Section 12 References

  1. Barreiro-Ures D, Francisco-Fernández M, Cao R, Fraguela BB, Doallo R, González-Andújar JL, Reyes M (2019) Analysis of interval-grouped data in weed science: The binnednp Rcpp package. Ecol Evol 9:10903–10915
  2. Bradford KJ (2002) Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Sci 50:248–260
  3. Brown, RF, DG Mayer (1988a) Representing Cumulative Germination. 1. A Critical Analysis of Single-value Germination Indices. Annals of Botany 61:117–125
  4. Brown, RF, DG Mayer (1988b) Representing Cumulative Germination.: 2. The Use of the Weibull Function and Other Empirically Derived Curves. Annals of Botany 61:127–138
  5. Catara, S., Cristaudo, A., Gualtieri, A., Galesi, R., Impelluso, C., Onofri, A. (2016). Threshold temperatures for seed germination in nine species of Verbascum (Scrophulariaceae). Seed Science Research 26, 30–46.
  6. Davison, A.C., Hinkley, D.V. (1997). Bootstrap methods and their application. Cambridge University Press, UK.
  7. Dutang, C, Goulet V and M. Pigeon (2008). actuar: An R Package for Actuarial Science. Journal of Statistical Software, vol. 25, no. 7, 1-37.
  8. Fay, MP, PA Shaw (2010) Exact and Asymptotic Weighted Logrank Tests for Interval Censored Data: The interval R Package. Journal of Statistical Software 36:1–34
  9. Gresta, F, G Avola, A Onofri, U Anastasi, A Cristaudo (2011) When Does Hard Coat Impose Dormancy in Legume Seeds? Lotus and Scorpiurus Case Study. Crop Science 51:1739–1747
  10. Keshtkar E, Kudsk P, Mesgaran MB (2021) Perspective: Common errors in dose–response analysis and how to avoid them. Pest Manag Sci 77:2599–2608
  11. Mesgaran, M.B., Mashhadi, H.R., Alizadeh, H., Hunt, J., Young, K.R., Cousens, R.D., 2013. Importance of distribution function selection for hydrothermal time models of seed germination. Weed Research 53, 89–101. https://doi.org/10.1111/wre.12008
  12. Michael P. Fay, Pamela A. Shaw (2010). Exact and Asymptotic Weighted Logrank Tests for Interval Censored Data: The interval R Package. Journal of Statistical Software, 36(2), 1-34. URL https://www.jstatsoft.org/v36/i02/.
  13. Onofri, A, F Gresta, F Tei (2010) A new method for the analysis of germination and emergence data of weed species. Weed Research 50:187–198
  14. Onofri, A, MB Mesgaran, F Tei, RD Cousens (2011) The cure model: an improved way to describe seed germination? Weed Research 51:516–524
  15. Onofri, A, MB Mesgaran, P Neve, RD Cousens (2014) Experimental design and parameter estimation for threshold models in seed germination. Weed Research 54:425–435
  16. Onofri, A., Benincasa, P., Mesgaran, M.B., Ritz, C. (2018). Hydrothermal-time-to-event models for seed germination. European Journal of Agronomy 101, 129–139.
  17. Onofri, A., Mesgaran, M., & Ritz, C. (2022). A unified framework for the analysis of germination, emergence, and other time-to-event data in weed science. Weed Science, 1-13. doi:10.1017/wsc.2022.8
  18. Onofri, Andrea, Hans Peter Piepho, and Marcin Kozak (2019). Analysing Censored Data in Agricultural Research: A Review with Examples and Software Tips. Annals of Applied Biology, 174, 3-13.
  19. Onofri, Andrea, Paolo Benincasa, M B Mesgaran, and Christian Ritz (2018). Hydrothermal-Time-to-Event Models for Seed Germination. European Journal of Agronomy 101: 129–39.
  20. Pace, R., Benincasa, P., Ghanem, M.E., Quinet, M., Lutts, S. (2012). Germination of untreated and primed seeds in rapeseed (brassica napus var oleifera del.) under salinity and low matric potential. Experimental Agriculture 48, 238–251.
  21. Ritz C, Jensen SM, Gerhard D, Streibig JC (2019). Dose-response analysis using R CRC Press. USA
  22. Ritz, C., Baty, F., Streibig, J. C., Gerhard, D. (2015). Dose-Response Analysis Using R PLOS ONE, 10(12)
  23. Therneau T (2021). A Package for Survival Analysis in R. R package version 3.2-11, <URL: https://CRAN.R-project.org/package=survival>.
  24. Wickham, H, G Grolemund (2016) R for data science: import, tidy, transform, visualize, and model data. First edition. Sebastopol, CA: O’Reilly. 492 pp.
  25. Yu, B., Peng, Y. (2008). Mixture cure models for multivariate survival data. Computational Statistics and Data Analysis 52, 1524–1532.
  26. Zeileis, A., Köll, S., Graham, N. (2020). Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R. J. Stat. Soft. 95. https://doi.org/10.18637/jss.v095.i01
  27. Alvarado, V., Bradford, K.J., 2002. A hydrothermal time model explains the cardinal temperatures for seed germination. Plant, Cell and Environment 25, 1061–1069.
  28. Baty, F., Ritz, C., Charles, S., Brutsche, M., Flandrois, J. P., Delignette-Muller, M.-L., 2014. A toolbox for nonlinear regression in R: the package nlstools. Journal of Statistical Software, 65, 5, 1-21.
  29. Bradford, K.J., 2002. Applications of hydrothermal time to quantifying and modelling seed germination and dormancy. Weed Science 50, 248–260.
  30. Catara, S., Cristaudo, A., Gualtieri, A., Galesi, R., Impelluso, C., Onofri, A., 2016. Threshold temperatures for seed germination in nine species of Verbascum (Scrophulariaceae). Seed Science Research 26, 30–46.
  31. Garcia-Huidobro, J., Monteith, J.L., Squire, R., 1982. Time, temperature and germination of pearl millet (Pennisetum typhoides S & H.). 1. Constant temperatures. Journal of Experimental Botany 33, 288–296.
  32. Kropff, M.J., van Laar, H.H. 1993. Modelling crop-weed interactions. CAB International, Books.
  33. Masin, R., Onofri, A., Gasparini, V., Zanin, G., 2017. Can alternating temperatures be used to estimate base temperature for seed germination? Weed Research 57, 390–398.
  34. Onofri, A., Benincasa, P., Mesgaran, M.B., Ritz, C., 2018. Hydrothermal-time-to-event models for seed germination. European Journal of Agronomy 101, 129–139.
  35. Ritz, C., Jensen, S. M., Gerhard, D., Streibig, J. C., 2019. Dose-Response Analysis Using R. CRC Press
  36. Rowse, H.R., Finch-Savage, W.E., 2003. Hydrothermal threshold models can describe the germination response of carrot (Daucus carota) and onion (Allium cepa) seed populations across both sub- and supra-optimal temperatures. New Phytologist 158, 101–108.
  37. Zeileis, A., 2006. Object-oriented computation of sandwich estimators. Journal of Statistical Software, 16, 9, 1-16.