Optimum Nitrogen Rates for Corn

Agronomy Research Summary – 2011


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Nitrogen (N) is typically the most yield-limiting nutrient for corn production, and represents one of the largest economic inputs associated with maximizing returns. This article reviews newly developed strategies for determining N recommendation rates and improving N use efficiency in corn production. This includes the latest procedures for estimating economically optimum nitrogen rates (EONR), known as the maximum return to nitrogen (MRTN) method, along with soil- and crop-based approaches for determining EONR.

Difficulties in Determining Optimum N Rates

Low N use efficiency in corn production is often associated with N losses from the system, primarily resulting from excess rainfall (Figure 1). Another contributor is the inability to accurately estimate EONR, the point where the last increment of N returns a grain yield increase large enough to pay for that N (Sawyer et al., 2006a). This breakdown occurs because corn exhibits a highly variable yield response to fertilizer N application due to highly variable weather conditions.

Inaccurate EONR estimates result in over-fertilization in some years and fields and under-fertilization in others. The former reduces grower profits through wasted fertilizer inputs and the latter through unattained yield goals (Shanahan et al, 2008). Hence, the need to improve N fertilizer management is clear, but the ability to accurately estimate EONR on a year-to-year and field-to-field basis remains elusive (van Es et al. 2007).

Yield-Goal Approach to N-Rate Decisions

Previous methods for developing N rate recommendations have often relied on the "yield goal" approach. This method begins by establishing a realistic yield goal based on field history and/or current capability. The amount of N required by the targeted yield is then determined from crop nutrient removal tables, (with a percentage added to account for losses). Finally, N "credits" from manure, preceding legume crops and soil organic matter mineralization are subtracted from the requirement. The result is the amount of nitrogen fertilizer that must be added to supply crop needs.

Also referred to as the "mass balance" approach, this method is simple and holds considerable appeal, but it is not without its shortcomings (Shanahan et al. 2008). The weakness in this approach is that the relationship between yield and EONR determined by this method appears to be very poor for humid regions of the Corn Belt where most corn is grown.

A summary of nearly 300 research studies revealed the limitations of the mass balance approach to N-rate determination. Researchers analyzed data from 298 previously reported experiments in five Corn Belt states in the U.S. where corn yield response to N was measured (Lory and Scharf, 2003). Their investigations showed that the recommended N rates determined by yield goal exceeded EONR by an average of 80 lb/acre. These results strongly support the notion that yield goal is a poor forecaster of corn N needs.

Figure 1

Figure 1. Corn field severely deficient in N due to excessive
rainfall and sustained wet field conditions.

An explanation for these observations provided by Grove and Schwab (2006) is that years with very favorable growing conditions (adequate but not excessive precipitation) also have favorable conditions for N mineralization from soil organic matter. Conversely, years with poor growing conditions tend to have lower rates of mineralization. Because of this additional mineralized N in high-yield years, corn requires about the same amount of fertilizer N regardless of the yield potential in a given year (this does not apply to low organic matter soils).


N-Rate Calculator Approach to Rate Decision

Because of problems with the "mass-balance" method, extension and crop advisors in several top corn producing states have embraced a new process of N-rate determination for corn. These states: Iowa, Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin are now using the so-called maximum return to N (MRTN) approach for N-rate decisions (Sawyer et al., 2006a). This method provides generalized or regional N-rate recommendations using a database compiled from a network of multiyear and multi-location N-rate field trials conducted in these states. This database is used in fitting yield response curves to N and conducting economic analyses of the yield responses. The approach is embodied in the "Corn Nitrogen Rate Calculator" available from Iowa State University Agronomy Extension: http://extension.agron.iastate.edu/soilfertility/nrate.aspx.

The rate with the largest average net return represents the MRTN (or "EONR"), and recommendations vary with grain-to-fertilizer price ratio. To illustrate operation of the calculator, two different N to corn price scenarios for a soybean-corn rotation in Iowa are presented (Figure 2). The first example is for corn at $4.50/bu and N at $0.50/lb and the second is for corn at $6.50/ bu and N at $0.50/lb. The calculator forecasts that MRTN for the two different price scenarios would be 124 and 138 lbs of N/acre, respectively. For a continuous corn rotation, the calculator estimates that MRTN would increase to 176 and 187 lbs of N/acre, respectively, for the same two price scenarios, reflecting the overall greater N requirements for continuous corn (data not shown).

It is worth noting that the flat net return surrounding the N rate at MRTN reflects small yield changes near optimum N, indicating that choosing an exact N rate is not critical to maximize profit. This range provides flexibility in rate selection for growers in addressing production risk and price fluctuations.

Figure 2

Figure 2. Maximum return to N (MRTN) for a soybean - corn rotation in Iowa. Recommendation is for N priced at $0.50/lb and corn at $4.50/bu (top) and $6.50/bu (bottom) using data from 172 research locations. Profitable N-rate range provides economic return within $1/acre of the MRTN. Source: Iowa State University Extension.

Advantages of MRTN Approach
The MRTN approach represents progress over the mass balance method in incorporating current fertilizer and grain price information and using updated N-response data sets from an extensive network of ongoing field trials. It also recognizes the N contribution of soil organic matter by adjusting N recommendations geographically (Sawyer et al., 2006a). However, as this approach provides generalized N-rate recommendations over large areas and years, it does not address the issue of year-to-year variability in temperature or rainfall (Grove and Schwab, 2006; van Es et al. 2007). Nonetheless, the MRTN approach is useful as a general guide for N rates.


Adjusting N Rates with Soil Testing

Another way for estimating EONR is the use of a variety of soil test procedures to approximate soil N supply for crop use. One of the first soil tests growers should consider for this goal is the Pre-Plant N Test (PPNT; Bundy et al., 1995). It measures nitrate or nitrate-plus-ammonium in the soil (typically from 0 to 24 inches) early in the season, and can be used to direct N-rate decisions prior to or at planting. This test can be used in situations with either high residual nitrate-N from the previous season or for manure applications, where it can provide some direction for adjusting early N application rates based on N inputs from organic matter. Use of the PPNT along with recognition of other nitrate-N credits (e.g., irrigation water) is especially useful for estimating EONR in the drier western Corn Belt (Shapiro et al, 2008). However, this procedure is less useful for estimating EONR in the more humid regions where nitrate-N losses can be high during the early season due to excess precipitation (van Es et al., 2007).

Figure 3

Figure 3. In this Pioneer N-rate research trial in Johnston, IA,
severe N deficiency was clearly visible in some low N-rate
treatments for continuous corn. (Percentages are the percent of
Iowa State University recommended N-rate applied as treatments.)

Another test for approximating available N and/or EONR is the pre-sidedress nitrate test (PSNT) developed by Magdoff et al. (1984). Results from this test provide an opportunity to adjust sidedress N rates (Blackmer et al., 1989) based on residual soil N present at the time of application. Sogbedji et al. (2000) found, for example, that the use of this test resulted in reduced N rates and consequently reduced nitrate leaching, with similar yields compared to the traditional yield goal method for estimating N fertilizer rates. Problems with this approach are the extensive requirements for soil sampling and sample analysis during a short time window in the spring.


Adjusting N Rates with Crop Monitoring

Recent developments in crop sensing allow for real-time estimation of crop N status during the growing season. Leaf chlorophyll meters (Sawyer et al., 2006b) or crop canopy sensors (Shanahan, 2010) have been proposed as a means for assessing leaf or canopy N status, typically for the purpose of in-season N application. This implies the use of high-clearance fertilizer application equipment and a reference strip that has been well-fertilized since planting.

Crop sensor technologies offer particular advantages for large acreages where pre-plant or in-season soil tests are simply too labor intensive for estimating EONR. Nevertheless, it should be recognized that there are areas where the sensor-based approach may not be appropriate, such as areas with low or erratic in-season precipitation (Shanahan, 2010).


Conclusions

Current N management schemes for estimating EONR have been largely designed to provide N-rate recommendations for applications made prior to or at planting. Though these methods have proven to vary in their ability to consistently predict seasonal corn N needs, they provide a starting point for answering the question of how much N to apply. Their major limitation is in providing static recommendations that cannot account for in-season weather changes that affect soil N availability and/or crop N uptake (van Es et al., 2007). For example, using the MRTN method, which is based on regionalized crop responses, can result in excessive fertilization in years with dry springs, and inadequate fertilization in years with high early season N losses (Sawyer et al., 2006a).

In practice, growers have often opted to use higher N rates in case losses occur due to excessively wet early season conditions (insurance fertilizer). This sometimes results in excessive fertilizer application, unnecessary expense, and increased N losses. Soil test methods like PPNT and PSNT provide opportunities to adjust N rates for variation in soil N supply. Similarly, crop sensing methods enable growers to better synchronize soil N supply with crop N needs, resulting in reduced N rates (Shanahan, 2010). However, these methods are not without their own shortcomings, including additional costs associated with acquiring and processing soil samples (van Es et al., 2007) as well as a narrower time window for in-season N applications. Hence, growers must carefully consider the pros and cons of each N management approach, using one or more strategies (or a combination) that maximize profit potential while minimizing risk.


References

Blackmer, A.M., D. Pottker, M.E. Cerrato, and J. Webb. 1989. Correlations between soil nitrate concentrations in late spring and corn yields in Iowa. J. Prod. Agric. 2:103-109.

Bundy, L.G., S.J. Sturgul, and R.W. Schmidt. 1995. #A3512 - Wisconsin's Preplant Soil Nitrate Test. UW-Extension, NPM. R-5-95-3M.

Grove, J.H, and G.J Schwab. 2006. The Corn Belt multi-state nitrogen rate calculator: Not reliable for Kentucky corn producers. Soil Science News & Views, Univ. of Kentucky Exten. Service. Vol. 26, No. 4, 2006.

Lory, J.A., and P.C. Scharf. 2003. Yield goal versus delta yield for predicting fertilizer nitrogen need in corn. Agron. J. 95: 994-999.

Magdoff, F.R., D. Ross, and J. Amadou. 1984. A soil test for nitrogen availability. Soil Sci. Soc. Am. J. 48:1301-1304.

Sawyer, J., E. Nafziger, G. Randall, L Bundy, G. Rehm, and B. Joern. 2006a. Concepts and rationale for regional nitrogen guidelines for corn. Iowa State Univ. Extension Publ. PM2015, 27 pp.

Sawyer, J., J. Lundvall, J. Hawkins, D. Barker, J. McGuire, and M. Nelson. 2006b. Sensing nitrogen stress in corn. Iowa State Univ. Extension Publ. PM2026, 4 pp.

Shanahan, J.F., N.R. Kitchen, W.R. Raun, and J.S. Schepers. 2008. Responsive in-season nitrogen management for cereals. Comput. Electron. Agric. 61:51-62.

Shanahan, J.F. 2010. Using crop sensors to improve corn nitrogen management. Crop Insights, Vol. 20, No. 6. Pioneer Hi-Bred, Johnston, IA.

Shapiro, C.A, R.B. Ferguson, G.W. Hergert, C.S. Wortmann, and D.T. Walters. 2008. Fertilizer suggestions for corn. University of Nebraska NebGuide EC117.

Sogbedji, J.M., H.M. van Es, C.L. Yang, L.D. Geohring, and F.R. Magdoff. 2000. Nitrate leaching and N budget as affected by maize N fertilizer rate and soil type. J. Environ. Qual. 29:1813-1820.

van Es, H.M., B.D. Kay, J.J. Melkonian, and J.M. Sogbedji. 2007. Nitrogen management for maize in humid regions: Case for a dynamic approach. In: T. Bruulsema (ed.) Managing Crop Nutrition for Weather. Intern. Plant Nutrition Institute Publ.


 
 
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