Alternative Benchmarks for Economically Optimal Rates of Nitrogen Fertilization for Corn

Discussions of N fertilizer needs for corn (Zea mays L.) usually are based on the assumption that economic optimum rates (EORs) of N provide the best benchmark to indicate the rates most likely to maximize profits for producers. We describe methods for calculating some additional benchmarks and illustrate how efforts to improve N management could be advanced by recognizing several alternative benchmarks simultaneously. We analyzed data from 54 small-plot trials with a new procedure that disaggregates problems associated with systematic model bias and variability in crop yield responses to fertilizer N. We compared the commonly used method for calculating EORs to a method that is described as discrete marginal analysis at near-optimal rates of N fertilization. We described methods for calculating the incremental break-even rate, the incremental rate that gives no crop response, and the incremental rate of N that gives the desired level of profit. Discussions of the relative merits of each benchmark suggest that problems associated with calculating and using the concept of EORs can be avoided by presenting the alternative benchmarks as proposed in this paper. ECONOMIC OPTIMUM RATES of fertilization as defined by Heady et al. (1955) and Heady and Dillon (1961) provide an essential benchmark when discussing N fertilization practices for corn. Economic optimum rates explicitly denote rates of N fertilization expected to maximize net returns (i.e., profits) from fertilizer applied per unit of land area. This benchmark has been widely used in agronomic studies (Black, 1993; Bock and Hergert, 1991; Colwell, 1994; Voss, 1975). Use of such a benchmark is reasonable because modern corn production is a business and, therefore, maximization of profits is often a primary consideration when selecting rates of N fertilization. Economic optimum rates also provide an essential benchmark when discussing or calculating any loss of profit that producers would suffer if rates of fertilization needed to be reduced to address problems related to environmental pollution (Aldrich, 1980; Huang et al., 2001). Spatial and temporal variability in crop responses to N fertilizer is a serious problem when calculating EORs (Blackmer et al., 1992; Mamo et al., 2003; Miao et al., 2006; Oberle and Keeney, 1990; Scharf et al., 2005). This variability is unavoidable because fertilizers are often applied before plants grow and because crop yield responses are greatly affected by weather and other factors that occur during plant growth. The problem of variability is exacerbated by the tendency of relationships between rates of N fertilization and yields to have only slight curvature at near-optimal rates of fertilization. Therefore, models used to quantitatively relate rates of N to yields often disagree substantially when calculating EORs (Black, 1993; Cerrato and Blackmer, 1990; Colwell, 1994; Frank et al., 1990; Heckman et al., 1996). The problems of model bias and variability in yield response interact because models inject significant systematic errors when curvatures are slight, because variability in yield response makes it difficult to identify the best model for any given site, and because the best model often varies due to differences observed in the nature of the response among sites. The problem of calculating EORs amid variability in yield response is great enough that many researchers have concluded that producers cannot afford the economic risk associated with using EORs and should apply additional N (Babcock, 1992; Bullock and Bullock, 1994; Sheriff, 2005; Yadav et al., 1997). It is suggested that this extra N, often called insurance N, enables producers to benefit from situations where conditions are favorable for unusually large yield responses to N and, therefore, unusually large profits from fertilization. Despite the fact that insurance N is often applied, quantitative methods have not been described for calculating optimal amounts of insurance N. A new multistep procedure has been developed to disaggregate the problems of model bias and variability in yield response when calculating EORs for N in corn (Kyveryga et al., 2007). This multistep procedure integrates established methods of calculating EORs and concepts of ex post and ex ante analyses. A sequence of steps and iteration of these steps makes it possible to minimize the problem of model bias in some steps while reducing unexplained variability in yield responses in other steps. Unexplained variability is reduced by forming categories that are based on information that is available to producers at the time of fertilization. When compared with methods used in the past, the new procedure places much greater emphasis on the need for collecting and analyzing samples of yield responses that randomly distributed within the specific areas of interest. The results are called category-specific ex post EORs. In contrast to EORs calculated for individual trials, numerical values for category-specific ex post EORs vary with the amounts of information inP.M. Kyveryga, Iowa Soybean Assoc., 4554 114th St., Urbandale, IA 50322; A.M. Blackmer, Dep. of Agronomy, Iowa State Univ., Ames, IA 50010; and T.F. Morris, Dep. of Plant Science, Univ. of Connecticut, Storrs, CT 06269. Received 26 Nov. 2006. *Corresponding author ([email protected]). Published in Agron. J. 99:1057–1065 (2007).