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意大利油橄榄(L. var)生长的物候模型。

A Phenological Model for Olive ( L. var ) Growing in Italy.

作者信息

Di Paola Arianna, Chiriacò Maria Vincenza, Di Paola Francesco, Nieddu Giovanni

机构信息

Institute for BioEconomy, National Research Council of Italy (IBE-CNR), 00100 Rome, Italy.

Impacts on Agriculture, Forests and Ecosystem Services Division, Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), 01100 Viterbo, Italy.

出版信息

Plants (Basel). 2021 May 31;10(6):1115. doi: 10.3390/plants10061115.

DOI:10.3390/plants10061115
PMID:34073124
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8230019/
Abstract

The calibration of a reliable phenological model for olive grown in areas characterized by great environmental heterogeneity, like Italy, where many varieties exist, is challenging and often suffers from a lack of observations, especially on budbreak. In this study, we used a database encompassing many phenological events from different olive varieties, years, and sites scattered all over Italy to identify the phases in which site-enlarged developmental rates can be well regressed against air temperature (Developmental Rate function, ) by testing both linear and nonlinear functions. A K-fold cross-validation (KfCV) was carried out to evaluate the ability of functions to predict phenological development. The cross-validation showed that the phases ranging from budbreak (BBCH 01 and 07) to flowering (BBCH 61 and 65) and from the beginning of flowering (BBCH 51) to flowering can be simulated with high accuracy ( = 0.93-0.96; RMSE = 3.9-6.6 days) with no appreciable difference among linear and nonlinear functions. Thus, the resulting represent a simple yet reliable tool for regional phenological simulations for these phases in Italy, paving the way for a reverse modeling approach aimed at reconstructing the budbreak dates. By contrast, and despite a large number of phases explored, no appreciable results were obtained on other phases, suggesting possible interplays of different drivers that need to be further investigated.

摘要

在像意大利这样环境异质性大且存在许多品种的地区,为橄榄建立可靠的物候模型并进行校准具有挑战性,而且常常缺乏观测数据,尤其是关于芽萌动的数据。在本研究中,我们使用了一个包含来自意大利各地不同橄榄品种、年份和地点的许多物候事件的数据库,通过测试线性和非线性函数,来确定哪些阶段的地点扩大发育速率可以很好地与气温进行回归分析(发育速率函数)。进行了K折交叉验证(KfCV)以评估函数预测物候发育的能力。交叉验证表明,从芽萌动(BBCH 01和07)到开花(BBCH 61和65)以及从开花开始(BBCH 51)到开花的阶段可以高精度模拟(= 0.93 - 0.96;RMSE = 3.9 - 6.6天),线性和非线性函数之间没有明显差异。因此,所得函数代表了一种简单而可靠的工具,可用于意大利这些阶段的区域物候模拟,为旨在重建芽萌动日期的反向建模方法铺平了道路。相比之下,尽管探索了大量阶段,但在其他阶段未获得明显结果,这表明需要进一步研究不同驱动因素之间可能的相互作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc0/8230019/c85bc73719b3/plants-10-01115-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc0/8230019/9c74461fccd9/plants-10-01115-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc0/8230019/274e329923c6/plants-10-01115-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc0/8230019/81fcdc2866bc/plants-10-01115-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc0/8230019/1f05b4a257c5/plants-10-01115-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc0/8230019/c85bc73719b3/plants-10-01115-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc0/8230019/9c74461fccd9/plants-10-01115-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc0/8230019/274e329923c6/plants-10-01115-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc0/8230019/81fcdc2866bc/plants-10-01115-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc0/8230019/1f05b4a257c5/plants-10-01115-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc0/8230019/c85bc73719b3/plants-10-01115-g005.jpg

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Predicting the start, peak and end of the Betula pollen season in Bavaria, Germany.预测德国巴伐利亚州桦树花粉季节的开始、高峰和结束。
Sci Total Environ. 2019 Nov 10;690:1299-1309. doi: 10.1016/j.scitotenv.2019.06.485. Epub 2019 Jun 30.
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Temperature and photoperiod drive spring phenology across all species in a temperate forest community.
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Is olive crop modelling ready to assess the impacts of global change?橄榄作物建模是否已准备好评估全球变化的影响?
Front Plant Sci. 2023 Nov 27;14:1249793. doi: 10.3389/fpls.2023.1249793. eCollection 2023.
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