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Forage Response to Swine Effluent: A Cox Nonnested Test ofAlternative Functional Forms Using a Fast Double Bootstrap

Published online by Cambridge University Press:  26 January 2015

Seong C. Park ,
B. Wade Brorsen ,
Arthur L. Stoecker  and
Jeffory A. Hattey
Seong C. Park
Affiliation:
Texas AgriLife Research-Vernon, and Department of Agricultural Economics, Texas A&M University, College Station, Texas
B. Wade Brorsen
Affiliation:
Department of Agricultural Economics, Oklahoma State University, Stillwater, Oklahoma
Arthur L. Stoecker
Affiliation:
Department of Agricultural Economics, Oklahoma State University, Stillwater, Oklahoma
Jeffory A. Hattey
Affiliation:
College of Food, Agricultural, and Environmental Sciences, Ohio State University, Columbus, Ohio
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Abstract

A Cox nonnested test is conducted using a fast double bootstrap (FDB) methodto select among three competing functional forms (linear response plateau,quadratic, and Mitscherlich-Baule) to model forage yield response tonitrogen applied with swine effluent. The quadratic is rejected in favor ofone of the other functional forms in all cases. The FDB pvalues differed slightly from the single bootstrap pvalues. Buffalograss was slightly more profitable than bermudagrass and hasthe ability to use almost as much nitrogen as bermudagrass.

Keywords

Cox nonnested testfast double bootstraplinear response plateauMitscherlich-Baulenitrogen responseswine effluentC12Q12
Type
Research Article
Information
Journal of Agricultural and Applied Economics , Volume 44 , Issue 4 , November 2012 , pp. 593 - 606
Copyright
Copyright © Southern Agricultural Economics Association 2012

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References

Ackello-Oguto, C., Paris, Q., and Williams, W.A. “Testing a Von Liebig Crop Response Function against Polynomial Specifications.” American Journal of Agricultural Economics 67(1985):873–80.10.2307/1241829Google Scholar
Adeli, A., and Varco, J.J. “Swine Lagoon Effluent as a Source of Nitrogen and Phosphorus for Summer Forage Grasses.” Agronomy Journal 93(2001):1174–81.10.2134/agronj2001.9351174xGoogle Scholar
Al-Kaisi, M., and Waskom, R.M. “Estimating Ammonia Loss from Sprinkler- Applied Swine Effluent.” Agronomy Journal 94(2002):1156–62.10.2134/agronj2002.1156Google Scholar
Allen, V.G., Brown, P., Kellison, R., Segarra, E., Wheeler, T., Dotray, P.A., Conkwright, J.C., Green, C.J., and Acosta-Martinez, V. “Integrating Cotton and Beef Production to Reduce Water Withdrawal from the Ogallala Aquifer in the Southern High Plains Regions.” Agronomy Journal 97(2005):556–67.10.2134/agronj2005.0556Google Scholar
Bauer, D.J., Preacher, K.J., and Gil, K.M. “Conceptualizing and Testing Random Indirect Effects and Moderated Mediation in Multilevel Models: New Procedures and Recommendations.” Psychological Methods 11(2006):142–63.10.1037/1082-989X.11.2.14216784335Google Scholar
Beran, R. “Prepivoting Test Statistics: A Bootstrap View of Asymptotic Refinements.” Journal of the American Statistical Association 83(1988):687–97.10.1080/01621459.1988.10478649Google Scholar
Berck, P., and Helfand, G. “Reconciling the Von Liebig and Differentiable Crop Production Functions.” American Journal of Agricultural Economics 72(1990):985–96.10.2307/1242630Google Scholar
Brink, G.E., Rowe, D.E., Sistani, K.R., and Adeli, A. “Bermudagrass Cultivar Response to Swine Effluent Application.” Agronomy Journal 95 (2003):597–601.10.2134/agronj2003.0597Google Scholar
Carreira, R.I., Stoecker, A.L., Epplin, F.M., Hattey, J.A., and Kizer, M.A. “Subsurface Drip Irrigation versus Center-Pivot Sprinkler for Applying Swine Effluent to Corn.” Journal of Agricultural and Applied Economics 38(2006):645–58.S1074070800022677Google Scholar
Carreira, R.I.R. “Economic Study of Alternatives Best Management Practices for Swine Effluent Application to Corn in Semiarid Climate.” Ph.D. dissertation, Oklahoma State University, Stillwater, OK, July 2004.Google Scholar
Coulibaly, N., and Brorsen, B.W. “Monte Carlo Sampling Approach to Testing Nonnested Hypotheses: Monte Carlo Results.” Econometric Reviews 18(1999):195–209.10.1080/07474939908800439Google Scholar
Cox, D.R. “Further Results on Tests of Separate Families of Hypotheses.” Journal of the Royal Statistical Society. Series B. Methodological 24(1962):406–24.Google Scholar
Davidson, R., and MacKinnon, J.G. “Improving the Reliability of Bootstrap Tests.” Discussion Paper No. 995. Queen's Institute for Economic Research, 2001.Google Scholar
Davidson, R., and MacKinnon, J.G. “Improving the Reliability of Bootstrap Tests with the Fast Double Bootstrap.” Computational Statistics & Data Analysis 51 (2007):3259–81.10.1016/j.csda.2006.04.001Google 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):597–603.10.2307/1243029Google Scholar
Gillen, R.L., and Berg, W.A. “Response of Perennial Cool-Season Grasses to Clipping in the Southern Plains.” Agronomy Journal 97(2005):125–30.10.2134/agronj2005.0125Google Scholar
Godfrey, L. “On the Asymptotic Validity of a Bootstrap Method for Testing Nonnested Hypotheses.” Economics Letters 94(2007):408–13.10.1016/j.econlet.2006.08.031Google Scholar
Godfrey, L. Bootstrap Tests for Regression Models. New York: Palgrave MacMillan, 2009.Google Scholar
Godfrey, L.G., and Santos Silva, J.M.C. “Bootstrap Tests of Nonnested Hypotheses: Some Further Results.” Econometric Reviews 23 (2004):325–10.Google Scholar
Goldman, N. “Statistical Tests of Models of DNA Substitution.” Journal of Molecular Evolution 36(1993):182–98.10.1007/BF00166252Google Scholar
Harri, A., Erdem, C., Coble, K.H., and Knight, T.O. “Crop Yield Distributions: A Reconciliation of Previous Research and Statistical Tests for Normality.” Applied Economic Perspectives and Policy 31(2009):163–82.Google Scholar
Heady, E.O., and Pesek, J. “A Fertilizer Production Surface with Specification of Economic Optima for Corn Grown on Calcareous Ida Silt Loam.” Journal of Farm Economics 36(1954):466–82.10.2307/1233014Google Scholar
Ishrat, H., Epplin, F.M., and Krenzer, E.G. Jr. “Planting Date Influence on Dual-Purpose Winter Wheat Forage Yield, Grain Yield, and Test Weight.” Agronomy Journal 95(2003):1179–88.10.2134/agronj2003.1179Google Scholar
Kaitibie, S., Epplin, F.M., Brorsen, B.W., Horn, G.W., Krenzer, E.G. Jr., and Paisley, S.I. “Optimal Stocking Density for Dual-Purpose Winter Wheat Production.” Journal of Agricultural and Applied Economics 35(2003):29–38.S1074070800005915Google Scholar
Kaitibie, S., Nganje, W.E., Brorsen, B.W., and Epplin, F.M. “A Cox Parametric Bootstrap Test of the Von Liebig Hypotheses.” Canadian Journal of Agricultural Economics 55(2007):15–25.10.1111/j.1744-7976.2007.00077.xGoogle Scholar
Kapetanios, G., and Weeks, M. “Non-Nested Models and the Likelihood Ratio Statistic: A Comparison of Simulation and Bootstrap Based Tests.” Working paper No. 490. Queen Mary University of London, April 2003.Google Scholar
Krall, J.M., and Schuman, G.E. “Integrated Dryland Crop and Livestock Production Systems on the Great Plains: Extent and Outlook.” Journal of Production Agriculture 9(1996):187–91.10.2134/jpa1996.0187Google Scholar
Krenzer, E.G. Jr., Tarrant, A.R., Bernardo, D.J., and Horn, G.W. “An Economic Evaluation of Wheat Cultivars Based on Grain and Forage Production.” Journal of Agriculture Production 9(1996):66–73.10.2134/jpa1996.0066Google Scholar
Lee, J.H., and Brorsen, B.W. “A Non-Nested Test of GARCH vs. EGARCH Models.” Applied Economics Letters 4(1997):765–68.10.1080/758528724Google Scholar
Letson, D., and McCullough, B.D. “Better Confidence Intervals: The Double Bootstrap with No Pivot.” American Journal of Agricultural Economics 80(1998):552–59.10.2307/1244557Google Scholar
Lowitt, R. American Outback: The Oklahoma Panhandle in the Twentieth Century. Lubbock, TX: Texas Tech University Press, 2006.Google Scholar
McAndrews, G.M., Liebman, M., Cambardella, C.A., and Richard, T.L. “Residual Effects of Composted and Fresh Solid Swine Manure on Soybean Growth and Yield.” Agronomy Journal 98(2006):873–82.10.2134/agronj2004.0078Google Scholar
McLachlan, G.J. “On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture.” Journal of the Royal Statistical Society. Series C, Applied Statistics 36(1987):318–24.Google Scholar
Paris, Q. “The Von Liebig Hypothesis.” American Journal of Agricultural Economics 74(1992):1019–28.10.2307/1243200Google Scholar
Paris, Q., and Knapp, K. “Estimation of Von Liebig Response Functions.” American Journal of Agricultural Economics 71(1989):178–86.10.2307/1241786Google Scholar
Park, S.C., Vitale, J., Turner, J.C., Hattey, J.A., and Stoecker, A. “Economic Profitability of Sustained Application of Swine Lagoon Effluent and Beef Feedlot Manure Relative to Anhydrous Ammonia in the Oklahoma Panhandle.” Agronomy Journal 102(2010):420–30.10.2134/agronj2009.0166Google Scholar
Park, S.C., Vitale, J., Turner, J.C., Hattey, J.A., and Stoecker, A. “Economic Potential of Intensified Forage Systems in the Southern Plains.” Journal of the American Society of Farm Managers and Rural Appraisers 74(2011):97–119.Google Scholar
Pesaran, B., and Pesaran, M.H. “A Nonnested Test of Level-Differenced versus Log-Differenced Stationary Models.” Econometric Reviews 14(1995):213–27.10.1080/07474939508800316Google Scholar
Pesaran, M.H., and Pesaran, B. “A Simulation Approach to the Problem of Computing Cox's Statistic for Testing Nonnested Models.” Journal of Econometrics 57(1993):377–92.10.1016/0304-4076(93)90072-DGoogle Scholar
Redfearn, D., Amali, B., Zhang, H., and Rice, C. Fertilizing Bermuda Grass Hay and Pasture. Stillwater, OK: Oklahoma Cooperative Extension Service, Fact Sheet PSS-2263, 2010.Google Scholar
SAS Institute Inc. SAS OnlineDoc® 9.1. Cary, NC: SAS Institute Inc., 2003.Google Scholar
Sims, J.T., and Wolf, D.C. “Poultry Waste Management: Agricultural and Environmental Issues.” Advances in Agronomy 52(1994):1–82.10.1016/S0065-2113(08)60621-5Google Scholar
Sutton, A.L., Nelson, D.W., Kelly, D.T., and Hill, D.L. “Comparison of Solid vs. Liquid Dairy Manure Applications on Corn Yield and Soil Composition.” Journal of Environmental Quality 15(1986):370–75.Google Scholar
Tembo, G., Brorsen, B.W., Epplin, F.M., and Tostã;o, E. “Crop Input Response Functions with Stochastic Plateaus.” American Journal of Agricultural Economics 90(2008):424–34.10.1111/j.1467-8276.2007.01123.xGoogle Scholar
Tumusiime, E., Brorsen, B.W., Mosali, J., Johnson, J., Locke, J., and Biermacher, J.T. “Determining Optimal Levels of Nitrogen Fertilizer Using Random Parameter Models.” Journal of Agricultural and Applied Economics 43(2011):541–52.S1074070800000067Google Scholar
Wagenmakers, E.J., Ratcliff, R., Gomez, P., and Iverson, G.J. “Assessing Model Mimicry Using the Parametric Bootstrap.” Journal of Mathematical Psychology 48(2004):28–56.10.1016/j.jmp.2003.11.004Google Scholar
Williams, D.A. “Discrimination between Regression Models to Determine the Pattern of Enzyme Synthesis in Synchronous Cell Cultures.” Biometrics 26(1970):23–32.10.2307/2529041Google Scholar