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Residue management systems and their implications for production efficiency

Published online by Cambridge University Press:  12 February 2007

Krishna P. Paudel*
Affiliation:
Department of Agricultural Economics and Agribusiness, 101 Agricultural Administration Building, Louisiana State University, Baton Rouge, LA 70803-5604, USA.
Luanne Lohr
Affiliation:
Department of Agricultural Economics and Agribusiness, 101 Agricultural Administration Building, Louisiana State University, Baton Rouge, LA 70803-5604, USA.
Miguel Cabrera
Affiliation:
Department of Crop and Soil Sciences, 3111 Miller Plant Sciences Bldg, University of Georgia, Athens, GA 30602-7272, USA.
*
*Corresponding author: Email: kpaudel@agcenter.lsu.edu

Abstract

Cotton production is the number one crop enterprise in Georgia in terms of revenue generation. However, due to continuous deterioration of soil quality with conventional tillage and chemical fertilizer application, the economic viability and sustainability of cotton production in Georgia are questionable. Residue management systems (RMSs) comprising winter cover crops were analyzed as an alternative to the existing system, which consists of conventional tillage and chemical fertilizer using yield benefit, net revenue, carbon sequestration, and yield efficiency criteria. Four different RMSs were examined for profitability and input efficiency. Four RMSs encompassing tillage versus no-till and chemical versus organic sources of plant nutrients were compared for their yield and net return differences. No-till and poultry litter with a cover crop was the only system with a positive return and crop yield based on the results from experimental data. Limited results from the experimental field were reinforced using a simulation study. When cotton yield is simulated with an alternative level of organic matter and nitrogen application, production function shows efficiency in input application at the higher level of organic matter. Regression results based on an erosion productivity impact calculator/environmental policy integrated climate (EPIC) simulation indicated that, in the long term, a no-till and poultry litter system may have promise in the region. The results from simulation confirm the results from the experimental study. This study reflected a need to change the cotton management system from the 200-year-old practice of employing intensively cultivated conventional tillage and chemical fertilizers to a new renewable resource-based system where residue management and organic sources of nutrients would be the key components.

Type
Review Article
Copyright
Copyright © Cambridge University Press 2006

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References

01Larson, J.A., Mapp, H.P., Verhalen, L.M. and Banks, J.C. 1996. Adapting a cotton model for decision analyses: a yield response evaluation. Agricultural Systems 50: 145167.CrossRefGoogle Scholar
02Trimble, S.W. 1974. Man-induced Soil Erosion on the Southern Piedmont. 17001970Soil Conservation Society of America, Ankeny, IA.Google Scholar
03Langdale, G.W., Wilson, R.L. Jr and Bruce, R.R. 1990. Cropping frequencies to sustain long term conservation tillage systems. Soil Science Society of America Journal 4: 193198.Google Scholar
04Hendrix, P.F. 1997. Long term patterns of plant production and soil carbon dynamics in a Georgia piedmont agroecosystem. In Paul, E.A., Paustian, K., Elliott, E.T. and Pole, C.V. (eds). Soil Organic Matter in Temperate Agroecosystems Long Term Experiments in North America. CRC Press, Boca Raton, FL. p. 235245.Google Scholar
05Mitchell, C.C., Arriaga, F.J. and Moore, D.A. 1995. Sixty years of continuous fertilization in central Alabama. In Proceedings of the 1995 Beltwide Cotton Conference Vol. 3: National Cotton Council of America.Memphis, TN.Google Scholar
06United States Department of Agriculture/National Agricultural Statistics Service. 1997. Agricultural Prices. USDA/NASS, Washington, DC. p. 19721997.Google Scholar
07Lal, R. and Kimble, J.M. 1997. Conservation tillage for carbon sequestration. Nutrient Cycling in Agroecosystems 49: 243253.Google Scholar
08Karlen, D.L., Eash, N.S. and Unger, P.W. 1992. Soil and crop management effects on soil quality indicators. American Journal of Alternative Agriculture 7: 4855.Google Scholar
09Clarke, H.R. 1992. The supply of non-degraded agricultural land. Australian Journal of Agricultural Economics 36: 3156.Google Scholar
10Hertel, T.W., Stiegert, K. and Vroomen, H. 1996. Nitrogen-land substitution in corn production: a reconciliation of aggregate and firm-level evidence. American Journal of Agricultural Economics 78: 3040.Google Scholar
11Burt, O.R. 1981. Farm-level economics of soil conservation in the Palouse area of the Northwest. American Journal of Agricultural Economics 63: 8392.Google Scholar
12Mitchell, C.C. and Entry, J.A. 1998. Soil C, N, and crop yields in Alabama's long-term ‘old rotation’ cotton experiment. Soil and Tillage Research 47: 331338.Google Scholar
13Hulugalle, N.R., Scott, F., Finlay, L.A., Entwistle, P.C. and Weaver, T.B. 2002. Cotton-based rotation systems on a sodic Vertosol under irrigation: effects on soil quality and profitability. Australian Journal of Experimental Agriculture 42: 341349.CrossRefGoogle Scholar
14Kim, K., Coxhead, I. and Barham, B.L. 2001. Measuring soil quality dynamics: a role for economists, and implications for economic analysis. Agricultural Economics 25: 1326.Google Scholar
15Kumar, K. and Goh, K.M. 2000. Crop residues and management practices: effects on soil quality, soil nitrogen dynamics, crop yield, and nitrogen recovery. Advances in Agronomy 68: 197319.Google Scholar
16Oriade, C.A. and Dillon, C.R. 1997. Developments in biophysical and bioeconomic simulation of agricultural systems: a review. Agricultural Economics 17: 4558.CrossRefGoogle Scholar
17King, R.P., Lybecker, D.W., Regmi, A. and Swinton, S.M. 1993. Bioeconomic models of crop production systems: design, development and use. Review of Agricultural Economics 15: 389401.Google Scholar
18Williams, J.R. 1985. The physical component of the EPIC model. In El-Swaify, S.A., Moldenhauer, W.C. and Lo, A. (eds). Soil Erosion and Conservation. Soil Science Society of America, Madison, WI. p. 272284.Google Scholar
19Givan, W. and Shurley, D. 1998. Crop enterprise cost analysis Athens, GA Cooperative Extension Service, Agricultural and Applied Economics, College of Agricultural and Environmental Sciences, University of GeorgiaGoogle Scholar
20Motta, A.C.V., Reeves, D.W. and Touchton, J.T. 2002. Tillage intensity effects on chemical indicators of soil quality in two coastal plain soils. Communication on Soil Science and Plant Analysis 33: 913932.CrossRefGoogle Scholar
21Izaurralde, R.C., McGill, W.B., Robertson, J.A., Juma, N.G. and Thurston, J.J. 2001. Carbon balance of the Breton classical plots over half a century. Soil Science Society of America Journal 65: 431441.CrossRefGoogle Scholar
22Del Grosso, S., Ojima, D., Parton, W., Mosier, A., Peterson, G. and Schimel, D. 2002. Simulated effects of dry land cropping intensification on soil organic matter and greenhouse gas exchanges using the DAYCENT ecosystem model. Environmental Pollution 116: S75S83.Google Scholar
23Davidson, R. and MacKinnon, J.G. 1981. Several tests for model specification in the presence of alternative hypotheses. Econometrica 49: 781793.CrossRefGoogle Scholar
24Frank, M.D., Beattie, B.R. and Embleton, M.E. 1990. A comparison of alternative crop response models. American Journal of Agricultural Economics 72: 597603.Google Scholar