International Journal of Biometeorology

, Volume 42, Issue 4, pp 177–182

Determining degree-day thresholds from field observations

  • R. L. Snyder
  • Donatella Spano
  • Carla Cesaraccio
  • Pierpaolo Duce
METHODS IN PHENOLOGY

DOI: 10.1007/s004840050102

Cite this article as:
Snyder, R., Spano, D., Cesaraccio, C. et al. Int J Biometeorol (1999) 42: 177. doi:10.1007/s004840050102

Abstract

 This paper compares several methods for determining degree-day (°D) threshold temperatures from field observations. Three of the methods use the mean developmental period temperature and simple equations to estimate: (1) the smallest standard deviation in °D, (2) the least standard deviation in days, and (3) a linear regression intercept. Two additional methods use iterations of cumulative °D and threshold temperatures to determine the smallest root mean square error (RMSE). One of the iteration methods uses a linear model and the other uses a single triangle °D calculation method. The method giving the best results was verified by comparing observed and predicted phenological periods using 7 years of kiwifruit data and 10 years of cherry tree data. In general, the iteration method using the single triangle method to calculate °D provided threshold temperatures with the smallest RMSE values. However, the iteration method using a linear °D model also worked well. Simply using a threshold of zero gave predictions that were nearly as good as those obtained using the other two methods. The smallest standard deviation in °D performed the worst. The least standard deviation in days and the regression methods did well sometimes; however, the threshold temperatures were sometimes negative, which does not support the idea that development rates are related to heat units.

Key words Growing degree-daysKiwifruitCherry treesPhenologyThreshold temperature

Copyright information

© International Society of Biometeorology 1999

Authors and Affiliations

  • R. L. Snyder
    • 1
  • Donatella Spano
    • 2
  • Carla Cesaraccio
    • 2
  • Pierpaolo Duce
    • 3
  1. 1.Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USAUS
  2. 2.Dipartimento di Economia e Sistemi Arborei, University of Sassari, Sassari, ItalyIT
  3. 3.Istituto per il Monitoraggio degli Agroecosistemi, CNR, Sassari, ItalyIT