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Computing statistical indices for hydrothermal times using weed emergence data

Published online by Cambridge University Press:  01 April 2011

R. CAO
Affiliation:
Faculty of Computer Science, Department of Mathematics, Campus de Eviña, s/n, A Coruña 15071, Spain
M. FRANCISCO-FERNÁNDEZ
Affiliation:
Faculty of Computer Science, Department of Mathematics, Campus de Eviña, s/n, A Coruña 15071, Spain
A. ANAND
Affiliation:
Faculty of Computer Science, Department of Mathematics, Campus de Eviña, s/n, A Coruña 15071, Spain
F. BASTIDA
Affiliation:
Polytechnic School, Department of Agroforestry Science, University of Huelva, Campus Universitario de La Rábida, Carretera de Palos de la Frontera s/n 21071 La Rábida, Palos de la Frontera (Huelva), Spain
J. L. GONZÁLEZ-ANDÚJAR
Affiliation:
CSIC, Institute for Sustainable Agriculture, Córdoba 4084, Spain
Corresponding
E-mail address:

Summary

Hydrothermal time (HTT) is a valuable environmental synthesis to predict weed emergence. However, weed scientists face practical problems in determining the best soil depth at which to calculate it. Two different types of measures are proposed for this: moment-based indices and probability density-based indices. Due to the monitoring process, it is not possible to observe the exact emergence time of every seedling; therefore, emergence times are not observed individually, seedling by seedling, but in an aggregated way. To address these facts, some new methods to estimate the proposed indices are derived, using grouped data estimators and kernel density estimators. The proposed methods have been exemplified with an emergence data set of Bromus diandrus. The results indicate that hydrothermal timing at 50 mm is more useful than that at 10 mm.

Type
Crops and Soils
Copyright
Copyright © Cambridge University Press 2011

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