Using growing degree days, agrometeorological variables, linear regression, and data mining methods to help improve prediction of sweetpotato harvest date in louisiana

HortTechnology

Volumn 19, Issue 1, 2009, Pages 133-144

  Villordon, Arthur ^a   Clark, Christopher ^a   Ferrin, Don ^a   LaBonte, Don ^a  

^a and Soil Sciences   (United States)

Author keywords

Heat units; Ipomoea batatas; Phenology

Indexed keywords

IPOMOEA BATATAS;

EID: 65449174033     PISSN: 10630198     EISSN: None     Source Type: Journal    
DOI: 10.21273/hortsci.19.1.133     Document Type: Article

References (48)

1. Arnold, C.Y. 1959. The determination and significance of the base temperature in a linear heat unit system. Proc. Amer. Soc. Hort. Sci. 74:430-445.
2. Arnold, C.Y. 1960. Maximum-minimum temperatures as a basic for computing heat units. Proc. Amer. Soc. Hort. Sci. 76: 683-692.
3. Benjamini, Y. and M. Leshno. 2005. Statistical methods for data mining, p. 565-588. In: O. Maimon and L. Rokach (eds.). Data mining and knowledge discovery handbook. Springer, New York.
4. Bui, E.N., B.L. Henderson, and K. Viergever. 2006. Knowledge discovery from models of soil properties developed through data mining. Ecol. Modell. 191:431-446.
5. Clapham, W.M. and J.M. Fedders. 2004. Modeling vegetative development of berseem clover (Trifolium alexandrinumL.) as a function of growing degree days using linear regression and neural networks. Can. J. Plant Sci. 84:511-517.
6. Data Mining Group. 2008. Data Mining Group. 1 Mar. 2008. http://www.dmg.org/.
7. Dufault, R.J. 1997. Determining heat unit requirements for broccoli harvest in coastal South Carolina. J. Amer. Soc. Hort. Sci. 122:169-174.
8. Edmond, J.B. andG.R. Ammerman. 1971. Sweet potatoes: Production, processing, marketing. AVI Publishing, Westport, CT.
9. Ekasingh, B., K. Ngamsomsuke, R.A. Letcher, and J. Spate. 2005. A data mining approach to simulating farmers' crop choices for integrated water resources management. J. Environ. Mgt. 77:315-325.
10. Everaarts, A.P. 1999. Harvest date prediction for field vegetables. A review. Gartenbauwissenschaft. 64:20-25.
11. Fayyad, U., D. Haussler, and P. Stoloroz. 1996. KDD for science data analysis: Issues and examples, p.50-56. In: E. Simoudis, J. Han, and U. Fayyad (eds.). p. 50-56 Proc. Second Knowledge Discovery and Data Mining Conf. 2-7 Aug. 1996 Menlo Park, CA.
12. Frank, E., M. Hall, L. Trigg, G. Holmes, and I.H. Witten. 2004. Data mining in bioinformatics using Weka. Bioinformatics 20:2479-2481.
13. Gill, M.K., T. Asefa, M. Kemblowski, and M. McKee. 2006. Soil moisture prediction using support vector machines. J. Amer.Water Resour.Assoc. 42:1033-1046.
14. Henderson, C.E., W.D. Potter, R.W. McClendon, and G. Hogenboom. 2000. Predicting aflatoxin contamination in peanuts: A genetic algorithm/neural network approach. Appl. Intell. 12:183-192.
15. Higley, L.G., L.P. Pedigo, and K.R. Ostlie. 1986. DEGDAY: A program for calculating degree-days, and assumptions behind the degree-day approach. Environ. Entomol. 15:999-1016.
16. Jenni, S., D. Cloutier, G. Bourgeois, and K.A. Stewart. 1996. A heat unit model to predict growth and development of muskmelon to anthesis of perfect flowers. J. Amer. Soc. Hort. Sci. 2:274-280.
17. Jenni, S., K.A. Stewart, G. Bourgeois, and D. Cloutier. 1998. Predicting yield and time to maturity of muskmelons from weather and crop observations. J. Amer. Soc. Hort. Sci. 123:195-201.
18. Kays, S.J. 1985. The physiology of yield in the sweet potato, p. 79-132. In: J. Bouwkamp (ed.). Sweetpotato products: A natural resource for the tropics, pages. CRC Press, Boca Raton, FL.
19. Khoshgoftaar, T. and E.B. Allen. 2001. Controlling overfitting in classificationtree models of software quality. Empir. Softw. Eng. 6:59-79.
20. Kim, Y.S. 2008. Comparison of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size. Expert Syst. Appl. 34:1227-1234.
21. Levin, N. and J. Zahavi. 2005. Data mining for target marketing, p. 1261- 1304. In: O. Maimon and L. Rokach (eds.). Data mining and knowledge discovery handbook. Springer, New York.
22. Louisiana Agriclimatic Information. 2008. Louisiana agriclimatic information. 1 Mar. 2008. http://www.lsuagcenter.com/weather/index.asp.
23. Maenhout, S., B. De Baets, G. Haesaert, and E. Van Bockstaele. 2007. Support vector machine regression for the prediction of maize hybrid performance. Theor. Appl. Genet. 115:1003-1013.
24. Maimon, O. and L. Rokach. 2005. Introduction to knowledge discovery in databases, p. 1-20. In: O. Maimon and L. Rokach (eds.). Data mining and knowledge discovery handbook. Springer, New York.
25. Park, S.J., C.S. Hwang, and P.L.G. Vlek. 2005. Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions. Agr. Systems 85:59-81.
26. Perry, K.B. and T.C. Wehner. 1996. A heat unit accumulation method for predicting cucumber harvest date. Hort- Technology 6:27-30.
27. Perry, K.B., T.C. Wehner, and G.L. Johnson. 1986. Comparison of 14 methods to determine heat unit requirements for cucumber harvest. HortScience 21:419-423.
28. Perry, K.B., Y. Wu, D. Sanders, J.T. Garrett, D. Decoteau, R. Nagata, R. Dufault, K.D. Batal, D. Granberry, and W. Mclaurin. 1997. Heat units to predict tomato harvest in the southeast USA. Agr. For. Meteorol. 84:249-254.
29. Ravi, V. and P. Indira. 1999. Crop physiology of sweet potato. Hort. Rev. (Amer. Soc. Hort. Sci.) 23:277-338.
30. Schwartz, M.D., G.J. Carbone, G.L. Reighard, and W.R. Okie. 1997. A model to predict peach phenology and maturity using meteorological variables. Hort- Science 32:213-216.
31. Seem, J.E., N. Creamer, and D.W. Monks. 2003. Critical weed-free period for Beauregard sweetpotato (Ipomoea batatas). Weed Technol. 17:686- 695.
32. Snyder, R.L. 2005. Degree days. 18 Aug. 2008. http://atm.ucdavis.edu/ ;biomet/DegreeDays/DegDay.htm.
33. Statsoft. 2008. Statsoft electronic textbook. 18 Aug. 2008. http://www.stat soft.com/textbook/stathome.html.
34. Stenzel, N.M.C., C.S.V.J. Neves, C.J. Marur, M.B.S. Scholz, and J.C. Gomes. 2006. Maturation curves and degree-days accumulation for fruits of 'Folha murcha' orange trees. Scientia Agricola 63:219-225.
35. Stoddard, S.C. and B. Weir. 2002. Sweetpotato research trials 2002 research progress report. Univ. California Coop. Ext. 15 Aug. 2008. http://cemerced.ucdavis.edu/files/19101.pdf.
36. Togari, Y. 1950. A study of tuberous root formation in sweet potato. Bul. Natl. Agr. Expt. Sta. Tokyo 68:1-96.
37. Tronstad, R., J.C. Silvertooth, and S. Husman. 2003. Irrigation termination of cotton: An economic analysis of yield, quality, and market factors. J. Cotton Sci. 7:86-94.
38. Turpin, K.M., D.R. Lapen, E.G. Gregorich, G.C. Topp, M. Edwards, N.B. McLaughlin, W.E. Curnoe, and M.J.L. Robin. 2005. Using multivariate adaptive regression splines (MARS) to identify relationships between soil and corn (Zea mays L.) production properties. Can. J. Soil Sci. 85:625-636.
39. U.S. Department of Agriculture. 2004. The commercial storage of fruits, vegetables, and florist and nursery stocks. 18 Aug. 2008. http://www.ba.ars. usda. gov/hb66/contents.html.
40. U.S. Department of Agriculture. 2005. United States standards for grades of sweetpotatoes. 18 Aug. 2008. http://www.ams.usda.gov/AMSv1.0/getfile? dDocName=STELPRDC5050330.
41. Vapnik, V., S. Golowich, and A. Smola. 1997. Support vector method for function approximation, regression estimation, and signal processing, p. 281-287.In: M. Mozer, M. Jordan, and T. Petsche (eds.). Advances in neural information processing systems 9. 2-5 Dec. 1996. Denver, CO.
42. Viator, R.P., R.C. Nuti, K.L. Edmisten, and R. Wells. 2005. Predicting cotton boll maturation period during degree days and other climatic factors. Agron. J. 97: 494-499.
43. Witten, I.H. and E. Frank. 2005. Data mining: Practical machine learning and techniques. Morgan Kaufman, San Francisco.
44. Wolfe, D.W., L.D. Albright, and J. Wyland. 1989. Modeling row cover effects on microclimate and yield: I. Growth response of tomato and cucumber. J. Amer. Soc. Hort. Sci. 114:562-568.
45. Wurr, D.C.E. and J.R. Fellows. 1984. The growth of three crisp lettuce varieties from different sowing dates. J. Agr. Sci. Cambridge 102:733-745.
46. Yang, C.C., S.O. Prasher, P. Enright, C. Madramootoo, M. Burgess, P.K. Goel, and I. Callum. 2003. Application of decision tree technology for image classification using remote sensing data. Agr. Systems 76:1101-1117.
47. Zalom, F.G., P.B. Goodell, L.T. Wilson, W.W. Barnett, and W.J. Bentley. 1983. Degree-days: The calculation and use of heat units in pest management. Univ. California Div. Agr. Natural Resources Lflt. 21373.
48. Zhang, P.G. 2005. Neural networks, p. 487-516. In: O. Maimon and L. Rokach (eds.). Data mining and knowledge discovery handbook. Springer, New York.