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Computing statistical indices for hydrothermal times using weed emergence data

  • R. CAO (a1), M. FRANCISCO-FERNÁNDEZ (a1), A. ANAND (a1), F. BASTIDA (a2) and J. L. GONZÁLEZ-ANDÚJAR (a3)...

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.

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*To whom all correspondence should be addressed. Email: mariofr@udc.es

References

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Bradford, K. J. (2002). Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Science 50, 248260.
Cao, R. (1993). Bootstrapping the mean integrated squared error. Journal of Multivariate Analysis 45, 137160.
Cao, R., Cuevas, A. & Fraiman, R. (1995). Minimum distance density-based estimation. Computational Statistics and Data Analysis 20, 611631.
Cao, R., Janssen, P. & Veraverbeke, N. (2001). Relative density estimation and local bandwidth selection with censored data. Computational Statistics and Data Analysis 36, 497510.
Colbach, N., Dürr, C., Roger-Estrade, J. & Caneill, J. (2005). How to model the effects of farming practices on weed emergence. Weed Research 45, 217.
Dorado, J., Sousa, E., Calha, I. M., González-Andújar, J. L. & Fernández-Quintanilla, C. (2009). Predicting weed emergence in maize crops under two contrasting climatic conditions. Weed Research 49, 251260.
Fernández-Quintanilla, C., Navarrete, L., González-Andújar, J. L., Fernández, A. & Sánchez, M. J. (1986). Seedling recruitment and age-specific survivorship and reproduction in populations of Avena sterilis ssp. ludoviciana. Journal of Applied Ecology 23, 945955.
Forcella, F., Benech-Arnold, R. L., Sánchez, R. & Ghersa, C. M. (2000). Modeling seedling emergence. Field Crops Research 67, 123139.
González-Manteiga, W., Cao, R. & Marron, J. S. (1996). Bootstrap selection of the smoothing parameter in nonparametric hazard rate estimation. Journal of the American Statistical Association 91, 11301140.
Grundy, A. C. (2003). Predicting weed emergence: a review of approaches and future challenges. Weed Research 43, 111.
Haj Seyed Hadi, M. R. & González-Andújar, J. L. (2009). Comparison of fitting weed seedling emergence models with nonlinear regression and genetic algorithm. Computers & Electronics in Agriculture 65, 1925.
Hunter, E. A., Glasbey, C. A. & Naylor, R. E. L. (1984). The analysis of data from germination tests. Journal of Agricultural Science, Cambridge 102, 207213.
Izquierdo, J., González-Andújar, J. L., Bastida, F., Lezaun, J. A. & Sánchez del Arco, M. J. (2009). A thermal time model to predict corn poppy (Papaver rhoeas) emergence in cereal fields. Weed Science 57, 660664.
Jones, M. C. & Sheather, S. J. (1991). Using nonstochastic terms to advantage in kernel-based estimation of integrated squared density derivatives. Statistics and Probability Letters 11, 511514.
Leblanc, M. L., Cloutier, D. C., Stewart, K. A. & Hamel, C. (2003). The use of thermal time to model common lambsquarters (Chenopodium album) seedling emergence in corn. Weed Science 51, 718724.
Leguizamón, E. S., Fernández-Quintanilla, C., Barroso, J. & González-Andújar, J. L. (2005). Using thermal and hydrothermal time to model seedling emergence of Avena sterilis ssp. ludoviciana in Spain. Weed Research 45, 149156.
Lesaffre, E., Komárek, A. & Declerck, D. (2005). An overview of methods for interval-censored data with an emphasis on applications in dentistry. Statistical Methods in Medical Research 14, 539552.
McGiffen, M., Spokas, K., Forcella, F., Archer, D., Poppe, S. & Figueroa, R. (2008). Emergence prediction of common groundsel (Senecio vulgaris). Weed Science 56, 5865.
Naylor, R. E. L. (1981). An evaluation of various germination indices for predicting differences in seed vigour in Italian ryegrass. Seed Science and Technology 9, 593600.
Onofri, A., Gresta, F. & Tei, F. (2010). A new method for the analysis of germination and emergence data of weed species. Weed Research 50, 187198.
Peto, R. (1973). Experimental survival curves for interval-censored data. Journal of the Royal Statistical Society, Series C: Applied Statistics 22, 8691.
R Development Core Team (2008). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
Ritz, C., Pipper, C., Yndgaard, F., Fredlund, K. & Steinrücken, G. (2010). Modelling flowering of plants using time-to-event methods. European Journal of Agronomy 32, 155161.
Royo-Esnal, A., Torra, J., Conesa, J. A., Forcella, F. & Recasens, J. (2010). Modeling the emergence of three arable bedstraw (Galium) species. Weed Science 58, 1015.
Ruppert, D. (1987). What is kurtosis? An influence function approach. American Statistician 41, 15.
Schutte, B. J., Regnier, E. E., Harrison, S. K., Schmoll, J. T., Spokas, K. & Forcella, F. (2008). A hydrothermal seedling emergence model for giant ragweed (Ambrosia trifida). Weed Science 56, 555560.
Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Monographs in Statistics and Applied Probability. London: Chapman and Hall.
Spokas, K. & Forcella, F. (2009). Software tools for weed seed germination modeling. Weed Science 57, 216227.
Sun, J. (2006). The Statistical Analysis of Interval-censored Failure Time Data. Statistics for Biology and Health. New York: Springer.
Turnbull, B. (1976). The empirical distribution function with arbitrarily grouped, censored and truncated data. Journal of the Royal Statistical Society, Series B: Methodology 38, 290295.
Wand, M. P. & Jones, M. C. (1995). Kernel Smoothing. CRC Monographs on Statistics and Applied Probability. London: Chapman and Hall.

Computing statistical indices for hydrothermal times using weed emergence data

  • R. CAO (a1), M. FRANCISCO-FERNÁNDEZ (a1), A. ANAND (a1), F. BASTIDA (a2) and J. L. GONZÁLEZ-ANDÚJAR (a3)...

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