# Agronomy Journal - Article

1. Vol. 105 No. 6, p. 1619-1625
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Published: September 6, 2013

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doi:10.2134/agronj2013.0166

# Testing Corn (Zea mays L.) Preseason Regional Nitrogen Recommendation Models in South Dakota

1. Ki-In Kim *a,
2. David Clayb,
3. Sharon Clayb,
4. Gregg C. Carlsonb and
5. Todd Trooienc
1. a Dep. of Soil, Water, and Climate, 439 Borlaug Hall, 1991 Upper Buford Circle, Univ. of Minnesota, St. Paul, MN 55108
b Box 2270A, Plant Science Dep., South Dakota State Univ., Brookings, SD 57006
c Agricultural Biosystems Engineering, South Dakota State Univ., Brookings, SD 57006

## Abstract

The purpose of a N recommendation model is to maximize profitability and minimize the impacts of agriculture on the environment. To achieve this goal, reliable recommendations must be developed and systematically tested. The objective of this study was to evaluate and test regional N recommendation models from South Dakota, western Minnesota, Iowa, and Nebraska for their suitability to improve South Dakota N recommendations. Data used to test the models were collected between 2002 and 2004 at Aurora and between 2004 and 2006 at Beresford and Watertown in eastern South Dakota. In this experiment, corn was responsive to N fertilizer, soil organic matter was relatively high (>30 g kg–1), manure was not applied, and drought conditions were not observed. Root mean square errors and bias of the different regional models were determined. Results showed that: (i) all models were unique and produced different N recommendations; (ii) economically optimum N rates (EONR) were sensitive to changing fertilizer costs and corn selling prices; (ii) water had a large impact on yield and N use efficiency; (iv) yields at the EONR were highly correlated (r = 0.60–0.73, P < 0.01) to the yield difference between fertilized and unfertilized plots; and (v) a modified South Dakota N recommendation model can be used to predict the impact of synergistic relationships between N and water.

### Abbreviations

EONR, economically optimum nitrogen rate; RMSE, root mean square error

Regional N recommendation models, such as yield goal based N recommendation models, have been used for corn production since the 1970s (Chang et al., 2003; Koch et al., 2004; Mulvaney et al., 2006). A generalized form of the regional yield goal model iswhere NR is the recommended N rate (kg ha–1), k is a constant that ranges from 21.4 to 26.8 kg N Mg–1 corn grain, and N credits include soil NO3, N provided by legumes, and N in irrigation water. If the yield goal model is viewed as a simplified version of a mass balance equation, then the difference between the N recommendation and k(yield goal) represents the N provided by the various pools. The advantages of Eq. [1] are that the model is easy to use and many people agree with the concept behind the model (high crop yields require more N than low crop yields). Although not designed for site-specific applications, modifications of this model are used for this purpose (Chang et al., 2003).

A disadvantage with using a yield goal N model is that there is a poor relationship between yield and the EONR because it does not consider (i) water as a nutrient, (ii) N mineralization, and (iii) synergistic relationships between water, N, and the microbial community (Kim et al., 2008). Kim et al. (2008) reported a synergistic relationship between N and water in a corn study. Therefore, these results have been attributed to inaccurate assessment of N mineralization. Also, the excessive application of water in some states causes major N losses through leaching and denitrification. As a result, many states have dropped or modified the yield goal based recommendation is favor of unique recommendation models for each state. For example, (i) the South Dakota and Nebraska models consider yield goals while the Minnesota and Iowa models do not; (ii) the South Dakota model considers 100% of the NO3–N contained in the surface 60 cm, while the Nebraska model considers 50% of the NO3–N contained in the surface 120 cm, and the western Minnesota model considers 60% of the NO3–N in the surface 60 cm; and (iii) only the Nebraska model considers organic matter. Three of the models (Nebraska, western Minnesota, and Iowa) consider the fertilizer cost/corn price ratio in the N recommendation, while the South Dakota model does not.

Validations of regional yield goal based N models for site-specific and whole-field applications have been mixed (Fox and Piekielek, 1995; Lory and Scharf, 2003). These results are attributed to: (i) scaling problems, i.e., using models designed for a specific purpose for applications that have not been validated (Mulvaney et al., 2006); (ii) using simplistic models that do not account for synergistic relationships between N and water (Kim et al., 2008); (iii) using NO3–N as an estimator of the soil N pool; and (iv) using N recommendation models that do not account for local climatic conditions. Five general approaches are available for solving these problems. The first approach is to increase the complexity of the current models. For example, as proposed by Kim et al. (2008), the k constant in Eq. [1] could be replaced with a variable. The second approach is to develop new mechanistic models that utilize digital soil and climatic data bases. Examples of mechanistic models that could be used include the CERES or Hybrid-Maize models (Jones and Kiniry, 1986; Yang et al., 2006). The third approach is to utilize field-specific N data (e.g., well-fertilized controls that are placed in the field). In the fourth technique, the N recommendations are modified to account for differential N mineralization across the landscape. A fifth technique would use some combination of the four techniques. The objective of this study was to evaluate and test regional N recommendation models from South Dakota, western Minnesota, Iowa, and Nebraska for their suitability to improve South Dakota N recommendations.

## MATERIALS AND METHODS

### Study Sites

Research was conducted between 2002 and 2004 at Aurora and between 2004 and 2006 at Watertown and Beresford in eastern South Dakota. Experimental details and methods were provided by Bly et al. (2004), Gerwing et al. (2004, 2005), Gelderman et al. (2006), Gelderman and Gerwing (2006), and Kim et al. (2008). The soil series at Aurora was a Brandt silty clay loam (a fine-silty, mixed, superactive, frigid Calcic Hapludoll). The surface horizon contained approximately 110 g sand, 580 g silt, and 310 g clay kg–1. Total N in the 0- to15- and 15- to 60-cm depths was approximately 5.1 and 10.2 Mg N ha–1, respectively. Total C in the 0- to 15- and 15- to 60-cm depths was approximately 44.6 and 78.5 Mg C ha–1, respectively. At Beresford and Watertown, the soils were an Egan silty clay loam (a fine-silty, mixed, superactive, mesic Udic Haplustoll) and a Brookings silty clay loam (a fine-silty, mixed, superactive, frigid Pachic Hapludoll). The parent materials at both sites were glacial till. Total soil organic C at both sites was between 20 and 30 g kg–1 (30–50 g kg–1 organic matter).

The experimental design at Aurora was a randomized split complete block design with four replications. Two main treatments were water and N. Two water regimes (rainfall and rainfall plus irrigation) and four N rates (0, 56, 112, and 168 kg N ha–1) were used. During the growing season from April through August, the natural precipitation was 42.5, 32.9, and 40.1 cm of water in 2002, 2003, and 2004, respectively, and additional irrigation was 10.8, 14.9, and 5 cm of water in 2002, 2003, and 2004, respectively. The experiment conducted at Aurora contained both medium (10 Mg ha–1) and high yield potential (12.54 Mg ha–1) conditions (Kim et al., 2008). The high yield potential environment was created by applying supplemental irrigation water. At Beresford and Watertown, the corn relied on natural precipitation. Yield goals at Watertown and Beresford were 10 Mg ha–1 (Bly et al., 2004; Gerwing et al., 2004, 2005; Gelderman et al., 2006; Gelderman and Gerwing, 2004, 2005, 2006). No manure was applied at any of the sites and the previous crop was either soybean [Glycine max (L.) Merr.], corn, or wheat (Triticum aestivum L.).

Composite soil samples from the surface 60 cm were collected in the spring before corn was planted at all sites. In the experiments described by Bly et al. (2004), Gerwing et al. (2004, 2005), Gelderman et., (2006), and Gelderman and Gerwing (2006), NO3 was extracted from 10 g of soil with 100 mL of 0.01 mol L–1 Al2(SO4)3 and 0.02 mol L–1 H3BO3 and analyzed for NO3–N with an electrode. In the study of Kim et al. (2008), NO3 was extracted from 10 g of soil with 100 mL of 1 mol L–1 KCl and analyzed for NO3–N using Cd reduction (Maynard and Kalra, 1993). In the experiment of Kim et al. (2008), NH4+–N and aboveground N uptake were also measured. At Aurora, grain samples were collected during harvest and analyzed for total N, d15N, and d13C on a ratio mass spectrometer (Clay et al., 2006a). Based on measured grain yields and d13C values, yield losses due to water and N stress were calculated (Clay et al., 2006b).

Nitrogen uptake of the aboveground plant parts was estimated by summing the N contents of grain and stover. Grain N use efficiency (NUE, %) was calculated aswhere Nplant is the N contained in the grain of the fertilized plot, Ncontrol is the N contained in the grain of the unfertilized plot within the block, and N rate is the amount of N applied (Kim et al., 2008).

In the unfertilized control plots, the percentage of soil N used was calculated aswhere the N net balance for medium and high water regimes within a block was calculated as

The net N balance was slightly lower in the medium than the high water regime. The higher N balance under the high water regime was attributed to the irrigation water containing NO3 (15–40 mg NO3–N kg–1) (Kim et al., 2008).

### Nitrogen Fertilizer Response and Economic Analysis Calculations

To assess if N recommendations could be improved by using an in-field diagnostic tool, N fertilizer responses (delta yield) were calculated aswhere YEONR is the grain yield at the EONR and Y0N is the grain yield in plots where N was not applied (Kachanoski et al., 1996; Lory and Scharf, 2003).

The EONRs were calculated by fitting the N response data to a second-degree quadratic and plateau model [Yield = a + b(N rate) + c(N rate)2]. The plateau was the maximum yield where further increases in N did not increase yield (Carlson et al., 2003; Scharf et al., 2005). For scenario testing, the EONRs were calculated for three N fertilizer cost/corn sale price ratios (2.80, 5.59, and 11.27) using a corn price of US$118 Mg–1 grain at 15.5% moisture and N fertilizer costs of US$330, US$660, and US$1330 Mg–1 N, respectively. These values were equivalent to the 0.05, 0.10, and 0.20 fertilizer/corn price ratios reported by Sawyer et al. (2006). The EONR is the point where the change in value of the yield [d(value of corn)] equals the change in value of the fertilizer [d(N fertilizer cost)] (Carlson et al., 2003). Mathematically, this expression is

### Nitrogen Recommendation Models

The EONRs for corn were calculated for each block and water stress environment using South Dakota, modified South Dakota, western Minnesota, Iowa, and Nebraska N recommendation models. The South Dakota N recommendation model iswhere NR is the N rate recommendation (kg N ha–1), YG is the yield goal (Mg ha–1), k is 21.4 kg N Mg–1 grain, STN is the amount of NO3–N contained in the surface 0 to 60 cm (kg N ha–1), and PCC is the previous crop credit (legume credit, 44 kg N ha–1) (Gerwing and Gelderman, 2005). Irrigation N was determined using the N mass balance approach (Kim et al., 2008). Nitrate-N was estimated using two different approaches. In the first approach, the amount of NO3–N contained in the surface 60 cm was measured (Gerwing and Gelderman, 2005). In the second approach, NO3–N was estimated to be 56 and 100 kg N ha–1 for corn following soybean and corn following corn or wheat. These values were based on soil test average values reported by Gelderman and Gerwing (2004, 2005, 2006). Based on the current South Dakota model (Gerwing and Gelderman, 2005), recommendations were not adjusted for corn or N prices.

The modified South Dakota N recommendation model iswhere f(k) is a variable that is a function based on the soil productivity level. This function was designed to account for synergistic relationships between water and N (Kim et al., 2008). Kim et al. (2008) developed a conceptual N model that explains this synergistic relationship. For highly productive systems, f(k) is 19.6 kg N Mg–1 grain and for moderately productive systems, f(k) is 21.4 kg N Mg–1 grain. These N recommendations were not adjusted based on corn or N prices.

The western Minnesota model iswhere the maximum return to N (MRTN) is the N fertilizer based on the N cost/crop price ratio and STN is the amount of NO3–N contained in the surface 0 to 60 cm (kg ha–1) (Rehm et al., 2006). In scenario testing, unirrigated corn was assigned an N requirement of 134 kg N ha–1, while irrigated corn was assigned an N requirement of 157 kg N ha–1. Based on the fertilizer cost/corn price ratios, these N recommendations were adjusted (Rehm et al., 2006).

The Iowa N recommendation is based on the MRTN and the previous crop. For corn following soybean, the N recommendations were 145, 123, 95 kg N ha–1 for the 2.8, 5.59, and 11.27 N cost/corn price ratios, respectively, and for corn following corn, the N recommendations were 205, 179, 143 kg N ha–1, respectively (Sawyer et al., 2006). The Iowa N recommendation is not adjusted based on preseason NO3–N.

The equation for the Nebraska model iswhere OM is organic matter content (up to 30 g organic matter kg–1), the soybean credit is 50 kg N ha–1, soil NO3–N is the average concentration (mg kg–1 soil) of soil NO3–N contained in the surface 120 cm, irrigation N was discussed above, fA is a correction factor for application time, and fR is correction factor for the corn/N price ratio (Ferguson et al., 2008). The fR values were 1.19, 1.05, and 0.78 for 0.05, 0.10, and 0.20 N cost/corn prices, respectively (Shapiro et al., 2008). Nitrate-N for the 60- to 120-cm depth was considered to be 3 mg kg–1 soil.

To evaluate the different models, root mean square error [RMSE = S(predicted value – observed value)2/n] and bias [Bias =(predicted value – observed value)/n] values were calculated. Correlation coefficients (r) between the different parameters were determined. A negative bias value indicates that, on average, the predicted recommendation underestimated the N recommendation, while a positive bias indicates that the model overestimated the recommendation. The RMSE values were compared with an F statistic. Significant differences are reported at the 0.05 level. The boundary conditions for the validation were: (i) manure was not applied; (ii) high NO3 levels were not expected; (iii) all sites were located on the eastern side of South Dakota; and (iv) all soils contained moderately high organic matter (>30 g organic matter kg–1 soil).

## RESULTS AND DISCUSSION

### Environmental Impacts on Yield

Corn yields in the region are typically limited by both N and water availability (Table 1; Clay et al., 2006b; Kim et al., 2008). Adding either N, water, or both had an additive effect on corn yields (Fig. 1). These yield were decreased due to N or water stress. These results were attributed to synergistic relationships between water and N and were attributed to water facilitating the transport of NO3 from the soil to the plant. These findings suggest that the relationship between the water and N cycles will impact the N use efficiency across different landscape positions, where areas with less plant-available water, such as summit and shoulder areas, may have relatively low N use efficiency, while areas with more available water will have higher N use efficiencies. Differential organic mineralization across the landscape may also impact N requirements (Clay et al., 2006a).

View Full Table | Close Full ViewTable 1.

The influence of N and water treatments on grain yields, yield losses due to N stress (YLNS), and yield losses due to water stress (YLWS) at Aurora, SD, in 2002, 2003, and 2004.

 Variable Grain yield YLNS YLWS kg ha–1 Yield potential of the soil Medium (dryland) 8500 1390 2120 High (irrigated) 9630 1140 1270 P value 0.004 >0.05 <0.01 N rate, kg ha–1 0 7510 2830 1660 56 9040 1360 1600 112 9890 430 1680 168 9800 430 1830 P value <0.001 <0.01 >0.05 LSD(0.05) 400 415 Year 2002 9070 1630 1350 2003 9660 1180 1160 2004 8450 980 2570 P value <0.001 <0.01 <0.01 LSD(0.05) 290 300 300
Fig. 1.

The relationship between yield and N (N fertilizer + inorganic soil N in the spring) under medium and high yield potential at Aurora from 2002 to 2004 (modified from Kim et al., 2008).

Generally increasing the fertilizer cost lowered the EONR. The EONR values were sensitive to the fertilizer cost/corn price ratio and were not influenced by the soil yield potential (Table 2). The different soil yield potentials were attributed to the irrigation water transporting N to the crop. Nitrogen fertilizer and soil-derived N use efficiency may be higher in areas with more rather than less available water. The soil N use efficiency was higher in the irrigated environment (high yield potential) than the dryland environment (moderate yield potential). For example, the soil N use efficiency was 67.7 and 61.6% in the high and moderate yield potential soils and the fertilizer use efficiency was 48 and 44%, respectively (Kim et al., 2008).

View Full Table | Close Full ViewTable 2.

The influence of year, soil yield potential, and a fertilizer cost/corn price ratio of 2.80, 5.59, or 11.27 on the economically optimum N rate (EONR) at Aurora, SD, in 2002, 2003, and 2004.

 Yield potential of the soil EONR Year 2.80 ratio 5.59 ratio 11.27 ratio kg N ha–1 2002 medium (dryland) 156 140 106 high (irrigated) 140 126 97 2003 medium 132 124 108 high 131 123 107 2004 medium 117 107 88 high 121 113 95 P value 0.635 0.616 0.680 2002–2004 medium 135 124 100 high 131 120 106 P value 0.554 0.622 0.863 2002 medium–high 148 132 102 2003 medium–high 132 124 107 2004 medium–high 119 110 92 P value 0.128 0.160 0.199 LSD(0.05)

### Predicting Nitrogen Recommendations

Historically, yield goal based approaches have been the basis for many N recommendations in the central portion of the United States. Several recent studies have questioned the value of these models (Derby et al., 2005; Lory and Scharf, 2003). Lory and Scharf (2003) suggested that the delta yield technique might be an alternative. The strength of the relationships among the corn’s response to N fertilizer, the EONR, and the yield at the EONR were impacted by the fertilizer/corn price ratio (Table 3). The N responseEONR values were poorly correlated with the EONR for the 2.80 and 5.59 fertilizer/corn price ratios and highly correlated with the EONR for the 11.27 fertilizer/corn price ratio. These results suggest that in-field assessment tools, such as the potential N response model, need further study. Others have reported that this approach can be used to estimate N fertilizer responses. Lory and Scharf (2003) reported that N responses were highly correlated with the EONR and that a linear equation could be used to describe data collected from five states (Illinois, Minnesota, Missouri, Pennsylvania, and Wisconsin). Kachanoski et al. (1996) and Braum et al. (1999) reported that within-field variation in the EONR for corn was related to potential N responses. Kachanoski et al. (1996) reported that in Canada the delta yield were correlated with the EONR and that delta yield can be used to estimate maximum economic N rates. Kim et al. (2008) showed that the yield goal approach can be improved by replacing a constant with a variable (Fig. 1.)

View Full Table | Close Full ViewTable 3.

The influence of the fertilizer cost/corn price ratios of 2.80, 5.59, and 11.27 on the correlation coefficients (r) between delta yield, the economically optimum N rate (EONR), and yield at the EONR at all study sites. A significant r value at P < 0.05 is 0.33.

 Delta yield EONR Yield at EONR Parameter 2.80 ratio 5.59 ratio 11.27 ratio 2.80 ratio 5.59 ratio 11.27 ratio 2.80 ratio 5.59 ratio 11.27 ratio Delta yield, 5.59 ratio 1.00 1.00 Delta yield, 11.27 ratio 0.98 0.99 1.00 EONR, 2.80 ratio –0.09 –0.10 –0.14 1.00 EONR, 5.59 ratio 0.11 0.10 0.07 0.97 1.00 EONR, 11.27 ratio 0.56 0.57 0.57 0.64 0.80 1.00 Yield at EONR, 2.80 ratio 0.73 0.71 0.66 –0.25 –0.13 0.21 1.00 Yield at EONR, 5.59 ratio 0.72 0.71 0.67 –0.26 –0.14 0.20 1.00 1.00 Yield at EONR, 11.27 ratio 0.70 0.69 0.67 –0.30 –0.18 0.18 0.98 0.99 1.00

### Comparisons among Nitrogen Recommendation Models

The RMSE and bias values were influenced by the fertilizer cost, corn price, and recommendation model. The RMSE values were directly related to the differences between the measured and predicted values (Table 4). A negative bias indicates that the recommendation was lower than the actual requirement. The South Dakota model generally underestimated the N recommendation for the 2.89 fertilizer/corn price ratio and overestimated the N requirement for the 11.27 fertilizer/corn price ratio. The best results were observed for the 5.59 ratio. These results were expected because the model did not adjust the recommendation based on the fertilizer cost/corn price ratio.

View Full Table | Close Full ViewTable 4.

The influence of N recommendation model on root mean square errors and bias. Data from all study sites were included in this analysis. For the constant N value calculations, the average NO3–N concentration as reported by the South Dakota Soil Testing Laboratories between 2002 and 2005 was used to estimate NO3–N (Gerwing and Gelderman, 2005). The constant N approach is recommended for fields where a soil sample is not collected.

 RMSE Fertilizer/corn price ratio South Dakota model Western Minnesota model Iowa model Nebraska model Modified South Dakota model 2.80 1871 (–16)† 1955 (–38) 3670 (48) 2042 (–21) 1935 (–27) 5.59 1414 (–5) 1832 (–36) 2107 (29) 1560 (–20) 1309 (–13) 11.27 2205 (17) 2316 (–36) 1629 (18) 1780 (–28) 1765 (9) Replacing measured NO3–N with a constant 2.80 1456 (–11) 1972 (–34) 1555(–19) 1287 (–21) 5.59 1006 (1) 1742 (–33) 1091(–16) 670 (–8) 11.27 1812 (23) 2082 (–32) 1608(–28) 1140 (15)
Bias values in parentheses.

Using long-term estimated NO3 concentrations rather than measured NO3–N concentrations either improved or did not impact N recommendations. These results were attributed to NO3 being only one of several plant-available N pools (NH4, mineralizable N, and NO3) and the fact that a portion of the NO3–N can be sorbed onto exchange sites (Clay et al., 2004). The N mass balances from data from the Aurora site showed that NO3–N represented <30% of the available N. Different results would be expected in soils containing less organic matter and/or greater amounts of NO3–N.

The western Minnesota model had slightly higher RMSE values and more negative bias than the South Dakota N recommendation model. Associated with the more negative bias were lower N fertilizer recommendations (Table 5). The relatively high RMSE value and negative bias may be associated with identifying these soils in the medium yield potential category.

View Full Table | Close Full ViewTable 5.

The influence of the state recommendation model and fertilizer cost/corn price ratio on the economically optimum N rate (EONR) and the N recommendations from South Dakota, western Minnesota, Iowa, and Nebraska. For the constant N value, the average NO3–N concentration as reported by the South Dakota Soil Testing Laboratories between 2002 and 2005 was used to estimate NO3–N (Gerwing and Gelderman, 2005). The constant N approach is recommended for fields where a soil sample is not collected.

 Fertilizer/corn ratio EONR South Dakota model Western Minnesota model Iowa model Nebraska model Modified South Dakota model kg N ha–1 Measured NO3–N 2.8 139 122 105 179 120 114 5.59 128 122 92 157 110 114 11.27 105 122 69 122 100 114 Replacing measured NO3–N with a constant 2.8 139 128 109 122 120 5.59 128 128 95 113 120 11.27 105 128 73 100 120

The Iowa N recommendation model, when compared with the South Dakota N recommendation model, had higher RMSE and bias values. The results for both the western Minnesota and Iowa N recommendation models might be related to these models providing an empirical fit to the synergistic relationships that occur between N and water, resulting in a different amount of N fertilizer being required to produce a corn crop in moderate and high yield potential soils. For example in a moderate yield potential environment, 9.2 kg of N might be required per megagram of grain, while in a high yield potential environment, 7.7 kg of N fertilizer might be required per megagram of grain.

The Nebraska N recommendation model is a yield goal model that also accounts for organic matter contents. Such an N recommendation model might be better for site-specific N recommendations. In many landscapes across the Midwest and Great Plains, organic C and N contain strong spatial structures, with footslope areas often having more organic matter than summit and shoulder areas. The Nebraska N recommendation model, when compared with the South Dakota model, had numerically higher RMSE values for the 2.80 and 5.59 ratios and a numerically lower value for the 11.27 ratio. Using measured NO3–N concentrations did not improve the Nebraska N recommendation.

The modified South Dakota N model had RMSE values either lower than or similar to the current South Dakota N model. We attribute this to the ability of the modified model to reduce the amount of N fertilizer required under high yield conditions. One factor contributing to this is the synergistic relationship between N and water. For example, Bauer et al. (1965) showed that in North Dakota water availability impacts N use by wheat. Clay et al. (2001) and Li et al. (2003) had similar results and reported that wheat N use efficiency in Montana was indirectly related to water stress. O’Neill et al. (2004) reported that in 13 experiments conducted in Nebraska, corn N use efficiency was numerically higher in high compared with moderate yield potential soils. Derby et al. (2005) reported that in North Dakota yield goal-based N recommendations overestimated the N requirement in high yield potential soils. Kim et al. (2008) developed a conceptual N model that could explain N and water synergistic relations. The model predicts that because NO3 is transported to the plant in the water transpiration stream, the amount of inorganic N transported to the plant is related to the amount of water transpired. The second factor that could result in reduced N fertilizer need is increased N mineralization in high yield environments.

### SUMMARY

The regional N recommendation models from South Dakota, western Minnesota, Iowa, and Nebraska and the modified South Dakota model were tested to see whether the current South Dakota model can be improved. The current South Dakota model could be improved by replacing the constant (k) in the South Dakota yield goal equation with a variable related to water availability. The modified South Dakota N recommendation model considers synergistic relationships between yield-limiting factors. The non-yield-goal models such as those in Iowa and western Minnesota most likely are the result of synergistic relationships occurring between water and N in those states; the amount of precipitation during the growing season is higher in Iowa and Minnesota than in South Dakota. These results suggest that more N fertilizer is needed in low-yield environments than high-yield environments in South Dakota. Different results would be expected in systems where high NO3–N concentrations exist or organic matter contents are lower (e.g., high manure applications or drought).

We believe that a better approach for a regional N recommendation model is to utilize a function that describes the mechanism as a function based on the soil productivity level rather than a block value. Further study may be needed to evaluate the models from other states where similar boundary conditions exist.

## Acknowledgments

Support for this research was provided in part by the South Dakota Soybean Research and Promotion Council, South Dakota Corn Utilization Board, Minnesota Corn Research and Promotion Council, NASA, USEPA, USDA-CSREES-406 (2004-51130-02248), and the South Dakota Agricultural Experiment Station.