Literature DB >> 20033431

Evaluation of different methods for determining growing degree-day thresholds in apricot cultivars.

Mirjana Ruml1, Ana Vuković, Dragan Milatović.   

Abstract

The aim of this study was to examine different methods for determining growing degree-day (GDD) threshold temperatures for two phenological stages (full bloom and harvest) and select the optimal thresholds for a greater number of apricot (Prunus armeniaca L.) cultivars grown in the Belgrade region. A 10-year data series were used to conduct the study. Several commonly used methods to determine the threshold temperatures from field observation were evaluated: (1) the least standard deviation in GDD; (2) the least standard deviation in days; (3) the least coefficient of variation in GDD; (4) regression coefficient; (5) the least standard deviation in days with a mean temperature above the threshold; (6) the least coefficient of variation in days with a mean temperature above the threshold; and (7) the smallest root mean square error between the observed and predicted number of days. In addition, two methods for calculating daily GDD, and two methods for calculating daily mean air temperatures were tested to emphasize the differences that can arise by different interpretations of basic GDD equation. The best agreement with observations was attained by method (7). The lower threshold temperature obtained by this method differed among cultivars from -5.6 to -1.7 degrees C for full bloom, and from -0.5 to 6.6 degrees C for harvest. However, the "Null" method (lower threshold set to 0 degrees C) and "Fixed Value" method (lower threshold set to -2 degrees C for full bloom and to 3 degrees C for harvest) gave very good results. The limitations of the widely used method (1) and methods (5) and (6), which generally performed worst, are discussed in the paper.

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Year:  2009        PMID: 20033431     DOI: 10.1007/s00484-009-0292-6

Source DB:  PubMed          Journal:  Int J Biometeorol        ISSN: 0020-7128            Impact factor:   3.787


  1 in total

1.  The role of temperature in the onset of the Olea europaea L. pollen season in southwestern Spain.

Authors:  C Galán; H García-Mozo; P Cariñanos; P Alcázar; E Domínguez-Vilches
Journal:  Int J Biometeorol       Date:  2001-02       Impact factor: 3.787

  1 in total
  9 in total

1.  Forecasting ragweed pollen characteristics with nonparametric regression methods over the most polluted areas in Europe.

Authors:  László Makra; István Matyasovszky; Michel Thibaudon; Maira Bonini
Journal:  Int J Biometeorol       Date:  2010-07-13       Impact factor: 3.787

2.  Growing degree-days for the 'Niagara Rosada' grapevine pruned in different seasons.

Authors:  Fábio Vale Scarpare; João Alexio Scarpare Filho; Alessandro Rodrigues; Klaus Reichardt; Luiz Roberto Angelocci
Journal:  Int J Biometeorol       Date:  2011-08-25       Impact factor: 3.787

3.  Heat accumulation period in the Mediterranean region: phenological response of the olive in different climate areas (Spain, Italy and Tunisia).

Authors:  Fátima Aguilera; Luis Ruiz; Marco Fornaciari; Bruno Romano; Carmen Galán; Jose Oteros; Ali Ben Dhiab; Monji Msallem; Fabio Orlandi
Journal:  Int J Biometeorol       Date:  2013-04-17       Impact factor: 3.787

4.  Predicting apricot phenology using meteorological data.

Authors:  Mirjana Ruml; Dragan Milatović; Todor Vulić; Ana Vuković
Journal:  Int J Biometeorol       Date:  2010-11-21       Impact factor: 3.787

5.  A new nonlinear method for calculating growing degree days.

Authors:  Guanglin Zhou; Quanjiu Wang
Journal:  Sci Rep       Date:  2018-07-05       Impact factor: 4.379

6.  Uncovering Olive Biodiversity through Analysis of Floral and Fruiting Biology and Assessment of Genetic Diversity of 120 Italian Cultivars with Minor or Marginal Diffusion.

Authors:  Luca Lombardo; Gianni Fila; Nicola Lombardo; Chiara Epifani; Donald H Duffy; Gianluca Godino; Amelia Salimonti; Samanta Zelasco
Journal:  Biology (Basel)       Date:  2019-08-28

7.  Analysis of Copernicus' ERA5 Climate Reanalysis Data as a Replacement for Weather Station Temperature Measurements in Machine Learning Models for Olive Phenology Phase Prediction.

Authors:  Noelia Oses; Izar Azpiroz; Susanna Marchi; Diego Guidotti; Marco Quartulli; Igor G Olaizola
Journal:  Sensors (Basel)       Date:  2020-11-09       Impact factor: 3.576

8.  Identification of superior late-blooming apricot (Prunus armeniaca L.) genotypes among seedling-originated trees.

Authors:  Zeinab Mashhadi; Ali Khadivi
Journal:  Food Sci Nutr       Date:  2022-01-18       Impact factor: 2.863

9.  Simulation Models of Leaf Area Index and Yield for Cotton Grown with Different Soil Conditioners.

Authors:  Lijun Su; Quanjiu Wang; Chunxia Wang; Yuyang Shan
Journal:  PLoS One       Date:  2015-11-04       Impact factor: 3.240

  9 in total

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