Statistical modeling and forecasting of fruit crop phenology under climate change

Phenology, the study of the association between biological development stages and variations in climate, has greatly increased in importance because of concerns arising from climate change. This paper presents a general stochastic approach to the modeling of the relationship between phenological eve...

Full description

Saved in:
Bibliographic Details
Published in:Environmetrics (London, Ont.) Ont.), 2014-12, Vol.25 (8), p.621-629
Main Authors: Cai, S., Zidek, James V., Newlands, Nathaniel K., Neilsen, Denise
Format: Article
Language:English
Subjects:
bloom date
Climate
Climate change
Crops
Fruits
global climate model
Greenhouse effect
Greenhouse gases
growing degree day
Mathematical models
phenological events
Phenology
prediction
predictive distribution
time-dependent covariates
time-to-event analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Phenology, the study of the association between biological development stages and variations in climate, has greatly increased in importance because of concerns arising from climate change. This paper presents a general stochastic approach to the modeling of the relationship between phenological events and climate variables, and gives a prediction method based on this approach to provide full predictive distributions for future events. The proposed methods are then applied to the modeling and prediction of the bloom dates of six high‐valued fruit crops. In particular, we use our approach to explore how the bloom dates are related to the accumulation of growing degree days, to provide a sensible estimate of an important parameter Tbase in phenological study, and to assess the prediction of bloom dates with a leave‐one‐out procedure. Most importantly, the impact of future climate change on bloom dates is studied with temperature outputs from well‐established coupled global climate models under a high greenhouse gases scenario. Copyright © 2014 John Wiley & Sons, Ltd.
ISSN:1180-4009
1099-095X
DOI:10.1002/env.2304