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DIVISION S-8—NUTRIENT MANAGEMENT & SOIL & PLANT ANALYSIS

Approaches to Management Zone Definition for Use of Nitrification Inhibitors

^a South Central Research & Extension Center, Univ. of Nebraska, Box 66, Clay Center, NE, 68933
^b Silsoe Research Institute, Wrest Park, Silsoe, Bedfordshire, UK, MK45 4HS

^* Corresponding author (rferguson{at}unl.edu)

	ABSTRACT

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

 
Site-specific use of nitrification inhibitors has been proposed as one means of reducing the cost of nitrification inhibitor use on fields that vary in their potential for leaching or denitrification. This study evaluated one approach for site-specific use of the nitrification inhibitor nitrapyrin (2-chloro-6-[trichloromethyl] pyridine) based on slope and surface texture. Treatments of uniform and variable N fertilizer and nitrapyrin were applied to field length strips in a center-pivot irrigated field planted to maize (Zea mays L.) from 1995 to 1998. Nitrapyrin application was controlled spatially according to an arbitrary leaching potential derived from slope and surface soil texture. Growing seasons were grouped into dry, wet, or average precipitation years. Alternative approaches for nitrapyrin management zones based on fuzzy cluster analysis of grain yield or soil apparent electrical conductivity (EC_a) were compared with the slope-texture management zone approach. There were small effects of nitrapyrin application method (none, uniform or variable) on grain yield. Uniform nitrapyrin application increased yield in a wet year. Fuzzy cluster analysis of grain yield or soil EC_a identified unique patterns of yield or soil EC_a. Yield clusters corresponded to patterns of water availability or excess. There were small effects of N or nitrapyrin application method on soil residual NO₃-N. Yield Cluster 3 soils had higher residual NO₃-N levels all 4 yr. In a wet year (1996) NO₃-N levels were lowest in yield Cluster 2 soils—in the other 3 yr of the study, there was no difference in NO₃-N levels between yield Clusters 1 and 2. Soil EC_a clusters corresponded closely to soil series. There were significant correlations between soil EC_a and yield clusters. Fuzzy cluster analysis has the potential to define management zones for use of nitrification inhibitors from relatively easily obtained spatial yield or soil EC_a, rather than expensive grid sampling of soil chemical and physical properties.

Abbreviations: ECa, apparent electrical conductivity • DGPS, differentially corrected global positioning satellite receiver • NCE, normalized classification entropy • PSA, particle-size analysis

	INTRODUCTION

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

 
NITRATE CONTAMINATION of ground water in parts of the central USA is often greatest where relatively shallow aquifers underlie coarse-textured soils that are used for irrigated maize production (Wu et al., 1997). Elevated NO₃–N is often associated with over-application of N fertilizers and irrigation water by producers anxious to remove those factors as barriers to optimum yield (Schepers et al., 1991), because the cost of over-application is minimal compared with the economic loss possible when under-applying. Numerous research and demonstration efforts have shown that the use of several management practices, including testing of soil and irrigation water for NO₃–N content, irrigation scheduling, and accounting for N credits from legumes and manures can substantially reduce the potential for NO₃–N leaching to ground water (Ferguson et al., 1991a; Khakural and Robert, 1993; Durieux et al., 1995; Pang et al., 1998; Saporito and Lanyon, 1998). However, even with careful management of N fertilizer and irrigation water, significant NO₃–N leaching can occur (Kessavalou et al., 1996).

The use of nitrification inhibitors with ammonium-based N fertilizers is recognized as one potential tool to improve N-use efficiency. The greatest potential benefit from nitrification inhibitors is on soils that frequently remain saturated during the early part of the growing season and are subject to denitrification, or on coarse-textured soils subject to leaching (Meisinger et al., 1980; Hoeft, 1984). However, positive response to the use of nitrification inhibitors (either increased yield or reduced NO₃–N leaching) is highly dependent on specific conditions and may most frequently be observed at suboptimal N rates (Walters and Malzer, 1990a, 1990b; Cerrato and Blackmer, 1990; Davies and Williams, 1995). Negative response to the use of a nitrification inhibitor (reduced apparent fertilizer N uptake) has been observed with late side-dress N/nitrapyrin application (Ferguson et al., 1991b). The use of nitrification inhibitors is one of several practices suggested for improving N-use efficiency and reducing the potential for NO₃–N leaching in areas vulnerable to ground water NO₃–N contamination (Central Platte Natural Resources District, 1998). However, use of nitrification inhibitors where not specifically required is often low, because of producer concerns regarding cost and efficacy. The cost-effectiveness of nitrification inhibitor use might be enhanced if site-specific application is employed. Whether to use a nitrification inhibitor, and perhaps even the application rate, might be determined by the risk of leaching or denitrification, processes that will vary depending on topography and soil properties and so may vary substantially within fields. Matching the use of nitrification inhibitors to local conditions could reduce the total cost of their use while maintaining the environmental benefit.

The definition and use of management zones, utilizing the spatial management tools of precision agriculture, has been proposed as a cost-effective approach to using spatial information for improved crop management (Fleming et al., 1999; Luchiari et al., 2000). There are numerous proposed methods for defining management zones (Gerwig et al., 2000; Fleming et al., 2000; Stewart and McBratney, 2000; Chang et al., 2000; Franzen et al., 2000), often varying with the management inputs under consideration, but generally relying on spatial information that is stable or predictable over time and related to crop yield (Doerge, 1999). One proposed mechanism for defining management zones is EC_a, which reflects the cumulative effect of a variety of soil properties (soil texture, clay content, cation-exchange capacity, solute content, etc.) and can be related to crop yield (Kitchen et al., 1999; Hartsock et al., 2000).

Cluster analysis is a tool that quantifies patterns of variability and reduces the empirical nature of defining management zones (Fridgen et al., 2000). Fuzzy cluster analysis allows cluster definition to be gradual and more closely aligned with natural gradients in soil formation and landscape position. The use of fuzzy cluster analysis for successive years of spatial yield data can take into account temporal variability in climate, crop species and even management factors (McBratney and deGruijter, 1992; Odeh et al., 1992; Lark and Stafford, 1997; Lark, 2001).

The objectives of this study were:(i) develop one approach to management zone definition for nitrification inhibitor use (based on slope–soil texture) and evaluate it in a field study, and (ii) compare two additional approaches to nitrification inhibitor management zone definition (based on fuzzy cluster analysis of grain yield over years or soil EC_a) to the slope–soil texture management zone method.

	MATERIALS AND METHODS

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

 
The field study was located on a center-pivot irrigated field in Hall County in the central Platte River Valley of Nebraska (40° 51' 17'' N lat., 98° 26' 32'' W long.). The study area (14.9 ha out of a 57-ha field) overlaid four soil series of moderately deep to shallow bottom lands adjacent to the Platte River (Fig. 1) : Platte–Wann complex (sandy, mixed, mesic Mollic Fluvaquents); Cass fine-sandy loam (coarse-loamy, mixed, mesic Fluventic Haplustolls); Volin silt loam (coarse-loamy, mixed, mesic Fluvaqentic Haplustolls); and Wann loam (coarse-loamy, mixed, mesic Fluvaquentic Haplustolls). The field was planted to maize before and during the study years of 1995 through 1998. The site was soil sampled in a grid pattern in the spring of 1995 using a coarse grid (24.3 m across rows by 122 m down rows), and then resampled using a finer grid in the spring of 1996 (6 m across rows by 61 m down rows). Both sampling schemes alternated in a triangular grid pattern (Fig. 1). Single cores 4.1 cm in diameter were collected at each grid intersection point to a depth of 0.9 m. Samples collected in 1995 were analyzed for soil organic matter (loss on ignition, Nelson and Sommers, 1996), pH, P (Bray-1 P, Kuo, 1996), available K (ammonium acetate extraction, Helmke and Sparks, 1996), and DTPA zinc in the 0- to 20-cm layer (Reed and Martens, 1996), and NO₃–N in the 0- to 20- and 20 to 90-cm layers (Cd reduction, Mulvaney, 1996). In addition, hand-feel texture, calibrated with hydrometer particle-size analysis (PSA) was determined for both 0- to 20- and 20- to 90-cm layers. Relative elevation was determined using a laser level at each grid intersection point (Fig. 2a) . A total of 57 points were sampled in 1995. Grid soil samples collected in 1996 were analyzed for soil organic matter and hand-feel texture (PSA calibrated) in the 0- to 20-cm layer, and NO₃–N in the 0- to 20- and 20- to 90-cm layers. The study area was resampled each fall at the finer grid density after harvest to determine 0- to 90-cm NO₃–N. A total of 401 grid points were sampled in 1996 and subsequent years. Treatments were arranged in a completely randomized design with factorial treatment levels applied to field length strips with four replications. Each treatment strip was 6.1 m wide (eight rows). Six treatments were used: Uniform N, no nitrapyrin; Variable N, no nitrapyrin; Uniform N, uniform nitrapyrin; Variable N, variable nitrapyrin; Uniform N, variable nitrapyrin; and Variable N, uniform nitrapyrin.

View larger version (75K): [in this window] [in a new window]   Fig. 1. Soil series and study layout. P-W: Platte-Wann complex; 3CS: Cass fine sandy loam; Vo: Volin silt loam; Wm: Wann loam.

View larger version (55K): [in this window] [in a new window]   Fig. 2. Study area relative (a) elevation in meters; (b) texture of the surface 0.2 m; (c) nitrapyrin application map (nitrapyrin applied to gray areas with variable nitrapyrin application strategy at rate of 0.56 kg ai ha-1); (d) 1997 N application map, typical of maps used with variable N treatments. All maps are produced from fine grid interpolated data.

The study was harvested each year with a yield-monitoring combine previously calibrated in buffer areas adjacent to the study. Lag time for grain flow through the combine was set according to the yield monitor manufacturer's specifications. Yield data were collected at 2-s intervals, with position determined by a DGPS receiver. Yield data were initially screened to remove outliers, then integrated into a single yield value for cells 30.4 m in length and 6.1 m width, centered on grid soil sample points. Typically, eight or nine yield observations fell within the boundaries of each cell, and were averaged to give the cell mean. This provided a single yield measurement that could be directly compared with soil information collected at the center of alternating cells.

Apparent soil electrical conductivity of the study area was evaluated 24 Mar. 1999, using a Veris 3100 sensor (Veris Technologies, Salina, KS). The sensor used six coulters (two of which applied an electrical current to the soil) to detect EC_a at two depths simultaneously—approximately 30 and 90 cm. The sensor position as determined by a DGPS receiver, along with EC_a readings from both depths, were recorded every second along transects approximately 12.5 m apart while driving at 19 km h^-1.

Alternative management zones based on EC_a or grain yield were developed for comparison to the slope–texture method used in the field evaluation. Fuzzy cluster analysis (Bezdek et al., 1984; Lark and Stafford, 1997) of EC_a using both layers of information, and grain yield using four sequential years of yield data, was conducted for the study area. In fuzzy cluster analysis objects (here sites in the field) are classified according to their values in m variables (here yield in four seasons or EC_a at two depths [0- to 30- and 0- to 90-cm layers]). A class is defined by a central concept, a characteristic combination of values in the m variables. Thus a class defined from the yield values has a characteristic pattern of season-to-season variation in yield. Any site in the field with known yields in the seasons used for the classification can be assigned a membership in each fuzzy class with membership values ranging from 0 (no resemblance to the characteristic pattern) to 1 (perfect resemblance). Partial memberships are therefore possible. The memberships in all classes of any one site in the field will be constrained to sum to 1. To scale yield equally across years, grain yield was standardized to zero mean and unit variance (observed yield was subtracted from the annual mean, then divided by the standard deviation). Fuzzy cluster analysis will generate k classes where k is determined by the user. To identify the number of distinct groups in a data set several classifications were performed with k = 2, 3, ... 8. The normalized classification entropy (NCE) was then calculated for each classification (McBratney and Moore, 1985; Lark, 2001). The classification for which NCE was minimized was selected as the optimum. In the case of grain yield over years, a classification into three clusters was selected. Fuzzy cluster analysis for EC_a was performed in a similar manner to yield, except that EC_a from two layers was used instead of yield over 4 yr. Again, a classification into three clusters was chosen.

Statistical analysis of treatment effects on yield was conducted using a mixed-models procedure (SAS v. 8, SAS Institute Inc, 1999). Semivariograms for grain yield were determined for each year using GS+ software, v. 3.1a (Gamma Design Software, 1998). The resulting isotropic semivariogram model was used in mixed models analysis (Littell et al., 1996). Treatments (N fertilizer and nitrapyrin methods) were considered fixed effects; replication, and replication by treatment interaction were considered random effects in the model. Each year was analyzed separately. Differences of least squares means were considered significant at P 0.05. For grain yield, any site with a cluster membership of 0.8 or greater in one of the classes of the selected classification was assigned primary membership in this class, and a discontinuous (nominal) classification variable was defined with levels corresponding to the class of primary membership. This classification variable was treated as a fixed effect in mixed models analysis of soil residual NO₃–N, along with N fertilizer and nitrapyrin methods. Replication was treated as a random effect in this model.

	RESULTS AND DISCUSSION

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

 
Grain Yield
The cumulative growing season precipitation (1 April–31 October) for the 4 yr of the study is shown in Fig. 3 . The years generally can be categorized with regard to growing season precipitation as ‘average’ (1995 and 1998), ‘wet’ (1996) or ‘dry’ (1997—although the cumulative precipitation at the end of the 1997 growing season approached the 30-yr normal). In particular, the month of May 1996 was quite wet, going from the driest year up to 26 April (Day 117) to the wettest year by 26 May (Day 147). Rainfall during the period 26 April to 26 May was 269 mm in 1996, compared with 85 mm in 1995, 69 mm in 1997, and 126 mm in 1998. This period covered the 4 to 5 wk following fertilizer N application, and would be expected to be the period in which nitrification inhibition would be most beneficial in protecting fertilizer N from leaching or denitrification.

View larger version (26K): [in this window] [in a new window]   Fig. 3. Cumulative growing season precipitation (1 April–31 October), 1995 through 1998.

The effects of nitrapyrin treatment strategy on grain yield are shown in Table 1. Overall, nitrapyrin had little or no influence on yield, depending on the year. There were no significant interactions between N application method and nitrapyrin strategy. In 1995 and 1998 (the two ‘average’ years) there were no effects of nitrapyrin application on grain yield. In 1996 (the ‘wet’ year), there was a significant yield increase with the uniform application of nitrapyrin, even though yield variability was much higher in 1996 than in other years. There was no benefit from variable nitrapyrin application on grain yield in 1996. In 1997 (the ‘dry’ year), there was a slight yield advantage to variable over uniform nitrapyrin application. The exact reason for this slight yield benefit is unclear. Ferguson et al. (1991b) found that nitrapyrin could reduce apparent fertilizer N uptake on similar soils, consistent with a temporary immobilization process, when applied with late side-dress N. Although the current study involved preplant N application, it may be that a similar mechanism occurred in 1997. In such a situation, the application of nitrapyrin only to areas of the field particularly vulnerable to N loss, or no application at all, is preferable to uniform nitrapyrin application.

View larger version (81K): [in this window] [in a new window]   Fig. 4. Grain yield patterns from the study area, 1995 through 1998; inverse distance-squared interpolated.

Yield Clusters
Figure 5 illustrates the normalized yield trends for the three clusters over the 4 yr of the study. Cluster 1 was always above average in yield; Cluster 2 was below the mean except in 1997; Cluster 3 was always well below the mean. Figure 6 illustrates the membership for the three clusters across the study area. Cluster 1 membership was in areas of the field that were consistently higher yielding, particularly in 1996. These areas corresponded to higher relative elevations that were not subject to ponding, or were coarser-textured and better drained (Fig. 2a & 2b). Cluster 2 membership was in the areas of the field that yielded somewhat below average except in 1997, the ‘dry’ year (Fig. 5). These areas of the field tended to be relatively low in elevation, higher in organic matter, and finer-textured. Cluster 2 areas tended to hold water, which was detrimental to crop growth in high precipitation years such as 1996, and probably resulted in N loss due to denitrification rather than leaching. However, in dryer years, the greater water holding capacity of Cluster 2 areas were beneficial. In 1997 yields in Cluster 2 were slightly above average, while yields in the better-drained Cluster 1 areas were the lowest of the 4 yr. There were substantial similarities between Cluster 2 areas of the study and areas designated for nitrapyrin application in slope/texture management zone approach (Fig. 2c).

View larger version (18K): [in this window] [in a new window]   Fig. 5. Normalized grain yield by clusters, 1995 through 1998.

View larger version (91K): [in this window] [in a new window]   Fig. 6. Grain-yield cluster membership.

Apparent Soil Electrical Conductivity (EC_a) Clusters
Figure 7 illustrates trends in normalized EC_a between the two sampling depths, and Fig. 8 the normalized EC_a cluster membership across the study area. In Cluster 1, EC_a was substantially lower at the surface than at 90 cm; in Cluster 2, EC_a was higher at the surface than at 90 cm. In Cluster 3 areas, EC_a was typically lower for both depths than over the rest of the field. These clusters as mapped in Fig. 8 corresponded closely to soil series as mapped in Fig. 1. Cluster 1 relates most closely to the Volin silt loam soil, which is well drained and has a depth to sand/gravel of 0.9 to 1.8 m. Cluster 2 corresponds to the Wann loam soil, which was less well drained than the Volin soil because of a higher clay content near the surface, but has a depth to sand/gravel of 0.6 to 0.9 m. Cluster 3 corresponds to Cass and Platte-Wann complex soils, which are well-drained, relatively shallow soils. Soil EC_a was measured in the spring of 1999, after the field study was conducted from 1995 through 1998. While soil EC_a may be expected to fluctuate seasonally to some degree, patterns of EC_a associated with soil texture would be expected to be relatively stable across years, as soil texture does not change significantly from year to year.

View larger version (15K): [in this window] [in a new window]   Fig. 7. Normalized apparent soil electrical conductivity (ECa), at depths of 30 and 90 cm.

View larger version (70K): [in this window] [in a new window]   Fig. 8. Soil apparent electrical conductivity (ECa) cluster membership

Residual Soil NO₃–N
Nitrogen application strategy (uniform vs. variable) had significant effects on soil residual NO₃–N in 1996 and 1998 (data not shown). In both years, variable N application slightly reduced residual soil NO₃–N. This suggests there was tendency for improved N-use efficiency (less residual NO₃–N remaining after harvest) using variable N application. However, actual differences in residual NO₃–N were slight—less than 0.5 mg kg^-1 NO₃–N—so the benefits from variable N application were minimal in terms of practical management.

The use of nitrapyrin had inconsistent effects on soil residual NO₃–N. In 1995, nitrapyrin use increased residual NO₃–N, with variable application of nitrapyrin resulting in the greatest residual NO₃–N. In 1997, uniform nitrapyrin application resulted in significantly lower residual NO₃–N compared with variable application, or no nitrapyrin. In 1997 and 1998, both uniform and variable application of nitrapyrin tended to reduce residual NO₃–N compared with no application of nitrapyrin. Again, the differences in soil residual NO₃–N overall were small, 1 mg kg^-1 or less, so impacts on management or potential NO₃–N leaching would be minimal.

Table 3 presents soil residual NO₃–N means for the 4 yr of the study, accounting for yield cluster effects in the statistical model. Primary cluster membership was assigned if yield cluster membership was 0.8 or greater at a given grid point. For most grid points, a primary cluster membership could be assigned. Few points had significant membership in more than one cluster.

	SUMMARY

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

 
This study evaluated one approach to defining nitrification inhibitor management zones, based on slope and surface soil texture, in a field study utilizing uniform and variable N and nitrapyrin management strategies. We found in ‘average’ years there was no advantage to the use of nitrapyrin, either applied uniformly or according to slope–texture management zones. In a ‘wet’ year, uniform nitrapyrin application helped protect yield to some extent, while variable nitrapyrin application according to the slope–texture management zones did not. In a ‘dry’ year, uniform nitrapyrin application depressed yield slightly relative to variable application, perhaps related to a temporary immobilization process. It appeared that defining nitrification inhibitor management zones based on surface properties of the soils studied—the slope–surface texture approach—did not effectively partition N loss processes that may occur throughout the root zone.

The alternative methods of describing nitrification inhibitor management zones (fuzzy cluster analysis of successive years yield maps, or fuzzy cluster analysis of soil EC_a) may have potential and warrant further study. While there was only moderate correlation at best between grain yield and any of the management zone approaches, fuzzy cluster analysis of yield over years and soil EC_a both seemed to identify areas within the field that may be vulnerable to NO₃–N loss, either via leaching or denitrification. Targeting the use of a nitrification inhibitor to only those areas of the field most vulnerable to loss makes sense particularly if there is some risk of yield depression with the use of a nitrification inhibitor.

	ACKNOWLEDGMENTS

 
The authors appreciate the assistance provided by Doug Thompson in the conduct of this study, for the use of his field and his efforts in fieldwork, and the help provided by Dean Krull and Mick Reynolds in soil sampling. The authors also appreciate the assistance of Gary Hergert in experimental design. Finally, the authors appreciate the financial support provided in part by Dow AgroSciences and the Central Platte Natural Resources District.

	NOTES

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

 
Journal series 13533.

¹ Mention or use of a product does not imply endorsement by the Univ. of Nebraska.

Received for publication October 29, 2001.

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

 

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