Modelling the effects of environmental conditions on apparent photosynthesis of Stipa bromoides by machine learning tools

Ecological Modelling

Volumn 129, Issue 2-3, 2000, Pages 245-257

  Dalaka, A ^a   Kompare, B ^b   Robnik Sikonja M ^b   Sgardelis, S P ^a  

^a Laboratory of Animal Physiology   (Greece)

^b UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Automatic model construction; Machine learning; P I curves; Regression trees

Indexed keywords

ECOLOGICAL MODELING; ENVIRONMENTAL CONDITIONS; IRRADIANCE; PHOTOSYNTHESIS; TEMPERATURE EFFECT;

GREECE;

STIPA BROMOIDES;

EID: 0034732853     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-3800(00)00237-4     Document Type: Article

References (28)

1. Boardman N.K. Comparative photosynthesis of sun and shade plants. Ann. Rev. PI. Phys. 28:1977;355-377.
2. Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and regression trees. 1984;Wadsworth, Belmont, CA.
3. 2. Ecol. Model. 73:1994;63-80.
4. Diamantopoulos J., Pantis J., Sgardelis S., Iatrou G., Pirintsos S., Papatheodorou E., Dalaka A., Stamou G., Cammeraat L.H., Kosmas C. The Petralona and Hortiatis field sites, Thessaloniki, Greece. Brandt C.J., Thorns J.B. Mediterranean Desertification and Land Use. 1996;229-245 Wiley, Chichester.
5. Duarte P. A mechanistic model of the effects of light and temperature on algal primary productivity. Ecol. Model. 82:1995;151-160.
6. Džeroski S., Grbović J., Walley W., Kompare B. Using machine learning techniques in the construction of models. II.: data analysis with rule induction. Ecol. Model. 95:1997;95-111.
7. Eilers P.H.C., Peeters J.C.H. Dynamic behavior of a model for photosynthesis and photoinhibition. Ecol. Model. 69:1993;113-133.
8. 3 species. Planta. 149:1980;78-90.
9. Frenette J.J., Demers S., Legendre L., Dodson J. Lack of agreement among models for estimating the photosynthetic parameters. Limnol. Oceanogr. 38:1993;679-687.
10. Gross L.J., Kirschbaum M.U.F., Pearchy R.W. A dynamic model of photosynthesis in varying light taking account of stomatal conductance, C3-cycle intermediates, photorespiration and Rubisco activation. Plant Cell. Environ. 14:1991;881-893.
11. Hahn B.D. Photosynthesis and Photorespiration: modelling the essentials. J. Theor. Biol. 151:1991;123-139.
12. Harley P.C., Baldocchi D.D. Scaling corbon dioxide and water vapour exchange from leaf to canopy in a deciduous forest. I.: leaf model parameterization. Plant Cell. Environ. 18:1995;1146-1156.
13. Harrison W.G., Platt T., Lewis M.R. The utility of light-saturation models for estimating marine primary productivity in the field: a comparison with conventional 'simulated' in situ methods. Can. J. Fish Aquat. Sci. 42:1985;864-872.
14. Karalič A. Employing linear regression in regression tree leaves. Neumann B. Proceedings of the Tenth European Conference on Artificial Intelligence. 1992;410-411 Wiley, Vienna.
15. Kompare, B., 1995. The use of artificial intelligence in ecological modelling. PhD thesis, Royal Danish School of Pharmacy, Department of General Chemistry, Environmental Chemistry, Copenhagen and University of Ljubljana, Faculty of Civil Engineering, Institute of Sanitary Engineering, Ljubljana.
16. Kompare B., Bratko I., Steinman F., Dzeroski S. Using machine learning techniques in the construction of models. Ecol. Model. 75/76:1994;617-628.
17. Li M., Vitanyi P. An Introduction to Kolmogorov Complexity and Its Applications. 1993;Springer-Verlag, New York.
18. Nikolov N.T., Massman W.J., Schoettle A.W. Coupling biochemical and biophysical processes at the leaf level - an equilibrium photosynthesis model for leaves of C-3 plants. Ecol. Model. 80:1995;205-235.
19. Platt T., Gallegos C.L., Harrison W.G. Photoinhibition of photosynthesis in natural assemblages of marine phytoplankton. J. Mar. Res. 38:1980;687-701.
20. Quinlan, J.R., 1993. Combining instance-based and model- based learning. In: Proceedings of the Tenth International conference on Machine learning. Morgan Kaufman Publications, San Mateo, California, pp. 236-243.
21. Robnik-Šikonja, M., 1997. CORE-a system that predicts continuous variables. Proceedings of Electrotehnical and Computer Science Conference ERK'97, Portoroz, Slovenia, pp. B145-B148.
22. Robnik-Šikonja M., Kononenko I. An adaptation of Relief for attribute estimation in regression. Fisher D. Machine Learning: Proceedings of the 14th International conference on Machine learning. 1997;296-304 Morgan Kaufman Publications, San Mateo, CA.
23. Robnik-Šikonja M., Kononenko I. Pruning regression trees with MDL. Prade H. Proceedings of 13th European Conference on Artificial Intelligence, ECAI'98. 1998;455-459 Wiley, New York.
24. Sims D.A., Pearcy R.W. Scaling sun and shade photosynthetic acclimation of Alocasia macrorrhiza to whole-plant performance - I.: carbon balance and allocation at different daily photon flux densities. Plant Cell. Environ. 17:1994;881-887.
25. SPSS for Windows Release 8.0.0, Standard version. SPSS Inc, Chicago, IL.
26. Steele J.H. Environmental control of photosynthesis in the sea. Limnol. Oceanogr. 7:1962;135-150.
27. Teskey R.O., Gholz H.L., Cropper W.P. Jr. Influence of climate and fertilization on net photosynthesis of mature slash pine. Tree Physiol. 14:1994;1215-1227.
28. Tutin T.G., Heywood V.H., Burges N.A., Moore D.M., Valentine D.H., Walters S.M., Webb O.A. Flora Europeae, Volume 5, Alismotaceae to Orchidaceae (Monocotyledones), CXCIII Gramineae. 1996;Cambridge University Press, Cambridge.