ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 46 Issue 2 > 2020,46(2):234-244. DOI:10.7519/j.issn.10000526.2020.02.009 Prev Next
Analysis on Limiting Factors and Construction of Prediction Model of Oilseed Rape Flowering Phenology
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Abstract:
Taking the beginning dates of flowering (BDF) as the research object, this paper analyzed the spatiotemporal variation trend of the BDF in Jiangsu Province from 1980 to 2016 by using the observation data and synchronous meteorological data. Combined with physiological characteristics of oilseed rape, the meteorological factors significantly related to the initial flowering period were selected, and the effect and sensitivity of meteorological factors were defined by path analysis. Moreover, the regression prediction model of initial flowering period was constructed. The results showed that under the background of climate change, the BDF shows a tendency of advancing year by year, with an average advance of more than three days in regions south of the Huaihe River. There are eight factors, that are significantly positively correlated to the BDF, which include: the beginning date of the daily average temperature (DAT) above 0℃ stably in previous year (X1), the ending date of the DAT above 5℃ stably (X2), the effective accumulated temperature above 0, 5 and 10℃ (X3, X4 and X5), the number of days with daily minimum temperature below 0 or 5 ℃ (X6 and X7) and average minimum temperature (X8) between budding stage and flowering stage (from early February to early March) in current year. The directly effects of X8, X7 and X4 rank the top three, and the relative determination degree of these three factors to the initial flowering period of rape is also ranked among the top three factors to the total contribution rate (TCR) of all factors to the R2 of the regression prediction model, while the direct effect of the remaining five factors are generally weaker than the indirect effect. And the remaining five factors mainly affect the BDF through X4, X7, X8, while X4, X7, X8 also exert some influence through these 5 factors. Removing any factor would change the direct and indirect effects on the BDF. In addition, the regression prediction model constructed by the above eight factors can explain 68.48% of the changes of the BDF (e.g. Gaochun Region), which is also suitable for some other regions. As far as Jiangsu Province is concerned, light and precipitation have little influence on the BDF, while heat condition is the main limiting factor. In a word, the prediction model of the BDF constructed in this paper can better reflect the rule of the BDF and the change of related heat factors.
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Clc Number:
S16，P49
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