al., 2000). Criteria used to delineate management zones vary. Topography and soil and crop canopy images can be used to identify management zones because they tend to reflect different soil properties, are noninvasive, and may be of low cost (Franzen et al., 1998; Schepers et al., 2000). Soil electrical conductivity (EC), which can be estimated using noninvasive electromagnetic induction methods, has been useful to estimate topsoil depth (to a claypan or other root growth–limiting layer) and physical and chemical soil properties and to explain yield variability (Doolittle et al., 1994; Kitchen et al., 1999; Myers et al., 2000). Yield maps can be used to define different soil productivity areas, which together with other layers of information, can be used as a basis for VR fertilization (Stafford et al., 1999). Colvin et al. (1997) showed, however, that stable within-field yield patterns over time are observed in some fields but not in others.
Benefits from VR liming may include larger yield increases in acidic areas and lime savings in high-pH areas, but crop response should offset likely higher costs of soil sampling and application (Pierce and Warncke, 2000). Bongiovanni and Lowenberg-DeBoer (2000) simulated corn and soybean yields using soil pH response functions from small-plot data and predicted larger annual returns with site-specific pH management. Soil test data from a field sampled by Borgelt et al. (1994) suggested that 3.4 to 4.5 Mg ha-1 lime was needed and that a uniform rate would have resulted in overliming of 9 to 12% of the field and underliming of 37 to 41% of the field. Mulla et al. (2000) estimated lime requirements of a 12-ha field by collecting soil samples from cells of various sizes (9 by 9, 18 by 18, or 100 by 100 m) and by simulating a sampling scheme based on near-infrared reflectance images of bare soil and soybean canopy. Areas needing lime were 1.3 ha for the 9- by 9-m scheme, 3.4 ha for the 18- by 18-m scheme, none for the 100- by 100-m scheme, and 0.6 ha for the targeted sampling scheme. Heiniger and Meijer (2000) used soil samples collected on 1-ha square grids from four eastern U.S. states to estimate amounts of lime required for uniform or VR application. Based on simulated corn yield response and soil pH data, they concluded that use of VR lime application would have resulted in an average profit increase of $4.03 ha-1 compared with the uniform application. Pierce and Warncke (2000) applied five lime treatments for corn and soybean to small field plots (4.5 by 30.5 m) located according to interpolated surfaces from soil samples collected from 30.5-, 61-, and 91.5-m cells. They reported that grid soil sampling did not accurately predict soil pH or lime requirements for corn or soybean.
Yield monitor maps, differential global positioning system (DGPS) receivers in combines, and a strip-trial methodology can be used to evaluate the effects of VRT or other site-specific management practices (Oyarzabal et al., 1996; Colvin et al., 1997; Mallarino and Wittry, 1997; Mallarino et al., 2001). Treatments are applied to narrow (usually the width is a multiple of the equipment width used to apply the treatments) and long strips (generally the length of the field), and crops are harvested with combines equipped with yield monitors and DGPS receivers. However, the flow meter data of the yield monitor cannot be expected to resolve detailed yield variation over spatial intervals of less than approximately 20 to 25 m (Lark et al., 1997). Much of the research on VR liming discussed previously focused on describing soil pH variation using various sampling strategies and simulated responses to lime. Moreover, when lime was applied, treatments did not compare yield response to FR and VR application using equipment used by farmers.
The objectives of this study were to (i) compare alternative soil-sampling schemes for describing soil pH variability over a field and (ii) assess the impacts of FR and VR lime application methods on soil pH and grain yield of a corn–soybean rotation using production agriculture equipment.
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MATERIALS AND METHODS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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The same lime source was used in both fields, had a 91% calcium carbonate equivalent (CCE) neutralizing value, and was predominantly calcitic (230 g kg-1 Ca and 25 g kg-1 Mg). All the material passed through a 4.75-mm screen, 93% through a 2.36-mm screen, and 34% though a 0.25-mm screen. The lime was spread with commercial broadcast spreaders (spinners) equipped with DGPS receivers and controllers. The equipment was calibrated by the commercial applicator following manufacturer's recommendations. For the VR method, lime was applied only when soil pH was <6.3. The lime rates were calculated to raise pH to 6.5 and ranged from 0 to 8.2 Mg ha-1 CCE in both fields (average rates applied for various pH ranges are shown in Table 1). Lime application surfaced maps were prepared from point-sampling data using the inverse distance method with a distance-weighing exponent value of 2 (Wollenhaupt et al., 1994). The fixed lime rates were 5.77 Mg ha-1 CCE in Field 1 and 4.62 Mg ha-1 CCE in Field 2 and were applied uniformly along all strips of the FR method within each field. Based on the collaborators inputs (farmer and local cooperative), the FR used in Field 1 was based on the average lime requirement of areas with pH < 5.8. In Field 2, the FR was defined as the average lime requirement of areas with pH < 6.3. Iowa State University current recommendations for corn and soybean are to lime soils with pH < 6.3 (15-cm depth) and raise it to pH 6.5 (Voss et al., 1999), except for a few soil associations with high-pH subsoil where lime is recommended only below pH 6.0 (but a target pH of 6.5 is still used). The lime was incorporated to a 12- to 15-cm depth by chisel plowing and disking. Uniform rates of N, P, and K were applied by the farmers following Iowa State University recommendations based on soil testing (for P and K) and corn yield potential (for N).
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Two additional sets of soil samples were collected from each field. In fall 1998, composite subsoil samples (three 5-cm-diam. cores) were collected from selected sampling points corresponding to the soil series present. Fourteen areas were sampled in Field 1 and 23 in Field 2. Each core was collected to a 91-cm depth and was divided into six 15-cm sections. Soil was analyzed for pH, and samples with pH > 7.5 were analyzed for CaCO3 and MgCO3 (Dreimanis, 1962) to calculate CCE. In the second set, soil samples (15-cm depth) were collected from transects laid out along strips that received the FR and VR treatments in two replications of each field and were analyzed for soil pH. The transects (four in each field) were laid out where pH data from the cell soil-sampling scheme suggested high pH variability along the strips. Composite soil samples (eight cores) were collected from 4.5-m2 areas spaced 6 m along 142 m in Field 1 and 135 m in Field 2.
Grain yield was measured and recorded using a combine equipped with an impact flow-rate yield monitor (Ag Leader Technol., Ames, IA) and a real-time DGPS receiver. Differential corrections were obtained through the U.S. Coast Guard AM signal. The monitors recorded yield data with a 9-s interval in 1998 and 1999 and a 1-s interval in 2000. The monitor was calibrated outside the experimental areas of the fields by weighing all grain harvested along several (at least four) combine passes over the entire length of the fields. Grain moisture was determined on the go by a sensor located in the combine auger, and grain yield was corrected to 155 g kg-1 H2O for corn and 130 g kg-1 H2O for soybean. Each combine pass was identified with a unique number that was recorded with the georeferenced yield data. The raw yield data were exported into ArcView (Environ. Syst. Res. Inst., Redlands, CA). Yield data were unaffected by field borders because the experimental areas were located at least 50 m from any border. Yield data from combine harvest passes that may have included crop rows from two treatment strips were not used for treatment evaluation. One or two 9-m-wide combine passes were used from each soybean strip, and two to four 6-m-wide combine passes were used from each corn strip. The yield monitor data were carefully analyzed for common errors such as incorrect geographic coordinates due to partial loss of good differential correction, the effects of waterways, and incorrect settings in the time lag for the grain path through the combine. Affected data were corrected (such as grain path lags) or deleted (for example, yield points near waterways and when the combine stopped within the trial area). Grain yield and soil pH data were exported from ArcView to appropriate files for statistical analysis with SAS (SAS Inst., 1996).
Grain yield responses to the treatments were analyzed using three statistical procedures. One procedure assumed a randomized complete block design (RCBD) for which the yield input data were yield means of each strip (the experimental units). In a second procedure, the spatial correlation of yield was accounted for in the RCBD-ANOVA by nearest-neighbor analysis (NNA). The NNA was used to calculate values of a covariate that was included in the RCBD-ANOVA for each field following a procedure used before (Hinz, 1987; Hinz and Lagus, 1991; Mallarino et al., 1998; Mallarino et al., 2001). The input data were means of yield monitor points recorded for areas delineated by the width of the combine header (9 m for soybean and 6 m for corn) and the length of the soil-sampling cell along the crop rows (52 m in Field 1 and 45 m in Field 2). The individual data recorded by the yield monitors were not directly used because of the known lack of accuracy of yield monitors over distances shorter than 20 to 25 m (Lark et al., 1997). The first step in the calculation was to obtain yield residuals by removing treatment and block effects with a RCBD-ANOVA. Afterwards, covariate values were calculated by subtracting each yield residual from the mean value of its four neighbors (one from each north, south, east, and west directions). The third procedure assessed treatment effects separately for parts of the fields with different pH following procedures used by Oyarzabal et al. (1996) and Mallarino et al. (1998)( 2001).
The yield and pH data input were means for areas defined by 0.1-ha soil-sampling cells. The initial pH values were used to classify each cell into five pH classes ( <5.70, 5.70–6.29, 6.3–7.2, or >7.2). There were at least 13 cells in a pH class, and the maximum number was 61. The F test from a one-way ANOVA was used to estimate the consistency of lime effects for each pH class. The numerator mean square (between groups) represented variation introduced by the treatments, and the denominator mean square (within groups) represented the average variation within treatments for cells with a similar pH classification. Tables with grain yield data for each pH class do not show results for the VR method for field areas with soil pH > 6.2 because this method was not a distinct treatment for these areas. Data for the treatment labeled control for the two high-pH classes are means of the control and VR lime treatments, and statistical tests correspond to an orthogonal contrast with the FR method.
The effect of the lime treatments on soil pH from each sampling date was evaluated using two procedures. One procedure assessed treatment effects on pH by an ANOVA that assumed a conventional RCBD and for which input data were pH means for each strip. The second assessed treatment effects on pH for areas of the field with pH within each pH class defined for the yield analyses and was the same type of ANOVA used to assess treatment effects on grain yield for areas with different soil pH.
Soil pH Assessment with Various Soil-Sampling Schemes
Simulations of soil-sampling schemes of various intensities were conducted for the two fields based on the soil samples collected immediately before liming. This methodology was previously developed and used by others (Franzen and Peck, 1995; Mulla et al., 2000; Pierce and Warncke, 2000). Six simulated schemes were sampling of 0.3-ha grid cell, 0.3-ha grid point, 0.7-ha grid cell, 0.7-ha grid point, soil series, and management zone. A vector map with associated information for each sampling scheme was created using ArcView. The pH data of the 0.3-ha grid cell were calculated by averaging the point data for three contiguous cells across each row of cells. The pH data for the 0.3-ha grid-point scheme corresponded to the single sampling point at the center cell of the same three cells. The pH data of the 0.7-ha grid cell were calculated by averaging the point data for eight contiguous cells in Field 1 (four cells across strips and two along strips) and six contiguous cells in Field 2 (three cells across strips and two along strips). The pH data of the 0.7-ha grid point were identified by randomly selecting one sampling point from the cells used to calculate mean pH for the 0.7-ha cells. The pH data for the soil-series scheme were the mean pH of all of the 0.1-ha sampling points included within each soil series.
Management zones were identified using five different approaches. Four approaches used individual attributes (yield, soil series, elevation, and EC maps), and one approach integrated this information into a management-zone scheme. For the yield zones, yield monitor maps from growing seasons before treatment application (three maps for Field 1 and two maps for Field 2) were used to create one yield-zone map for each field following a two-step procedure. First, four to five areas with different yield levels were delineated using ArcView in maps from each crop using equal intervals. Second, these maps were used (through visual observations) to create one map for each field that described seven yield zones in Field 1 and six zones in Field 2. Some field areas had consistently higher or lower yield over time compared with other areas and were identified as separate zones. At least one zone in each field corresponded to areas containing large temporal yield variability. For the soil-map zones, soil series (seven in Field 1 and six in Field 2) were obtained from digitized (1:12000 scale) soil survey maps (Andrews and Dideriksen, 1981). Elevation models and EC maps for the elevation and EC zones were obtained after harvesting the 1998 crops by driving a vehicle equipped with a high-precision DGPS receiver (4000 Total Station with a real-time kinematic system, Trimble, Sunnyvale, CA) and an electromagnetic induction sensor (EM-38, Geonics Limited, Mississauga, ON, Canada). Elevation and EC data for 320 observations (points) per hectare were imported into ArcView to create surface maps. The elevation range was approximately 8 m in both fields. The EC values ranged from approximately 8 to 70 mS m-1 in each field. Both elevation and EC values were mapped into four equidistant classes. An aerial digital color image (1-m resolution) of the soybean canopy was taken from each field in late June of one year. Each image was imported into ArcView, and although zones based on color differences were not delineated, visual observations of contrasting color differences were used to help create the integrated management-zone maps. The photos showed small areas (<10% of the experimental areas) with chlorotic soybean canopy. There was no attempt to identify the reason for the chlorosis. In this soil association, soybean chlorosis at early stages usually is associated with excess moisture, Fe deficiency induced by high soil pH, or severe infestation with soybean cyst nematode (Heterodera glycines).
Information collected and characterized for the individual zoning approaches was used to identify an integrated management-zone approach for both fields. The maximum number of zones that could have been defined was very large (784 for Field 1, for example, from seven yield zones, seven soil-series zones, four elevation zones, and four EC zones), and many would be very small and would have irregular shapes. Using our knowledge for these fields (including remote sensing, production system, and equipment requirements), we identified nine integrated zones in Field 1 and six in Field 2. This approach for identifying management zones integrates farmers' preferences into the zone identification process (Fleming et al., 2000). As an example, Fig. 1 shows yield, elevation, EC, soil series, and management zones for one field. The pH for each management zone is the mean of corresponding sampling points of the 0.1-ha cells.
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RESULTS AND DISCUSSION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Table 2 shows mean soil pH data for each treatment and sampling date. Data from the first sampling date (immediately before applying the lime treatments) showed that pre-existing pH of areas that would receive the three treatments did not differ significantly (P 0.05). Liming did not increase soil pH significantly in Field 1, but there were increasing trends for both application methods in all sampling dates. Lime increased soil pH in the 1999 sampling date of Field 2, and the VR method increased soil pH more than the FR method. Results for the 2000 sampling date of Field 2 are difficult to explain because only the VR method seemed to have increased soil pH. The lime main effect was not significant (P = 0.12), and the comparison between application methods was significant at P = 0.06. Strips that received the VR treatment had less soil pH variability (SD) than the control or FR treatments in the fall 1998 and 2000 sampling dates of Field 1. In Field 2, the FR treatment had the lowest SD in the two sampling dates after the lime application. However, it should be noted that the initial SD for plots that would later receive the FR treatment was lower than for the other treatments. A reduction in variability from either FR or VR liming can be explained by a larger pH increase in acid areas than in high-pH areas.
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0.05) in the two more acidic pH classes (except for one acidic pH class in the 2000 sampling date of Field 2). The VR method increased pH more than the FR method for soil within the most acidic class of Field 1 but not in Field 2. A larger pH increase in areas with the most acid soil with the VR method is reasonable because more lime was applied with this method than with the FR method. The FR method did not affect soil pH in the neutral or high-pH classes.
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0.01) for the FR method compared with the control in the 6.3 to 7.2 pH class. This result would be possible if excess lime had detrimental effects on yield through a reduction in availability of other nutrients (McLean and Brown, 1984). However, we did not detect treatment differences for soils with pH > 7.2.
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2% CCE) at some depth (0–91 cm). Thirty-eight percent were calcareous at all depths, and 51% were calcareous at a 30- to 9-cm depth. Several sampling points had acid soil in the 0- to 15-cm layer but had calcareous subsoil below that depth. It is likely that a potential detrimental impact of acid pH of surface soil on crop yield was offset by high-pH subsoil. Current Iowa State University lime recommendations for corn and soybean (Voss et al., 1999) consider a soil pH 6.0 (15-cm depth) sufficient for these crops when subsoils are calcareous although advise liming to pH 6.5 if lime is required. Our data suggest that the critical pH level should be lower.
Another possible reason for a lack of response to VR liming was very high small-scale variability of soil pH. Figure 2
shows soil pH data for the intensive sampling conducted along eight transects and the corresponding 0.1-ha cell data. Soil pH varied from 5.4 up to 8.0 over distances of about 50 m in most transects. In some sections, soil pH varied about 2 pH units over a 12-m distance although sometimes changes were more gradual. There was a good agreement between the transect data and the cell data even with such a high small-scale variability in Field 1, which suggests that for this portion of the field, the cell data accurately represented the pH of the small area sampled. However, there was more discrepancy between the transect and cell data in two transects of Field 2. This result may be explained by high soil pH variability along multiple directions, which coincides with results of previous research for P and K (Mallarino, 1996). The high small-scale pH variation suggests that the pH class assignment for VR liming based on a 0.2-ha grid sampling may have not been entirely correct and could partly explain a lack of response to the VR lime method in apparently most acidic areas. For example, pH data from a Field 2 transect (VR treatment, Replication 2) suggests that lime is required, but the cell pH data from the same area suggests that no lime is required (soil pH is 6.3). These observations suggest that in these soils (Clarion–Nicollet–Webster soil association), even the very intensive grid soil sampling used may not represent soil pH variability well and may not produce a reasonable interpolated map. Furthermore, even if soil samples were collected with the extremely high intensity used in the transects, current VRT equipment used by cooperatives or distributors cannot manage such a small-scale variation.
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0.05) between soybean yield and soil pH of unlimed areas in 1998 and 1999 (data not shown), which explained 45% of the yield variability in 1998 (Field 1) and 54% in 1999 (Field 2). Thus, an apparent negative effect of the FR liming on soybean yield for high-pH areas could be explained by low yield in high-pH areas. Correlations between corn yield and soil pH were negative in 1999 and explained 46% of yield variability but were positive in 2000 and explained 36% of yield variability. These relationships likely are explained by differences in soil moisture. The low-lying and high-pH soils of this soil association (such as the series Canisteo, Harps, Okoboji, and Webster) are prone to excessive moisture in seasons with above-average rainfall. The 50-yr average rainfall recorded in a weather station located 10 km from the fields (Perry, IA) for the March–September period is 640 mm (U.S. Dep. of Commerce, 1951–2000). The 1998 and 1999 rainfall for the same period was 816 and 827 mm, respectively, but was 408 mm in 2000. In wet years, like 1998 and 1999, excessive moisture may limit yield in the low areas, but in dry years (like 2000), the same areas may have an advantage compared with the rest of the field, especially with corn. Kaspar et al. (2000) worked on similar soils and found a negative correlation between corn yield and elevation when rainfall was less than normal during the growing season but a positive correlation when rainfall was greater than normal. Moreover, Jaynes and Colvin (1997) found that the yield spatial pattern and structure vary over time for this soil association mainly due to changing rainfall patterns.
Soil pH Assessment with Various Soil-Sampling Schemes
The mean pH values for sampling units of various sampling schemes ranged from 6.6 to 6.9 for Field 1 and from 6.6 to 7.0 for Field 2 (Table 6). However, the pH range and SD within a field were smaller for the soil-map and management-zone schemes than for more intensive sampling schemes. This suggests that these schemes were effective in separating areas with contrasting pH. The smaller pH range for most grid-cell and zone-sampling schemes suggests, however, that large sampling units pool areas with large pH variation. The soil-map scheme had the lowest pH range and was the least effective in separating areas with distinctly different pH in Field 1. The soil-map and management-zone schemes were less effective in Field 2. The size of field areas that would be classified into four pH classes by each sampling strategy varied markedly. The two most acid pH classes were merged in one class because this pH range represents the area with greatest potential for yield increase. In Field 1, the less intensive sampling schemes resulted in a smaller area that would be limed compared with more intensive schemes. However, this was not always the case in Field 2, probably because one large management zone (10.1 ha) with a mean pH of 6.03 significantly increased the area that would be limed. The least intensive sampling schemes also resulted in smaller high-pH areas, especially in Field 1.
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0.01) for all schemes, which suggests that all schemes identified field areas with contrasting soil pH. However, the sizes of the F values confirm previous interpretations based on pH and SD in suggesting that, in this field, the elevation and EC schemes were more effective in reducing within-zone pH variability and increasing pH differences between zones. In contrast to results for Field 1, pH values, SD, and F tests suggested that all zone schemes provided similar information about pH variability.
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Future developments of on-the-go automated soil-testing systems should markedly decrease the cost of soil sampling and improve the accuracy of soil nutrient maps (Sudduth et al., 1997). Birrell et al. (1999) and Adamchuk et al. (1999) have developed real-time soil nutrient analysis sensors to determine soil pH that showed a reasonably good relationship (R2 = 0.83) with manually collected soil samples. Although these early automated soil-sampling systems provide analysis of soil acidity with lower accuracy than standard laboratory methods, they should improve the quality of the soil maps because much higher spatial resolution of soil sampling can be achieved (Adamchuk et al., 1999). However, in fields with very high small-scale pH variability, these advances should be accompanied by advances in VRT equipment effectiveness to apply lime accurately and precisely over very short distances.
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CONCLUSIONS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Soil pH information provided by a 0.1-ha point-grid sampling, which is more intensive and costly than grid-sampling schemes used in the Corn Belt, may not provide more useful pH data than less intensive zone-sampling schemes. This is due to extreme variation at a scale much smaller than the distance between grid points. Zone-sampling schemes may not provide better information about soil pH variability than intensive grid-sampling schemes, but they offer more flexibility to reduce the number of samples depending on particular field conditions, soil-sampling objectives, and economic conditions. Although no sampling scheme will alleviate the limitations of current VRT equipment to manage high small-scale variability, the results showed that VR liming is a better alternative to FR liming in these soils because it provides a reasonable way of avoiding lime application to large high-pH areas.
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NOTES |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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REFERENCES |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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