Abstract:

To quantitatively assess the impact of meteorological conditions on the quality of sand pears, the key meteorological factors affecting the quality of sand pears were screen out by using the methods of correlation analysis, multiple stepwise regression analysis and weighted calculation based on the quality data of sand pears from 2018 to 2024 and the meteorological observation data of the same period. The climate quality evaluation models and fruit climate quality classification index for Jinshan sand pears were constructed. The results showed that the soluble solids content of sand pears was significantly positively correlated with the daily temperature range and sunshine duration during the fruit expansion period, with correlation coefficients of 0.75 and 0.76 respectively, while it was significantly negatively correlated with the minimum temperature during the fruit expansion period and the precipitation at maturity stage, with correlation coefficients of -0.81 and -0.73 respectively. The single fruit weight had the greatest positive correlation with the precipitation during the fruit expansion period, with a correlation coefficient of 0.96. The climate quality grade indicators for the super-grade sand pear were as follows: the average daily minimum temperature within 35 days before picking was no more than 24.0℃, the daily temperature range within15 days before picking was no less than 7.0℃, the sunshine duration within 31 days before picking was no less than 175.6 hours, the precipitation within 36 days before picking was no less than 186.0 mm, and the precipitation within 10 days before picking was no more than 66.0 mm. Upon verification, the sand pear climate quality evaluation models established in this paper can effectively reflect the impact of climatic conditions on sand pear quality. This research outcome can provide a scientific decision-making reference for sand pear production and relevant management departments.

Key words: Jinshan sand pear, climate quality, meteorological factors, correlation analysis, regression model