Tea is a very important cash crop in Rwanda, as it provides crucial income and employment for farmers in poor rural areas. From 2017 to 2020, this study was intended to determine the impact of seasonal rainfall on tea...Tea is a very important cash crop in Rwanda, as it provides crucial income and employment for farmers in poor rural areas. From 2017 to 2020, this study was intended to determine the impact of seasonal rainfall on tea output in Rwanda while still considering temperature, plot size (land), and fertiliser for tea plantations in three of Rwanda’s western, southern, and northern provinces, western province with “Gisovu” and “Nyabihu”, southern with “Kitabi”, and northern with “Mulindi” tea company. The study tested the level of statistical significance of all considered variables in different formulation of panel data models to assess individual behaviour of independent variables that would affect tea production. According to this study, a positive change in rainfall of 1 mm will increase tea production by 0.215 percentage points of tons of fresh leaves. Rainfall is a statistically significant variable among all variables with a positive impact on tea output Qitin Rwanda’s Western, Southern, and Northern provinces. Rainfall availability favourably affects tea output and supports our claim. Therefore, there is a need for collaboration efforts towards developing sustainable adaptation and mitigation options against climate change, targeting tea farming and the government to ensure that tea policy reforms are targeted towards raising the competitiveness of Rwandan tea at local and global market.展开更多
It is significant to estimate terrestrial net primary productivity (NPP) accurately not only for global change research, but also for natural resources management to achieve sustainable development. Remote sensing dat...It is significant to estimate terrestrial net primary productivity (NPP) accurately not only for global change research, but also for natural resources management to achieve sustainable development. Remote sensing data can describe spatial distribution of plant resources better. So, in this paper an NPP model based on remote sensing data and climate data is developed. And 1km resolution AVHRR NDVI data are used to estimate the spatial distribution and seasonal change of NPP in China. The results show that NPP estimated using remote sensing data are more close to truth. Total annual NPP in China is 2.645X109 tC. The spatial distribution of NPP in China is mainly affected by precipitation and has the trend of decreasing from southeast to northwest.展开更多
This research aims to improve the forecasting precision of electric quantity. It is discovered that the total electricity consumption considerably increased during the Spring Festival by the analysis of the electric q...This research aims to improve the forecasting precision of electric quantity. It is discovered that the total electricity consumption considerably increased during the Spring Festival by the analysis of the electric quantity time series from 2002 to 2007 in Shandong province. The festival factor is ascertained to be one of the important seasonal factors affecting the electric quantity fluctuations, and the multiplication model for forecasting is improved by introducing corresponding variables and parameters. The computational results indicate that the average relative error of the new model decreases from 4.31% to 1.93% and the maximum relative error from 14.05% to 6.52% compared with those of the model when the festival factor is not considered. It shows that introducing the festival factor into the multiplication model for electric quantity forecasting evidently improves the precision.展开更多
目的探讨自回归移动平均(autoregressive integrated moving average model,ARIMA)乘积季节模型在盐城市手足口病发病趋势预测的可行性。方法利用盐城市2009年1月至2015年12月的手足口病月发病率建立ARIMA乘积季节模型,并对2016年手足...目的探讨自回归移动平均(autoregressive integrated moving average model,ARIMA)乘积季节模型在盐城市手足口病发病趋势预测的可行性。方法利用盐城市2009年1月至2015年12月的手足口病月发病率建立ARIMA乘积季节模型,并对2016年手足口病发病趋势进行预测。结果盐城市手足口病预测模型为ARIMA(1,0,1)(1,1,0)12,该模型的参数估计具有统计学意义,拟合优度检验统计量最小Normalized BIC=2.997,残差序列检验统计量Ljung-Box=20.692(P>0.05),残差为白噪声,模型能够拟合出手足口病的发病趋势,且实际值都在95%可信区间内,但模型拟合的平均误差率为41.296%,检验模型预测效果的平均误差率为23.998%,模型预测精度高于拟合精度。结论运用ARIMA乘积季节模型能够对盐城市手足口病发病趋势进行预测和动态分析,对手足口病预防控制产生积极的指导作用。展开更多
文摘Tea is a very important cash crop in Rwanda, as it provides crucial income and employment for farmers in poor rural areas. From 2017 to 2020, this study was intended to determine the impact of seasonal rainfall on tea output in Rwanda while still considering temperature, plot size (land), and fertiliser for tea plantations in three of Rwanda’s western, southern, and northern provinces, western province with “Gisovu” and “Nyabihu”, southern with “Kitabi”, and northern with “Mulindi” tea company. The study tested the level of statistical significance of all considered variables in different formulation of panel data models to assess individual behaviour of independent variables that would affect tea production. According to this study, a positive change in rainfall of 1 mm will increase tea production by 0.215 percentage points of tons of fresh leaves. Rainfall is a statistically significant variable among all variables with a positive impact on tea output Qitin Rwanda’s Western, Southern, and Northern provinces. Rainfall availability favourably affects tea output and supports our claim. Therefore, there is a need for collaboration efforts towards developing sustainable adaptation and mitigation options against climate change, targeting tea farming and the government to ensure that tea policy reforms are targeted towards raising the competitiveness of Rwandan tea at local and global market.
基金National Natural Science Foundation of China, No. 49871055 No. 39990490 key basic research project of China, No. 95-Y-38
文摘It is significant to estimate terrestrial net primary productivity (NPP) accurately not only for global change research, but also for natural resources management to achieve sustainable development. Remote sensing data can describe spatial distribution of plant resources better. So, in this paper an NPP model based on remote sensing data and climate data is developed. And 1km resolution AVHRR NDVI data are used to estimate the spatial distribution and seasonal change of NPP in China. The results show that NPP estimated using remote sensing data are more close to truth. Total annual NPP in China is 2.645X109 tC. The spatial distribution of NPP in China is mainly affected by precipitation and has the trend of decreasing from southeast to northwest.
基金The Forecasting Research Base of Chinese Academy of Sciences in Xi an Jiaotong University,the National Natural Science Foundation of China (No.70773091)
文摘This research aims to improve the forecasting precision of electric quantity. It is discovered that the total electricity consumption considerably increased during the Spring Festival by the analysis of the electric quantity time series from 2002 to 2007 in Shandong province. The festival factor is ascertained to be one of the important seasonal factors affecting the electric quantity fluctuations, and the multiplication model for forecasting is improved by introducing corresponding variables and parameters. The computational results indicate that the average relative error of the new model decreases from 4.31% to 1.93% and the maximum relative error from 14.05% to 6.52% compared with those of the model when the festival factor is not considered. It shows that introducing the festival factor into the multiplication model for electric quantity forecasting evidently improves the precision.
