Soil-Sampling Alternatives and Variable-Rate Liming for a Soybean by oum18845


									                                      PRECISION AGRICULTURE
   Soil-Sampling Alternatives and Variable-Rate Liming for a Soybean–Corn Rotation
                                           Agustin A. Bianchini and Antonio P. Mallarino*

                           ABSTRACT                                                graphical coordinates of each sample or cells to create
   Precision agriculture technologies can be used to manage soil pH.               maps. The sampling intensity required for effective use
This study compared soil-sampling schemes for pH and evaluated                     of VR technology (VRT) is not clearly defined and may
variable-rate (VR) liming for soybean [Glycine max (L.) Merr.] and                 be different for different soil tests, fields, and geographic
corn (Zea mays L.). Global positioning systems and yield monitors                  regions. Research (Wollenhaupt et al., 1994; Franzen
were used in two fields. Soils were Typic Hapludolls, Aquic Haplu-                 and Peck, 1995) showed that grid soil sampling at densi-
dolls, and Typic Endoaquolls. Treatments were a control and a fixed-               ties of 0.4 and 0.1 ha further increase accuracy of soil
rate (FR) or VR liming based on a 0.2-ha soil-sampling scheme. Soil                test mapping. However, research (Wollenhaupt et al.,
pH (15-cm depth) ranged from 5.4 to 8.4, and most subsoils were
                                                                                   1994; Mallarino, 1996) has also shown that soil test vari-
calcareous. Treatments were applied before soybean to long and repli-
cated strips. Grain yield and soil pH were measured during 3 yr in
                                                                                   ability within cells of that size can be very high and that
one field and 2 yr in the other. Initial pH; canopy photos; and soil               use of systematically aligned sampling points may lead
survey, elevation, and electrical-conductivity maps were used to simu-             to large error when soil test patterns tend to be cyclic.
late sampling schemes based on larger cells or zones. Liming increased                Zone sampling has recently been suggested to reduce
(P 0.05) corn yield in one site-year (230 kg ha 1), and liming meth-               number of samples and sampling costs while main-
ods did not differ. The VR method applied less lime (56–61%) and                   taining acceptable information about nutrient variation
reduced pH variability in one field. The lack of response was explained            within fields. Sampling by zone assumes that sampling
by high subsoil pH and high small-scale variation of topsoil pH. Sam-              areas can be identified on the basis of zones with differ-
pling schemes based on 0.7-ha cells or zones identified smaller acid and
                                                                                   ent soil or crop characteristics across a field and that
alkaline areas than schemes based on small cells. Results suggest that
                                                                                   patterns are likely to remain temporally stable (Franzen
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                  et al., 2000). Criteria used to delineate management
apply less lime than a FR method in soils similar to those in this study.          zones vary. Topography and soil and crop canopy im-
                                                                                   ages can be used to identify management zones because
                                                                                   they tend to reflect different soil properties, are nonin-
                                                                                   vasive, and may be of low cost (Franzen et al., 1998;
T     he value of liming acid soils to increase soil pH
     to values optimal for crops is well known. McLean
and Brown (1984) presented in a detailed review the
                                                                                   Schepers et al., 2000). Soil electrical conductivity (EC),
                                                                                   which can be estimated using noninvasive electromag-
beneficial effects of lime in corn and soybean for the                             netic induction methods, has been useful to estimate
U.S. Corn Belt. Soil testing is a useful diagnostic tool,                          topsoil depth (to a claypan or other root growth–limiting
but high variability is often observed for pH and other                            layer) and physical and chemical soil properties and to
nutrients in farmer’s fields (Cahn et al., 1994; Cambar-                           explain yield variability (Doolittle et al., 1994; Kitchen
della et al., 1994; Mallarino, 1996; Pierce and Warncke,                           et al., 1999; Myers et al., 2000). Yield maps can be
2000). Variability patterns of these nutrients sometimes                           used to define different soil productivity areas, which
are related to soil map units but not when fertilization                           together with other layers of information, can be used
and liming have increased soil test values and created                             as a basis for VR fertilization (Stafford et al., 1999).
new patterns of variability (Franzen and Peck, 1995;                               Colvin et al. (1997) showed, however, that stable within-
Mallarino, 1996). Fields where fertilizers have been                               field yield patterns over time are observed in some fields
banded or where high rates of nutrients and manure                                 but not in others.
were used show large small-scale nutrient variability                                 Benefits from VR liming may include larger yield
(Peck and Melsted, 1973; Mallarino, 1996). Different                               increases in acidic areas and lime savings in high-pH
sampling schemes can be used to collect soil samples                               areas, but crop response should offset likely higher costs
from fields. Grid soil sampling began to be used exten-                            of soil sampling and application (Pierce and Warncke,
sively in the early 1990s in the Corn Belt, and it refers                          2000). Bongiovanni and Lowenberg-DeBoer (2000)
to a process whereby a field is divided into many smaller                          simulated corn and soybean yields using soil pH re-
cells for sampling purposes. Soil sampling for pH, P,                              sponse functions from small-plot data and predicted
and K often is based on square 1- to 2-ha grids (Sawyer,                           larger annual returns with site-specific pH management.
1994). The results of analyses are combined with geo-                              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
Dep. of Agron., Iowa State Univ., Ames, IA 50011. Iowa Agric. and
Home Econ. Exp. Stn. Journal Paper no. J-19556. Project 4062. This                 Abbreviations: CCE, calcium carbonate equivalent; EC, electrical
project was supported in part by the Iowa Soybean Promotion Board.                 conductivity; DGPS, differential global positioning system; FR, fixed
Received 8 Oct. 2001. *Corresponding author (                rate; NNA, nearest-neighbor analysis; RCBD, randomized complete
                                                                                   block design; SD, standard deviation; VR, variable rate; VRT, vari-
Published in Agron. J. 94:1355–1366 (2002).                                        able-rate technology.

1356                                     AGRONOMY JOURNAL, VOL. 94, NOVEMBER–DECEMBER 2002

and that a uniform rate would have resulted in overlim-                   native soil-sampling schemes for describing soil pH vari-
ing of 9 to 12% of the field and underliming of 37 to                     ability over a field and (ii) assess the impacts of FR and
41% of the field. Mulla et al. (2000) estimated lime                      VR lime application methods on soil pH and grain yield
requirements of a 12-ha field by collecting soil samples                  of a corn–soybean rotation using production agricul-
from cells of various sizes (9 by 9, 18 by 18, or 100 by                  ture equipment.
100 m) and by simulating a sampling scheme based
on near-infrared reflectance images of bare soil and                                 MATERIALS AND METHODS
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,                         Grain Yield and Soil pH Response Study
none for the 100- by 100-m scheme, and 0.6 ha for the                        A field response study was conducted from 1998 to 2000
targeted sampling scheme. Heiniger and Meijer (2000)                      in one field (Field 1) and from 1999 to 2000 in another field
used soil samples collected on 1-ha square grids from                     (Field 2) using a strip-trial methodology, DGPS, VRT, and
four eastern U.S. states to estimate amounts of lime                      grain yield monitors. The fields were located in central Iowa
required for uniform or VR application. Based on simu-                    (Boone County), had soils of the Clarion–Nicollet–Webster
lated corn yield response and soil pH data, they con-                     soil association, and were managed with a 2-yr corn–soybean
cluded that use of VR lime application would have                         rotation. Areas of approximately 15 ha in Field 1 and 18 ha
                                                                          in Field 2 that were located at least 50 m from field borders
resulted in an average profit increase of $4.03 ha 1 com-
                                                                          were selected for the experiments. Besides the Clarion, Ni-
pared with the uniform application. Pierce and Warncke                    collet, and Webster soil series, the Field 1 had the series
(2000) applied five lime treatments for corn and soybean                  Canisteo (Typic Hapludoll), Crippin (Aquic Hapludoll),
to small field plots (4.5 by 30.5 m) located according to                 Harps (Typic Calciaquoll), and Storden (Typic Eutrodepts).
interpolated surfaces from soil samples collected from                    Field 2 had the series Canisteo, Clarion, Harps, Nicollet, and
30.5-, 61-, and 91.5-m cells. They reported that grid                     two variants (silty-clay loam and mucky-silt loam) of Okoboji
soil sampling did not accurately predict soil pH or lime                  (Cumulic Vertic, Endoaquoll). Treatments were a control, a
requirements for corn or soybean.                                         FR lime application method, and a VR application method
   Yield monitor maps, differential global positioning                    based on a surfaced map from an intensive grid soil-sampling
system (DGPS) receivers in combines, and a strip-trial                    scheme and were applied once before the first soybean crop.
                                                                          There were four replications (blocks) in Field 1 and three in
methodology can be used to evaluate the effects of VRT                    Field 2. The individual treatment strip size was 18 by 624 m
or other site-specific management practices (Oyarzabal                    in Field 1 and 24 by 900 m in Field 2. The strip width was
et al., 1996; Colvin et al., 1997; Mallarino and Wittry,                  decided on the basis of the spreading width of the commercial
1997; Mallarino et al., 2001). Treatments are applied to                  lime spreaders used and the width of the farm combine head-
narrow (usually the width is a multiple of the equipment                  ers. Permanent plastic pipes were buried at each corner of
width used to apply the treatments) and long strips                       the experimental areas, and the geographical coordinates were
(generally the length of the field), and crops are har-                   recorded with a hand-held DGPS receiver. In Field 1, soil
vested with combines equipped with yield monitors and                     samples were collected in November 1997; the lime was ap-
DGPS receivers. However, the flow meter data of the                       plied on 23 Apr. 1998; and soybean was planted on 10 May.
                                                                          In Field 2, soil samples were collected in October 1998; the
yield monitor cannot be expected to resolve detailed                      lime was applied on 16 Dec. 1998; and soybean was planted
yield variation over spatial intervals of less than approxi-              on 28 May 1999. Soil samples (12 cores, 15-cm depth) were
mately 20 to 25 m (Lark et al., 1997). Much of the                        collected from areas approximately 80 m2 in size randomly
research on VR liming discussed previously focused on                     located (using computer software) within 0.2-ha cells (a grid-
describing soil pH variation using various sampling                       point sampling procedure). Samples were dried in an oven at
strategies and simulated responses to lime. Moreover,                     35 C, ground to pass a 2-mm screen, and analyzed in dupli-
when lime was applied, treatments did not compare                         cates. Soil pH was measured using a 1:1 (w/v) soil/water ratio
yield response to FR and VR application using equip-                      and a 10-min shaking time (Watson and Brown, 1998). Lime
ment used by farmers.                                                     requirement was calculated for each sampling point using the
                                                                          SMP (Shoemaker–McLean–Pratt) buffer method as described
   The objectives of this study were to (i) compare alter-                by Watson and Brown (1998).
                                                                             The same lime source was used in both fields, had a 91%
Table 1. Field areas and lime rates for the fixed-rate and variable-      calcium carbonate equivalent (CCE) neutralizing value, and
  rate liming methods.                                                    was predominantly calcitic (230 g kg 1 Ca and 25 g kg 1 Mg).
Field     Treatment       pH class      Field area        Lime rate†      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.
                                            ha          Mg ha 1 CCE‡
                                                                          The lime was spread with commercial broadcast spreaders
1          Fixed             All           14.6             5.77
           Variable          All           14.6             2.54          (spinners) equipped with DGPS receivers and controllers. The
                              5.7           1.3             6.73          equipment was calibrated by the commercial applicator fol-
                           5.7–6.2          5.7             4.56          lowing manufacturer’s recommendations. For the VR method,
                              6.2           7.6             0             lime was applied only when soil pH was 6.3. The lime rates
2          Fixed             All           18.0             4.62          were calculated to raise pH to 6.5 and ranged from 0 to 8.2
           Variable          All           18.0             1.80
                              5.7           3.6             4.69
                                                                          Mg ha 1 CCE in both fields (average rates applied for various
                           5.7–6.2          5.6             2.30          pH ranges are shown in Table 1). Lime application surfaced
                              6.2           8.8             0             maps were prepared from point-sampling data using the in-
† Rates for the variable-rate method are weighed averages (lime was not
                                                                          verse distance method with a distance-weighing exponent
  applied with the variable-rate method when soil pH was 6.2).            value of 2 (Wollenhaupt et al., 1994). The fixed lime rates
‡ CCE, calcium carbonate equivalent.                                      were 5.77 Mg ha 1 CCE in Field 1 and 4.62 Mg ha 1 CCE in
                          BIANCHINI & MALLARINO: SOIL SAMPLING FOR pH AND VARIABLE-RATE LIMING                                 1357

Field 2 and were applied uniformly along all strips of the FR      treatment evaluation. One or two 9-m-wide combine passes
method within each field. Based on the collaborators inputs        were used from each soybean strip, and two to four 6-m-wide
(farmer and local cooperative), the FR used in Field 1 was         combine passes were used from each corn strip. The yield
based on the average lime requirement of areas with pH             monitor data were carefully analyzed for common errors such
5.8. In Field 2, the FR was defined as the average lime require-   as incorrect geographic coordinates due to partial loss of good
ment of areas with pH        6.3. Iowa State University current    differential correction, the effects of waterways, and incorrect
recommendations for corn and soybean are to lime soils with        settings in the time lag for the grain path through the combine.
pH      6.3 (15-cm depth) and raise it to pH 6.5 (Voss et al.,     Affected data were corrected (such as grain path lags) or
1999), except for a few soil associations with high-pH subsoil     deleted (for example, yield points near waterways and when
where lime is recommended only below pH 6.0 (but a target          the combine stopped within the trial area). Grain yield and
pH of 6.5 is still used). The lime was incorporated to a 12- to
                                                                   soil pH data were exported from ArcView to appropriate files
15-cm depth by chisel plowing and disking. Uniform rates of
N, P, and K were applied by the farmers following Iowa State       for statistical analysis with SAS (SAS Inst., 1996).
University recommendations based on soil testing (for P and           Grain yield responses to the treatments were analyzed using
K) and corn yield potential (for N).                               three statistical procedures. One procedure assumed a ran-
   To evaluate the impact of the lime treatments on soil pH        domized complete block design (RCBD) for which the yield
over time, soil samples were collected from all strips immedi-     input data were yield means of each strip (the experimental
ately before the lime application and after each crop harvest      units). In a second procedure, the spatial correlation of yield
using a more intensive sampling scheme than the one used to        was accounted for in the RCBD-ANOVA by nearest-neighbor
define the VRs. In Field 1, soil samples were collected in April   analysis (NNA). The NNA was used to calculate values of a
(spring) 1998 immediately before liming and in late October        covariate that was included in the RCBD-ANOVA for each
or November (fall) 1998, 1999, and 2000. In Field 2, samples       field following a procedure used before (Hinz, 1987; Hinz and
were taken in November (fall) 1998 immediately before liming       Lagus, 1991; Mallarino et al., 1998; Mallarino et al., 2001).
and again in November 1999 and 2000. One composite sample          The input data were means of yield monitor points recorded
(12 cores, 15-cm depth) was collected from an area approxi-        for areas delineated by the width of the combine header (9 m
mately 80 m2 in size located at the center of each of 144 cells    for soybean and 6 m for corn) and the length of the soil-
in Field 1 and 180 cells in Field 2. The width of each cell        sampling cell along the crop rows (52 m in Field 1 and 45 m
coincided with the strip width (18 m in Field 1 and 24 m in        in Field 2). The individual data recorded by the yield monitors
Field 2), and the length along the strips was 52 m in Field 1      were not directly used because of the known lack of accuracy
and 45 m in Field 2. Thus, the area represented by each sample     of yield monitors over distances shorter than 20 to 25 m (Lark
approximately corresponded to 0.1-ha (0.09 ha in Field 1 and       et al., 1997). The first step in the calculation was to obtain
0.11 ha in Field 2).
                                                                   yield residuals by removing treatment and block effects with
   Two additional sets of soil samples were collected from
each field. In fall 1998, composite subsoil samples (three 5-cm-   a RCBD-ANOVA. Afterwards, covariate values were calcu-
diam. cores) were collected from selected sampling points          lated by subtracting each yield residual from the mean value
corresponding to the soil series present. Fourteen areas were      of its four neighbors (one from each north, south, east, and
sampled in Field 1 and 23 in Field 2. Each core was collected      west directions). The third procedure assessed treatment ef-
to a 91-cm depth and was divided into six 15-cm sections. Soil     fects separately for parts of the fields with different pH follow-
was analyzed for pH, and samples with pH 7.5 were analyzed         ing procedures used by Oyarzabal et al. (1996) and Mallarino
for CaCO3 and MgCO3 (Dreimanis, 1962) to calculate CCE.            et al. (1998, 2001).
In the second set, soil samples (15-cm depth) were collected          The yield and pH data input were means for areas defined
from transects laid out along strips that received the FR and      by 0.1-ha soil-sampling cells. The initial pH values were used
VR treatments in two replications of each field and were           to classify each cell into five pH classes ( 5.70, 5.70–6.29,
analyzed for soil pH. The transects (four in each field) were      6.3–7.2, or 7.2). There were at least 13 cells in a pH class,
laid out where pH data from the cell soil-sampling scheme          and the maximum number was 61. The F test from a one-way
suggested high pH variability along the strips. Composite soil     ANOVA was used to estimate the consistency of lime effects
samples (eight cores) were collected from 4.5-m2 areas spaced      for each pH class. The numerator mean square (between
6 m along 142 m in Field 1 and 135 m in Field 2.                   groups) represented variation introduced by the treatments,
   Grain yield was measured and recorded using a combine           and the denominator mean square (within groups) repre-
equipped with an impact flow-rate yield monitor (Ag Leader         sented the average variation within treatments for cells with
Technol., Ames, IA) and a real-time DGPS receiver. Differen-       a similar pH classification. Tables with grain yield data for
tial corrections were obtained through the U.S. Coast Guard        each pH class do not show results for the VR method for field
AM signal. The monitors recorded yield data with a 9-s interval    areas with soil pH 6.2 because this method was not a distinct
in 1998 and 1999 and a 1-s interval in 2000. The monitor
                                                                   treatment for these areas. Data for the treatment labeled con-
was calibrated outside the experimental areas of the fields
                                                                   trol for the two high-pH classes are means of the control and
by weighing all grain harvested along several (at least four)
combine passes over the entire length of the fields. Grain         VR lime treatments, and statistical tests correspond to an
moisture was determined on the go by a sensor located in the       orthogonal contrast with the FR method.
combine auger, and grain yield was corrected to 155 g kg 1            The effect of the lime treatments on soil pH from each
H2O for corn and 130 g kg 1 H2O for soybean. Each combine          sampling date was evaluated using two procedures. One proce-
pass was identified with a unique number that was recorded         dure assessed treatment effects on pH by an ANOVA that
with the georeferenced yield data. The raw yield data were         assumed a conventional RCBD and for which input data were
exported into ArcView (Environ. Syst. Res. Inst., Redlands,        pH means for each strip. The second assessed treatment effects
CA). Yield data were unaffected by field borders because the       on pH for areas of the field with pH within each pH class
experimental areas were located at least 50 m from any border.     defined for the yield analyses and was the same type of AN-
Yield data from combine harvest passes that may have in-           OVA used to assess treatment effects on grain yield for areas
cluded crop rows from two treatment strips were not used for       with different soil pH.
1358                                  AGRONOMY JOURNAL, VOL. 94, NOVEMBER–DECEMBER 2002

            Soil pH Assessment with Various                             Information collected and characterized for the individual
                  Soil-Sampling Schemes                              zoning approaches was used to identify an integrated manage-
                                                                     ment-zone approach for both fields. The maximum number
   Simulations of soil-sampling schemes of various intensities       of zones that could have been defined was very large (784 for
were conducted for the two fields based on the soil samples          Field 1, for example, from seven yield zones, seven soil-series
collected immediately before liming. This methodology was            zones, four elevation zones, and four EC zones), and many
previously developed and used by others (Franzen and Peck,           would be very small and would have irregular shapes. Using
1995; Mulla et al., 2000; Pierce and Warncke, 2000). Six simu-       our knowledge for these fields (including remote sensing, pro-
lated schemes were sampling of 0.3-ha grid cell, 0.3-ha grid         duction system, and equipment requirements), we identified
point, 0.7-ha grid cell, 0.7-ha grid point, soil series, and man-    nine integrated zones in Field 1 and six in Field 2. This ap-
agement zone. A vector map with associated information for           proach for identifying management zones integrates farmers’
each sampling scheme was created using ArcView. The pH               preferences into the zone identification process (Fleming et
data of the 0.3-ha grid cell were calculated by averaging the        al., 2000). As an example, Fig. 1 shows yield, elevation, EC,
point data for three contiguous cells across each row of cells.      soil series, and management zones for one field. The pH for
The pH data for the 0.3-ha grid-point scheme corresponded            each management zone is the mean of corresponding sampling
to the single sampling point at the center cell of the same          points of the 0.1-ha cells.
three cells. The pH data of the 0.7-ha grid cell were calculated        Soil pH data for the six schemes were compared by study
by averaging the point data for eight contiguous cells in Field 1    of several descriptive statistics and GIS maps. Field areas
(four cells across strips and two along strips) and six contiguous   represented by each pH class were calculated for each scheme
cells in Field 2 (three cells across strips and two along strips).   to determine how the schemes would have estimated the size
The pH data of the 0.7-ha grid point were identified by ran-         of the area that should receive lime. The two lower pH classes
domly selecting one sampling point from the cells used to            were merged into one class for pH 6.3 to represent in one
calculate mean pH for the 0.7-ha cells. The pH data for the          class the area that could have been limed. Average soil pH
soil-series scheme were the mean pH of all of the 0.1-ha sam-        and standard deviation (SD) were determined for the soil-
pling points included within each soil series.                       map, large-grid, and integrated management-zone schemes
   Management zones were identified using five different ap-         and also for the yield, elevation, and EC zones used to deter-
proaches. Four approaches used individual attributes (yield,         mine the integrated management-zone scheme. Also, F tests
soil series, elevation, and EC maps), and one approach inte-         based on a two-way ANOVA using SAS PROC GLM (SAS
grated this information into a management-zone scheme. For           Inst., 1996) were used to compare pH variability between and
the yield zones, yield monitor maps from growing seasons             within zones for the zone-sampling schemes (soil-map, yield,
before treatment application (three maps for Field 1 and two         elevation, EC, and integrated management-zone schemes).
maps for Field 2) were used to create one yield-zone map for         For each scheme, the numerator of the F ratio was the mean
each field following a two-step procedure. First, four to five       square arising from differences between average pH across
areas with different yield levels were delineated using Arc-         zones, and the denominator was a pooled mean square for
View in maps from each crop using equal intervals. Second,           pH variation within zones. A statistically significant difference
these maps were used (through visual observations) to create         would suggest that the sampling scheme was effective in identi-
one map for each field that described seven yield zones in           fying field areas with contrasting soil pH. The size of the F
Field 1 and six zones in Field 2. Some field areas had consis-       value can be interpreted as an index of the effectiveness of
tently higher or lower yield over time compared with other           each sampling scheme to reduce within-zone pH variability
areas and were identified as separate zones. At least one zone       and increase pH differences between zones.
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                        RESULTS AND DISCUSSION
(1:12 000 scale) soil survey maps (Andrews and Dideriksen,
1981). Elevation models and EC maps for the elevation and EC
                                                                                   Lime Use and Soil pH
zones were obtained after harvesting the 1998 crops by driving          The VR method applied 56% less lime than the FR
a vehicle equipped with a high-precision DGPS receiver (4000         in Field 1 and 61% less in Field 2 (Table 1). The average
Total Station with a real-time kinematic system, Trimble, Sun-       variable lime rates used in areas with soil pH 5.7 were
nyvale, CA) and an electromagnetic induction sensor (EM-38,          markedly higher than the FR in Field 1 (17%) but were
Geonics Limited, Mississauga, ON, Canada). Elevation and
EC data for 320 observations (points) per hectare were im-
                                                                     only slightly higher in Field 2 (1%). In areas with soil
ported into ArcView to create surface maps. The elevation            pH values between 5.7 and 6.2, the VR method applied
range was approximately 8 m in both fields. The EC values            significantly less lime than the FR method in both fields
ranged from approximately 8 to 70 mS m 1 in each field. Both         (18% less in Field 1 and 50% less in Field 2). Other
elevation and EC values were mapped into four equidistant            research has shown that VR application reduces lime
classes. An aerial digital color image (1-m resolution) of the       application rates (Heiniger and Meijer, 2000). The dif-
soybean canopy was taken from each field in late June of one         ferences in lime use may vary, however, depending on
year. Each image was imported into ArcView, and although             the distribution of pH values and lime needs. In our
zones based on color differences were not delineated, visual         fields, although soil pH ranged from 5.5 to 8.2 in Field
observations of contrasting color differences were used to help      1 and from 5.4 to 8.4 in Field 2, large areas had pH
create the integrated management-zone maps. The photos
                                                                     6.2 and were not limed with the VR method. Also, there
showed small areas ( 10% of the experimental areas) with
chlorotic soybean canopy. There was no attempt to identify           is no widely accepted criterion to decide the fixed lime
the reason for the chlorosis. In this soil association, soybean      rate to use in field experiments or when producers
chlorosis at early stages usually is associated with excess mois-    choose to apply a uniform rate over a field.
ture, Fe deficiency induced by high soil pH, or severe infesta-         Table 2 shows mean soil pH data for each treatment
tion with soybean cyst nematode (Heterodera glycines).               and sampling date. Data from the first sampling date
                               BIANCHINI & MALLARINO: SOIL SAMPLING FOR pH AND VARIABLE-RATE LIMING                                                  1359

Fig. 1. Examples (using Field 2) of grain-yield zones, elevation zones, electrical-conductivity zones, soil-series zones, and integrated manage-
   ment zones.

(immediately before applying the lime treatments)                              0.05). Liming did not increase soil pH significantly in
showed that pre-existing pH of areas that would receive                        Field 1, but there were increasing trends for both appli-
the three treatments did not differ significantly (P                           cation methods in all sampling dates. Lime increased
Table 2. Descriptive statistics of soil pH for each treatment across the two fields sampled.
                                                                                    Descriptive statistics‡                             Lime effect§
Field           Sampling date†             Treatment          Mean           Max             Min              Range     SD           Lime             F-V
                                                                                             pH                                              P   F
1             Initial (spring 1998)         No lime            6.78          8.18            5.55             2.63      0.96          NS              NS
                                            Fixed              6.81          8.18            5.68             2.50      0.95
                                            Variable           6.78          8.20            5.50             2.70      0.95
              Fall 1998                     No lime            6.62          8.10            5.30             2.80      1.10          NS              NS
                                            Fixed              6.80          8.25            5.63             2.62      0.97
                                            Variable           6.88          8.50            5.75             2.75      0.92
              Fall 1999                     No lime            6.54          8.05            5.35             2.70      1.03          NS              NS
                                            Fixed              6.78          8.00            5.60             2.40      0.92
                                            Variable           6.79          8.13            5.65             2.48      0.91
              Fall 2000                     No lime            6.65          8.18            5.40             2.78      1.06          NS              NS
                                            Fixed              6.95          8.10            5.85             2.25      0.89
                                            Variable           6.99          8.15            5.75             2.40      0.83
2             Initial (fall 1998)           No lime            6.62          8.10            5.40             2.70      0.96          NS              NS
                                            Fixed              6.52          8.05            5.28             2.77      0.88
                                            Variable           6.61          8.35            5.25             3.10      0.96
              Fall 1999                     No lime            6.65          8.10            5.53             2.58      0.88          0.04            0.05
                                            Fixed              6.73          8.10            5.60             2.50      0.72
                                            Variable           6.90          8.05            5.45             2.60      0.79
              Fall 2000                     No lime            6.69          8.23            5.73             2.50      0.91          NS              0.06
                                            Fixed              6.66          8.18            5.45             2.73      0.82
                                            Variable           6.89          8.18            5.40             2.78      0.87
† The initial soil sampling was done immediately before applying the lime (spring 1998 in Field 1 and fall 1998 in Field 2). Any other sampling was done
  in fall (October or November) of each year.
‡ Max, maximum soil test value; Min, minimum soil test value; SD, standard deviation.
§ Lime, orthogonal comparison of the control vs. the mean of the two limed treatments; F-V, orthogonal comparison of the fixed-rate and variable-rate
  lime treatments.
¶ NS, not significant at P 0.1.
1360                                      AGRONOMY JOURNAL, VOL. 94, NOVEMBER–DECEMBER 2002

Table 3. Soil pH for different sampling dates, treatments, and pH classes for two fields.
                                                                                Soil pH by sampling date and pH class
                                                      Spring 1998†                  Fall 1998‡                 Fall 1999                   Fall 2000
Field        pH class         Treatment          pH           P      F§      pH            P        F   pH            P         F   pH            P         F
1                5.7           No lime          5.63              NS         5.45           0.01¶       5.49           0.01¶        5.55           0.01¶
                               Fixed            5.69                         5.65                       5.69                        5.95
                               Variable         5.61                         6.00                       5.93                        6.23
             5.7–6.29          No lime          5.99              NS         5.70           0.01¶       5.70           0.01         5.78           0.01¶
                               Fixed            5.97                         5.95                       5.97                        6.13
                               Variable         5.96                         6.18                       5.98                        6.29
             6.3–7.2           No lime          6.76              NS         6.50              NS       6.54               NS       6.59           0.09
                               Fixed            6.57                         6.86                       6.65                        6.95
                 7.2           No lime          7.84              NS         7.76              NS       7.68               NS       7.81               NS
                               Fixed            7.87                         7.82                       7.79                        7.92
2                5.7           No lime                                       5.55              NS       5.78           0.01         5.93               NS
                               Fixed                                         5.51                       6.01                        5.82
                               Variable                                      5.51                       6.12                        5.99
             5.7–6.29          No lime                                       6.00              NS       6.15           0.01         6.10           0.02
                               Fixed                                         6.03                       6.45                        6.33
                               Variable                                      6.03                       6.34                        6.34
             6.3–7.2           No lime                                       6.61              NS       6.75               NS       6.69               NS
                               Fixed                                         6.58                       6.71                        6.70
                 7.2           No lime                                       7.85              NS       7.79               NS       7.89               NS
                               Fixed                                         7.81                       7.60                        7.66
† Initial soil sampling immediately before liming in Field 1.
‡ Second soil sampling (first after liming) in Field 1 and initial soil sampling (immediately before liming) in Field 2.
§ Probability of orthogonal comparisons between the control and the mean of the two application methods for the two lower pH classes and between
  the fixed-rate method and the mean of the control and variable-rate method for the two higher pH classes (no lime was applied with the variable-rate
  method when pH was 6.2).
¶ An orthogonal comparison between the fixed-rate and variable-rate methods was significant at P 0.1.

soil pH in the 1999 sampling date of Field 2, and the VR                      no effect in Field 2 (Table 4). The means for the RCBD
method increased soil pH more than the FR method.                             analysis correspond to observed yields, and means for
Results for the 2000 sampling date of Field 2 are difficult                   the RCBD-NNA are least square means that were ad-
to explain because only the VR method seemed to have                          justed for the spatial correlation of yield. The RCBD
increased soil pH. The lime main effect was not signifi-                      and RCBD-NNA means for each treatment were almost
cant (P     0.12), and the comparison between applica-                        exactly the same, a result that was observed in other
tion methods was significant at P        0.06. Strips that                    studies (Mallarino et al., 1998). However, adjusting for
received the VR treatment had less soil pH variability                        spatial correlation with NNA reduced the standard error
(SD) than the control or FR treatments in the fall 1998                       of treatment means. The 1999 corn showed a positive
and 2000 sampling dates of Field 1. In Field 2, the FR                        small response to lime (230 kg ha 1), which was statisti-
treatment had the lowest SD in the two sampling dates                         cally significant with both methods of analysis. The
after the lime application. However, it should be noted                       RCBD-NNA method of analysis suggested a very small
that the initial SD for plots that would later receive the                    negative soybean response in 1998 (P           0.07). This
FR treatment was lower than for the other treatments.                         negative response was mainly due to a lower yield of
A reduction in variability from either FR or VR liming                        the VR method and is difficult to explain.
can be explained by a larger pH increase in acid areas                           Table 5 shows yield means by pH class, treatment,
than in high-pH areas.                                                        and year for both fields. In Field 1, liming had no influ-
   Table 3 shows mean soil pH data for each treatment                         ence on yield in any pH class. This result makes the
and each of four pH classes. Results for the first soil-                      small field-average positive response to lime of the 1999
sampling date (before liming) indicated no significant                        corn crop in Field 1 difficult to explain although there
differences between treatments. As expected, the lime                         was a small nonsignificant responsive trend for the 5.7
treatments usually increased soil pH (P       0.05) in the                    to 6.29 pH class. The corn crop of Field 2 showed lower
two more acidic pH classes (except for one acidic pH                          yield (P 0.01) for the FR method compared with the
class in the 2000 sampling date of Field 2). The VR                           control in the 6.3 to 7.2 pH class. This result would be
method increased pH more than the FR method for soil                          possible if excess lime had detrimental effects on yield
within the most acidic class of Field 1 but not in Field                      through a reduction in availability of other nutrients
2. A larger pH increase in areas with the most acid soil                      (McLean and Brown, 1984). However, we did not detect
with the VR method is reasonable because more lime                            treatment differences for soils with pH 7.2.
was applied with this method than with the FR method.                            There could be several reasons for the small or nonex-
The FR method did not affect soil pH in the neutral or                        istent crop response to lime. One likely reason is the
high-pH classes.                                                              presence of high-pH (calcareous) subsoils in both fields.
                                                                              Eighty-nine percent of the deep sampling points had
             Grain Yield Response to Lime                                     soil pH 7.4 and were calcareous ( 2% CCE) at some
  The lime treatments had little effect on mean corn                          depth (0–91 cm). Thirty-eight percent were calcareous
and soybean yields along the strips of Field 1 and had                        at all depths, and 51% were calcareous at a 30- to 9-cm
                            BIANCHINI & MALLARINO: SOIL SAMPLING FOR pH AND VARIABLE-RATE LIMING                                                         1361

Table 4. Effect of lime application on corn and soybean yields evaluated by two methods of analysis.
                                                                                            Method of statistical analysis†
                                                                               RCBD                                                 RCBD-NNA
Field        Crop          Year        Treatment        Yield            SE‡        Lime§            F-V¶          Yield            SE    Lime              F-V
                                                                     1                                                          1
                                                             kg ha                          P    F                      kg ha                    P      F
1          Soybean         1998          No lime         3 980            71          NS#            NS             3 988           23    0.07              NS
                                         Fixed           3 975                                                      3 954
                                         Variable        3 887                                                      3 896
           Corn            1999          No lime        11 118           101          0.1            NS            11 122           58    0.02              NS
                                         Fixed          11 337                                                     11 324
                                         Variable       11 376                                                     11 386
           Soybean         2000          No lime         3 154            23          NS             NS             3 148            5    NS                NS
                                         Fixed           3 156                                                      3 160
                                         Variable        3 143                                                      3 145
2          Soybean         1999          No lime         3 306            32          NS             NS             3 301           12    NS                NS
                                         Fixed           3 304                                                      3 305
                                         Variable        3 277                                                      3 281
           Corn            2000          No lime         9 184            27          NS             NS             9 178           10    NS                NS
                                         Fixed           9 138                                                      9 143
                                         Variable        9 181                                                      9 181
† RCBD, randomized complete block design (observed means and statistics); RCBD-NNA, least square means and statistics from RCBD analysis combined
  with nearest-neighbor analysis (NNA).
‡ SE, average standard error of the least square means.
§ Lime, significance of the orthogonal comparison between the control and the mean of the two application methods.
¶ F-V, significance of the orthogonal comparison between the two application methods.
# NS, not significant at P 0.1.

depth. Several sampling points had acid soil in the 0-                          distances of about 50 m in most transects. In some sec-
to 15-cm layer but had calcareous subsoil below that                            tions, soil pH varied about 2 pH units over a 12-m
depth. It is likely that a potential detrimental impact of                      distance although sometimes changes were more grad-
acid pH of surface soil on crop yield was offset by high-                       ual. There was a good agreement between the transect
pH subsoil. Current Iowa State University lime recom-                           data and the cell data even with such a high small-scale
mendations for corn and soybean (Voss et al., 1999)                             variability in Field 1, which suggests that for this portion
consider a soil pH 6.0 (15-cm depth) sufficient for these                       of the field, the cell data accurately represented the pH
crops when subsoils are calcareous although advise lim-                         of the small area sampled. However, there was more
ing to pH 6.5 if lime is required. Our data suggest that                        discrepancy between the transect and cell data in two
the critical pH level should be lower.                                          transects of Field 2. This result may be explained by
   Another possible reason for a lack of response to VR                         high soil pH variability along multiple directions, which
liming was very high small-scale variability of soil pH.                        coincides with results of previous research for P and K
Figure 2 shows soil pH data for the intensive sampling                          (Mallarino, 1996). The high small-scale pH variation
conducted along eight transects and the corresponding                           suggests that the pH class assignment for VR liming
0.1-ha cell data. Soil pH varied from 5.4 up to 8.0 over                        based on a 0.2-ha grid sampling may have not been

Table 5. Soybean and corn yield by pH class and treatment for two fields.
Field             Year              Crop               pH class                 No lime                Fixed                Variable                 Statistics†
                                                                                                      kg ha                                           P F
1                 1998            Soybean                  5.7                   4 513                 4 477                     4 603                 NS‡
                                                       5.7–6.29                  4 228                 4 287                     4 196                 NS
                                                        6.3–7.2                  4 089                 4 031                                           NS
                                                           7.2                   3 497                 3 537                                           NS
                  1999            Corn                     5.7                  11 713                11 716                    11 583                 NS
                                                       5.7–6.29                 11 508                11 674                    11 637                 NS
                                                        6.3–7.2                 11 556                11 587                                           NS
                                                           7.2                  10 816                10 923                                           NS
                  2000            Soybean                  5.7                   3 192                 3 226                     3 163                 NS
                                                       5.7–6.29                  3 134                 3 166                     3 180                 NS
                                                        6.3–7.2                  3 226                 3 230                                           NS
                                                           7.2                   3 110                 3 110                                           NS
2                 1999            Soybean                  5.7                   3 690                 3 539                     3 609                 NS
                                                       5.7–6.29                  3 485                 3 424                     3 400                 NS
                                                        6.3–7.2                  3 327                 3 389                                           NS
                                                           7.2                   2 940                 2 979                                           NS
                  2000            Corn                     5.7                   8 891                 8 913                     8 685                 NS
                                                       5.7–6.29                  9 009                 8 833                     8 949                 0.01
                                                        6.3–7.2                  9 196                 8 934                                           NS
                                                           7.2                   9 234                 9 315                                           NS
† Statistical significance of orthogonal comparisons between the control and the mean of two application methods for the two lower pH classes and
  between the fixed-rate method and the mean of the control and variable-rate method for the two higher pH classes (no lime was applied with the
  variable-rate method when pH was 6.2).
‡ NS, not significant at P 0.1. An orthogonal comparison between the two application methods never was significant at P 0.1.
1362                                   AGRONOMY JOURNAL, VOL. 94, NOVEMBER–DECEMBER 2002

entirely correct and could partly explain a lack of re-                    Webster soil association), even the very intensive grid
sponse to the VR lime method in apparently most acidic                     soil sampling used may not represent soil pH variability
areas. For example, pH data from a Field 2 transect                        well and may not produce a reasonable interpolated
(VR treatment, Replication 2) suggests that lime is re-                    map. Furthermore, even if soil samples were collected
quired, but the cell pH data from the same area suggests                   with the extremely high intensity used in the transects,
that no lime is required (soil pH is 6.3). These observa-                  current VRT equipment used by cooperatives or distrib-
tions suggest that in these soils (Clarion–Nicollet–                       utors cannot manage such a small-scale variation.

Fig. 2. Intensive soil-sampling data from transects compared to the cell data for the fixed-rate and variable-rate application methods in Fields
   1 and 2.
                             BIANCHINI & MALLARINO: SOIL SAMPLING FOR pH AND VARIABLE-RATE LIMING                                         1363

   The influence of factors other than pH on yield could                      range and SD within a field were smaller for the soil-map
also explain small and inconsistent response to lime and                      and management-zone schemes than for more intensive
especially the small negative response trend of soybean                       sampling schemes. This suggests that these schemes
in 1998. There was a negative linear relationship (P                          were effective in separating areas with contrasting pH.
0.05) between soybean yield and soil pH of unlimed                            The smaller pH range for most grid-cell and zone-sam-
areas in 1998 and 1999 (data not shown), which ex-                            pling schemes suggests, however, that large sampling
plained 45% of the yield variability in 1998 (Field 1)                        units pool areas with large pH variation. The soil-map
and 54% in 1999 (Field 2). Thus, an apparent negative                         scheme had the lowest pH range and was the least effec-
effect of the FR liming on soybean yield for high-pH                          tive in separating areas with distinctly different pH in
areas could be explained by low yield in high-pH areas.                       Field 1. The soil-map and management-zone schemes
Correlations between corn yield and soil pH were nega-                        were less effective in Field 2. The size of field areas that
tive in 1999 and explained 46% of yield variability but                       would be classified into four pH classes by each sampling
were positive in 2000 and explained 36% of yield vari-                        strategy varied markedly. The two most acid pH classes
ability. These relationships likely are explained by dif-                     were merged in one class because this pH range repre-
ferences in soil moisture. The low-lying and high-pH                          sents the area with greatest potential for yield increase.
soils of this soil association (such as the series Canisteo,                  In Field 1, the less intensive sampling schemes resulted
Harps, Okoboji, and Webster) are prone to excessive                           in a smaller area that would be limed compared with
moisture in seasons with above-average rainfall. The                          more intensive schemes. However, this was not always
50-yr average rainfall recorded in a weather station lo-                      the case in Field 2, probably because one large manage-
cated 10 km from the fields (Perry, IA) for the March–                        ment zone (10.1 ha) with a mean pH of 6.03 significantly
September period is 640 mm (U.S. Dep. of Commerce,                            increased the area that would be limed. The least inten-
1951–2000). The 1998 and 1999 rainfall for the same                           sive sampling schemes also resulted in smaller high-pH
period was 816 and 827 mm, respectively, but was 408 mm                       areas, especially in Field 1.
in 2000. In wet years, like 1998 and 1999, excessive mois-                       Table 7 shows within-zone mean soil pH and statistics
ture may limit yield in the low areas, but in dry years                       used to compare the pH variability within zones for the
(like 2000), the same areas may have an advantage com-
                                                                              soil-map, yield, elevation, EC, and integrated manage-
pared with the rest of the field, especially with corn.
                                                                              ment-zone schemes. In Field 1, the soil-map and inte-
Kaspar et al. (2000) worked on similar soils and found
                                                                              grated management-zone schemes had the largest pH
a negative correlation between corn yield and elevation
                                                                              difference across zones, and the SD for the zones was
when rainfall was less than normal during the growing
                                                                              intermediate compared with other schemes. The yield-
season but a positive correlation when rainfall was
                                                                              zone scheme had the smallest pH range across zones,
greater than normal. Moreover, Jaynes and Colvin
(1997) found that the yield spatial pattern and structure                     and it was the only sampling scheme that would have
vary over time for this soil association mainly due to                        resulted in no lime requirement (soil pH was 6.2 for
changing rainfall patterns.                                                   all zones). Although SD was low in one yield zone (0.13
                                                                              in Zone 5), much higher SD values for other yield zones
                                                                              suggest that this scheme was less effective than other
           Soil pH Assessment with Various                                    schemes in reducing within-zone variability. The eleva-
                 Soil-Sampling Schemes                                        tion-zone scheme had the lowest range of SD and an
   The mean pH values for sampling units of various                           intermediate pH range across the units, which confirms
sampling schemes ranged from 6.6 to 6.9 for Field 1 and                       other research results (Franzen et al., 1998; Luchiari et
from 6.6 to 7.0 for Field 2 (Table 6). However, the pH                        al., 2000) suggesting that elevation may be a good source

Table 6. Area for each sampling unit, number of sampling units, and descriptive statistics of soil pH for different soil-sampling schemes.
                                                                                       Soil pH                       Total area by pH class
Field         Sampling scheme            Area†           of units‡        Mean          Range      SD§         6.3          6.3–7.2            7.2
                                            ha                                                                                ha
1            Small grid point               0.1             144            6.81          2.70      0.94        6.9            1.4             6.1
             Medium grid point              0.3              48            6.78          2.63      0.95        6.9            1.5             6.0
             Medium grid cell               0.3              48            6.81          2.38      0.86        6.3            1.8             6.3
             Large grid point               0.7              18            6.93          2.48      0.94        5.6            1.8             7.0
             Large grid cell                0.7              18            6.81          2.08      0.75        4.1            4.8             5.5
             Soil-map zones              0.5–4.6              7            6.64          1.75      0.73        5.4            5.1             3.9
             Management zones            0.5–2.1              9            6.68          2.03      0.60        4.5            7.3             2.6
2            Small grid point               0.1             180            6.60          3.10      0.93        9.2            3.1             5.7
             Medium grid point              0.3              60            6.62          2.70      0.95        9.9            2.1             6.0
             Medium grid cell               0.3              60            6.60          2.65      0.87        9.3            2.7             6.0
             Large grid point               0.7              30            6.68          2.70      0.95       10.2            1.1             6.7
             Large grid cell                0.7              30            6.57          2.32      0.72        6.7            6.7             4.6
             Soil-map zones              0.9–6.7              6            6.95          1.86      0.80        9.7            4.2             4.1
             Management zones            1.0–10.1             6            6.91          1.82      0.81       11.9            0.0             6.1
†Size of each sampling unit. The two numbers indicate the smallest and largest sampling zones.
‡Number of sampling units for each soil-sampling scheme.
§SD, standard deviation.
1364                                        AGRONOMY JOURNAL, VOL. 94, NOVEMBER–DECEMBER 2002

Table 7. Soil pH means and standard deviation (SD) for soil-map, yield, elevation, electrical-conductivity (EC), and management-
  zone schemes.
                    Soil-map zones                  Yield zones               Elevation zones                  EC zones                 Management zones
Field          Zone       pH         SD      Zone          pH     SD     Zone       pH           SD     Zone       pH           SD     Zone     pH      SD
1               1        6.42        0.92     1            7.08   0.94    1         7.20         0.75    1         6.12         0.65    1        7.01   0.95
                2        6.98        0.89     2            7.39   0.93    2         7.63         0.67    2         6.05         0.53    2        6.24   0.51
                3        5.85        0.15     3            6.39   0.88    3         6.05         0.47    3         7.40         0.74    3        7.17   0.85
                4        7.53        0.68     4            6.72   0.87    4         6.16         0.69    4         7.62         0.56    4        6.48   0.65
                5        6.14        0.64     5            7.85   0.13                                                                  5        6.73   0.98
                6        5.97        0.52     6            6.83   1.14                                                                  6        5.80   0.23
                7        7.60        0.63     7            6.73   0.72                                                                  7        6.68   0.94
                                                                                                                                        8        6.22   0.75
                                                                                                                                        9        7.83   0.32
    F tests†        16.7, P 0.001                 4.0, P    0.001             54.4, P 0.011                  55.4, P 0.001                13.3, P 0.001
2               1          5.99   0.60        1          5.96     0.56    1          7.80   0.26         1          6.04   0.43         1        7.85   0.14
                2          6.65   0.87        2          7.81     0.19    2          6.87   0.93         2          6.17   0.73         2        7.29   0.76
                3          6.14   0.50        3          7.51     0.64    3          5.89   0.45         3          7.35   0.79         3        6.03   0.68
                4          7.49   0.27        4          7.36     0.71    4          6.27   0.23         4          7.80   0.19         4        6.27   0.17
                5          7.60     -         5          6.26     0.17                                                                  5        6.28   0.23
                6          7.85   0.30        6          5.99     0.63                                                                  6        7.75   0.36
    F tests         48.8, P 0.001                 52.3, P 0.001               57.9, P    0.001               56.5, P    0.001             55.5, P 0.001
† F value and probability for comparisons of between-zone and within-zone pH variability for each sampling scheme.

of information to delineate management zones. All EC                             than schemes based on larger cells or zones but are
zones also had low SD values, and the pH range across                            more expensive (especially in large fields) due to high
zones was intermediate compared with other schemes.                              sampling and analysis costs. The effectiveness of tradi-
Areas with high EC were associated with areas of high                            tional sampling by soil-map unit (based on commonly
pH, and correlation coefficients were 0.67 for Field 1                           used soil survey maps) still used by many producers
and 0.70 for Field 2. These results should be interpreted                        could be improved by using other layers of information
carefully, however, because areas with high EC and high                          to develop management-zone schemes. A management-
pH tended to be at low elevations, were moderately                               zone scheme likely will represent small-scale soil test
poorly drained, had higher egg counts of soybean cyst                            variability less accurately than intensive grid-sampling
nematode, and yielded less seasons with higher precipi-                          schemes when the number of sampling units is compara-
tation. The F tests showed a significant difference (P                           tively lower. However, the management-zone concept
0.01) for all schemes, which suggests that all schemes                           is flexible to accommodate different information layers
identified field areas with contrasting soil pH. However,                        as well as different fields, sampling objectives, and eco-
the sizes of the F values confirm previous interpretations                       nomic conditions.
based on pH and SD in suggesting that, in this field,                               Future developments of on-the-go automated soil-
the elevation and EC schemes were more effective in                              testing systems should markedly decrease the cost of
reducing within-zone pH variability and increasing pH                            soil sampling and improve the accuracy of soil nutrient
differences between zones. In contrast to results for                            maps (Sudduth et al., 1997). Birrell et al. (1999) and
Field 1, pH values, SD, and F tests suggested that all                           Adamchuk et al. (1999) have developed real-time soil
zone schemes provided similar information about pH                               nutrient analysis sensors to determine soil pH that
variability.                                                                     showed a reasonably good relationship (R2 0.83) with
   Our results coincide with other research in showing                           manually collected soil samples. Although these early
that elevation and EC maps can be useful tools to delin-                         automated soil-sampling systems provide analysis of soil
eate management zones although correlations among                                acidity with lower accuracy than standard laboratory
elevation, EC, and soil properties, as well as the basis                         methods, they should improve the quality of the soil
for their use to delineate management zones, vary among                          maps because much higher spatial resolution of soil
fields and regions (Jaynes et al., 1995; Hartsock et al.,                        sampling can be achieved (Adamchuk et al., 1999).
2000). Our work and that of the previous authors also                            However, in fields with very high small-scale pH vari-
showed that soil series are not always properly identified                       ability, these advances should be accompanied by ad-
by soil survey maps and that EC maps are useful tools                            vances in VRT equipment effectiveness to apply lime
to complement soil survey maps. Furthermore, results                             accurately and precisely over very short distances.
show that various options are available when farmers
need to take soil samples to decide lime application in
these soils. Specific recommendations that apply to all
fields and conditions are not possible because the effec-                          Fixed-rate or VR liming had no meaningful effect on
tiveness of different sampling schemes and layers of                             crop yield even though a soil-sampling method more
information vary greatly across regions, with the main                           intensive than any method being used in production
objective of the soil sampling, and with economic con-                           agriculture showed that 15% of the areas had pH 5.4
siderations. Intensive grid-sampling schemes being used                          to 5.7 and 35% had pH 5.7 to 6.2. The VR method
in the Corn Belt (which are based on 0.7- to 1.0-ha cells)                       usually increased pH of acidic areas more than the FR
are more effective in describing small-scale variability                         method and applied 56% less lime in one field and 61%
                              BIANCHINI & MALLARINO: SOIL SAMPLING FOR pH AND VARIABLE-RATE LIMING                                                1365

less in the other. The lack of yield response could be                        Franzen, D.W., A.D. Halvorson, and V.L. Hofman. 2000. Manage-
                                                                                 ment zones for soil N and P levels in the Northern Great Plains
explained by predominant calcareous subsoil and by
                                                                                 [CD-ROM]. In P.C. Robert et al. (ed.) Proc. Int. Conf. on Precision
very high small-scale pH variability although this possi-                        Agric., 5th, Bloomington, MN. 16–19 July 2000. ASA, CSSA, and
bility could not be confirmed with the methods used.                             SSSA, Madison, WI.
Irregular patterns with a variation of about 2 pH units                       Franzen, D.W., and T.R. Peck. 1995. Field soil sampling density for
within 10 to 20 m were common. Although this variabil-                           variable rate fertilization. J. Prod. Agric. 8:568–574.
                                                                              Hartsock, N.J., T.G. Mueller, G.W. Thomas, R.I. Barnhisel, K.L.
ity should not have affected crop response to FR liming,                         Wells, and S.A. Shearer. 2000. Soil electrical conductivity variability
it may have reduced the effectiveness of VR liming.                              [CD-ROM]. In P.C. Robert et al. (ed.) Proc. Int. Conf. on Precision
Results suggest that no lime is needed in this soil associa-                     Agric., 5th, Bloomington, MN. 16–19 July 2000. ASA, CSSA, and
tion when the topsoil has pH        5.4 and subsoils are                         SSSA, Madison, WI.
calcareous although other soils could respond differ-                         Heiniger, R.W., and A.J. Meijer. 2000. Why variable rate application
                                                                                 of lime has increased grower profits and acceptance of precision
ently.                                                                           agriculture in the southeast [CD-ROM]. In P.C. Robert et al. (ed.)
   Soil pH information provided by a 0.1-ha point-grid                           Proc. Int. Conf. on Precision Agric., 5th, Bloomington, MN. 16–19
sampling, which is more intensive and costly than grid-                          July 2000. ASA, CSSA, and SSSA, Madison, WI.
sampling schemes used in the Corn Belt, may not pro-                          Hinz, P.N. 1987. Nearest neighbor analysis in practice. Iowa State J.
                                                                                 Res. 62:199–217.
vide more useful pH data than less intensive zone-sam-                        Hinz, P.N., and J.P. Lagus. 1991. Evaluation of four covariate types
pling schemes. This is due to extreme variation at a                             used for adjustment of spatial variability. p. 118–126. In Proc. Appl.
scale much smaller than the distance between grid                                Stat. in Agric. Conf., Kansas State Univ., Manhattan, KS. 28–30
points. Zone-sampling schemes may not provide better                             Apr. 1991. Dep. of Stat., Kansas State Univ. Manhattan
information about soil pH variability than intensive                          Jaynes, D.B., and T.S. Colvin. 1997. Spatiotemporal variability of corn
                                                                                 and soybean yield. Agron. J. 89:30–37.
grid-sampling schemes, but they offer more flexibility                        Jaynes, D.B., T.S. Colvin, and J. Ambuel. 1995. Yield mapping by
to reduce the number of samples depending on particu-                            electromagnetic induction. p. 383–394. In P.C. Robert (ed.) Proc.
lar field conditions, soil-sampling objectives, and eco-                         Int. Conf. on Site-Specific Manage. for Agric. Syst., 2nd, Blooming-
nomic conditions. Although no sampling scheme will                               ton, MN. 27–30 Mar. 1994. ASA, CSSA, and SSSA, Madison, WI.
alleviate the limitations of current VRT equipment to                         Kaspar, T.C., T.S. Colvin, D.B. Jaynes, D.L. Karlen, D.E. James,
                                                                                 D.W. Meek, D. Pullido, and H. Butler. 2000. Estimating corn yield
manage high small-scale variability, the results showed                          using six years of yield data and terrain attributes [CD-ROM]. In
that VR liming is a better alternative to FR liming in                           P.C. Robert et al. (ed.) Proc. Int. Conf. on Precision Agric., 5th,
these soils because it provides a reasonable way of                              Bloomington, MN. 16–19 July 2000. ASA, CSSA, and SSSA, Madi-
avoiding lime application to large high-pH areas.                                son, WI.
                                                                              Kitchen, N.R., K.A. Sudduth, and S.T. Drummond. 1999. Soil electri-
                                                                                 cal conductivity as a crop productivity measure for claypan soils.
                           REFERENCES                                            J. Prod. Agric. 12:607–617.
Adamchuk, V.I., M.T. Morgan, and D.R. Ess. 1999. An automated                 Lark, R.M., J.V. Stafford, and H.C. Bolam. 1997. Limitations on the
   sampling system for measuring soil pH. Trans. ASAE 42:885–891.                spatial resolution of yield mapping for combinable crops. J. Agric.
Andrews, W.F., and R.O. Dideriksen. 1981. Soil survey of Boone                   Eng. Res. 66:183–193.
   County, Iowa. USDA, Washington, DC (available online at http://            Luchiari, A., J. Shanahan, D. Francis, M. Schlemmer, J. Schepers, M. (Verified 25 July 2002.)                             Liebig, A. Schepers, and S. Payton. 2000. Strategies for establishing
Birrell, S.J., J.W. Hummel, R.G. Hoeft, and T.R. Peck. 1999. Develop-            management zones for site specific nutrient management [CD-
   ment and field testing of real-time soil nutrient sensors for precision       ROM]. In P.C. Robert et al. (ed.) Proc. Int. Conf. on Precision
   fertilizer application. p. 65–70. In Proc. Illinois Fert. Conf., Peoria,      Agric., 5th, Bloomington, MN. 16–19 July 2000. ASA, CSSA, and
   IL. 25–27 Jan. 1999. Univ. of Illinois, Urbana.                               SSSA, Madison, WI.
Bongiovanni, R., and J. Lowenberg-DeBoer. 2000. Economics of vari-            Mallarino, A.P. 1996. Spatial variability of phosphorus and potassium
   able rate lime in Indiana. Precis. Agric. 2:55–70.                            in no-tilled soils for two sampling scales. Soil Sci. Soc. Am. J. 60:
Borgelt, S.C., S.W. Searcy, B.A. Stout, and D.J. Mulla. 1994. Spatially          1473–1481.
   variable liming rates: A method for determination. Trans. ASAE             Mallarino, A.P., M.U. Haq, D. Wittry, and M. Bermudez. 2001. Varia-
   37:1499–1507.                                                                 tion in soybean response to early-season foliar fertilization among
Cahn, M.D., J.W. Hummel, and B.H. Brouer. 1994. Spatial analysis                 and within fields. Agron. J. 93:1220–1226.
   of soil fertility for site-specific crop management. Soil Sci. Soc.        Mallarino, A.P., and D.J. Wittry. 1997. Use of DGPS, yield monitors,
   Am. J. 58:1240–1248.                                                          soil testing, and variable rate technology to improve phosphorus
Cambardella, C.A., T.B. Mooreman, J.M. Novak, T.B. Parkin, D.L.                  and potassium management. p. 267–275. In Proc. Integrated Crop
   Karlen, R.F. Turco, and A.E. Konopka. 1994. Field-scale variability           Manage. Conf., Ames, IA. 17–18 Nov. 1997. Iowa State Univ.
   of soil properties in central Iowa soils. Soil Sci. Soc. Am. J. 58:           Ext., Ames.
   1501–1511.                                                                 Mallarino, A.P., D.J. Wittry, D. Dousa, and P.N. Hinz. 1998. Variable-
Colvin, T.S., D.B. Jaynes, D.L. Karlen, D.A. Laird, and J.R. Ambuel.             rate phosphorus fertilization: On-farm research methods and evalu-
   1997. Yield variability within a central Iowa field. Trans. ASAE              ation for corn and soybean. p. 687–696. In P.C. Robert et al. (ed.)
   40:883–889.                                                                   Proc. Int. Conf. on Precision Agric., 4th, St. Paul, MN. 19–22 July
Doolittle, J.A., K.A. Sudduth, N.R. Kitchen, and S.J. Indorante. 1994.           1998. ASA, CSSA, and SSSA, Madison, WI.
   Estimating depths to claypans using electromagnetic induction              McLean, E.O., and J.R. Brown. 1984. Crop response to lime in the
   methods. J. Soil Water Conserv. 49:572–575.                                   midwestern United States. p. 267–303. In F. Adams (ed.) Soil acidity
Dreimanis, A. 1962. Quantitative gasometric determination of calcite             and liming. Agron. Monogr. 12. 2nd ed. ASA, CSSA, and SSSA,
   and dolomite by using Chittick Apparatus. J. Sediment. Petrol. 32:            Madison, WI.
   520–529.                                                                   Mulla, D.J., A.C. Sekely, and M. Beatty. 2000. Evaluation of remote
Fleming, K.L., D.G. Westfall, D.W. Wiens, and M.C. Brodahl. 2000.                sensing and targeted soil sampling for variable rate application of
   Evaluating farmer defined management zone maps for variable                   lime [CD-ROM]. In P.C. Robert et al. (ed.) Proc. Int. Conf. on
   rate fertilizer application. Precis. Agric. 2:201–215.                        Precision Agric., 5th, Bloomington, MN. 16–19 July 2000. ASA,
Franzen, D.W., L.J. Cihacek, V.L. Hofman, and L.J. Swenson. 1998.                CSSA, and SSSA, Madison, WI.
   Topography-based sampling compared to grid sampling in the                 Myers, D.B., N.R. Kitchen, K.A. Sudduth, and R.J. Miles. 2000. Esti-
   Northern Great Plains. J. Prod. Agric. 11:364–370.                            mation of a soil productivity index on claypan soils using soil electri-
1366                                    AGRONOMY JOURNAL, VOL. 94, NOVEMBER–DECEMBER 2002

   cal conductivity [CD-ROM]. In P.C. Robert et al. (ed.) Proc. Int.       to regionalize fields into potential management units. p. 225–237.
   Conf. on Precision Agric., 5th, Bloomington, MN. 16–19 July 2000.       In P.C. Robert et al. (ed.) Proc. Int. Conf. on Precision Agric., 4th,
   ASA, CSSA, and SSSA, Madison, WI.                                       St. Paul, MN. 19–22 July 1998. ASA, CSSA, and SSSA, Madi-
Oyarzabal, E.S., A.P. Mallarino, and P.N. Hinz. 1996. Using precision      son, WI.
   framing technologies for improving applied on-farm research. p.       Sudduth, K.A., J.W. Hummel, and S.J. Birrell. 1997. Sensors for site-
   379–387. In P.C. Robert et al. (ed.) Proc. Int. Conf. on Precision      specific management. p. 183–210. In F.J. Pierce and E.J. Sadler
   Agric., 3rd, Bloomington, MN. 23–26 June 1996. ASA, CSSA, and           (ed.) The state of site-specific management for agriculture. ASA,
   SSSA, Madison, WI.                                                      CSSA, and SSSA, Madison, WI.
Peck, T.R., and S.W. Melsted. 1973. Field sampling for soil testing.     United States Department of Commerce. 1951–2000. Climatological
   p. 67–75. In L.M. Walsh and J.D. Beacon (ed.) Soil testing and          data. Natl. Oceanic and Atmos. Administration, Natl. Clim. Data
   plant analysis. SSSA Book Ser. 3. SSSA, Madison, WI.                    Cent., Asheville, NC (summaries available online at http://mesonet.
                                                                  (Verified 25 July 2002.)
Pierce, F.J., and D.D. Warncke. 2000. Soil and crop response to vari-
                                                                         Voss, R.D., J.E. Sawyer, A.P. Mallarino, and R. Killorn. 1999. General
   able-rate liming for two Michigan fields. Soil Sci. Soc. Am. J. 64:     guide for crop nutrient recommendations in Iowa. Publ. PM 1688
   774–780.                                                                (Revised). Iowa State Univ. Ext., Ames.
SAS Institute. 1996. SAS/STAT user’s guide. Release 6.11. SAS Inst.,     Watson, M.E., and J.R. Brown. 1998. pH and lime requirement. p.
   Cary, NC.                                                               13–16. In J.R. Brown (ed.) Recommended chemical soil test proce-
Sawyer, J.E. 1994. Concepts of variable-rate technology considera-         dures for the North Central Region. Recommended North Central
   tions for fertilizer application. J. Prod. Agric. 7:195–201.            Regional Res. Publ. 221 (Revised). SB 1001. Missouri Agric. Exp.
Schepers, J.S., M.R. Schlemmer, and R.B. Ferguson. 2000. Site-specific     Stn., Columbia.
   considerations for managing phosphorus. J. Environ. Qual. 29:125–     Wollenhaupt, N.C., R.P. Wolkowski, and M.K. Clayton. 1994. Map-
   130.                                                                    ping soil test phosphorus and potassium for variable-rate fertilizer
Stafford, J.V., R.M. Lark, and H.C. Bolam. 1999. Using yield maps          application. J. Prod. Agric. 7:441–448.

To top