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

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.

THERMAL TIME FOR REPRODUCTIVE PHENOLOGICAL STAGES OF PINEAPPLE GUAVA (Acca sellowiana (O. Berg) Burret)

“…Se estimaron las temperaturas base (Tb) de cuatro etapas fenológicas de la feijoa a saber: desde la aparición de botones florales a la antesis, de la antesis al cuaje del fruto, del cuaje del fruto a la cosecha y de la aparición de botones florales a la cosecha, para lo cual se realizó la sumatoria de las temperaturas registradas en cada localidad para cada una de las diferentes etapas fenológicas y luego se minimizó el Coeficiente de Variación (CV) de las cantidades de calor calculados (GDC) a partir de un rango de temperaturas entre 0 y 12 ºC para cada una de las diferentes etapas consideradas en este estudio. La temperatura óptima (Tb) para cada etapa, corresponde con la temperatura a la cual se obtiene el CV más pequeño utilizando un modelo de regresión de segundo grado (Yang et al, 1995;Salazar, 2006;Pulido et al, 2008;Ruml et al, 2010;Salazar-Gutierrez et al, 2013). La herramienta Solver para Excel®, se utiliza para la estimación de la Tb; Solver es un procedimiento no lineal iterativo que realiza la primera estimación con los valores iniciales asignados a los parámetros de la ecuación y luego aumenta o disminuye el valor de los parámetros hasta que encuentra el valor mínimo de la suma de los cuadrados de las desviaciones.…”

Spatial-Temporal Simulation of LAI on Basis of Rainfall and Growing Degree Days

“…Particularly in the areas of crop phenology and development, the concept of heat units, measured in GDD, has vastly improved description and prediction of phenological events compared to other approaches such as time of year or number of days e.g., [30,31]. The heuristic indicator of GDD is calculated by summing up the positive daily differences between mean temperature and a baseline temperature [32,33]. GDD is a measurement unit that combines temperature and time such that the development duration of an organism's life cycle, or any stage or portion of the life cycle, decreases as the temperature increases [34] and describe the timing of biological processes [29,35].…”

The Influence of Chilling and Heat Accumulation on Bloom Timing, Bloom Length and Crop Yield in Almonds (Prunus dulcis (Mill.))

“…Ruml (1999) acknowledges that base temperatures vary across environments and also across modeling methods (Ruml, Vukovic & Milatovic, 2010;Snyder, Spano, Cesaraccio & Duce, 1999).…”