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.
In marketing or medical research, especially in psychiatrics, it is very often necessary to define preference of examinees against defined object (persons, products, or phenomena). A question that is related to the object of preference is defined as like degree of like (positive preference) or as like degree of dislike (negative preference), where estimation is done as in scholar system (nominal or ordinal characteristics), with marks 1 through 5. Rank of objects achieved is very often expressed as average, which is not a good measure for realistic object ranking. In this paper, a coefficient of preference is presented as an effort to rank object more efficiently than average or other methods for ranking, especially in the meaning of preference. Preference is essential for humankind for decision making. One of the measures is Coefficients of Preference in Ranking (CPR) as shown.
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