Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-08T15:25:43.301Z Has data issue: false hasContentIssue false

Variation in Marginal Response to Nitrogen Fertilizer between Locations

Published online by Cambridge University Press:  28 April 2015

Dale K. Graybeal*
Affiliation:
North Carolina State University

Abstract

A logistic growth equation with time and location varying parameters was used to model corn response to applied nitrogen. A nonlinear dummy-variable regression model provided a parsimonious representation of site and time effects on parameter values. The model was used to test for the equality of the mean marginal product of nitrogen fertilizer between locations on the coastal plain of North Carolina. Monte Carlo simulation and bootstrap simulation were used to construct finite sample covariance estimates. Results support rejection of the hypothesis that mean marginal products are equal when nitrogen is applied at 168 kg/ac. A comparison of bootstrapped errors and asymptotic errors suggests that results based on asymptotic theory are fairly reliable in this case.

Type
Invited Paper Sessions
Copyright
Copyright © Southern Agricultural Economics Association 2000

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Berek, P. and Helfand, G.. “Reconciling the von Liebig and Differentiable Crop Production Functions.” American Journal of Agricultural Economics 72 (1990):985996.CrossRefGoogle Scholar
Cerrato, M.E. and Blackmer, A.M.. “Comparison of Models for Describing Corn Yield Response to Nitrogen Fertilizer.” Agronomy Journal 82(1990):138143.CrossRefGoogle Scholar
Efron, B.Bootstrap Methods: Another Look at the Jackknife.” Annals of Statistics 7, 1(1979):126.CrossRefGoogle Scholar
Frank, M.D., Beattie, B.R., and Embleton, M.E.. “A Comparison of Alternative Crop Response Models.” American Journal of Agricultural Economics 72 (1990):597603.CrossRefGoogle Scholar
Gallant, R.A.Nonlinear Statistical Models. New York: John Wiley & Sons, New York, 1987.CrossRefGoogle Scholar
Greene, W.H.Econometric Analysis, Third Edition. Upper Saddle River, NJ: Prentice Hall, 1997.Google Scholar
Heady, E.O., Peseck, J.T., and Brown, W.G.. Crop Response Surfaces and Economic Optima in Fertilizer Use. Ames, Iowa: Iowa Ag. Exp. Sta. Res. Bull. 424, 1955.Google Scholar
Kamprath, Eugene J.Nitrogen Studies with Corn on Coastal Plain Soils. Raleigh, NC: NC ARS T.B. 282, 1986.Google Scholar
Krinskey, I. and Robb, A.L.. “Three Methods for Calculating the Statistical Properties of Elasticities: A Comparison.” Empirical Economics 16(1991):199209.CrossRefGoogle Scholar
Lentner, M. and Bishop, T.. Experimental Design and Analysis. Blacksburg, VA: Valley Book Company, 1986.Google Scholar
Lowenberg-DeBoer, J. and Boehlje, M.. “Revolution, Evolution, or Dead-end: Economic Perspectives on Precision Agriculture.” In: Precision Agriculture, eds., Robert, P.C., Rust, R.H., and Larson, W.E., Madison, WI: ASA-CSSA-SSA, 1996.Google Scholar
Myers, R.H.Classical and Modern Regression with Applications. Boston: Duxbury Press, 1986.Google Scholar
Overman, A.R., Wilson, D.M., and Kamprath, E.J.. “Estimation of Yield and Nitrogen Removal by Corn.” Agronomy Journal 86 (1994):10121016.CrossRefGoogle Scholar
Paris, Q.The von Liebig Hypothesis.” American Journal of Agricultural Economics 74 (1992):10191028.CrossRefGoogle Scholar