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SITE-SPECIFIC MANAGEMENT

Different Techniques to Identify Management Zones Impact Nitrogen and Phosphorus Sampling Variability

^a Plant Science Dep., South Dakota State Univ., Brookings, SD 57007
^b Southeast Research Farm, South Dakota Agric. Exp. Stn., South Dakota State Univ., Beresford, SD 57004
^c Univ. of Missouri, Columbia, MO 65211

^* Corresponding author (david_clay{at}sdstate.edu).

Received for publication September 26, 2002.

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY REFERENCES

 
The efficiency of the management zone approach to improve fertilizer recommendations relies on accurately locating zone boundaries. The objective of this study was to determine the impact of different techniques of identifying management zones on soil NO^-₃–N and Olsen-P (sodium bicarbonate extractable-P) sampling variability. Soil samples were collected on a 60 by 60 m or denser grid, in three fields (65, 53, and 40 ha). These samples were analyzed for NO^-₃–N and Olsen-P. Soil nutrient data was used to simulate the effect of different techniques to identify P and N management zone boundaries. Approaches evaluated for locating management zone sampling boundaries included: (i) sampling areas impacted by old homesteads or animals separately from the rest of the field; (ii) sampling different grid cells; (iii) use of geographic information systems (GIS) or cluster analysis to identify zones based on apparent electrical conductivity (EC_a), elevation, aspect, and distance (connectedness); and (iv) sampling each soil series separately. An F statistic was used to determine if the sampling approach reduced nutrient sampling variability. Results suggested that: (i) old homesteads or areas impacted by animals should be sampled separately from the rest of the field; (ii) grid-cell sampling was more consistent in reducing within zone soil-test variability than the other techniques tested; and (iii) zones that are not continuous should be sampled and managed separately.

Abbreviations: EC_a, apparent electrical conductivity • GIS, geographic information systems • MZA, Management Zone Analyst software • NRCS, National Resource Conservation Service • s²_p, pooled variance

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY REFERENCES

 
A KEY PRECISION AGRICULTURE CONCEPT is that fertilizer recommendations can be improved by accounting for in-field nutrient variability (Chang et al., 2000). Commonly used strategies for obtaining soil nutrient spatial information are grid-point, grid-cell, and management-zone sampling. In grid-point sampling, samples are collected from specific points within fields, and kriging or some other estimation approach is used to develop contour maps (Wollenhaupt et al., 1994; Chang et al., 1999). Grid sampling provides excellent soil nutrient information for land managers if the points are close enough to assure spatial dependence. However, developing a universal recommendation for grid distance is difficult because different fields may have different sampling requirements. For example, Wollenhaupt et al. (1994) had one recommendation for fields in the nonresponsive categories (91-m grid) and a different recommendation for fields in the responsive range (61-m grid). Other problems with grid-point sampling are that important information may be missed when grid distances are too large, and many farmers perceive that grid-point sampling is not profitable.

Grid-cell sampling is an approach where a composite sample is collected from a block with a specified size (Wollenhaupt et al., 1994). The sample from each block is analyzed and the resulting value represents the average value of the cell. Many current traditional soil sampling strategies contain some aspect of grid-cell sampling. Ferguson et al. (1998) and Buchholz (1999) recommended that in Nebraska and Missouri, respectively, the largest sampling area in a field should be 8 ha or less. In Montana, Jacobsen (1998) recommended that the largest sampling area should be 40 ha.

The management zone approach is based on the hypothesis that a field is a mosaic of different habitats with each having unique characteristics that influence soil properties and management (Doerge, 1999; Fleming et al., 2000). Information that can be used to identify the different management zones include (i) prior experience, (ii) yield maps, (iii) soil survey maps, (iv) topography, (v) soil drainage, (vi) EC_a, (vii) soil color, (viii) soil organic matter, (ix) soil nutrients, (x) remote sensing, and (xi) soil moisture. One of the first techniques used to identify different zones was to spread as many maps as possible on a table and to draw lines around the different zones. For example, Fleming et al. (1999) used aerial photographs as templates for farmer-developed productivity management zones in two center-pivot irrigated fields located in Colorado. In these fields, nutrient concentrations (NO^-₃, K, and Zn) and yields were high in farmer-identified high productivity areas, and soil organic matter, percentage clay, and EC_a were correlated to yield.

More recently, researchers have investigated if computer classification procedures available in GIS or cluster analysis can be used to improve and automate the process of defining management zones. For example, Fridgen (2000) and Fridgen et al. (2000) used cluster analysis to identify zone boundaries. Fridgen et al. (2000) reported that in Missouri, approximately 54% of the yield variation was explained by cluster analysis of EC_a, elevation, and slope information. Once boundaries are identified, samples from each zone can be collected, analyzed, and used for developing management recommendations. Questions that need to be resolved with attributed-based soil sampling include (i) should samples from two areas with similar attributes, that are not physically connected, be composited?; (ii) should large management zones be split into several smaller zones?; and (iii) what classification procedures should be used to identify management zones?

Several studies have compared different approaches to define zone boundaries. Mallarino and Wittry (2001) evaluated the impact of management zone sampling (digitized soil survey and targeted soil sampling) on fertilizer recommendations. They reported that targeted soil sampling correctly fertilized a higher percentage of the fields than schemes based on field averages and digitized soil survey maps. Franzen et al. (1998) compared topography-based sampling with grid sampling. They reported that NO^-₃–N and P concentrations from management zone demarcation based on topographic information were correlated to values calculated on a 60-m grid. Franzen et al. (2002) compared published soil survey, Order 1 soil survey (scale 1:6600), topography-based, and grid-sampling approaches and reported that published soil surveys should not be used for identifying N management zones unless the soil patterns were verified with other zone development tools. Additional studies are needed to compare traditional approaches for collecting soil samples with computer classification of soil attributes. The objective of this study was to determine the impact of different techniques for identifying management zones on soil NO^-₃–N and Olsen-P sampling variability.

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY REFERENCES

 
Study Fields and Soil Samples
This research was conducted at Moody (65 ha), Brookings (53 ha), and Beresford (40 ha) sites. All sites were located in eastern South Dakota and had a crop sequence of corn (Zea mays L.) followed by soybean [Glycine max (L.) Merr.]. The Moody field was located at 44°10' N, 96°37' W. The Brookings field was located at 44°14' N, 96°39' W. The Beresford site was located at 43°03' N, 96°53' W. Dominant soils at Moody were Cubden (fine-silty, frigid Aeric Calciaquoll), Waubay (fine-silty, mixed, superactive, frigid Pachic Hapludoll), Kranzburg (fine-silty, mixed, superactive, frigid Calcic Hapludoll), and Vienna (fine-loamy, mixed, superactive, frigid Calcic Hapludoll). Dominant soils at Brookings were Barnes (fine-loamy, mixed, superactive, frigid Calcic Hapludoll), Brookings (fine-silty, mixed, superactive, frigid Aquic Hapludolls), McIntosh (fine-silty, mixed, superactive, frigid Aeric Calciaquoll), and Vienna. Dominant soils at Beresford were Egan (fine-silty, mixed, superactive, mesic Udic Haplustolls) and Chancellor (fine, smectitic, mesic Vertic Argiaquoll). Additional details about the soils at these sites are available in Clay et al. (2001).

Soil samples from the 0- to 15- and 15- to 60-cm depths were collected at Moody from a 30- by 30-m slightly offset grid before planting corn in 1995. At Brookings, soil samples from the same depths were collected from a 60- by 30-m slightly offset grid before planting soybean in 1997. At Beresford, soil samples from the same depths were collected from a 60- by 60-m slightly offset grid following soybean harvest in October 1997. Each grid sample consisted of 15 individual cores that were collected within 1 m of the center of the grid (Clay et al., 1997). Each sampling point was located by walking a prescribed number of steps from the previous sampling point. Relative to the edge of the field, the starting points for each transect were slightly different. The net result of this sampling approach was grid points that were slightly offset. The sampled points were located by latitude, longitude, and elevation by carrier phase differential global positioning system (DGPS) equipment. The vertical and horizontal error of the system was 2 cm (Johansen et al., 2001).

Soil samples were prepared for analysis by air-drying (35°C) and grinding to <2 mm in diameter. Olsen-P was extracted from samples collected from the 0- to 15-cm soil depth with 0.5 M NaHCO₃ at a pH value of 8.5 (Olsen and Sommers, 1982). The soil extract was filtered, a color reagent containing ascorbic acid and molybdate was added, and color development was measured on a colorimeter set at 882 nm (Murphy and Riley, 1962). Inorganic N (NH₄–N and NO₃–N) was extracted from samples collected at the 0- to 15- and 15- to 60-cm soil depths with 1.0 M KCl with a 10:1 solution to soil ratio and analyzed on an Astoria Analyzer 300 (Astoria-Pacific Inc., Clackamas, OR) by the Cd reduction method (Maynard and Kalra, 1993).

Apparent electrical conductivity in the vertical configuration was measured with an EM38 (Geonics Ltd., Mississauga, ON, Canada) at each sampling point by placing the instrument in the vertical configuration on the ground and recording the measured value (Clay et al., 2001). At all sites, EC_a was collected in the spring of 1997 following thawing and before planting and fertilizing. Black-and-white aerial photographs of the sites (1950–1965) were obtained from county USDA National Resource Conservation Service (NRCS) offices. The IKONOS satellite collected false color composite images (blue, red, and near infrared) at Brookings and Moody on 17 May 2001. This image has a spatial resolution of 4 m.

Locating Boundary Lines between Management Zones
Techniques for locating boundary lines were based on (i) sampling areas impacted by old homesteads or animals separately from the rest of the field; (ii) sampling different grid cells; (iii) use of GIS (Environmental Systems Research Institute, 1996) or cluster analysis (USDA-ARS, 2000) to identify zones based on EC_a, elevation, aspect, and distance (connectedness); and (iv) sampling each soil series separately. All simulated sampling approaches used the same database and the simulated P and N concentrations were equal to the average value of grid samples contained within a zone.

Locating Old Homestead or Animal-Impacted Areas
Old aerial photographs (1950–1985) along with other evidence were used to locate old homesteads and areas where animals had been grazed. The other evidence included the existence of old windmills, trees, haystacks, or entry roads into the fields. On the basis of this classification, each field was split into two areas: old homestead or areas impacted by animals, and the remaining portion of the field. Only one area impacted by homesteads or animals was identified in each field. Aerial photographs taken of the Brookings field in 1956 showed the location of an old homestead (Fig. 1) , and an aerial photograph of the Moody field collected in 1984 revealed the existence of haystacks. This area of the field was also adjacent to a homestead and therefore most likely had been grazed.

View larger version (93K): [in this window] [in a new window]   Fig. 1. Black-and-white images (top) collected from the Brookings, SD, field in 1956 and (bottom) a near-infrared image collected in May 2001.

Geographic Information Systems Classification
ArcView GIS (Environmental Systems Research Institute, 1996) was used to characterize EC_a, elevation, aspect, and distance information into management zones. For the EC_a–elevation classification, 10 EC_a–elevation zones were identified in a two-step process. In the first step, the fields were split into five different elevation zones. Each zone had a range that was approximately equal to the difference between the maximum and minimum divided by five. These five zones were then separated into areas that were greater than or less than the average EC_a value. For classification based on EC_a and aspect, the fields were separated into five different equal EC_a classes by the same approach as described above. The EC_a classes were subdivided by aspects (316° to 45°, 46° to 135°, 136° to 225°, and 226° to 315°).

For classification based on EC_a and distance (nonconnectedness), a two-step process was to identify management zones. In the first step, the field was separated into five different equal EC_a classes. Each zone had a range that was approximately equal to difference between the maximum and minimum EC_a value divided by five. In the second step, each large group was further subdivided into areas that were connected or not connected. If the groups were connected, they were identified as a single zone. If the groups were not connected, they were characterized as two zones.

Cluster Analysis Classification
Mahalanobis distance and fuzzy c-means unsupervised clustering algorithms within Management Zone Analyst 1.0 (MZA) were used to identify different clusters (Johnson, 1998; Fridgen, 2000). The software is available at USDA-ARS (2000). The combination of three variables (EC_a, elevation, and aspect) was used by MZA. The number of management zones selected for characterization was associated with the minimum Normalized Classification Entropy and Fuzziness Performance Index values (Fridgen, 2000). The number of zones selected was dependent on the data used in the classification. For comparative purposes, the management zone of selected approaches at Moody are shown in Fig. 2 . Similar maps were developed for the other fields (data not shown).

View larger version (95K): [in this window] [in a new window]   Fig. 2. The selected data layers and classification maps for Moody, SD, are: (a) Olsen-P contour map, (b) black-and-white aerial photo collected in 1984, (c) management zones based on geographic information systems (GIS) analysis of electrical conductivity (EC) and elevation information, (d) management zones based on cluster analysis of EC and elevation information, (e) management zones based on an Order 1 soil survey, and (f) management zones based on GIS analysis of EC and distance information. For c, d, e, and f each color represents a different management zone.

Statistics and Calculations
Summary statistics, skewness values (Ott, 1977), and semivariograms for Olsen-P and NO^-₃–N were determined. The models used to describe the relationship between sampling distance and the semivariances were exponential, spherical, and linear. The model that accounted for the most variation was the criterion for selecting a given model.

Once management zone boundaries were identified, the mean and variance of each zone was calculated. These variances were used to calculate pooled variances by the equation

[1]

where z was the number of sampling zones, n_i was the number of samples within zone i, and s²_i was the variance within zone i (Steel and Torrie, 1980). An F value at P 0.1 was used to determine significant differences.

The P recommendation for corn from each zone was calculated by the equation

[2]

where STP is the soil test P (mg kg^-1 P) and YG is the yield goal (10.67 Mg ha^-1) at 15.5% moisture (Gerwing and Gelderman, 1998). The N fertilizer recommendation for corn was calculated by the equation

[3]

where STN is kg NO₃–N contained in the surface 61 cm of soil and PCC is the previous crop credit (e.g., legume credit, 44.8 kg ha^-1 N) (Gerwing and Gelderman, 1998).

At each grid point within a management zone, the fertilizer recommendation was compared with the grid point fertilizer recommendation. For P, if the difference between the management zone P recommendation and the grid P recommendation was <-5 or >5 kg ha^-1 P, then the grid point was characterized as over- or underfertilized, respectively. If the difference was -5 and 5 kg P ha^-1, then the site was characterized as correctly fertilized. For N, if the difference between the management zone N recommendation and the grid N recommendation was <-10 or >10 kg ha^-1 N, then the grid point was characterized as over- and underfertilized, respectively. If the difference was -10 and 10 kg ha^-1 N, then the site was characterized as correctly fertilized.

	RESULTS AND DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY REFERENCES

 
If the precision and accuracy of soil tests are influenced by nutrient variability, then it makes logical sense that sampling techniques with low variability will have higher fertilizer recommendation reproducibility than sampling techniques with high variability. To evaluate the relationships among sampling approach, soil nutrient variability, and fertilizer recommendations, the paper was divided into three sections: (i) Olsen P and nitrate-N population distributions and spatial variability (field characteristics); (ii) the effects of management zone sampling on Olsen-P and resulting fertilizer recommendations; and (iii) the effects of management zone sampling on nitrate-N concentrations and resulting fertilizer recommendations.

Field Characteristics
Olsen-P and NO^-₃–N concentrations had skewness values > 0 and medians less than the means (Table 1). These results indicated that both Olsen-P and NO^-₃–N were not normally distributed. Kravchenko and Bullock (1999) had similar results for fields located in Illinois, Indiana, and Iowa. Soil Olsen-P concentrations were greater in old homestead or grazing areas than the remaining portions of the field (Table 1). Franzen et al. (1998) had similar results in a field located near Valley City, ND, where P level patterns were consistent between years, and very high P levels were observed where livestock were fed until 1960.

Soil Olsen-Phosphorus
At Moody, removing the sampling points within the old homestead or grazing area from the whole field data set reduced the variance (Table 2). Splitting the field further into 4-ha grid cells or classifying the site by GIS with EC_a–distance information reduced s²_p. Classification based on only EC_a information (data not shown) or EC_a–elevation or EC_a–aspect did not reduce s²_p. These results suggest that at this site, nutrient spatial dependence is important and that areas not physically connected should not be composited. Use of cluster analysis or the Order 1 soil survey to characterize management zones did not reduce s²_p.

View this table: [in this window] [in a new window]   Table 2. The influence of sampling approach on Olsen-P (mg kg-1) pooled variances at Moody, SD. The information used in classification approaches were combinations of apparent electrical conductivity (ECa), elevation (elev.), distance, and aspect.

View this table: [in this window] [in a new window]   Table 3. The influence of sampling approach on Olsen-P (mg kg-1) pooled variances at Brookings, SD. The information used in classification approaches were combinations of apparent electrical conductivity (ECa), elevation (elev.), distance, and aspect.

View this table: [in this window] [in a new window]   Table 4. The influence of sampling approach on Olsen-P (mg kg-1) pooled variances at Beresford, SD. The information used in classification approaches were combinations of apparent electrical conductivity (ECa), elevation (elev.), distance, and aspect.

In the three fields, when the homestead or grazing area was sampled separately, splitting the field into 4-ha (or 3.5-ha) grid cells further reduced s²_p. These results were attributed to spatial dependence. Strong spatial dependence (nugget-to-sill ratio < 0.25) was observed in Brookings and Beresford, while in Moody only moderate (nugget-to-sill ratio between 0.25 and 0.75) spatial dependence was observed (Table 1). Strong and moderate spatial dependence indicates that as the sampling points become farther away, Olsen-P values become less correlated. The importance of distance on reducing nutrient variability was supported by the observation that the only classification approach that further reduced s²_p (two of three sites) was GIS classification of EC_a–distance information. These results suggest that zones that are not continuous should be sampled and managed separately.

Including areas impacted by a homestead or animals in the data set reduced the whole field P recommendation and increased the amount of the field that was underfertilized at Moody (Table 5). Splitting the field into 4-ha grid cells increased fertilizer rates when compared with the whole field recommendation. When the homestead or animal-impacted areas were sampled separately, splitting the field into 4-ha grid cells reduced overfertilized areas from 31.5 to 27.6% and reduced underfertilized areas from 57.1 to 45.0%. Similar improvements were observed when the old homestead was not sampled separately.

Nitrate-Nitrogen
At Moody, sampling areas impacted by either an old homestead or animals separately from the rest of the field did not reduce the NO^-₃–N field variance (Table 6). However, s²_p was reduced by grid-cell sampling (4, 7, and 16 ha) and GIS classification of EC_a–distance information. Cluster analysis or classification based EC_a–elevation or EC_a–aspect did not reduce s²_p. At Brookings, sampling the old homestead separately from the whole field reduced the NO^-₃–N field variance from 8.1 to 6.3 (Table 7). The s²_p was further reduced by 3.5-ha grid-cell sampling. Classification based on soil attributes did not reduce NO^-₃–N s²_p. At Beresford, sampling the homestead separately from the rest of the field reduced the variance from 54 to 18 (Table 8). After removing the homestead from the database, the only approach that reduced s²_p further was 4-ha grid-cell sampling. When the homestead or animal-impacted areas were not sampled separately from the rest of the field, the only approach that reduced s²_p was GIS classification based on EC_a–aspect information.

View this table: [in this window] [in a new window]   Table 6. The influence of sampling approach on NO3–N (mg kg-1) pooled variances at Moody. The information used in classification approaches were combinations of apparent electrical conductivity (ECa), elevation (elev.), distance, and aspect.

View this table: [in this window] [in a new window]   Table 7. The influence of sampling approach on NO3–N (mg kg-1) pooled variances at Brookings, SD. The information used in classification approaches were combinations of apparent electrical conductivity (ECa), elevation (elev.), distance, and aspect.

View this table: [in this window] [in a new window]   Table 8. The influence of sampling approach on NO3–N (mg kg-1) pooled variances at Beresford, SD. The information used in classification approaches were combinations of apparent electrical conductivity (ECa), elevation (elev.), distance, and aspect.

Grid-cell sampling is not new and has been compared with several other approaches for obtaining spatial information. Mueller et al. (2001) reported that kriged maps based on a 100-m grid, grid cell, and simulated soil map units has similar prediction efficiencies, which were poor. Wollenhaupt et al. (1994) compared grid cell vs. grid-point sampling for P and K recommendations in two Wisconsin fields. Wollenhaupt et al. (1994) recommended that if (i) a single rate of fertilizer was to be applied, then grid-cell sampling is better than whole-field sampling; (ii) the soil test P and K levels are in the nonresponsive categories, then the field should be sampled on a 91-m grid; and (iii) the field is in the response category, then the field should be sampled on a 61-m grid. Wollenhaupt et al. (1994) and this study had one major difference: the interpretation. In Wisconsin, yields are higher than those in the northern Great Plains, and therefore the potential to recover sampling costs are greater in Wisconsin than the northern Great Plains. On the basis of the recommendations of Wollenhaupt et al. (1994), approximately 64 and 144 samples would be collected from fields sampled on a 91- and 61-m grids. Many farmers in the northern Great Plains are not willing to collect this number of samples because they perceive this as unprofitable.

	SUMMARY

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY REFERENCES

 
This study investigated the impact of different techniques for identifying N and P management zones on s²_p. When compared with the whole field, soil sampling based on the Order 1 soil survey did not reduce the Olsen-P and NO^-₃–N s²_p. The failure of the Order 1 soil survey to reduce Olsen-P and NO^-₃–N s²_p was attributed to the impact of prior management on nutrient variability.

Generally, the lowest s²_p values resulted from grid-cell sampling or areas impacted by an old homestead and animals separately from the rest of the field. The net effect of 4-ha (3.5-ha) grid-cell sampling when compared with whole-field sampling was to reduce the percentage of the land underfertilized. These results were attributed to Olsen-P and NO^-₃–N having strong to moderate spatial dependence (Table 1). Spatial structure was important because in grid-cell sampling, distance is the only criterion used for identifying the zones. Sampling zones based on grid-cell sampling (3.5 and 4 ha) had shorter distances between all potential sampling points than any other technique tested. For example, in the 4-ha grid cells, the maximum distance between potential sampling points was 283 m, while distances between potential sampling points for the Order 1 soil survey could be >800 m. Although not always significant, management zones based on EC_a–distance information for soil test P (two of three sites) and nitrate-N (one of three sites) had lower s²_p than classification approaches that did not consider distance. These results were attributed to EC_a–distance classification separating areas with similar EC_a values that were not physically connected to each other into two different zones. These results point out the importance of distance between sampling points, and suggest that soil samples collected at similar landscape positions that are separated by long distances should not be combined.

The effect of sampling areas impacted by homesteads or animals separately from the rest of the field indicates that events of 30 to 50 yr ago still affect nutrient variability today. Many current soil sampling guidelines suggest that these areas should be sampled separately from the rest of the field (Jacobsen, 1998; Buchholz, 1999; Kleinjan, 2002). Results from this study confirm these recommendations. Findings from this study suggested that (i) soil sampling variation can be reduced by grid-cell sampling; (ii) old homestead or grazing areas should be sampled separately from the rest of the field; and (iii) zones that are not continuous should be sampled and managed separately.

	ACKNOWLEDGMENTS

 
Support for this project came in part from the South Dakota Soybean Research and Promotion Council, United Soybean Board, North Central Soybean Research Program, USDA-CSREES, and NASA.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY REFERENCES

 
South Dakota Exp. Stn. paper no. 3337.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY REFERENCES

 

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