Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-25T17:51:12.941Z Has data issue: false hasContentIssue false
  • English
  • Français

Incorporation of Within-Season Yield Growth into a Mathematical Programming Sugarcane Harvest Scheduling Model

Published online by Cambridge University Press:  28 April 2015

Michael E. Salassi ,
Lonnie P. Champagne  and
Benjamin L. Legendre
Michael E. Salassi
Affiliation:
Department of Agricultural Economics and Agribusiness, Louisiana State University Agricultural Center Sugarcane Research Unit, Agricultural Research Service, U. S. Department of Agriculture
Lonnie P. Champagne
Affiliation:
Department of Agricultural Economics and Agribusiness, Louisiana State University Agricultural Center Sugarcane Research Unit, Agricultural Research Service, U. S. Department of Agriculture
Benjamin L. Legendre
Affiliation:
Department of Agricultural Economics and Agribusiness, Louisiana State University Agricultural Center Sugarcane Research Unit, Agricultural Research Service, U. S. Department of Agriculture
Get access Rights & Permissions [Opens in a new window]

Abstract

This study focuses on the development of a optimal harvest scheduling mathematical programming model which incorporates within-season changes in perennial crop yields. Daily crop yield prediction models are estimated econometrically for major commercially grown sugarcane cultivars. This information is incorporated into a farm-level harvest scheduling linear programming model. The harvest scheduling model solves for an optimal daily harvest schedule which maximizes whole farm net returns above harvesting costs. Model results are compared for a commercial sugarcane farm in Louisiana.

Keywords

sugarcaneharvest schedulingwithin-season yield growth
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 32 , Issue 3 , December 2000 , pp. 507 - 519
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

Alexander, Alex G. “Chapter 11—Maturation and Natural Ripening, in Sugarcane Physiology,” A Comprehensive Study of the Saccharum Source-to-Sink System, Elsevier, 1973.Google Scholar
Barnes, A.C.The Sugar Cane, New York: John Wiley & Sons, Inc, 1974.Google Scholar
Brumelle, Shelby, Granot, Daniel, Halme, Merja, and Vertinsky, Ilan, “A Tabu Search Algorithm for Finding Good Forest Harvest Schedules Satisfying Green-up Constraints,” European Journal of Operational Research 106 (1998):408–424.CrossRefGoogle Scholar
Chang, Y.S. “The Trend of Sucrose Accumulation During Maturation of Sugarcane with Special Reference to the Maturity of Sugarcane Cultivars,” Report of the Taiwan Sugar Research Institute 148 (1995):1–9.Google Scholar
Crane, Donald R., Spreen, T.H., Alvarez, J., and Kidder, G.. An Analysis of the Stubble Replacement Decision for Florida Sugarcane Growers, Agricultural Experiment Station, Institute of Food and Agricultural Sciences, University of Florida. Bulletin 822, 1982.Google Scholar
Daust, David K., and Nelson, John D., “Spatial Reduction Factors for Strata-Based Harvest Schedules,” Forest Science 39 (1993):152–165.Google Scholar
Faw, Wade E, 1998 Sugarcane Harvesting Schedule, Sugarcane Circular Letter No. 11-98, Louisiana Cooperative Extension Service, Louisiana State University Agricultural Center.Google Scholar
Griffiths, W.E., Hill, R.C., and Judge, G.G.. Learning and Practicing Econometrics, New York: John Wiley & Sons, Inc, 1993.Google Scholar
Higgins, A.J., Muchow, R.C., Rudd, A.V., and Ford, A.W., “Optimising Harvest Date in Sugar Production: A Case Study for the Mossman Mill Region in Australia—I. Development of Operations Research Model and Solution,” Field Crops Research 57(1998):153–162.CrossRefGoogle Scholar
Lass, L.W., Callihan, R.H., and Everson, D.O., “Forecasting the Harvest Date and Yield of Sweet Corn by Complex Regression Models,” Journal of the American Society for Horticultural Science 118(1993):450–455.CrossRefGoogle Scholar
Malezieux, E., “Predicting Pineapple Harvest Date in Different Environments, Using a Computer Simulation Model,” Agronomy Journal 86(1994):609–617.CrossRefGoogle Scholar
Millhollon, R.W., and Legendre, B.L., “Sugarcane Yield as Affected by Annual Glyphosate Ripener Treatments,” American Society of Sugar Cane Technologists Journal 16 (1996):7–12.Google Scholar
Muchow, R.C., Higgins, A.J., Rudd, A.V., and Ford, A.W., “Optimising Harvest Date in Sugar Production: A Case Study for the Mossman Mill Region in Australia—I. Sensitivity to Crop Age and Crop Class Distribution,” Field Crops Research 57(1998):243–251.CrossRefGoogle Scholar
Nelson, John, Douglas Brodie, J., and Sessions, John, “Integrating Short-Term, Area-Based Logging Plans with Long-Term Harvest Schedules,” Forest Science 37 (1991):101–122.Google Scholar
Salassi, M.E., and Milligan, S.B., “Economie Analysis of Sugarcane Variety Selection, Crop Yield Patterns, and Ratoon Crop Plow Out Decisions,” Journal of Production Agriculture 10(1997):539–545.CrossRefGoogle Scholar
Semenzaio, R., “A Simulation Study of Sugar Cane Harvesting,” Agricultural Systems 47(1995):427–437.CrossRefGoogle Scholar
United States Department of Agriculture, National Agricultural Statistics Service, Crop Production Annual Summary, 1996 annual summary, Cr Pr 2-1 (97), January 1997.Google Scholar
United States Department of Agriculture, National Agricultural Statistics Service, Crop Production Annual Summary, 1999 annual summary, Cr Pr 2-1 (00), January 2000.Google Scholar
United States Department of Agriculture, Economic Research Service, Sugar and Sweetener Situation and Outlook Report, SSS-227, January 2000.Google Scholar
United States Department of Agriculture, Economic Research Service “U.S.-Mexico Sweetener Trade Mired in Dispute,” Agricultural Outlook, September 1999, pp. 17–20.Google Scholar
Van Deusen, Paul C, “Habitat and Harvest Scheduling Using Bayesian Statistical Concepts,” Canadian Journal of Forest Research 26 (1996):1375–1383.CrossRefGoogle Scholar
White, H., “A Heteroskedasticity-Consistent Co-variance Matrix Estimator and a Direct Test of Heteroskedasticity,” Econometrica 48(1980):817–838.CrossRefGoogle Scholar
Wolf, S., “Predicting Harvesting Date of Processing Tomatoes by a Simulation Model,” Journal of the American Society for Horticultural Science 111(1986):11–16.CrossRefGoogle Scholar