The Spatial and Temporal Prognosis of Oilseed Yield in Shandong Province
Based on the data about oilseed yield of 87 country units in Shandong province, the paper performed the Morans I computerization to analyze the spatial autocorrelation characteristics of the oilseed yield on country level. Results showed that the spatial pattern of the oilseed yield presented the significant agglomeration characteristics, the Morans I coefficient of 14 country units was noted quadrant HH, which displayed the country units with a high oilseed yield (above the average) surrounded by country units with high oilseed yield (above the average), the Morans I coefficient of 4 country units was noted quadrant LH, which showed the country units with low value surrounded by country units with high values, the Morans I coefficient of 22 country units was noted quadrant LL, which indicated the country units with low oilseed yield surrounded by country units with low oilseed yield, the autocorrelation of the other country units was not evident. The study also carried out to predict the total oilseed yield with ARIMA (2,1,2) model on basis of time series data, in order to explore the trend of the total oilseed yield in Shandong province, the average relative error between observation value and prediction value is 2.12％ only using statistical oilseed yield data during 1978-2008, the better reliability. In a word, Morans I coefficient and ARIMA (2,1,2) model can fairly clarify the spatial and temporal status of oilseed yield. Whats more, the study is to provide a better understanding of temporal and spatial patterns of oilseed yield in Shandong province.
Shandong Province Oilseed yield ARIMA model Spatial autocorrelation
Yujian Yang Jianhua Zhu Shubo Wan Xiaoyan Zhang
S & T Information Engineering Technology Center of Shandong Academy of Agricultural Science, Jinan 2 Shandong Academy of Agricultural Science, Jinan 250100, P.R. China
国际会议
南昌
英文
146-157
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）