Agricultural management practices are responsible for almost two-thirds of the variations in potato tuber yield. In order to answer the research question about the remaining variability of the tuber yield, we hypothesized that climate extremes partly explain the missing component of variations of the tuber yield. Therefore, this research attempts to bridge this knowledge gap in order to generate a knowledge base for future strategies. A climate extreme dataset of the Prince Edward Island (PEI) was computed by averaging the data of five meteorological stations. In detail, changing patterns of 20 climate extreme indices were computed with ClimPACT2 software for 30 years (1989-2018) data of PEI. Statistical significance of the trends and their slope values were determined with the Mann-Kendall test and Sen's slope estimates, respectively. Average of daily mean temperature (TMm), mean daily minimum temperature (TNm) and the occurrence of continuous dry days (CDD), significantly increased by 0.77 °C, 1.17 °C and 3.33 days., respectively, during the potato growing seasons (May-October) of the past three decades. For this period daily temperature range (DTR), frost days (FD), cold days (TX10p), cold nights (TN10p) and warmest days (TXx) showed decreasing trends of -1.01 °C, -3.75 days, -5.67 days, -11.40 nights, and -2.00 days, respectively. The principal component analysis showed that DTR, TXx, CDD, and TNm were the main factors affecting seasonal variations of tuber yield. The multiple regression model attributed ~39% of tuber yield variance to DTR, TXx, CDD, and TNm. However, these indices explained individually 21%, 19%, 16%, and 4% variation to the tuber yield, respectively. The remaining variation in the tuber yield explained by other yield affecting factors. The information generated from this study can be used for future planning about agricultural management strategies in the Island, for example, the provision of water resources for supplemental irrigation of crops during dry months.
- Agricultural management practices
- Extreme indices
- Meteorological data
- Sustainable agriculture
- Time series